Inflammatory stromal and T cells mediate human bone marrow niche remodeling in clonal hematopoiesis and myelodysplasia.

Текст сохранён. Часть иллюстраций пока доступна только в первоисточнике; загрузка будет проверена повторно.

Introduction

Aging is associated with a decline in hematopoietic system function and alterations in the composition of the bone marrow (BM) stem cell niche. Furthermore, it increases the risk for hematopoietic stem and progenitor cells (HSPC) to accumulate somatic mutations in cancer-driver genes 1 . Some of these mutations confer a competitive advantage and drive clonal expansion 2 . This phenomenon, when the variant allele frequency (VAF) exceeds 2% in the absence of hematological neoplasms, is termed clonal hematopoiesis with indeterminate potential (CHIP) 3 , 4 and is detected in more than 10% of adults over 65 5 , 6 . Most recurrent CHIP mutations affect DNA methylation regulators ( DNMT3A, TET2, ASXL1 ) and have been associated through a complex array of confounding factors with increased risks of non-malignant disorders such as cardiovascular disease and rise in overall mortality 7 – 10 . Importantly, CHIP may precede and increase the risk of developing hematologic malignancies such as myelodysplastic syndromes (MDS) and transformation to acute myeloid leukemia (AML) 11 – 13 . As CHIP is increasingly recognized as a biomarker for the risk of blood malignancy development, understanding its impact on the BM microenvironment may provide a better insight into the mechanisms driving disease progression.

MDS represents a group of hematological malignancies arising from mutated HSPCs, resulting in ineffective hematopoiesis, BM dysplasia, peripheral blood cytopenia, and predisposition to secondary AML 14 . While MDS shares some genetic features with CHIP, such as mutations in epigenetic regulators, it also exhibits cytogenetic aberrations and additional mutations, especially in RNA splicing factors ( SF3B1 , SRSF2 , ZRSR2 ) 15 , 16 . For instance, SF3B1 mutations are highly predictive for the occurrence of anemia and diagnosis of myeloid neoplasm 17 . In particular SF3B1 mutations are found in 80% of low-risk MDS cases characterized with ring sideroblasts (RS), thereby defining a distinct MDS- SF3B1 subtype within MDS-RS 18 – 20 .

The BM microenvironment (BM niche), including immune cells, endothelium, and mesenchymal stromal cells (MSC), constitutes specialized niches that orchestrate HSPC differentiation and maintenance, bone homeostasis, and immunity 21 , 22 . These niches undergo remodeling with aging, marked by a shift of MSC differentiation towards the adipogenic lineage and increased adipocytes (fatty BM) 23 – 25 . Another hallmark of BM aging is the emergence of inflammaging, a sterile low-grade inflammatory state characterized by elevated levels of pro-inflammatory factors including IL-1β, IL-6, and TNFα 26 – 28 . Inflammaging is associated with a decreased stem cell function 29 , 30 and has been observed across multiple organ systems 31 , 32 . In CHIP carriers, elevated blood serum levels of IL-6, CXCL8, IL-18, and TNFɑ have been detected compared to age-matched controls 33 – 35 . Furthermore, the establishment of an inflammatory BM state has been shown to be beneficial for clonal expansion of TET2 - and DNMT3A -mutant HSPCs in mouse models as well as in human CHIP cohort studies 30 , 35 – 38 .

Extensive remodeling of the BM niche has been observed in hematopoietic malignancies, fostering an environment that supports malignant cells. This remodeling particularly influences the stromal components, which can alter the functions of resident immune cells, such as T cell-mediated responses 39 – 41 . In MDS, in vitro cultured MDS-derived BM MSCs show an inflammatory program 42 , decreased proliferation capacity, and reduced HSPC support 43 compared to healthy donor-derived BM MSCs. A recent study in a DNMT3A -mutant CHIP mouse model (equivalent to a human VAF of 1–4%) suggests that mutated HSPCs can induce senescence in BM MSCs through pro-inflammatory cytokines including IL-6 and TNFα 44 . However, in vivo studies on the MDS BM niche cells remain scarce, and the extent to which CHIP-mutant clones can remodel the BM microenvironment remains unclear, particularly in the human context and at single-cell level.

In this study, we investigate the BM microenvironment in aged controls, CHIP carriers ( DNMT3A , TET2 ), and low-risk MDS patients ( DNMT3A , TET2 , spliceosome mutations) using multimodal approaches, including targeted RNA profiling, flow cytometry, single-cell sequencing, and imaging. Our analyses reveal distinct alterations in stromal and immune compartments, marked by the emergence of an inflammatory MSC subset in CHIP that further expands in MDS. We show that these MSCs act alongside IFN-responsive T cells to remodel the niche and interact with mutated HSPCs in ways that may sustain malignant hematopoiesis. Together, these findings contribute to a deeper understanding of the inflammatory human BM microenvironment in CHIP and MDS and open avenues for therapeutic intervention.

Results

Distinct inflammatory and immune microenvironment profiles in CHIP and MDS bone marrow

To understand how the microenvironment is altered across CHIP and MDS, we analyzed bone marrow (BM) samples from a balanced cohort of 84 donors, including 35 age-matched Controls, 17 CHIP donors (VAF ≥ 2%), and 32 MDS patients, the majority of whom had low-risk disease and were newly diagnosed or disease-modifying treatment-naïve cases (i.e., erythropoietin or supportive care only) (Fig. 1A , and Table 1 ). DNMT3A and TET2 were the most frequently mutated genes across most CHIP and MDS donors. In addition, the majority of MDS patients also harbored at least one mutation in a spliceosomal gene, such as SF3B1 ( n = 16) , SRSF2 , or U2AF1 ( n = 6) or carry only DNMT3A/TET2 mutations ( n = 10) (Table 1 , Supplementary Data 1 , Supplementary Fig. 1A ). Low-frequency mutations (VAF < 2%) identified in a few controls showed no association with clinical features (including CRP values), supporting their classification as non-CHIP in our BM cohort (Supplementary Data 1 , Supplementary Fig. 1A ). Blood count analysis showed equal proportions of leukocytes across the mutational spectrum of the MDS cohort but indicated increased ring sideroblasts (RS) and anemia specifically in SF3B1 -mutated MDS (Supplementary Fig. 1B ), which is consistent with the current WHO consensus classification of MDS-RS 45 , 46 . Fig. 1 Inflammatory expression profiling in bulk bone marrow across CHIP and MDS. A Circular heat map of the BoHemE cohort ( NCT02867085 ), showing age distribution (boxplot, top left), sex (female: magenta, male: blue), splicing mutations ( SF3B1 , SRSF2; wildtype: green, mutant: magenta), and conditions (green: Control, purple: CHIP, orange: MDS). Rings indicate inclusion (black boxes) in various analyses such as scRNA-seq, NanoString, flow cytometry, and immunofluorescence analysis. B Schematic of the experimental workflow. Bone marrow (BM) aspirates were processed into BM mononucleocytes (BM-MNCs) using Ficoll separation or only erythrocyte lysis. Ficoll-treated samples were used for NanoString profiling (Immune Exhaustion and PanCancer Immune panels) and erythrocyte-lysed samples for flow cytometry analysis to quantify major cell types (see Supplementary Data 2 for NanoString panels and Supplementary Data 3 for flow cytometry panels). Created in BioRender. Prummel, K. ( https://BioRender.com/pzpb028 ). C Principal component analysis (PCA) scatter plots showing the separation of Control, CHIP, and MDS samples ( n = 12/condition) by the Immune Exhaustion (top) and PanCancer Immune (bottom) panels. Each dot represents a sample, colored by condition (green: Control, purple: CHIP, orange: MDS). D Gene Set Enrichment Analysis (GSEA) results illustrating gene sets significantly up- or downregulated in MDS vs. Control (orange) and CHIP vs. Control (purple). P values were adjusted using Benjamini–Hochberg procedure. E Heatmap of NanoString-derived genes differentially expressed between MDS and Control samples (from D ) onto scRNA-seq data from healthy BM 47 . Genes are averaged by cell type and Z-scored across cell types for visualization. F Flow cytometry analysis comparing the relative proportions of CD34 + /CD117 + HSPCs/myeloblasts, CD19 + B cells, and CD10 + CD19 + B cell progenitors within the CD45 + cell population across Control ( n = 20), CHIP ( n = 14), and MDS ( n = 24) donors. Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA with FDR for multiple comparison correction. Source data are provided in the Source Data file. VSMC vascular smooth muscle cell, n.s. not significant. Table 1 Clinical characteristics of experimental cohort Variable Control CHIP MDS Total number of donors 35 17 32 Sex Female 27 10 15 Male 8 7 17 Age (years) Median 71 77 72 Range 60–79 63–84 60–89 Mutations (number of donors with most representative % VAF)* Epigenetic regulators (DNMT3A, TET2, ASXL1) N/A 14 10 SF3B1 (K700E and others) N/A 1 16 Other splicing factors N/A 0 6 Other mutations (PPMD1, SRP72) N/A 2 0 IPSS-R (number of donors) IPSS-R ≤ 3.5 (low) N/A N/A 17 IPSS-R > 3.5 - 4.5 (intermediate) N/A N/A 10 IPSS-R > 4.5 (high) N/A N/A 5 MDS Risk group (number of donors) Low-Risk (LR-MDS) N/A N/A 27 High-Risk (HR-MDS) N/A N/A 5 Treatment status Newly diagnosed (no treatment) N/A N/A 10 Treatment-naïve cases (i.e., erythropoietin or supportive care only) N/A N/A 18 Hypomethylating agent (AZA) N/A N/A 3 Lenalidomide N/A N/A 1 * VAF variant allele frequency; only somatic mutations with VAF ≥ 2% in defined genes are considered in this study. See details in Supplementary Data 1 and Supplementary Fig. 1

To gain a comprehensive overview of microenvironmental alterations, we profiled bulk gene expression in total BM mononuclear cells (MNC) using two NanoString nCounter panels targeting 773 immune-related (Immune Exhaustion) and 730 cancer inflammation-associated (PanCancer Immune Profiling) genes respectively (Supplementary Data 2 , and Fig. 1B ). Principal component analysis (PCA) revealed a separation of MDS samples from CHIP and Control along the main principal component in both panels, with only one MDS outlier (Fig. 1C ; Methods ). Correlation heatmaps of the top 10 principal components (PC1–10) with clinical metadata showed no strong associations, apart from the expected alignment with VAF, indicating that the transcriptional patterns were largely independent of other clinical parameters (Supplementary Fig. 2A ). Although cluster separation appeared slightly more distinct in the Immune Exhaustion panel, both panels consistently captured transcriptional divergence in MDS, reflecting broad remodeling of the BM immune milieu.

Differential gene expression analysis identified 123 genes significantly altered between MDS and Controls (FDR < 0.05), with the majority being downregulated in MDS BM (Supplementary Fig. 2B ). Gene set enrichment analysis (GSEA) revealed upregulation of TNFα and IFNα pathways in MDS, indicating a pro-inflammatory environment, while T cell-related processes were downregulated (Fig. 1D ).

To further identify which cell types contributed to the transcriptional changes in the MDS niche, we mapped the differentially expressed genes onto a recently published single-cell atlas of healthy BM 47 . For each gene, we computed average expression across annotated cell types and clustered the resulting gene-by-cell-type matrix (Supplementary Fig. 2C ), followed by grouping cell types with similar patterns into the major BM cell populations (Fig. 1E ). This revealed that genes downregulated in MDS are predominantly expressed by lymphoid populations, including T cells, pre/pro-B cells, mature B cells, and B plasma cells (e.g., CD3, CD8, CD4, CD22, IGH, CD38, CD19 ) (Fig. 1E ). This overall loss of B cells was confirmed by immunophenotyping of the BM (Fig. 1F , Supplementary Fig. 1C ) and is consistent with prior studies on low-risk (LR) MDS and SF3B1- mutated MDS 48 – 51 . In contrast, the total numbers of CD3 + T cells, CD4 + /CD8 + subsets, NK cells, NKT cells, and granulocytes were unchanged across BM conditions (Supplementary Fig. 1C, D ). Together with the upregulation of several pro-inflammatory genes in MDS BM (e.g., TNF, IFNL1, CCR9 ) (Fig. 1E ), these findings suggest the presence of a phenotypically preserved but functionally altered T cell population contributing to the inflammatory milieu in MDS.

In addition to lymphoid alterations, MDS donors exhibited transcriptional shifts across other BM compartments, including genes specific to early myeloid precursor cells (Fig. 1E ), likely reflected by the elevated presence of MDS myeloblasts (CD34 + /CD117 + ) as confirmed by immunophenotyping (Fig. 1F ). Moreover, dendritic cell–specific genes were reduced, whereas neutrophil- and myeloid progenitor–associated transcripts were upregulated, suggesting changes in abundance or functional state of these populations (Fig. 1E ). Lastly, stromal gene expression revealed reduced levels of TWIST and SFRP1 (markers involved in MSC identity and differentiation) while endothelial-associated genes ( BMP2, IFIT3 ) were upregulated in MDS (Fig. 1E ). Since these genes are important modulators of HSPC and osteogenesis 52 – 55 , our data indicate that both stromal and endothelial compartments might also undergo significant remodeling in MDS BM.

Although CHIP donors did not show statistically significant expression changes compared to Controls (Supplementary Fig. 2D ), B and T cell-related gene sets were upregulated relative to MDS (Supplementary Fig. 2E ), indicating preserved lymphoid integrity. Notably, stratification by clone size further revealed that CHIP donors with VAF ≥ 5%, primarily carrying DNMT3A mutations clustered more closely to MDS in the PCA (Supplementary Fig. 2F ) and showed increased expression of inflammatory and proliferative markers compared to CHIP/Control donors with VAF < 5% (Supplementary Fig. 2G ). This suggests that a subset of CHIP donors may already harbor an inflammatory BM microenvironment. In contrast, Controls with low-frequency mutations (VAF < 2%) showed no association with molecular features (Supplementary Fig. 1A , and Supplementary Data 1 ), underscoring their distinction from CHIP donors.

Overall, the transcriptional analysis of bulk BM reveals a pro-inflammatory microenvironment that emerges in high-VAF CHIP and becomes more pronounced in MDS, accompanied by distinct changes in cellular composition. These include expansion of immature myeloid cells, selective loss of B cells, and remodeling of the T cell, stromal, and endothelial compartments, suggesting stepwise BM niche disruption across distinct disease states.

Single-cell RNA-seq reveals inflammatory stromal and lymphocyte subsets in the BM niche of CHIP and MDS

To further investigate the observed remodeling of the BM microenvironment in CHIP and MDS, we employed single cell transcriptomics on selected BM populations from a representative subset of the total cohort, comprising 3 Control, 3 CHIP ( DNMT3A and/or TET2 ), and 4 MDS patients ( SF3B1/SRSF2 and DNMT3A and/or TET2 ) (Fig. 2A , Supplementary Fig. 1A ; more info in Supplementary Data 5 ). We focused on three key compartments of the BM: non-hematopoietic stromal fraction (negative gating strategy: CD45 - CD235a - CD71 - CD14 - CD38 - ), HSPCs (CD45 + CD34 + CD235a - CD71 - CD14 - ), and T cells (CD45 + CD3 + ), while minimizing the contamination by erythroid and other immature cells including MDS-RS (Supplementary Fig. 3A ). To enhance stromal cell recovery, we additionally sorted CD271⁺ cells for plate-based scRNA-seq (CEL-Seq2). CD271, a well-established marker of human BM stromal cells 56 , 57 , labeled the predominant stromal population in our selected BM cohort compared to other known stromal markers (Supplementary Fig. 3B–E ). Fig. 2 Identification of BM cell subsets in Control, CHIP, and MDS by scRNA-seq. A Schematic of the experimental design using donor-derived BM aspirates and FFPE trephine bone biopsies. Created in BioRender. Prummel, K. ( https://BioRender.com/kdi1w0u ). B Uniform manifold approximation and projection (UMAP) of 24,639 cells across the major cell populations of scRNA-seq from donors highlighted in Fig. 1A and Fig. 2A, colored by Donor ID and split by condition. C UMAP of 12,557 HSPCs, with clusters annotated by cell type. D Dot plot of marker gene expression in HSPCs. Circle size corresponds to percent of cells expressing the given gene and color the expression level. The heatmap shows average AUCell scores of marker transcription factors based on regulons inferred in this study. E UMAP of 9,962 T cells, with clusters annotated and colored by cell type. F Dot plot of T cell marker expression and heatmap of average AUCell scores 65 . G UMAP of 1,396 stromal cells integrated between 10x and CEL-Seq2 data, colored by cell type annotation. H Dot plot of stromal cell markers. I Average expression of genes upregulated in MDS vs. Control in the NanoString data, shown across our scRNA-seq cell type clusters. Source data are provided in the Source Data file and Supplementary Data 7 .

After donor demultiplexing based on genotype deconvolution ( Methods ), quality control, and cell calling (Supplementary Fig. 4A–E ), we identified a population of hematopoietic cells in the stromal gate despite the removal of CD38 + /CD71 + /CD235a + cells (Supplementary Fig. 4F ; Methods ). These cells resembled monoblasts and erythroblasts (or RS) and predominantly originated from MDS donors; and were therefore removed for the main analyses. Altogether, these filtering steps yielded in total 24,693 cells, with between 1,190 and 4,393 high-quality cells per donor (median 3,644 UMIs, 1,610 unique genes, and 6.2% mitochondrial reads per cell; Supplementary Data 4 ; Supplementary Fig. 4A ). A UMAP representation followed by broad cell type classification revealed the expected 3 major compartments: 1,396 stromal cells ( CXCL12 and LEPR ), 9,962 T cells ( CD3E and CD247 ), and 12,257 HSPCs ( CD34 and SPINK2 ) (Fig. 2B ), along with a small population of CD14 -low monocytes, which was unexpectedly recovered from MDS donors despite CD14 - gating.

To further refine the cell populations, we integrated the scRNA-seq data for each major cell population across all donors using Seurat v4 ( Methods ) and generated a UMAP embedding for each broad cell type (T cells, stroma, HSPCs including CD14-low monocytes). We next performed Leiden clustering and annotated the populations using canonical marker genes, reference human T cells and HSPC atlases ( Methods 58 – 65 ), and gene regulatory network inference (SCENIC 66 ) to identify key transcription factor regulons ( Methods 60 – 63 , 66 ). In total, we defined 13 HSPC clusters, 9 T cell clusters, and 6 stromal cell clusters (Fig. 2C–H , and Supplementary Fig. 4G–J ). For the HSPC compartment, we identified HSC and multipotent progenitors (HSC/MPP; HLF, AVP, GPR56, MECOM ), along with lymphoid-primed multipotent progenitors (LMPP; SPINK2, CD34, PROM1 ). B lymphoid progenitors comprised both Pro-B cells, ( CD96 , DNTT, EBF1) and Pre-B cells ( BACH2, PAX5 ). Myeloid commitment was represented by granulocyte-monocyte progenitors (GMP; PRTN3, MPO) and plasmacytoid-dendritic cell progenitors (pDC-Pr; IRF8, CUX2 ). In addition, the eosinophil–basophil–mast cell progenitor population (EBMP; ENPP3 ,  IKZF2 ) was resolved alongside, megakaryocyte-erythroid progenitors (MEP; GATA2) , megakaryocyte progenitors (MkPr; PF4, PBX1) , and erythroid progenitors (EryPr; APOC1, KLF1 ). Finally, we identified a CD14-low myeloid population ( CD68, MFAB, SPI1 ) that was reminiscent of non-classical CD14-low monocytes. These were previously defined as patrolling monocytes involved in the innate local surveillance of tissues and shown to expand in blood malignancies 67 – 69 . Two low-quality (high_mito) or donor-specific clusters were excluded from further interpretation (Fig. 2C, D ).

We annotated 9 T cell populations using a combination of marker genes and regulon activities of specific transcription factors, including those inferred from SCENIC and from our previously established T cell-specific regulons 63 , 65 , 70 , 71 . The CD4 clusters included: CD4 naive/central memory (CD4 naive/CM ; SELL, CCR7 ), CD4 effector memory (CD4 EM ; IL7R, KLRB1, AQP3 ), a mixed CD4 population, and regulatory T cells (Treg; IL2RA , RTKN2 , FOXP3 regulon activity). Among CD8 T cells, we distinguished CD8 naive/central memory (CD8 naive/CM ; CCL5, GZMK ), CD8 terminally differentiated effector memory re-expressing CD45RA (T EMRA ; FCGR3A, GZMB, GNLY ), as well as IFN-responsive CD8 effector cells (CD8 IFN-responsive; OASL, IFIT2 ). Additionally, we observed a mixed CD4/CD8 heat-shock protein-expressing population (CD4/CD8 HSP + ; DNAJB1, HSP90AA1, HSPA1A ) and mucosal-associated invariant T cells (MAIT; SLC4A10, ME1 ) (Fig. 2E,F ).

Within the stromal compartment, we annotated 6 distinct subpopulations based on a BM reference atlas 47 , including two adipogenic CXCL12 abundant reticular (Adipo-CAR1/2; sharing CXCL12, LEPR , and EBF3 , and distinguished by PPARG and LPL - Adipo-CAR1 and ALPL and PDGFRB - Adipo-CAR2), chondrogenic- ( SOX9 ), and osteogenic-lineage ( RUNX2, SPP1, NCAM1, BGLAP) MSCs, as well as neural crest-derived Schwann-like cells ( NRG1, NRXN1 ), which express CD271 (NGFR) akin to MSCs 72 , 73 and were enriched among our CD271 + population (Fig. 2G, H ). Notably, we identified a stromal population expressing inflammation-associated genes, including CD44 and IL1R1 74 , 75 , which we refer to as inflammatory MSCs (iMSCs). Other stromal cells, including endothelial cells, vascular smooth muscle cells/pericytes, or mature osteocytes and adipocytes could not be recovered from BM aspirates, either due to their low abundance or mechanical fragility during cell isolation.

The differentially expressed genes from the bulk BM transcriptomics analysis in the larger cohort were highly expressed in MDS-specific cell populations (iMSCs, IFN-responsive T cells, and non-classical CD14-low monocytes), which did not exist in the healthy BM reference, confirming that we profiled the relevant cell populations (Fig. 2I ).

Overall, our single-cell data reveals distinct, disease-associated subpopulations within the stromal, T cell, and HSPCs across the CHIP and MDS BM. Integration with the bulk transcriptomics data of the larger cohort highlights the emergence of inflammatory populations in MDS, particularly iMSCs and IFN-responsive T cells, underscoring their potential role in BM niche remodeling.

Inflammatory MSCs are exclusively present in CHIP and hematologic malignancies

To further investigate the stromal cell populations, we quantified the subtype changes across Control, CHIP, and MDS samples. The most notable shift was the emergence of the iMSC population, present exclusively in CHIP and MDS (Fig. 3A , Supplementary Fig. 4H , Supplementary Data 4 ). Although bulk BM from CHIP donors lacked a significant pro-inflammatory signature (Supplementary Fig. 2D ), our scRNA-seq approach sensitively captured these rare iMSCs, highlighting its ability to resolve subtle and low-abundance cellular changes. This iMSC population was detected in 2 out of 3 CHIP donors and all MDS donors examined, albeit in varying proportions (Supplementary Data 4 ). In addition to the expansion of iMSCs, MDS patients exhibited a marked depletion of both Adipo-CAR1 and Adipo-CAR2 MSCs, while CHIP donors showed a specific loss of Adipo-CAR1. This is corroborated by the reduced expression of adipocyte-specific markers ( ACACA, FABP4 ) in the NanoString MDS data (Fig. 1E ), and by histological analysis, showing a significant reduction of BM adipocytes in MDS (Fig. 3B, C , and Supplementary Fig. 5A–C ). These findings are in accordance with previous reports linking adipocyte loss to ineffective hematopoiesis in LR-MDS 76 – 78 . Fig. 3 Identification of a distinct inflammatory MSC population in BM of CHIP and MDS. A Spider plot showing stromal cell types in Control (green), CHIP (purple), and MDS (orange). Proportions are probit-transformed; black outlines indicate significant differences compared to Control. Statistical test: Fisher’s exact test. B Giemsa-stained BM sections from Control, CHIP, and MDS patients. Scale bars: 100 µm. C Scatter plot quantifying BM cellularity (cell/adipocyte ratio) across conditions (Control n = 30, CHIP n = 9, MDS n = 16 patients) (see Supplementary Fig. 5A–C ). Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA, Tukey’s test. D Dot plot of extracellular matrix (ECM)-related and inflammatory gene markers (from 74 ) across stromal cell populations. Circle size represents percentage of cells expressing the gene, while color intensity represents scaled expression. E Violin plots of stromal genes upregulated in MDS and detected in the NanoString data. F Immunofluorescence of BM sections showing IL-1R1 (red) and CD271 (yellow) co-staining in Control, CHIP, and MDS. Insets highlight areas containing inflammatory IL-1R1 + CD271 + stromal cells. DAPI (blue) stains nuclei. Scale bars: 25 µm. G Bar graph quantifying IL-1R1 + MSCs across Control ( n = 2), CHIP ( n = 3), and MDS ( n = 3) samples. Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA, Tukey’s test. H , I Flow cytometry of CD44 expression on Lin – CD271 + CD73 + MSCs from Control ( n = 5), CHIP ( n = 5), and MDS ( n = 13) samples. MDS samples are divided into splice ( n = 9, SF3B1 , SRSF2 ) and non-spliceosome ( n = 4) mutated. Histogram ( H ) and percentage of CD44 + MSCs ( I ). Means with SD are shown. Statistical test: one-way ANOVA, Dunnett test. J , K Violin plots of inflammatory signature scores in stromal cells, stratified by condition ( J ) and cell subtypes ( K ). The signature score represents the cumulative expression of inflammation-related genes. Statistical test: two-sided Wilcoxon rank-sum test. L UMAP integration of our scRNA-seq dataset with three published human BM datasets (healthy, AML, and MM 47 , 74 , 83 ) using Harmony 177 . The stromal cells are colored by inflammatory signature scores. Arrowhead marks iMSCs. M UMAP of stromal cell populations in Control, CHIP, and MDS from BM aspirates (left), healthy digested hip bones (middle) 47 , and AML 83 and MM BM aspirates (right) 74 . Cluster labels follow original published annotations, inflammation-associated clusters are labeled iMSC. A , J , K P values were adjusted using Benjamini–Hochberg procedure. Source data are provided in the Source Data file and Supplementary Data 7 .

Given their striking enrichment in CHIP and MDS, we next characterized the transcriptional features of the iMSC population. They displayed high expression of inflammatory mediators ( CD51, CD44 , IL1R1 , PTGS2 (a prostaglandin synthase involved in inflammatory responses), CXCL8, CCL2 ) as well as genes involved in pro-inflammatory extracellular matrix (ECM) remodeling ( ADAM12, FN1, LAMC1, ITGA5, ITGB1, ITGB3 ) and profibrotic collagens ( COL4A1, COL4A2, COL6A2, COL6A 3) (Fig. 3D ). Additionally, iMSCs expressed several genes that were also significantly upregulated in bulk BM, including CHUK (encoding IKK-alpha, crucial for NF-κB signaling activation and regulation), BMP2 , and PTGS2 (Fig. 3E ) 79 , 80 , further linking iMSCs as active players of structural remodeling of the niche.

The presence of iMSCs was confirmed by in situ immunofluorescence staining for IL-1R1 in FFPE BM tissue, revealing higher IL-1R1 + cell counts in MDS compared to Control and CHIP donors (Fig. 3F, G ). Despite the overall low IL-1R1 + cell counts in CHIP, discrete clusters of IL-1R1 + /CD271 + cells were still detectable in CHIP BM sections (Supplementary Fig. 6A ). Additionally, flow cytometry on a subset of our full cohort of BM aspirates (Fig. 1A , Supplementary Fig. 1A ; n = 22) further validated the presence of iMSCs in CHIP and MDS using CD44 and CD51/61 markers. In particular, we observed a significant increase of CD44 + iMSC within the CD271 + /CD73 + stromal compartment in MDS, harboring spliceosome mutations (Fig. 3H, I , and Supplementary Fig. 6B–D ). CD44 is linked to stromal inflammation and ECM production through TGFβ and RhoA-YAP signaling 81 , 82 .

Inflammatory alterations in stromal cells have been reported in other BM malignancies such as AML 83 and mature B cell-derived Multiple Myeloma (MM) 74 , where they are linked to cancer-associated fibroblast (CAF)-like signatures 84 . Using a MSC-inflammatory signature derived from these prior studies (21 genes, Supplementary Data 5 , Methods ), we confirmed MSCs from our Controls lacked inflammatory features (Fig. 3J ), while CHIP/MDS-specific iMSCs showed high inflammation scores (Fig. 3J, K ).

To further quantify the transcriptional similarities of our iMSCs to those previously described in other BM malignancies, we integrated our dataset with published scRNA-seq data from BM stromal compartments of healthy, MM, and NPM1 -mutant AML patients ( Methods 47 , 74 , 83 ). This revealed that the iMSC cluster shared between CHIP and MDS represents a subset of the iMSCs found in AML and MM (Fig. 3L, M , and Supplementary Fig. 7A ). This shared iMSC cluster across multiple BM malignancies exhibited a high inflammatory signature and was nearly absent in healthy BM 47 and disease-free controls from the MM and AML datasets 74 , 83 (Fig. 3L , and Supplementary Fig. 7A ). These findings suggest a slightly increased inflammatory niche in CHIP and a more pronounced expansion of an iMSC subset in MDS, which partially resembles other BM malignancies, such as AML and MM. This suggests the existence of a shared stromal response to (chronic) inflammatory stimuli across diverse BM diseases.

iMSCs differ between CHIP and MDS in their HSPC-support signatures

To explore how stromal remodeling and inflammation affect the HSPC support, we curated a literature-based gene signature of known HSPC-support factors (46 genes, Supplementary Data 5 ) 85 and assessed its expression across the stromal populations. We observed that the adipo-MSCs (Adipo-CAR1 + 2) showed the highest HSPC support score (Fig. 4A ). Given the loss of these populations in MDS, and to a lesser extent in CHIP (Fig. 3A , Supplementary Fig. 7B ), indicates a reduced capacity of the stromal compartment to maintain HSPCs in CHIP, with this impairment being even more pronounced in MDS. Indeed, quantifying the in situ CXCL12 protein levels, an essential HSPC support and maintenance factor 86 , relative to the number of CD271 + stromal cells in BM biopsies from our cohort, revealed a significantly lower CXCL12/CD271 ratio in MDS compared to CHIP and Control (Fig. 4B, C ). This suggests that either MSCs in MDS express lower levels of CXCL12, or that the proportion of CXCL12-expressing CD271 + MSCs is reduced. This is consistent with the overall reduction in the HSPC-support signature in MDS stromal populations compared to Controls when pooling all MSC populations (Fig. 4D ). Fig. 4 Mapping HSPC support in stromal BM populations. A Violin plots of HSPC-support signature scores across the stromal cell types. B Immunofluorescence of BM sections showing CXCL12 (magenta) and CD271 (yellow) expression in Control, CHIP, and MDS. Insets highlight regions with distinct co-localization patterns. DAPI (blue) stains the nuclei. Scale bars: 25 µm. C Dot plot of CXCL12 + cells among CD271 + stromal cells (Control/CHIP n = 15 and MDS n = 9 donors). Medians with 95% confidence intervals are shown. Statistical test: two-sided Welch’s t -test. D Violin plots of HSPC-support signature scores in stromal cells from scRNA-seq across Control, CHIP, and MDS. The signature score represents the cumulative expression of the HSPC-support. E Scatter plots showing the correlation between the HSPC-support and inflammation signature scores across stromal cells between conditions. Different colors represent the distinct stromal clusters and different shapes represent the single-cell datasets (circles 10x, triangles CEL-Seq2). Black trend lines indicate the direction of the correlation in each condition. Linear fit and 95% confidence intervals are calculated by the function geom_smooth from ggplot2 . Statistical test: student’s t -test for PCC. F Violin plots of the HSPC-support signature scores specifically in Osteo-chondro MSCs, Adipo-CAR1/2, and iMSCs across conditions. G Sankey diagram summarizing the proportions of NICHES-inferred cell-cell interactions 87 between HSPCs and stromal cell types across Control, CHIP, and MDS. Interaction proportions highlight the changes of supportive interactions in MDS. A , D , F Statistical test: two-sided Wilcoxon rank-sum test with Benjamini–Hochberg procedure. Stroma-HSPC schematic created in BioRender. Prummel, K. ( https://BioRender.com/8aemiyg ). Source data are provided in the Source Data file.

Notably, in CHIP-derived stromal cells, we observed a slight anti-correlation between the HSPC-support and the inflammatory signatures (Fig. 4E , PCC ≈ -0.11, p value ≈ 0.013, Student’s t -test for PCC), implying that CHIP-iMSCs can provide limited HSPC support. In contrast, in the MDS stromal compartment, we observed a positive association between the inflammatory and HSPC-support signatures across all MSC populations (Fig. 4E , PCC ≈ 0.58, p value < 2.2e-16, Student’s t -test for PCC), suggesting a partial retention of HSPC-supportive functions particularly the MDS-iMSCs. In line, MDS-iMSCs expressed higher levels of CXCL12 and KITLG compared to CHIP-iMSCs (Supplementary Fig. 7C ), although their overall support capacity remained lower than Adipo-MSCs in Control and CHIP (Fig. 4F ). A similar trend was observed in iMSCs from AML patients in the dataset from Chen and colleagues 83 (Supplementary Fig. 7D ). GSEA comparing iMSCs from MDS versus CHIP revealed upregulation of stress and inflammatory pathways in MDS, including UV response, apoptosis, mTORC1 signaling, and TNFα signaling via NF-κB (Supplementary Fig. 7E ), indicating MDS-iMSCs have heightened inflammatory activation in comparison to CHIPiMSCs. These observations suggest that, although MDS shows reduced HSPC-supportive capacity alongside iMSC expansion, both inflammatory and HSPC-support programs may still partially coexist within individual stromal cells.

To assess potential stromal-HSPC interactions, we applied the computational tool NICHES 87 to infer ligand-receptor activity between MSCs subsets and HSPCs. In Controls, Adipo-CAR1 cells emerged as the primary HSPC-interacting cell population (Fig. 4G ), consistent with their known supportive role (Fig. 4A ). In CHIP, this interaction shifted mainly toward Adipo-CAR2, while in MDS the iMSC population became the dominant stromal population to interact with HSPCs (Fig. 4G ). Notably, these iMSC-HSPC interactions were significantly more frequent in MDS than CHIP (OR ~ 3.3; p value < 2.2e-16), supporting that iMSCs in CHIP and MDS, though transcriptionally similar, are functionally distinct.

Altogether, these results suggest that while both CHIP and MDS display inflammatory stromal remodeling, their iMSCs differ in functional output: iMSCs in CHIP show limited hematopoietic support, whereas iMSCs in MDS retain partial capacity to interact with and support HSPCs.

MDS blasts contribute to iMSC remodeling

Next, we sought to understand how clonal HSPCs may contribute to stromal remodeling and the emergence of iMSCs. First, we analyzed the HSPC composition across Control, CHIP, and MDS donors. The most notable change was the significant reduction of Pre-B/Pro-B cells in both CHIP and MDS compared to Control (Fig. 5A , Supplementary Fig. 2I ), consistent with the diminished (mature) B cell signatures seen in our NanoString data and the reduced frequencies of CD10 + /CD19 + B cells detected by flow cytometry (Fig. 1E, F , and Supplementary Fig. 1C ). Furthermore, HSC/MPPs were slightly but significantly reduced in MDS while showing a modest increase in CHIP. Apart from that, no HSPC population displayed a marked expansion in CHIP or MDS, with the exception of an expansion of CD14-low monocytes, previously reported in other blood malignancies such as B cell leukemia 69 . Fig. 5 Characterization of HSPCs and their effect on BM inflammation. A Spider plot of the relative abundances of HSPC subtypes across Control (green), CHIP (purple), and MDS (orange) donors. Proportions are probit-transformed; black outlines indicate significant differences compared to Controls. Statistical test: Fisher’s exact test. B UMAP of HSPC cells showing the pseudotime and schematic outline of main differentiation trajectories. C Smoothed expression of pro-inflammatory cytokine genes CXCL8 and IL1B along the pseudotime in 3 main differentiation trajectories of HSPCs. Smoothing and 95% confidence intervals are calculated by the function geom_smooth from ggplot2 (posterior distribution of coefficients of a cubic spline generalized additive regression model used for smoothing). D UMAP of HSPCs showing the SpliceUp-predicted SF3B1 mutational status ( SF3B1 WT blue, SF3B1 MU T red). Dashed box highlights the erythroid progenitors, further analyzed in ( E , F ). E Differential expression between SpliceUp-predicted SF3B1 WT and SF3B1 MUT erythroid progenitors. Statistical test: DESeq2. Red dots: adjusted p value < 0.05. F Gene Set Enrichment Analysis (GSEA) results with the Biological Process ontology from Gene Ontology database showing the top 20 significant sets enriched between SF3B1 WT and SF3B1 MUT erythroid progenitors. G Schematic visualization of the primary HSPC-MSC co-culture experiment. BM MSCs were cultured either alone or with CD34 + HSPC from Control, CHIP or MDS donors (ratio 1:1). After 96 hrs, the supernatant was collected and subjected to subsequent protein quantification using an Olink panel. The cells were harvested and subjected to scRNA-seq. Created in BioRender. Prummel, K. ( https://BioRender.com/v0w0jhd ). H Olink quantification of 4 secreted cytokines after 96 hrs of co-culture. Mean normalized protein expression (NPX) values (mean of 3 experimental replicates) across conditions (MSC mono-culture (MSC-only), Control CHIP, and MDS, n = 3/group) are displayed. Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA, Tukey’s test. I scRNA-seq of co-cultures showing normalized RNA expression of cytokines in ( H ) across the stromal (triangles) and HSPC (circles) compartments after MSC mono-culture (MSC-only), and Control and MDS co-cultures ( n = 3/group). Medians with 95% confidence intervals are shown. Statistical test: two-sided unpaired Student’s t -test. A , E , F P values were adjusted using Benjamini–Hochberg procedure. Source data are provided in the Source Data file and Supplementary Data 7 . n.s. not significant.

Differential expression in all HSPCs comparing MDS versus Control followed by GSEA revealed a significant enrichment for pro-inflammatory pathways, such as TNFα via NF-κB in MDS (Supplementary Fig. 8A, B ). Among the individual HSPC populations, this upregulation was most pronounced in GMPs and LMPPs, suggesting these populations may drive the overall enrichment (Supplementary Fig. 8A ). While CHIP HSPCs showed a similar trend, the difference did not reach statistical significance (Supplementary Fig. 8A ).

To further investigate lineage-specific contribution to inflammation, we applied Monocle3 to infer the HSPC differentiation trajectories (erythroid, myeloid, and lymphoid) 88 and quantified specific pro-inflammatory gene expression across pseudotime (Fig. 5B ). We found that CHIP-derived HSPCs expressed higher levels of pro-inflammatory genes CXC L8 and IL1β across all lineages compared to Controls, suggesting a broader low-grade inflammatory state. In MDS, HSPCs showed moderate increases in CXCL8 expression across all lineages, with a pronounced increase specifically in the monocyte population (Fig. 5C ).

To determine whether clonal SF3B1 MDS HSPCs themselves contribute to the inflammatory environment, we applied SpliceUp 89 , our custom algorithm that infers mutational status based on the presence of characteristic mis-splicing events associated with SF3B1 mutations ( Methods ; n = 3, Fig. 5D ). By aggregating data across multiple splice sites, SpliceUp circumvents the sparsity inherent in scRNA-seq reads, providing improved sensitivity compared to direct mutant allele detection. While a minority of SF3B1 -mutant cells may still be identified as wild-type due to residual sparsity, this approach enabled reliable classification of SF3B1 mutant ( SF3B1 MUT ) and wild-type ( SF3B1 WT ) HSPCs, offering a robust framework for downstream comparative analyses.

We performed differential gene expression analysis between predicted SF3B1 WT and SF3B1 MUT HSPCs across the 3 SF3B1 -mutated MDS donors in a pseudo-bulk approach. This analysis included primarily MDS-derived erythroid progenitor cells that were inadvertently enriched during FACS-based stromal isolation. (Supplementary Fig. 4 , Fig. 5D , and Supplementary Fig. 8C ). Overall, we identified 61 differentially expressed genes between SF3B1 MUT and SF3B1 WT cells (Fig. 5E ), none of which were significantly upregulated in the MDS bulk BM transcriptomic data (Fig. 1E ), suggesting that the SF3B1 MUT HSPCs contribute minimally to the inflammatory milieu within the MDS. Moreover, GSEA analysis revealed that SF3B1 MUT cells upregulated pathways involved in cytoplasmic translation, cellular respiration, and nucleoside metabolism, while SF3B1 WT cells enriched pathways are related to cell differentiation, signaling, and glycerolipid metabolism (Fig. 5F ). This suggests that SF3B1 MUT cells exhibit an adaptation towards increased cell proliferation in lieu of terminal differentiation capacity, a hallmark of altered HSPC kinetics, which aligns with previous studies identifying this shift as a defining characteristic of MDS 90 , 91 .

To functionally investigate how mutated HSPCs influence stromal cells, we established a co-culture system using primary BM-derived MSCs and CD34 + HSPCs isolated from BM aspirates from donors of each group (Control, CHIP, MDS, n = 3/condition, all from the study cohort) (Fig. 5G , Supplementary Fig. 9A ). Cells were cultured together for 96 hrs, and secreted proteins in the culture supernatants were profiled using the Olink platform, focusing on factors related to hematopoietic support and inflammation (Supplementary Data 6 , Supplementary Fig. 9B ).

After 96 hrs, Control and CHIP HSPCs formed numerous cobblestone areas, which are clusters of HSCs/MPPs, while MDS HSPCs produced very few (Supplementary Fig. 9A ). In the secretome, the most striking observation was that CXCL12, along with other hematopoietic support factors such as M-CSF (CSF1), GM-CSF (CSF2) and CCL2, were strongly induced in the co-cultures with Control and CHIP-derived HSPCs but remained at baseline levels in co-cultures with MDS-derived HSPCs (Fig. 5H ). scRNA-seq of the co-cultures confirmed that these HSPC-supportive mediators were predominantly expressed by the stromal cells, except for CCL2 (Fig. 5I ). This is consistent with our earlier observation of overall reduced CXCL12 in primary MDS patient samples at both the RNA and protein levels (Fig. 4B,C ).

Consistent with our observations in primary BM (Fig. 5C,E-F , Supplementary Fig. 9A ), MDS HSPCs did not induce inflammatory programs in stromal cells (Supplementary Fig. 9B ), and HSPC intrinsic expression of pro-inflammatory cytokines such as IL-1β, CXCL8, and various interferons were even lower in MDS compared to healthy HSPCs (Supplementary Fig. 9B ).

To model a more advanced disease stage, we also co-cultured BM-MSCs with the MDS-L cell line, which represents a more differentiated, blast-like MDS population (Supplementary Fig. 10A ). Secretome profiling using the Luminex platform revealed that, in contrast to MDS HSPCs, MDS blasts induced inflammatory factors such as IL-1ɑ, MIP1, IL-1RA, while also suppressing key hematopoietic and angiogenic-support factors including CXCL12, VEGFA, and HGF (Supplementary Fig. 10B,C ). These findings suggest a disease-stage-specific modulation of stromal function.

Overall, these results imply that in healthy BM, HSPCs actively engage with the stromal niche to establish supportive feedback loops, a function that appears compromised in MDS-HSPCs. In addition, MDS blasts seem to induce a pro-inflammatory environment that may favor their malignant survival and contribute to disease progression.

MDS-specific IFN-responsive T cells interact with iMSC in MDS

To better understand the immune contributors to the inflammatory signature in CHIP and MDS BM, we investigated the T cell landscape in our dataset. Three distinct T cell populations were significantly differentially abundant in Controls: Heat Shock Protein (HSP) + T cells were elevated in CHIP and MDS, while IFN-responsive and cytotoxic CD8 + T EMRA /T EFF cells were specifically enriched in MDS (Fig. 6A , Supplementary Fig. 4J ). In addition, a modest but significant increase in Tregs was observed in MDS (Fig. 6A ), consistent with our immunophenotyping data based on CD4 and FoxP3 expression (Supplementary Fig. 1D ). While HSP + T cells have been previously described in colorectal cancer, their biological function remains unclear 92 . In contrast, both IFN-responsive and cytotoxic CD8 +  cells were recently associated with exacerbated inflammation in autoimmune diseases such rheumatoid arthritis 93 – 95 . Fig. 6 Characterization of T cells in BM of Control, CHIP, and MDS. A Spider plot of relative T cell population abundance across Control (green), CHIP (purple), and MDS (orange) donors. Proportions are probit-transformed; black outlines indicate significant differences compared to Controls. Statistical test: Fisher’s exact test with Benjamini–Hochberg procedure. B UMAPs showing the normalized expression of genes enriched in CHIP and/or MDS-associated T cells subsets: CD4/CD8 HSP + , CD8 + T EMRA , and CD8 + IFN-responsive. C Immunofluorescence of MDS BM sections showing HSP60, OASL, and GNLY (red; left to right) representing the CD4/CD8 HSP + , CD8 IFN-responsive T cells, and CD8 + T EMRA , respectively, with CD3 (yellow). Insets highlight co-localization. DAPI (blue) stains the nuclei. Arrows indicate double-positive cells. Scale bars: 25 µm. D Quantification of marker-positive cells from ( B ) among all CD3 + cells ( n = 3/condition). Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA, Tukey’s test. E Violin plots of IFNy and TNF gene expression across T cell subsets. Each dot represents the expression level of an individual cell. F Sankey diagram summarizing the proportions of NICHES-inferred cell-cell interactions between T cells and different stromal cell types across Control, CHIP, and MDS samples. Stroma-T cell schematic created in BioRender. Prummel, K. ( https://BioRender.com/wg4r69i ). G Numbers of interactions between T cells (receiver) per individual stromal cell (sender) inferred by NICHES. Point jitter is added to visualize overlapping points. H , I Total interactions between Adipo-CARs ( H ) or iMSCs ( I ) and T cell subtypes (receivers) across conditions. Numbers of interactions are normalized by the number of all possible interactions given the population sizes. J Expression of ligand-receptor pairs marking the interactions between iMSCs and CD8 + IFN-responsive cells. Source data are provided in the Source Data file and Supplementary Data 7 . n.s. not significant.

We validated the expansion of these inflammation-related T cell subsets in BM using immunofluorescence of CD3 + T cell compartment with previously reported markers also expressed in our scRNA-seq data on a subset of our cohort. We used OASL for IFN-responsive T cells 96 , GNLY for cytotoxic T EMRA /T EFF cells 97 , and HSP60 for HSP + T cells 98 (Fig. 6C , and Supplementary Fig. 11 ). The OASL-expressing IFN-responsive and GNLY-expressing cytotoxic T EMRA /T EFF cells showed a significant increase in MDS (Fig. 6D ), confirming our scRNA-seq observations. In CHIP, the expansion of HSP + T cells was visible but not statistically significant (Fig. 6D ). These data confirmed that the T cells analyzed in the scRNA-seq data were not solely circulating T cells present in the BM vasculature.

IFN-responsive T cells showed elevated transcript levels of pro-inflammatory cytokines TNFα and IFNγ in the normalized scRNA-seq data (Fig. 6E ), suggesting they may contribute to immune activation through self- or cross-activation mechanisms, thereby functioning as potent drivers of inflammation. Additionally, we previously identified a similar IFN-responsive T cell population in the BM of AML patients after allogeneic stem cell transplantation associated with imminent relapse 65 , suggesting these T cells may be ineffective in mounting anti-leukemic responses. Furthermore, comparison of the bulk BM expression signature upregulated in MDS versus Control in our NanoString data revealed that IFN-responsive T cells highly expressed genes involved in the inflammatory response ( IFIT1/2/3 , TNF ; Fig. 2I ). Together, these findings suggest that aberrant IFN-responsive CD8 T cells might be key contributors to the inflammatory milieu in MDS.

Using NICHES 87 , we investigated potential interactions between stromal populations and T cells. In both Control and CHIP samples, the Adipo-CAR1 and Adipo-CAR2 populations exhibited the most interactions with T cells respectively, whereas in MDS, iMSCs were the major interacting stromal population (Fig. 6F, G ). Furthermore, the T cell interactions in MDS differed quite dramatically from Control and CHIP where Adipo-CARs interacted with all CD4/CD8 populations: mostly with Tregs, CD4 naive/CM , and CD4 mixed cells in CHIP, while in MDS, their interactions were primarily with IFN-responsive T cells and CD4 EM cells (Fig. 6H ). Finally, while MDS-iMSCs strongly interacted with IFN-responsive and CD4 EM T cells, CHIP-iMSCs showed very little interactions with any T cell population (Fig. 6I ) , corroborating the observation that the iMSCs in CHIP and MDS represent two distinct functional populations.

We investigated the specific ligand-receptor interactions mediating the crosstalk between iMSCs and T cells in MDS. In particular, we identified several pairs enriched in MDS, including IL7 , expressed by iMSCs, interacting with IL7R ( CD127 ) on IFN-responsive T cells. Both the ligand (expressed by iMSCs) and receptor (on T cells) are upregulated in MDS compared to CHIP (Fig. 6J ). IL-7R expression can be induced by IFN signaling and IL-7 is a regulator of T cell differentiation and homeostasis 99 . Other prominent ligand-receptor interactions included Fibronectin 1 ( FN1 ), secreted by iMSCs, interacting with Integrin 4A ( ITG4A ) on T cells, as well as Thrombospondin 1 ( THBS1 ), an extracellular matrix glycoprotein produced by iMSC, interacting with CD47 on T cells (Fig. 6J ).

These results indicate that IFN-responsive T cells may contribute to the inflammatory signaling via TNFα and IFNγ secretion, while the blast-induced ECM remodeling of iMSCs in MDS may specifically attract inflammatory T cell populations.

Aberrant cross-talk of MSCs and mutated HSPCs promotes vascular niche remodeling in MDS

Our integration of the targeted bulk RNA-seq data with healthy BM indicated elevated expression of inflammation-associated genes in endothelial cells in MDS (Fig. 1G ). Previous studies have described expansion and remodeling of BM vasculature and increased expression of angiogenic factors including VEGFA in myeloid malignancies, including MDS 100 – 103 . Due to the limited availability of endothelial cells in human BM aspirates and the challenges in capturing them through FACS and scRNA-seq, we assessed the angiogenic potential of BM cell types: HSPCs, T cells, and stromal populations (Fig. 7A , and Supplementary Fig. 12A–C ). We found that the Adipo-CAR and iMSC populations were primarily responsible for angiogenesis-related condition-dependent changes (Fig. 7A , Supplementary Fig. 12C ), while other cell populations showed minimal angiogenic potential (Supplementary Fig. 12A, B ). This is in line with the reported localization of Adipo-CARs in vascular BM niches in mouse and human BM 47 , 104 – 106 . Specifically, Adipo-CARs in MDS patients upregulated several known secreted vasculature-remodeling factors, including, VEGFA and FSTL1 , which were also secreted by iMSCs both in CHIP and MDS (Fig. 7B ). These results suggest that on top of increased inflammation and reduced HSPC-support, the remodeled stroma in MDS may reshape the BM vasculature. We leveraged immunofluorescence imaging on FFPE BM biopsies to investigate this increased angiogenic potential within the MDS niche on the arteriolar and sinusoidal blood vessels (Fig. 7C ). We quantified the microvasculature density (MVD) for both arteriolar (UEA1 + CD105 - ) and sinusoidal (UEA1 - CD105 + ) vasculature, which revealed a significant increase in MVD for both structures in MDS compared to Control and CHIP (Fig. 7D, E ). Additionally, we observed a reduction in the average distance between sinusoidal and arteriolar vessels in MDS compared to CHIP and Control (Fig. 7F ). Fig. 7 Assessment of vascular alterations in human CHIP, MDS, and a CHIP Dnmt3a mouse model. A Violin plots of angiogenesis signature scores across stromal cell types comparing Control, CHIP, and MDS. B Violin plots of representative genes within the angiogenesis signature across the stromal cell types comparing Control, CHIP, and MDS. C Immunofluorescence of BM sections showing UEA1 (green, all vasculature) and CD105 (red, sinusoids) and DAPI (blue) across Control, CHIP, and MDS. Asterisks are highlighting autofluorescence from erythrocytes. Scale bars: 25 µm. D , E Quantification of CD105 + sinusoids ( D ) and CD105 - UEA1 + arterioles/arteries ( E ) across conditions ( n = 3/condition). Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA, Tukey’s test. F Scatter plot depicting the nearest distance of the vasculature (in µm) in Control, CHIP, and MDS samples. Shortened distances in MDS indicate disrupted vasculature organization. Medians with 95% confidence intervals are shown. Statistical test: one-way ANOVA with FDR correction. G Schematic of the CHIP Dnmt3a -mutant mouse study groups: femurs were collected from young control (24 weeks), young Dnmt3a R878H (24 weeks), and old control (68 weeks), n = 3/group. Created in BioRender. Prummel, K. ( https://BioRender.com/pqyu7b8 ). H Giemsa staining from young control, young Dnmt3a R878H , and old control mouse femurs, illustrating reduced cellularity in young Dnmt3a R878H and old mice ( n = 3/group). Scale bars: 200 µm. I Bar plot showing the cellularity (BM/adipocyte ratio) across the groups ( n = 3/group). Both young Dnmt3a R878H and old control mice display significantly reduced cellularity compared to young controls. Means with SD are shown. Statistical test: one-way ANOVA, Tukey’s test. J Immunofluorescence of EMCN (red) and DAPI (blue) in femur sections from young control, young Dnmt3a R878H , and old control mice. Scale bars: 100 µm. K Quantification of the total sinusoid area in the femur ( n = 3/condition). Means with SD are shown. Statistical test: two-way ANOVA, Tukey’s test. L Quantification of the mean sinusoidal area (sinusoid size) in the femur ( n = 3/condition). Means with SD are shown. Statistical test: one-way ANOVA with FDR correction. M Schematic illustration summarizing BM niche remodeling in CHIP and LR-MDS. A , B Statistical test: two-sided Wilcoxon rank-sum test with Benjamini–Hochberg procedure. Created in BioRender. Guezguez, B. ( https://BioRender.com/8tl5dtf ). Source data are provided in the Source Data file and Supplementary Data 7 . n.s. not significant.

Despite the presence of iMSCs with angiogenic potential within CHIP donors (Fig. 7A, B ), no significant vascular changes were observed (Fig. 7C–F ). Given the correlation between osteoporosis risk and a higher VAF% of the CHIP mutation DNMT3A R882H ( > 10%) 107 , 108 , we further explored the yet uncharted impact of VAF on vasculature remodeling. We utilized a CHIP mouse model carrying the Dnmt3a R878H mutation, which is homologous to a common human DNMT3A R882H CHIP mutation, where the majority of hematopoietic cells carry this mutation 109 . We analyzed the general BM morphology and vasculature within femurs of young control mice (24 weeks), young Dnmt3a R878H/+ mice (24 weeks), and old control mice (68 weeks) (Fig. 7G ). First, we showed that Dnmt3a R878H/+ decreased the overall cellularity in BM, driven by the expansion of adipocytes particularly in the femoral head (Fig. 7H,I ). This phenotype may reflect impaired bone integrity, consistent with prior reports of increased marrow adiposity and bone loss in Dnmt3a -mutant CHIP mouse models 107 , 110 . Next, we observed that young Dnmt3a -mutant mice showed increased sinusoidal vessel density (Fig. 7J, L ) and general morphological alterations including increased dilation of the sinusoidal vasculature (Fig. 7K ), which is in line with our observations in human MDS (Fig. 7C-F ). Arterioles and arteries did not show any significant differences between conditions (Supplementary Fig. 12D ). This suggests that the higher burden of mutant cells in CHIP may drive BM niche architecture remodeling, however, additional studies in human individuals with a higher CHIP VAF will be needed to validate this.

Altogether, these results suggest that mutant HSPCs can significantly alter the BM niche vasculature, potentially via stromal remodeling. These findings provide new insights into how CHIP may prime the BM microenvironment for malignant transformation.

Discussion

Prior culture-based and bulk transcriptomic studies have suggested an inflammatory skewing of MSCs in aging and chronic BM disorders 42 , 111 – 115 . Our study builds upon these observations by providing a comprehensive single-cell characterization of the BM microenvironment across healthy aged controls, CHIP, and MDS, revealing inflammatory remodeling of the hematopoietic BM niche, characterized by coordinated stromal and immune perturbations (Fig. 7M ).

A central finding is the emergence of a distinct inflammatory mesenchymal stromal cell (iMSC) population, absent in healthy controls but expanded in CHIP and MDS. These iMSCs display a pro-inflammatory transcriptional profile and may serve as active responders to hematopoietic stress. Their presence in individuals with relatively low-risk mutations ( DNMT3A , TET2 , and SF3B1 ) suggests that early clonal events can coincide with - or potentially initiate - niche remodeling. Although these mutations differ in their leukemic potential 116 , 117 , they have all been linked to inflammatory signaling: DNMT3A -mutant HSPCs are associated with increased levels of inflammatory cytokines and can modulate stromal cell fate 35 , 107 , 118 , while SF3B1 mutations are associated with dysregulated innate immune activation 119 – 122 . Emerging evidence, including work by Jakobsen et al. 35 and Scherer et al. 123 , points to a more complex relationship. Rather than mutations alone driving inflammation, a pre-existing inflammatory microenvironment may act as a selective force promoting the expansion of mutant HSPCs. In line with these studies, our data supports a strong association of CHIP and MDS with an inflamed BM niche. While the origin of inflammation in non-malignant clonal states like CHIP remains uncertain, our findings suggest that MDS blasts can directly induce inflammatory remodeling of stromal cells. Moreover, a parallel study by Chen et al. 124 independently identifies iMSCs in a genetically diverse LR-MDS cohort, reinforcing the robustness and broader relevance of this inflammatory stromal phenotype.

Phenotypically, iMSCs in our study were characterized by elevated CD44 and IL-1R1 expression, both canonical NF-κB target genes induced by TNF and IL-1 signaling. These features mirror cancer-associated fibroblasts (CAFs) in solid tumors 125 , where IL-1R1 has been linked to enhanced motility, inflammatory signaling, and immune suppression 126 . The shared pro-inflammatory program between iMSCs and CAFs suggests a conserved stromal response to chronic inflammatory cues. This is further supported by the skewed MSC lineage composition observed in CHIP and MDS, marked by expansion of osteo-lineage subsets and contraction of adipogenic progenitors, consistent with the lineage shift commonly associated with inflammatory microenvironments 127 , 128 .

While both CHIP and MDS display the presence of iMSCs, our data suggest transcriptional and functional divergence between these states. In CHIP, inflammatory and hematopoietic support features within MSCs tend to be mutually exclusive (i.e., iMSCs have low support signatures), whereas in MDS these features become increasingly decoupled and can co-exist within the same iMSC population. Functional in vitro data support these observations: whereas healthy and CHIP HSPCs induced expression of stem cell-supportive factors such as CXCL12 in MSCs, MDS HSPCs and MDS-L (LR-MDS blast-like) cells failed to do so. This aligns with our single-cell data showing reduced CXCL12 expression in MDS stroma. Notably, MDS-L also triggered inflammatory activation in MSCs, suggesting that early niche remodeling is already occurring in LR-MDS disease. The loss of stem cell support observed in LR-MDS stroma contrasts with findings on HR-MDS by Jann et al. 129 , who identified a CXCL12–high expressing MSC subset (StemCAR) enriched in response to high-risk MDS blasts and diminished following hypomethylating agent therapy. These discrepancies may reflect disease stage–specific effects on MSC function. Future studies are necessary to delineate how MSC programs diverge across the spectrum of low- and high-risk MDS.

These stromal changes are accompanied by vascular remodeling. In MDS, we observed increased angiogenic gene expression (e.g., VEGFA ) in Adipo-CARs and iMSCs, correlating with sinusoidal expansion and increased BM permeability. This is consistent with previous reports showing that cultured MDS-derived MSCs express elevated levels of VEGFA and ANGPTL4 42 . While human low-VAF CHIP donors lacked overt vascular changes, high-VAF Dnmt3a CHIP mice exhibited clear vascular remodeling, including disorganized sinusoids and barrier dysfunction. This suggests that vascular remodeling in CHIP may be contingent on clonal burden, and becomes more pronounced at later stages of clonal expansion. Increased vascular permeability may facilitate aberrant HSPC activation, mobilization, and clonal dominance; thereby offering a potential mechanistic link between inflammation, vascular dysfunction, and disease progression. Spatially resolved transcriptomics and high-resolution vascular imaging will be essential to delineate how angiogenic remodeling and niche support vary across distinct anatomical compartments of the human BM in CHIP and MDS.

The iMSC expansion also coincided with selective changes in the BM T cell landscape. CHIP donors were marked by enrichment of HSP-expressing CD4⁺/CD8⁺ T cells, a subset not well-defined functionally but implicated in chronic inflammatory responses 130 , 131 . In MDS, IFN-responsive CD8⁺ T cells were expanded and showed predicted interactions with iMSCs via IL-7/IL-7R and ITGA4/FN1 axes. This crosstalk may reinforce local inflammation and T cell retention in the BM. Additionally, the cytotoxic CD8 + T EMRA cells present in MDS may harbor tumor-specific TCRs, which is consistent with the notion of a systemic immune response 132 , 133 . Thus, there is a need for increasing research efforts, including TCR-sequencing, to deepen our understanding of T cell biology and implication in CHIP and MDS.

Transcriptional profiling also revealed other dysregulated immune cell types in MDS, including non-classical monocytes, characterized by elevated expression of inflammatory markers such as ADORA2A and the alarmin S100A8 . A recent preclinical study demonstrated that S100A9, secreted by monocytes/macrophages and forming a heterodimer with S100A8, can drive inflammatory activation of stromal cells and impair hematopoietic function in MDS 134 . These findings raise the possibility that monocyte-driven inflammation may contribute to the disrupted immune microenvironment in MDS, however, further investigation is needed to clarify their functional role and interplay with stromal alterations.

The transcriptional signature of iMSCs appears conserved across hematologic malignancies. We observed strong overlap between iMSCs identified in CHIP/MDS and those reported in AML 83 and multiple myeloma (MM) 74 . Similar stromal phenotypes and vascular remodeling have also been reported in MGUS, the precursor state to MM and the lymphoid analogue of CHIP 135 , 136 . Together, these findings support a unifying model in which early oncogenic or clonal events - regardless of lineage - either trigger a conserved stromal inflammatory response or are selected by an inflammatory environment.

The presence of iMSCs in both premalignant (CHIP, MGUS) and malignant (MDS, AML, MM) states raises important questions about their ontogeny, plasticity, and therapeutic relevance. Whether iMSCs arise from pre-existing CAR-like populations or represent a distinct lineage under inflammatory stress remains to be resolved. Their stability and reversibility in vivo, particularly in response to anti-inflammatory or niche-targeted interventions, may inform their utility as biomarkers or therapeutic targets. Future efforts should focus on spatial mapping, clonal tracing, and stromal manipulations models to define roles of iMSCs and aberrant immune cells in early hematologic disease.

In conclusion, our study identifies inflammatory stromal remodeling as a key hallmark in both CHIP and MDS, with iMSCs emerging as key mediators. While CHIP HSPCs preserve the capacity to induce niche support, MDS HSPCs fail to do so, linking stromal dysfunction to impaired hematopoiesis in MDS. Overall, these findings underscore the role of the microenvironment in early myeloid disease and may contribute to the development of future strategies aimed at intercepting pre-malignant hematopoiesis.

Methods

Patient cohort and tissue material processing

Overall, BM samples from 32 MDS patients, 17 CHIP donors, and 35 healthy (Control) donors were analyzed within this study. Human BM aspirate and hip and femur trephine core biopsies were obtained from informed consenting donors enrolled in the BoHemE study ( NCT02867085 ). All human material have been approved by local ethics committees at the University Hospital Dresden (TU Dresden, reference nr.: EK 393092016) and University Hospital Leipzig (University of Leipzig, Faculty of Medicine, reference nr.: 137/19-1k) and conducted in accordance with the Declaration of Helsinki. A detailed overview of the patient cohort, including age and sex, can be found in Supplementary Data 1 . We aimed for a sex-balanced cohort and included sex-specific analyses for the NanoString data (Fig. 1 ) and BM cellularity (Supplementary Fig. 5B, C ). Aspirates were processed by density gradient centrifugation using Ficoll-Paque Premium 1.073 (Cytiva) to deplete erythrocytes and were stored in freezing medium (90% FCS + 10% DMSO (Sigma-Aldrich)) in liquid nitrogen. Trephine cores were fixed in 4% paraformaldehyde (PFA) for 24 hrs, then transferred to PBS with sodium azide (0.3%) until further processing. Samples were decalcified for 48 hrs using Osteosoft (Merck KGaA) at 37 °C and embedded in paraffin.

Animal models

Femurs from control and DNMT3A-R878H mutant mice were obtained from The Jackson Laboratory, USA. Briefly, Dnmt3a fl-R878H/+ mice (JAX:032289) were crossed with B6.Cg-Tg(Mx1-cre)1Cgn/J mice (JAX:003556, referred to as Mx-Cre). In all experiments, control (+/+) mice carried a single copy of the Mx-Cre allele. Mice were maintained under controlled environmental conditions with a 12 hr light/12 hr dark cycle. Ambient temperature was kept at 18–21 °C, with relative humidity maintained at ~55%. To induce Mx-Cre, mice were injected intraperitoneally with 15 mg/kg high molecular weight polyinosinic-polycytidylic acid (polyI:C) (InvivoGen) once every other day for a total of five injections. In all experiments, mice were used > 4 weeks after polyI:C administration. Following the Jackson Laboratory recommendations for aging stages, mice up to 26 weeks (6–7 months) were considered young, while mice 68 weeks (15–16 months) and older were considered old. Due to the development of ulcerative dermatitis, old Dnmt3a fl-R878H/+ mice could not be included in the study. All experimental mice were female and were euthanized by cervical dislocation at 26 and 68 weeks of age for phenotypic analysis and tissue collection. Femurs were fixed in 4% PFA at 4 °C overnight and stored in PBS with sodium azide (0.3%). After decalcification, bones were paraffin embedded. The Jackson Laboratory Institutional Animal Care and Use Committee approved all experiments.

Flow cytometry of BM aspirates

Diagnostic flow cytometry

Diagnostic flow cytometry was performed within 24 hrs after BM aspiration. Prior to antibody staining, erythrocytes were removed by lysis for 10 min at room temperature (RT) using BD Pharm-Lyse (1:10 dilution with distilled water; BD Biosciences), followed by two washing steps with PBS. For surface labeling, the cells were incubated with one of the five 8-color antibody panels (see Supplementary Data 3 for antibody panels) for 15 min at RT in the dark 137 . The five panels allowed a comprehensive analysis of the BM aspirates as proposed in the guidelines of the iMDSFlow 138 . Subsequently, cells were washed twice and resuspended in PBS. Samples were stored at 4 °C and measured within 1 hr. Samples were acquired on a Canto II (BD Biosciences). At least 200,000 events were acquired per sample. For data analysis, a hierarchical gating strategy according to the iMDSFlow 138 was applied: (1) exclusion of doublets (FSC-A vs. FSC-H) and (2) of debris (FSC vs. SSC), (3) gating of CD45 dim/+ leukocytes (SSC vs. CD45).

T cells and inflammatory MSC flow cytometry

BM aspirates from the curated BoHemE study ( NCT02867085 ) were thawed in 100% FCS (Thermo Scientific) + 100 µg/ml DNAse I (Sigma-Aldrich), centrifuged at 300 g for 5 min and suspended in DMEM (Gibco) + 10% FCS + 100 µg/ml DNAse I. When necessary, cell pellets were treated with 1X Red Blood Cell Lysis (Invitrogen) for 10 min at RT. Cells were washed with 2% FCS/PBS. Live/dead staining (e.g., Zombie Aqua, Biolegend) was performed in PBS, followed by antibody staining for 30 min at 4 °C (see Supplementary Data 3 for antibody panels and dilutions). Stained cells were analyzed with a FACSAria Fusion (BD Biosciences) or Symphony A3 (BD Biosciences).

Immunohistochemistry and histopathological stainings of FFPE bone marrow sections

Serial sections of human and mouse FFPE BM biopsies were prepared at 3–5 μm thickness on coated microscope slides (Dako FLEX, Agilent) and processed for immunohistochemistry/immunofluorescence (IHC-IF). Deparaffinization (30 min), rehydration (2×10 min), and antigen retrieval (10 min) were performed by heating sections in Trilogy™ buffer (Cell Marque, Millipore Sigma) at 105 °C and 1.2 bar for 10 min in a pressure cooker. Sections were permeabilized with 0.25% Triton-X (Sigma) in PBS for 10 min, then blocked for 30 min with 5% normal donkey serum (Jackson ImmunoResearch) in PBS-Tween20 (0.05%, Sigma). Primary antibodies were incubated at RT for 1 hr in 1% normal donkey serum PBS-Triton-X100 (0.2%). Samples were washed twice with 1% donkey serum PBS-Tween20 (0.05%) for 5 min, then incubated with secondary antibody for 1 hr at RT. Samples were washed again with 1% donkey serum PBS-Tween20 (0.05%) for 5 min, then incubated for 3 min with the TrueView (Vector Laboratories) quenching reagent to reduce tissue autofluorescence. Slides were washed with PBS for 5 min, mounted with DAPI-containing mounting medium (Abcam), and imaged. All used antibodies in IHC-IF are detailed in Supplementary Data 3 . Additional FFPE sections of BM biopsies were processed for standard pathology diagnosis with Giemsa staining by the Core Facility Biobank of the UMC Mainz.

Image data acquisition and analysis

IHC-IF stained sections were imaged for semi-quantitative analysis on an Opera Phenix (PerkinElmer) high-content screening system with 40x objective (water, NA 1.1). Image analysis was done using Harmony 4.8 (PerkinElmer), detailed workflows for image analysis can be found in Supplementary Fig. 5 .

Giemsa-stained sections were imaged using an EVOS M5000 microscope with 4x objective (air, NA 0.13). Captured images of human BM biopsy sections were analyzed for cellularity analysis using the Weka-Segmentation plugin for Fiji; for each condition, one representative image was used as training data 139 . For each sample, three different fields of views were analyzed and the resulting probability maps were checked for successful segmentation. The detailed workflow can be found in Supplementary Fig. 5 . For the Giemsa staining of mouse femurs, cellularity analysis was conducted using QuPath software with the MarrowQuant plugin 140 .

Co-culture experiments

HSPC – MSC co-cultures

Primary BM-derived MSCs were isolated from a healthy young male donor (32 years old) and used at passage 1 (P1), after informed consent and in accordance with procedures approved by the local ethics committee (Comité de Protection des Personnes–Île-de-France V, Hôpital Saint-Antoine, Paris) and with the Declaration of Helsinki. For secretome and scRNA-seq analysis, MSCs were seeded at a density of 30,000 cells per well in gelatin-coated 12-well plates (Costar, Corning Incorporated) with 1 ml Myelocult H5100 medium (Stemcell Technologies) with 1 µM Hydroxycortisone (Sigma). After 24 hrs, HSPCs were isolated from BM aspirates using CD34 + magnetic bead–based positive selection (Miltenyi Biotec) and added to the MSCs at a 1:1 ratio (30,000 HSPCs per well). Co-cultures were maintained for 96 hrs under standard conditions (37 °C, 5% CO₂, > 90% humidity). For each donor, three technical replicates were set up. Additional control wells containing MSC mono-cultures and medium-only wells (Myelocult in gelatin-coated plates without cells) were included in each experiment. Three independent rounds of co-culture experiments were performed, each including one donor per condition (Control, CHIP, and MDS) to minimize batch effects, resulting in three biological replicates per condition (see Supplementary Fig. 9A ). After 96 hrs, brightfield images were acquired with a Nikon Eclipse TS100 (with 4x (X4, X0.13, WD 16.5) and 40x (X40, X0.55, WD 2.1) objectives) to capture the culture morphology.

Next, cells were trypsinized (Trypsin-EDTA, Gibco) and centrifuged at 300 g for 5 min to separate cells from supernatants. Supernatants were centrifugated at 10,000 g for 10 min at 4 °C, aliquoted, and stored at -70/80 °C for secretome analysis. The single cell suspensions were immediately processed for scRNA-seq.

MDS-L – MSC co-cultures

The human BM stromal cell line hTERT-MSC (Cat. No. T0523, Applied Biological Materials Inc.) is maintained with aMEM (Gibco) supplemented with 7% human Platelet Lysate (hPL, Macopharma) and Penicillin/Streptomycin (Gibco). The MDS-L cell line (derived from a refractory anemia with ring sideroblasts (RARS) patient and SF3B1 WT 141 ) were kindly gifted by Dr. Kaoru Tohyama (Kawasaki Medical School) and cultured in RPMI 1640 containing Glutamax (Gibco), supplemented with 10% FCS (Gibco), 40 ng/ml GM-CSF (Peprotech), and Penicillin/Streptomycin.

All cell lines were maintained up to 70–80% confluency under standard conditions (37 °C, 5% CO₂, > 90% humidity). All cell lines are tested at regular periods for mycoplasma and authenticated by Single Nucleotide Polymorphism (SNP)-profiling (Multiplexion, Heidelberg, Germany).

For secretome analysis, hTERT-MSCs were seeded at a density of 10,000 cells in 96-well Spheroid Microplates (Corning, USA) in 100 µl aMEM + 7% hPL per well and cultured for up to 72 hrs to allow MSC spheroid formation. Afterwards, 1,000 MDS-L cells were added to the MSC spheroids in fresh culture media. MSC spheroid and MDS-L mono-cultures were included as controls. After 96 hrs of co-culture, 50 µl supernatant was collected per well and pooled across wells (up to 200 µl per condition) to collect sufficient material for downstream analyses. Next, the supernatants were centrifuged at 300 g for 5 min, followed by 10,000 g for 5 min to exclude any cell debris, and stored at -20 °C until further use for cytokine analysis.

Secretome analysis

Olink (HSPC – MSC co-cultures)

Secretome profiling of the HSPC–MSC co-cultures was performed using a customized Olink ® Flex panel with 30 targets (Olink Proteomics AB, Supplementary Data 6 ). Supernatant was undiluted and incubated with 30 paired oligo-nucleotide-labeled antibodies. After hybridization and DNA polymerization, the reporter sequences were quantified using real-time PCR (BioMark, Fluidigm), and data were processed using the software NPX Manager (Olink Proteomics AB). The resulting threshold cycle (Ct)-data were processed for quality control and normalized, using internal and external controls. The protein levels were reported in absolute values (pg/ml) and normalized as Normalized Protein eXpression (NPX) values. The three technical replicates were merged taking the mean and used for further analysis.

Luminex (MDS-L – MSC co-cultures)

Cytokine profiling was performed using a custom 20-plex ProcartaPlex kit (Invitrogen, ThermoFisher, USA, Supplementary Data 6 ). Supernatants and standards were processed in duplicate on a MAGPIX instrument (Luminex Corp), following the manufacturer’s instructions. The MAGPIX software (Luminex Corp) was used to calculate the cytokine concentrations after the collection of standard curves (pg/ml).

All measured cytokine concentrations were assessed for statistical evaluation and graphical reports were established with Graphpad Prism version 10.4.1 (GraphPad Software, San Diego, California, USA).

NanoString nCounter gene expression analysis

36 whole BM aspirates (12 MDS patients, 12 CHIP donors, and 12 age-matched Controls), curated as part of the BoHemE study ( NCT02867085 ), were used for NanoString analysis. Total mRNA was isolated using AllPrep DNA/RNA Mini Kit (Qiagen) following the manufacturer’s instructions. 150–300 ng RNA was extracted per sample. The expression of 1255 unique genes were analyzed in the NanoString nCounter Pro Analysis System using the PanCancer Immune profiling panel and Immune Exhaustion panel (NanoString Technologies). The nSolver software package and nSolver Advanced Analysis module (NanoString Technologies) were used to evaluate the determined transcript counts. Quality-checked raw data was normalized utilizing the geometric mean of the housekeeping reference genes and the code sets’ internal positive controls were used to exclude samples that are outliers. Statistics were calculated in R based on normalized and log2-scaled counts applying an empirical Bayes moderated t-statistics tests using limma (v.3.58.1) 142 and corrected for multiple testing adopting the Benjamini-Hochberg procedure. Genes were determined differentially expressed with FDR < 0.05. For comparing CHIP and Control samples, log2-fold changes were ranked based on their magnitude without applying an FDR cut-off and determined as differential with an absolute fold change > 0.5. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of differentially expressed genes was done with clusterProfiler v.4.10.1 143 using all genes measured in the same panel as background. P values of GO and KEGG enrichment results were corrected for multiple testing applying Benjamini-Hochberg procedure and determined significant with an FDR < 0.05. Top 10 significant GO- and KEGG terms were selected by magnitude of FDR for plotting. Additionally, significantly changed genes were submitted to Gene Set Enrichment Analysis (GSEA) against the Hallmark and the C6 gene sets 144 , 145 .

Healthy BM single cell atlas integration with NanoString data

Publicly available healthy BM scRNA-seq dataset from 47 was acquired. Expression of genes found to be significantly up- or down-regulated between the MDS and Control samples in NanoString analysis (both panels used) at FDR < = 0.05 was aggregated based on either the cell type annotation provided by the authors (Supplementary Fig. 2E ) or a simplified annotation grouping related cell types (Fig. 1E ) using Seurat’s AverageExpression function and visualized using the ComplexHeatmap R package 146 .

Cell isolation/processing of hematopoietic and non-hematopoietic cells for scRNA-seq

BM aspirates from a representative subset of donors in this study (3 age-matched Controls, 3 CHIP, and 4 MDS donors), curated as part of the BoHemE study ( NCT02867085 ), were included for scRNA-seq. The samples were thawed in 100% FCS + 100 µg/ml DNAse I, centrifuged at 300 g for 5 min and suspended in DMEM + 10% FCS + 100 µg/ml DNAse I. When necessary, cell pellets were treated with 1X Red Blood Cell Lysis for 10 min at RT. Cells were washed with 2% FCS/PBS. Live/dead staining (FV780, Thermo Fisher) was performed in PBS, followed by antibody staining for 30 min at 4 °C in CellCover (Anacyte Laboratories) (see Supplementary Data 3 for antibody panel and dilutions). Stained cells were sorted into PBS + 5% BSA with a FACSAria™ Fusion (BD Biosciences) equipped with a 130 µm nozzle into BSA-coated LoBind 1.5 ml tubes (Eppendorf).

scRNA sequencing

10x Genomics (droplet-based)

Primary patient samples

The HSPC and T cell fractions were combined and subjected to scRNA-seq in parallel with the stromal fraction, ensuring clean separation of the populations and minimizing contamination of blood or immune cells in the stromal fractions. 10x Genomics was performed using the Chromium Next GEM (Gel Bead-In Emulsions) Single Cell 3’ kit (v3.1, 10x Genomics) according to the manufacturer’s instructions. We pooled the T cells (CD45 high CD34 - CD14 - CD235a - CD71 - CD3 + ) and HSPC (CD45 dim CD34 + CD14 - CD235a - CD71 - ) fractions in 1 lane and the stromal cells (CD34 - CD45 - CD14 - CD235a - CD71 - CD38 - ) in a separate lane. Sorting and single-cell processing was performed in 3 batches on different days, with each batch consisting of a mix of Control, CHIP, and MDS donors to correct for batch effects. Libraries were prepared based on manufacturer’s instructions, with 13–14 PCR cycles for the final library generation. Following, the libraries were equimolarly pooled and sequenced using Illumina NextSeq 2000 with P2 and P3 flow cells.

HSPC-MSC co-cultures

The HSPC-MSC co-cultures (including both stromal and HSPC fractions) were subjected to scRNA-seq, using Chromium Next GEM Single Cell 3’ kit (v3.1, 10x Genomics), according to manufacturer’s instructions. Single-cell processing was performed in three independent batches, each containing one donor per condition (Control, MDS) and a MSC mono-culture control to correct for batch effects. We loaded each condition into a separate 10x lane. Libraries were prepared based on manufacturer’s instructions and sequenced using Illumina NovaSeqX plus.

CEL-Seq2 (plate-based)

The CEL-Seq2 protocol was carried out as according to published information 147 : CD45 - CD14 - CD235a - CD71 - CD38 - CD271 + single cells were sorted in 5 384-well plates containing 5 μl of CEL-Seq2 primer solution in mineral oil (24 bp polyT stretch, a 4 bp random molecular barcode (UMI), a cell-specific barcode, the 5 ’ -Illumina TruSeq small RNA kit adapter, and a T7 promoter), provided by Single Cell Discoveries (Utrecht, the Netherlands). After sorting, the plates were centrifuged for 1 min at 1000 g and immediately placed on ice and stored at −70 °C. Single Cell Discoveries further processed the plates. In brief, ERCC Spike-in RNA Mix (Ambion, 0.02 μl of 1:50,000 dilution) was added to each well before cell lysis with heat shocking. Reverse transcription and second-strand synthesis reagents were dispensed using the Nanodrop II (GC biotech). After generation of cDNA from the original mRNA, all cells from one plate were pooled and the pooled sample was amplified linearly with in vitro transcription 148 . To generate sequencing libraries, RPI-series index primers were used for library PCR. Libraries were sequenced on an Illumina Nextseq500 using 75 bp paired-end sequencing.

SNP analysis for genotyping of donors in scRNA-seq

Experimental

gDNA was isolated from donors BM cells using the Macherey-Nagel NucleoSpin Tissue kit (Fisher Scientific) and was provided in a concentration ranging from 50 - 140 ng/ul for genotyping using the Infinium CoreExome-24 v1.4 BeadChip (Illumina), including 567,218 markers.

Computational

Raw intensity data were genotyped using the Illumina Array Analysis Platform Genotyping Command Line Interface v1.1. Resulting GTC files were converted to VCF using Illumina’s GTCtoVCF (v1.2.1) software ( https://github.com/Illumina/GTCtoVCF ) with GRCh37 reference genome and ‘--skip-indels‘ option. All VCF files were merged and indexed using bcftools 149 .

To increase the number of usable genotypes, we performed imputation using the Michigan Imputation Server 150 . VCF files were split by chromosome and for each of the autosomes imputation was performed using HRC (Version r1.1 2016) reference panel 151 , Eagle v2.4 phasing 152 , and an Rsq filter threshold of 0.3. Imputed genotypes for individual chromosomes were merged and lifted over to GRCh38 using the LiftoverVcf program from Picard tools by the Broad Institute ( https://broadinstitute.github.io/picard/ ).

scRNA-seq preprocessing and quality control

10x Genomics data

Primary patient samples

Reads were aligned to the GRCh38 (v.2020-A) reference genome and quantified using cellranger count (10x Genomics, v.7.0.0). To assign individual cells to the corresponding donors, we used Souporcell 153 with a list of common variants from 1000 genomes project 154 filtered to include variants with ≥2% allele frequency and a list of known genotypes derived from the SNP analysis. To increase the power of cell assignments, BAM files from individual libraries that share the same donors were combined and provided as input to Souporcell. Counts from cellranger count were adjusted for ambient RNA contamination using SoupX 155 . Corrected counts were analyzed using scDblFinder 156 and used for downstream analysis using Seurat v4.1.1 157 .

Cells with less than 200 genes and more than 50% mitochondrial genes per cell as well as the genes found in fewer than 3 cells were excluded from the downstream analysis. After the first round of preprocessing, normalization, and clustering, we identified 2 clusters that were enriched in doublets identified by Souporcell (Supplementary Fig. 4B, C ). Additionally, we identified several clusters of hematopoietic cells deriving from libraries of pooled stromal cells, namely erythroid progenitor cells, monocyte progenitor cells, and plasma cells (Supplementary Fig. 3F ). All cells were classified as doublets by either Souporcell or scDblFinder, from doublet-enriched clusters, and clusters corresponding to hematopoietic cells deriving from the stromal libraries pools were removed and normalization, clustering and dimensionality reduction was performed again.

HSPC-MSC co-cultures

Reads were aligned to the GRCh38 (v.2024-A) reference genome and quantified using cellranger count (10x Genomics, v.9.0.0). Resulting count tables were used for downstream analysis using Seurat v4.1.1 157 . Cells with fewer than 500 and more than 8000 genes expressed as well as those with more than 20% mitochondrial genes per cell were excluded from further analysis.

CEL-Seq2 data

CEL-Seq2 data was processed using a custom Snakemake 158 pipeline. In short, a hybrid genome reference and annotation was prepared by combining human genome reference GRCh38 and a gene annotation from Gencode Release 32 with the reference sequences and annotation of the Ambion ERCC RNA Spike-In Mix (Thermo Fisher Scientific). Raw Fastq files were aligned against the hybrid reference using STAR aligner (v2.7.10b) 159 . Resulting BAM files were filtered to remove reads without an assigned cell barcode or UMI using SAMtools 149 and GNU grep. Filtered BAM files were used to create the gene count tables using the dropEst pipeline (v0.8.6) 160 with adjusted settings for CEL-Seq2 experiment and counting of any UMIs that overlap either exonic or intronic regions. The results were additionally corrected for UMI collisions and sequencing errors using dropEst. Corrected counts were used for downstream analysis and integrations using Seurat v4.1.1 157 . Cells with fewer than 1000 UMI counts or belonging to a sample with fewer than 10 cells recovered were removed.

Normalization, dimensionality reduction, and clustering scRNA-seq data

Primary BM samples

Following quality control and prior to dimensionality reduction, the raw counts were normalized using SCTransform v2 161 . 3000 most variable features were selected using SCTransform. Ribosomal, mitochondrial, sex chromosome genes and transcripts were excluded from the variable features. Selected features were used for principal component analysis (PCA) 162 and then uniform manifold approximation and projection (UMAP) on the first 50 principal components 163 . Cells were then grouped into clusters using the Leiden algorithm 164 and the optimal clustering resolution was determined using Clustree 165 .

Full dataset was classified into 3 populations of interest: HSPCs, T cells, and stromal cells; and each subset was normalized separately using SCTransfrom as described above. For integration within the HSPC and T cell populations, Seurat objects were split either based on disease status for T cells or the donor ID for HSPCs, renormalized using SCTransform as described above, and integrated using anchor-based workflow from Seurat. Stromal population from 10x was integrated with the CEL-Seq2 data based on the experiment type using anchors integration.

Marker genes of the unsupervised clusters were identified using Seurat’s FindAllMarkers function. Genes considered were detected in at least 25% of cells per cluster (min.pct=0.25) and had at least 0.25 log2-fold change relative to all cells outside the cluster (logfc.threshold=0.25, only.pos=T). Differentially expressed genes between clusters were identified using likelihood ratio test for logistic regression model predicting group membership (test = ”LR”).

HSPC-MSC co-cultures

Raw counts were Log-normalized using NormalizeData function. Individual samples were integrated using the Seurat’s anchor-based integration. Top 2000 most variable features were identified using FindVariableFeatures function and used to perform the PCA. Cells were clustered using the Leiden algorithm with the resolution parameter of 0.01 resulting in 2 clusters separating HSPC and stromal cells. Individual cells coming from the MSC mono-culture condition, but clustering with HSPCs were removed from further analysis. Average expression of genes corresponding to proteins in the Olink secretome analysis was calculated per sample separately for HSPC and stromal cells.

Regulon activity quantification in scRNA-seq data

Transcription factor regulons were inferred using the pySCENIC method (v.0.11.1) 166 via a custom Snakemake pipeline. Raw expression data was randomly split into 10 subsets. Initial co-expression modules were constructed using the GRNBoost2 algorithm and enriched for transcription factor motifs using the human motif databases v10 (hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather and hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather) from cisTarget ( https://resources.aertslab.org/cistarget/databases/ ) for each of the subsets with 50 repeats each. Resulting regulons were filtered to only include target genes that were identified for a given regulon in at least 5 independent runs and combined. Regulon activities were quantified using the AUCell method of pySCENIC. For the T cells populations, regulons identified in Mathioudaki et al. 65 were used to compute AUCell scores in our scRNA data using the AUCell R package 66 .

Cell type annotation in scRNA-seq data

Cell clusters from integrated populations (HSPCs, T cells, and stromal cells) were manually annotated based on the expression of marker genes and AUCell activity scores of regulons. For the unfiltered dataset used in SpliceUp predictions, we included HSPCs that came from the sorting for stroma and performed an additional label transfer using the BoneMarrowMap R package 167 with the reference dataset from Roy et al 60 . Cells corresponding to erythroid progenitors were aggregated and added to the “EryPr cluster” that we defined previously.

Differential gene expression in scRNA-seq between conditions

Using the annotated cell types within each population, we aggregated all of the raw counts per gene per donor within each cell type. Prior to testing, genes were filtered to include only the ones that had at least 2 fragments per million present in at least half of the donors in either testing condition. We used the DESeq2 R package 168 to test differential expression between conditions with donor’s sex as a covariate in the design formula. The resulting estimates for effect sizes were further corrected using the ashr method 169 via DESeq2’s function lfcShrink. Resulting log2-fold change values were used to perform Gene Set Enrichment Analysis (GSEA) 145 against the Hallmark gene sets 170 from MSigDB 171 via the clusterProfiler 172 R package. Enriched terms with adjusted p value <= 0.05 were used for visualization, unless specified otherwise. For the comparison of iMSCs between CHIP and MDS, we only used data from 10x, because sample size was not big enough to properly account for multiple covariates.

Cell-cell interactions analysis of scRNA-seq data

To infer the potential cell-cell interactions in the scRNA-seq data, we used the NICHES method 87 . The fully integrated dataset was split based on donor identity and for each donor-specific subset the RunNICHES function from the NICHES package was run with Omnipath 173 as a database for ligand-receptor interactions. Individual datasets were merged using the merge.Seurat function of Seurat and filtered to remove cell pairs with fewer than 5 interactions inferred. Interactions specific to cell populations of interest were selected using the Seurat’s FindMarkers function.

For visualization of the number of interactions per cell pair in Fig. 6H,I , the inferred number of interactions was divided by the total number of possible cell pair combinations that could come from the corresponding cell types in each donor, i.e., within each donor a product of the numbers of cells belonging to corresponding cell types.

Single-cell trajectory inference scRNA-seq data

To construct single cell trajectories, we used the Monocle3 88 , 174 , 175 v1.3.1 R package. A subset of the integrated HSPC cells was converted to a “cell_data_set” object using the as.cell_data_set function from SeuratWrappers R package v.0.3.1. Dimensionality reduction was performed using Monocle 3’s function reduce_dimension with max_components=3, reduction_method = “UMAP” 176 , preprocess_method = “PCA”. Following, the cells were again clustered with the Leiden algorithm 164 using the cluster_cells function with resolution=0.01. Monocle 3’s learn_graph function was used to construct the trajectory graph. Three main trajectories — myeloid, lymphoid, and erythroid — were interactively constructed with Monocle 3’s order_cells function.

Integration of single-cell data with public datasets

Three publicly available BM scRNA-seq datasets 47 , 74 , 83 were integrated with the scRNA-seq dataset from this study (see Table 2 ). Stromal cells were extracted from each dataset based on author-provided annotations using the subset function in Seurat (v5.0.3). The datasets were merged into a single list of Seurat objects and subjected to standard preprocessing, including normalization, variable feature identification, and data scaling using the default values. Principal component analysis (PCA) was initially applied as dimensionality reduction. Data integration was performed using Harmony 177 , using the donor, dataset, and sequencing technology (10x Genomics or CEL-Seq2) as integration variables (group.by.vars), and PCA as the reduction method. Further dimensionality reduction was achieved using Uniform Manifold Approximation and Projection (UMAP) based on the 50 Harmony components. The Seurat layers were joined using the JoinLayers function from Seurat and the k nearest neighbors were computed by applying the FindNeighbors function. Clustering of the cells was conducted with Seurat’s FindClusters function, using a resolution of 0.2 to identify broad cellular populations. Table 2 Overview of bone marrow scRNA-seq datasets included in the comparative and integrative analyses Author Journal Reference Condition Technology Chen et al. Blood Cancer Discov, Sep 2023 83 Acute Myeloid Leukemia, Healthy 10x Genomics De Jong et al. Nat Immunol, June 2021 74 Multiple Myeloma, Healthy 10x Genomics Bandyopadhyay et al. Cell, June 2024 47 Healthy 10x Genomics This study - - CHIP, LR-MDS, Healthy 10x Genomics, CEL-Seq2

Curated inflammatory and HSPC-support signature scores

Gene signatures were manually curated partly based on 85 for the HSPC-support signature and based on 74 and 178 for the inflammatory signature ( Supplementary Data 5 ) . To quantify the signature scores, we used the AddModuleScore 179 function from the Seurat package.

Prediction and analysis of SF3B1 -mutated cells

Prediction of SF3B1 -mutant clones with SpliceUp

To investigate the differences between the SF3B1 wild-type (WT) and mutant clones in MDS samples, we applied the SpliceUp tool (v0.15.2) 89 . SpliceUp leverages known missplicing events associated with SF3B1 mutations in hematological malignancies to quantify the usage of normal and cryptic splice sites and use the relative difference between the two to predict the mutational status of SF3B1 in individual cells. By aggregating the information across multiple gene transcripts across the genome, SpliceUp can overcome the sparsity of scRNA-seq data. In lieu of the ground truth about the mutational status of an individual cell, SpliceUp fits a modified logistic regression model using the sample status of SF3B1 mutation as the response variable. A threshold of 10% for false-positive rate was applied to control the error rate of the model. To increase the power of downstream analyses, we applied SpliceUp to the subset of our 10x data that still included the erythroid progenitors from libraries sorted for stromal populations (see above under “Cell type annotation”). Classification was performed based on the list of SF3B1 -associated 3’ alternative splice site and skipped exon missplicing events provided by the authors using mean imputation strategy. Cells were considered for classification if at least 2 missplicing events were covered in the sequencing data.

Differential gene expression between SF3B1 WT and mutant clones

To quantify differential gene expression between cells predicted to be SF3B1 WT or SF3B1 MUT , we used aggregated expression of each gene from the subset of erythroid progenitors (Fig. 5D ) based on the donor identity and predicted mutational status. We then used the DESeq2 to test for differentially expressed genes between the groups of mutant and WT cells using the donor identity as a covariate. Estimated effect sizes were further corrected using the ashr method 169 and p values adjusted using Benjamini-Hochberg procedure.

Quantification and statistics

NanoString analyses

To examine associations between PanCancer/ImmuneExhaustion PCA scores and clinical parameters of bone marrow samples (Supplementary Fig. 2A ), we evaluated linear relationships between the top ten principal components (PC1–PC10) and selected metadata variables (age, variant allele frequency (VAF), and C-reactive protein (CRP)). Pearson’s correlation coefficient was used to quantify associations, and statistical significance was determined using a two-sided unpaired Student’s t -test.

(sc)RNA-seq analyses

To assess enrichment of specific cell types in CHIP and MDS versus Control (Fig. 3A , and Fig. 5A , Fig. 6A ), we performed a one-vs-rest comparison by computing the number of cells per cell type against all other types within the same general population across conditions. Fisher’s exact test was used to assess statistical significance, with Benjamini–Hochberg correction for multiple testing. Cell populations with adjusted p values < 0.05 were considered significantly enriched.

For cell–cell interaction analysis between stromal subpopulations and HSPCs (Fig. 4G , and Fig. 6F ), we quantified the total number of interactions per stromal cell type using NICHES and tested for differences using Fisher’s exact test, followed by Benjamini–Hochberg correction.

Differential expression of individual HSPC-support and angiogenesis-support genes across stromal subsets in CHIP and MDS (Fig. 4F , and Fig. 7B ) was tested using the two-sided Wilcoxon rank-sum test.

In co-culture scRNA-seq experiments (Fig. 5I ), the log-transformed average expression of selected genes was compared between conditions using a two-sided unpaired Student’s t -test.

Flow cytometry analyses

For the diagnostics flow cytometry, the differences in the abundance of major BM populations were assessed by one-way ANOVA followed by Benjamini–Hochberg correction for multiple testing (Fig. 1F , and Supplementary Fig. 1C ). T cell subsets were compared across conditions using one-way ANOVA with post hoc Tukey’s test (Supplementary Fig. 1D ). Inflammatory MSC populations were analyzed using an one-way ANOVA with Dunnett’s test (Fig. 3I ) and a two-way ANOVA followed by Benjamini–Hochberg correction (Supplementary Fig. 6C ). Further details on all flow cytometry statistical tests are provided in the Source Data.

Imaging on human and mouse BM tissue

For imaging of the histopathological stains (Giemsa) and immunofluorescence on human and mouse FFPE bone tissue sections, the following tests were applied: Welch’s t -test for CXCL12 + MSCs quantification (Fig. 4C ); one-way ANOVA with post hoc Tukey’s test for human BM cellularity (Fig. 3C ), IL-1R1 + MSCs (Fig. 3G ), GNLY + , HSP60 + , and OASL + T cells (Fig. 6D ), human vasculature metrics (Fig. 7D,E ), nearest distance of objects to vasculature (Fig. 7F ), and mouse BM cellularity (Fig. 7I ); one-way ANOVA with FDR (Benjamini–Hochberg) correction for mean sinusoidal and arteriolar vessel area in mouse BM (Fig. 7L , Supplementary Fig. 12F ); and two-way ANOVA with post hoc Tukey’s test for total vasculature quantification in mouse BM (Fig. 7K , Supplementary Fig. 12E ). Full statistical details for imaging data are provided in the Source Data.

Co-culture secretome analysis (Olink and Luminex)

For secretome measurements in co-culture experiments with primary HSPCs or MDS-L cells and BM MSCs (Fig. 5 , Supplementary Fig. 9 , 10 ), we applied one-way ANOVA with post hoc Tukey’s test to identify significant differences in cytokine and secreted factor levels across conditions. Full details are available in the Source Data.

Peripheral blood counts in MDS patients

Differences in peripheral blood parameters across MDS mutational subgroups ( SF3B1 , other splicing factors, DNMT3A/TET2/ASXL1 ) were tested using one-way ANOVA with post hoc Tukey’s correction (Supplementary Fig. 1B ). Full details are available in the Source Data.

All statistical analyses were performed in R or GraphPad Prism (version 10.4.1). For ANOVA, data were tested for normal distribution (Shapiro-Wilk and Kholmogorov-Smirnov), and for sample sizes (n < 10), QQ plots were generated to guide the selection of appropriate statistical tests.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Supplementary Data 6 Supplementary Data 7 Reporting Summary Transparent Peer Review file

Source data

Source Data

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Karin D. Prummel, Kevin Woods, Maksim Kholmatov.

Supplementary information

The online version contains supplementary material available at 10.1038/s41467-025-65803-y.

Acknowledgements

We thank the tissue donors of the BoHemE study for their contribution as well as the CHOICE consortium for providing the biomaterial, especially Katharina Zolden. We thank Pr. François Delhommeau, Dr. Pierre Hirsch, and Frédéric de Vassoigne (Saint Antoine Hospital, Paris) for the healthy donor BM-derived MSCs. We thank the tissue bank of the University Medical Clinic in Mainz for processing the samples, as well as the microscopy core facility of the Institute for Molecular Biology (IMB Mainz) for their technical support. We thank the EMBL’s FCCF and GeneCore facilities for technical support for the single-cell experiments and sequencing. We thank the ICM’s iGenSeq core facility and Y. Marie and M. Coutelier for single-cell sequencing and demultiplexing. We also thank Single Cell Discoveries for processing the CEL-Seq2 experiments. We thank the TU Dresden Flow Facility and Anna-Lena Baumann, Kristin Möbus, and Claudia Richter for support and data analysis. We thank the DKFZ Genomics and Proteomics Core Facility for the genome-wide SNP analysis and Olink analysis. We thank the EMBL IT for providing the infrastructure and support in performing the data analysis. We thank Jean-Karim Heriche for assistance in creation and hosting of the Shiny web application. We are grateful to members from the Zaugg and Guezguez groups for their valuable input throughout the process. Additionally, we thank the Deplancke group (EPFL) for their insightful discussions. Schematics throughout the manuscript were made with BioRender ( biorender.com ). This work was supported by the German Cancer Consortium (DKTK) Joint Funding Program (DKTK CHOICE) to B.G., M.S., and U.P.; the José Carreras Leukemia Foundation (DJCLS, 04/R 2018) to B.G.; the European Union (ERC, epiNicheAML, 101044873) to J.B.Z.; M.K. is funded by the EC H2020 MSCA-ITN Project ENHPATHY (grant agreement number 860002) to J.B.Z.; the SNSF (P2ZHP3_199669) and EMBO (538-2021) Postdoctoral Fellowships to K.D.P.; the Deutsche Forschungsgemeinschaft (DFG, Project ID 318346496 – SFB 1292) to M.T.; National Institutes of Health grants R01DK118072, R01 AG069010, and U01AG077925 to J.J.T.; J.J.T. is a Scholar of the Leukemia & Lymphoma Society; National Institutes of Health grant F31DK127573 and The Tufts University Scheer-Tomasso Fund philanthropic gift to L.S.S. Part of this work was supported by Cancer United Research Associating Medicine (CURAMUS), the French National Cancer Institute, Ministry of Health, and ITMO Cancer of Aviesan as part of the 2021–2030 Cancer Control Strategy (INCA-DGOS-Inserm_12560) to C.C. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Author contributions

K.D.P., K.W., J.B.Z., and B.G. conceived the project and designed the study. K.D.P. and K.W. performed the single-cell and flow cytometry experiments. K.W. performed the MDS-L co-cultures, Luminex, and imaging experiments. M.K. processed and analyzed the scRNA-seq data, NanoString data, and integrated analysis with other datasets. E.S. analyzed the NanoString data and supported experiments. E.V. integrated and analyzed the publicly available scRNA-seq datasets. K.D.P., M.L., C.C., B.G., and T.J. designed and performed primary HSPC co-cultures and subsequent single-cell experiments. K.D.P. performed the Olink experiment. G.P. and K.S. supported secretomics analysis. M.K. and P.L.M. developed the SpliceUp algorithm. R.W. performed the NanoString experiment, and M.S. supervised the analysis. L.S.S. performed the in vivo genetic induction of CHIP in mice and isolated femurs. S.W. and U.O. performed diagnostic flow cytometry, retrieved clinical information, and incorporated patient data analysis. M.W. performed flow cytometry. K.R., M.T., and U.P. provided administrative and core facility support and patient samples. K.D.P., M.K., J.B.Z., and B.G. analyzed and compiled the data. K.D.P., K.W., M.K., J.B.Z., and B.G. wrote the manuscript with input from all co-authors. C.C., E.L., M.S, J.J.T., J.B.Z., and B.G. supervised (certain aspects of) the project.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Funding

Open Access funding enabled and organized by Projekt DEAL.

Data availability

Single-cell profiling and NanoString nCounter gene expression data has been newly generated. The scRNA-seq and NanoString data have been deposited in Figshare under the (10.6084/m9.figshare.27643503). Raw data are available on the Gene Expression Omnibus (GEO): 10x and CEL-Seq2 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE309534 ), ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE309536 ), and NanoString ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE309538 ), ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE309540 ). Data not deposited on GEO are available upon reasonable request through the corresponding authors. For interactive exploration of our scRNA-seq data we created a Shiny web application available ( https://shiny-portal.embl.de/shinyapps/app/11_cellmds ). The Shiny app was created using the ShinyCell R package 180 . Source data are provided with this manuscript. Source data are provided with this paper.

Code availability

Code used for the analyses of Nanostring nCounter gene expression data and single-cell transcriptomic profiling are available at ( https://git.embl.org/grp-zaugg/BM_CHIP_MDS ).

Competing interests

The authors declare no competing interests.

References

1. Winter S Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche Leukemia 2024 38 936 946 10.1038/s41375-024-02226-6 38514772 PMC11073997 Winter, S. et al. Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche. Leukemia 38 , 936–946 (2024). 38514772 10.1038/s41375-024-02226-6 PMC11073997

2. Challen GA Goodell MA Clonal hematopoiesis: mechanisms driving dominance of stem cell clones Blood 2020 136 1590 1598 32746453 10.1182/blood.2020006510 PMC7530644 Challen, G. A. & Goodell, M. A. Clonal hematopoiesis: mechanisms driving dominance of stem cell clones. Blood 136 , 1590–1598 (2020). 32746453 10.1182/blood.2020006510 PMC7530644

3. Jaiswal, S. & Ebert, B. L. Clonal hematopoiesis in human aging and disease. Science 366 , eaan4673 (2019). 10.1126/science.aan4673 PMC8050831 31672865

4. Goldman, E. A., Spellman, P. T. & Agarwal, A. Defining clonal hematopoiesis of indeterminate potential: evolutionary dynamics and detection under aging and inflammation. Cold Spring Harb Mol Case Stud 9 , a006251 (2023). 10.1101/mcs.a006251 PMC10240836 36889927

5. Fabre MA Vassiliou GS The lifelong natural history of clonal hematopoiesis and its links to myeloid neoplasia Blood 2024 143 573 581 10.1182/blood.2023019964 37992214 Fabre, M. A. & Vassiliou, G. S. The lifelong natural history of clonal hematopoiesis and its links to myeloid neoplasia. Blood 143 , 573–581 (2024). 37992214 10.1182/blood.2023019964

6. Guermouche H High prevalence of clonal hematopoiesis in the blood and bone marrow of healthy volunteers Blood Adv. 2020 4 3550 3557 10.1182/bloodadvances.2020001582 32761230 PMC7422125 Guermouche, H. et al. High prevalence of clonal hematopoiesis in the blood and bone marrow of healthy volunteers. Blood Adv. 4 , 3550–3557 (2020). 32761230 10.1182/bloodadvances.2020001582 PMC7422125

7. Ahmad H Jahn N Jaiswal S Clonal hematopoiesis and its impact on human health Annu. Rev. Med. 2023 74 249 260 10.1146/annurev-med-042921-112347 36450282 Ahmad, H., Jahn, N. & Jaiswal, S. Clonal hematopoiesis and its impact on human health. Annu. Rev. Med. 74 , 249–260 (2023). 36450282 10.1146/annurev-med-042921-112347

8. Weeks LD Ebert BL Causes and consequences of clonal hematopoiesis Blood 2023 142 2235 2246 10.1182/blood.2023022222 37931207 PMC10862247 Weeks, L. D. & Ebert, B. L. Causes and consequences of clonal hematopoiesis. Blood 142 , 2235–2246 (2023). 37931207 10.1182/blood.2023022222 PMC10862247

9. Fabre MA The longitudinal dynamics and natural history of clonal haematopoiesis Nature 2022 606 335 342 10.1038/s41586-022-04785-z 35650444 PMC9177423 Fabre, M. A. et al. The longitudinal dynamics and natural history of clonal haematopoiesis. Nature 606 , 335–342 (2022). 35650444 10.1038/s41586-022-04785-z PMC9177423

10. Mitchell E Clonal dynamics of haematopoiesis across the human lifespan Nature 2022 606 343 350 10.1038/s41586-022-04786-y 35650442 PMC9177428 Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606 , 343–350 (2022). 35650442 10.1038/s41586-022-04786-y PMC9177428

11. van Zeventer IA Evolutionary landscape of clonal hematopoiesis in 3,359 individuals from the general population Cancer Cell 2023 41 1017 1031.e4 10.1016/j.ccell.2023.04.006 37146604 van Zeventer, I. A. et al. Evolutionary landscape of clonal hematopoiesis in 3,359 individuals from the general population. Cancer Cell 41 , 1017–1031.e4 (2023). 37146604 10.1016/j.ccell.2023.04.006

12. Weeks, L. D. et al. Prediction of risk for myeloid malignancy in clonal hematopoiesis. NEJM Evid . 2 , 10.1056/evidoa2200310 (2023). 10.1056/evidoa2200310 PMC10361696 37483562

13. Bernstein N Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis Nat. Genet. 2024 56 1147 1155 10.1038/s41588-024-01755-1 38744975 PMC11176083 Bernstein, N. et al. Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis. Nat. Genet. 56 , 1147–1155 (2024). 38744975 10.1038/s41588-024-01755-1 PMC11176083

14. Cazzola M Myelodysplastic syndromes N. Engl. J. Med. 2020 383 1358 1374 10.1056/NEJMra1904794 32997910 Cazzola, M. Myelodysplastic syndromes. N. Engl. J. Med. 383 , 1358–1374 (2020). 32997910 10.1056/NEJMra1904794

15. Sperling AS Gibson CJ Ebert BL The genetics of myelodysplastic syndrome: from clonal haematopoiesis to secondary leukaemia Nat. Rev. Cancer 2017 17 5 19 10.1038/nrc.2016.112 27834397 PMC5470392 Sperling, A. S., Gibson, C. J. & Ebert, B. L. The genetics of myelodysplastic syndrome: from clonal haematopoiesis to secondary leukaemia. Nat. Rev. Cancer 17 , 5–19 (2017). 27834397 10.1038/nrc.2016.112 PMC5470392

16. Ogawa S Genetics of MDS Blood 2019 133 1049 1059 10.1182/blood-2018-10-844621 30670442 PMC6587668 Ogawa, S. Genetics of MDS. Blood 133 , 1049–1059 (2019). 30670442 10.1182/blood-2018-10-844621 PMC6587668

17. Todisco G Moura PL Hellström-Lindberg E Clinical manifestations of clonal hematopoiesis: What has SF3B1-mutant MDS taught us? Semin. Hematol. 2022 59 150 155 10.1053/j.seminhematol.2022.08.002 36115692 Todisco, G., Moura, P. L. & Hellström-Lindberg, E. Clinical manifestations of clonal hematopoiesis: What has SF3B1-mutant MDS taught us? Semin. Hematol. 59 , 150–155 (2022). 36115692 10.1053/j.seminhematol.2022.08.002

18. Papaemmanuil E Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts N. Engl. J. Med. 2011 365 1384 1395 10.1056/NEJMoa1103283 21995386 PMC3322589 Papaemmanuil, E. et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl. J. Med. 365 , 1384–1395 (2011). 21995386 10.1056/NEJMoa1103283 PMC3322589

19. Malcovati L SF3B1-mutant MDS as a distinct disease subtype: a proposal from the International Working Group for the Prognosis of MDS Blood 2020 136 157 170 10.1182/blood.2020004850 32347921 PMC7362582 Malcovati, L. et al. SF3B1-mutant MDS as a distinct disease subtype: a proposal from the International Working Group for the Prognosis of MDS. Blood 136 , 157–170 (2020). 32347921 10.1182/blood.2020004850 PMC7362582

20. Khoury JD The 5th edition of the World Health Organization classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms Leukemia 2022 36 1703 1719 10.1038/s41375-022-01613-1 35732831 PMC9252913 Khoury, J. D. et al. The 5th edition of the World Health Organization classification of haematolymphoid tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia 36 , 1703–1719 (2022). 35732831 10.1038/s41375-022-01613-1 PMC9252913

21. Crane GM Jeffery E Morrison SJ Adult haematopoietic stem cell niches Nat. Rev. Immunol. 2017 17 573 590 10.1038/nri.2017.53 28604734 Crane, G. M., Jeffery, E. & Morrison, S. J. Adult haematopoietic stem cell niches. Nat. Rev. Immunol. 17 , 573–590 (2017). 28604734 10.1038/nri.2017.53

22. Woods K Guezguez B Dynamic changes of the bone marrow niche: mesenchymal stromal cells and their progeny during aging and leukemia Front. Cell Dev. Biol. 2021 9 714716 10.3389/fcell.2021.714716 34447754 PMC8383146 Woods, K. & Guezguez, B. Dynamic changes of the bone marrow niche: mesenchymal stromal cells and their progeny during aging and leukemia. Front. Cell Dev. Biol. 9 , 714716 (2021). 34447754 10.3389/fcell.2021.714716 PMC8383146

23. Meunier P Aaron J Edouard C Vignon G Osteoporosis and the replacement of cell populations of the marrow by adipose tissue. a quantitative study of 84 iliac bone biopsies Clin. Orthop. Relat. Res. 1971 80 147 154 10.1097/00003086-197110000-00021 5133320 Meunier, P., Aaron, J., Edouard, C. & Vignon, G. Osteoporosis and the replacement of cell populations of the marrow by adipose tissue. a quantitative study of 84 iliac bone biopsies. Clin. Orthop. Relat. Res. 80 , 147–154 (1971). 5133320 10.1097/00003086-197110000-00021

24. Moerman EJ Teng K Lipschitz DA Lecka-Czernik B Aging activates adipogenic and suppresses osteogenic programs in mesenchymal marrow stroma/stem cells: the role of PPAR-gamma2 transcription factor and TGF-beta/BMP signaling pathways Aging Cell 2004 3 379 389 10.1111/j.1474-9728.2004.00127.x 15569355 PMC1850101 Moerman, E. J., Teng, K., Lipschitz, D. A. & Lecka-Czernik, B. Aging activates adipogenic and suppresses osteogenic programs in mesenchymal marrow stroma/stem cells: the role of PPAR-gamma2 transcription factor and TGF-beta/BMP signaling pathways. Aging Cell 3 , 379–389 (2004). 15569355 10.1111/j.1474-9728.2004.00127.x PMC1850101

25. Lai, M. C. et al. Enhancer-priming in ageing human bone marrow mesenchymal stromal cells contributes to immune traits. BioRxiv 10.1101/2021.09.03.458728 (2021).

26. Pangrazzi L “Inflamm-aging” influences immune cell survival factors in human bone marrow Eur. J. Immunol. 2017 47 481 492 10.1002/eji.201646570 27995612 PMC5434810 Pangrazzi, L. et al. “Inflamm-aging” influences immune cell survival factors in human bone marrow. Eur. J. Immunol. 47 , 481–492 (2017). 27995612 10.1002/eji.201646570 PMC5434810

27. Caiado, F., Pietras, E. M. & Manz, M. G. Inflammation as a regulator of hematopoietic stem cell function in disease, aging, and clonal selection. J. Exp. Med . 218 , e20201541 (2021). 10.1084/jem.20201541 PMC8210622 34129016

28. Zhao J Ghimire A Liesveld J Marrow failure and aging: The role of “Inflammaging” Best. Pract. Res. Clin. Haematol. 2021 34 101283 10.1016/j.beha.2021.101283 34404535 Zhao, J., Ghimire, A. & Liesveld, J. Marrow failure and aging: The role of “Inflammaging”. Best. Pract. Res. Clin. Haematol. 34 , 101283 (2021). 34404535 10.1016/j.beha.2021.101283

29. Ermolaeva M Neri F Ori A Rudolph KL Cellular and epigenetic drivers of stem cell ageing Nat. Rev. Mol. Cell Biol. 2018 19 594 610 10.1038/s41580-018-0020-3 29858605 Ermolaeva, M., Neri, F., Ori, A. & Rudolph, K. L. Cellular and epigenetic drivers of stem cell ageing. Nat. Rev. Mol. Cell Biol. 19 , 594–610 (2018). 29858605 10.1038/s41580-018-0020-3

30. Zioni N Inflammatory signals from fatty bone marrow support DNMT3A driven clonal hematopoiesis Nat. Commun. 2023 14 2070 10.1038/s41467-023-36906-1 37045808 PMC10097668 Zioni, N. et al. Inflammatory signals from fatty bone marrow support DNMT3A driven clonal hematopoiesis. Nat. Commun. 14 , 2070 (2023). 37045808 10.1038/s41467-023-36906-1 PMC10097668

31. Yousefzadeh MJ An aged immune system drives senescence and ageing of solid organs Nature 2021 594 100 105 10.1038/s41586-021-03547-7 33981041 PMC8684299 Yousefzadeh, M. J. et al. An aged immune system drives senescence and ageing of solid organs. Nature 594 , 100–105 (2021). 33981041 10.1038/s41586-021-03547-7 PMC8684299

32. Li X Inflammation and aging: signaling pathways and intervention therapies Signal Transduct. Target. Ther. 2023 8 239 10.1038/s41392-023-01502-8 37291105 PMC10248351 Li, X. et al. Inflammation and aging: signaling pathways and intervention therapies. Signal Transduct. Target. Ther. 8 , 239 (2023). 37291105 10.1038/s41392-023-01502-8 PMC10248351

33. Bick AG Inherited causes of clonal haematopoiesis in 97,691 whole genomes Nature 2020 586 763 768 10.1038/s41586-020-2819-2 33057201 PMC7944936 Bick, A. G. et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature 586 , 763–768 (2020). 33057201 10.1038/s41586-020-2819-2 PMC7944936

34. Cook EK Comorbid and inflammatory characteristics of genetic subtypes of clonal hematopoiesis Blood Adv. 2019 3 2482 2486 10.1182/bloodadvances.2018024729 31434682 PMC6712533 Cook, E. K. et al. Comorbid and inflammatory characteristics of genetic subtypes of clonal hematopoiesis. Blood Adv. 3 , 2482–2486 (2019). 31434682 10.1182/bloodadvances.2018024729 PMC6712533

35. Jakobsen NA Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging Cell Stem Cell 2024 31 1127 1144.e17 10.1016/j.stem.2024.05.010 38917807 PMC11512683 Jakobsen, N. A. et al. Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging. Cell Stem Cell 31 , 1127–1144.e17 (2024). 38917807 10.1016/j.stem.2024.05.010 PMC11512683

36. Liao M Aging-elevated inflammation promotes DNMT3A R878H-driven clonal hematopoiesis Acta Pharm. Sin. B 2022 12 678 691 10.1016/j.apsb.2021.09.015 35256939 PMC8897035 Liao, M. et al. Aging-elevated inflammation promotes DNMT3A R878H-driven clonal hematopoiesis. Acta Pharm. Sin. B 12 , 678–691 (2022). 35256939 10.1016/j.apsb.2021.09.015 PMC8897035

37. SanMiguel JM Distinct tumor necrosis factor alpha receptors dictate stem cell fitness versus lineage output in Dnmt3a-mutant clonal hematopoiesis Cancer Discov. 2022 12 2763 2773 10.1158/2159-8290.CD-22-0086 36169447 PMC9716249 SanMiguel, J. M. et al. Distinct tumor necrosis factor alpha receptors dictate stem cell fitness versus lineage output in Dnmt3a-mutant clonal hematopoiesis. Cancer Discov. 12 , 2763–2773 (2022). 36169447 10.1158/2159-8290.CD-22-0086 PMC9716249

38. Abegunde SO Buckstein R Wells RA Rauh MJ An inflammatory environment containing TNFα favors Tet2-mutant clonal hematopoiesis Exp. Hematol. 2018 59 60 65 10.1016/j.exphem.2017.11.002 29195897 Abegunde, S. O., Buckstein, R., Wells, R. A. & Rauh, M. J. An inflammatory environment containing TNFα favors Tet2-mutant clonal hematopoiesis. Exp. Hematol. 59 , 60–65 (2018). 29195897 10.1016/j.exphem.2017.11.002

39. Li Z Philip M Ferrell PB Alterations of T-cell-mediated immunity in acute myeloid leukemia Oncogene 2020 39 3611 3619 10.1038/s41388-020-1239-y 32127646 PMC7234277 Li, Z., Philip, M. & Ferrell, P. B. Alterations of T-cell-mediated immunity in acute myeloid leukemia. Oncogene 39 , 3611–3619 (2020). 32127646 10.1038/s41388-020-1239-y PMC7234277

40. Brück O Immune profiles in acute myeloid leukemia bone marrow associate with patient age, T-cell receptor clonality, and survival Blood Adv. 2020 4 274 286 10.1182/bloodadvances.2019000792 31968078 PMC6988390 Brück, O. et al. Immune profiles in acute myeloid leukemia bone marrow associate with patient age, T-cell receptor clonality, and survival. Blood Adv. 4 , 274–286 (2020). 31968078 10.1182/bloodadvances.2019000792 PMC6988390

41. Rodriguez-Sevilla JJ Colla S T-cell dysfunctions in myelodysplastic syndromes Blood 2024 143 1329 1343 10.1182/blood.2023023166 38237139 Rodriguez-Sevilla, J. J. & Colla, S. T-cell dysfunctions in myelodysplastic syndromes. Blood 143 , 1329–1343 (2024). 38237139 10.1182/blood.2023023166

42. Medyouf H Myelodysplastic cells in patients reprogram mesenchymal stromal cells to establish a transplantable stem cell niche disease unit Cell Stem Cell 2014 14 824 837 10.1016/j.stem.2014.02.014 24704494 Medyouf, H. et al. Myelodysplastic cells in patients reprogram mesenchymal stromal cells to establish a transplantable stem cell niche disease unit. Cell Stem Cell 14 , 824–837 (2014). 24704494 10.1016/j.stem.2014.02.014

43. Geyh S Insufficient stromal support in MDS results from molecular and functional deficits of mesenchymal stromal cells Leukemia 2013 27 1841 1851 10.1038/leu.2013.193 23797473 Geyh, S. et al. Insufficient stromal support in MDS results from molecular and functional deficits of mesenchymal stromal cells. Leukemia 27 , 1841–1851 (2013). 23797473 10.1038/leu.2013.193

44. Mistry, J. J. et al. Mesenchymal stromal cell senescence induced by Dnmt3a -mutant hematopoietic cells is a targetable mechanism driving clonal hematopoiesis and initiation of hematologic malignancy. Preprint at BioRxiv 10.1101/2024.03.28.587254 (2024).

45. Huber S SF3B1 mutated MDS: blast count, genetic co-abnormalities and their impact on classification and prognosis Leukemia 2022 10.1038/s41375-022-01728-5 36261576 PMC9712089 Huber, S. et al. SF3B1 mutated MDS: blast count, genetic co-abnormalities and their impact on classification and prognosis. Leukemia 10.1038/s41375-022-01728-5 (2022). 36261576 10.1038/s41375-022-01728-5 PMC9712089

46. Lee W-H Comparison of the 2022 world health organization classification and international consensus classification in myelodysplastic syndromes/neoplasms Blood Cancer J. 2024 14 57 10.1038/s41408-024-01031-9 38594285 PMC11004131 Lee, W.-H. et al. Comparison of the 2022 world health organization classification and international consensus classification in myelodysplastic syndromes/neoplasms. Blood Cancer J. 14 , 57 (2024). 38594285 10.1038/s41408-024-01031-9 PMC11004131

47. Bandyopadhyay S Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging Cell 2024 187 3120 3140.e29 10.1016/j.cell.2024.04.013 38714197 PMC11162340 Bandyopadhyay, S. et al. Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging. Cell 187 , 3120–3140.e29 (2024). 38714197 10.1016/j.cell.2024.04.013 PMC11162340

48. Winter S Mutations in the splicing factor SF3B1 are linked to frequent emergence of HLA-DRlow/neg monocytes in lower-risk myelodysplastic neoplasms Leukemia 2024 38 1427 1431 10.1038/s41375-024-02249-z 38632316 PMC11147767 Winter, S. et al. Mutations in the splicing factor SF3B1 are linked to frequent emergence of HLA-DRlow/neg monocytes in lower-risk myelodysplastic neoplasms. Leukemia 38 , 1427–1431 (2024). 38632316 10.1038/s41375-024-02249-z PMC11147767

49. Kahn JD Regulatory T cells and progenitor B cells are independent prognostic predictors in lower risk myelodysplastic syndromes Haematologica 2015 100 e220 e222 10.3324/haematol.2014.116657 25749828 PMC4450633 Kahn, J. D. et al. Regulatory T cells and progenitor B cells are independent prognostic predictors in lower risk myelodysplastic syndromes. Haematologica 100 , e220–e222 (2015). 25749828 10.3324/haematol.2014.116657 PMC4450633

50. Sternberg A Evidence for reduced B-cell progenitors in early (low-risk) myelodysplastic syndrome Blood 2005 106 2982 2991 10.1182/blood-2005-04-1543 16076868 Sternberg, A. et al. Evidence for reduced B-cell progenitors in early (low-risk) myelodysplastic syndrome. Blood 106 , 2982–2991 (2005). 16076868 10.1182/blood-2005-04-1543

51. Mortera-Blanco T SF3B1-initiating mutations in MDS-RSs target lymphomyeloid hematopoietic stem cells Blood 2017 130 881 890 10.1182/blood-2017-03-776070 28634182 PMC5572789 Mortera-Blanco, T. et al. SF3B1-initiating mutations in MDS-RSs target lymphomyeloid hematopoietic stem cells. Blood 130 , 881–890 (2017). 28634182 10.1182/blood-2017-03-776070 PMC5572789

52. Arthur A Twist-1 enhances bone marrow mesenchymal stromal cell support of hematopoiesis by modulating CXCL12 expression Stem Cells 2016 34 504 509 10.1002/stem.2265 26718114 Arthur, A. et al. Twist-1 enhances bone marrow mesenchymal stromal cell support of hematopoiesis by modulating CXCL12 expression. Stem Cells 34 , 504–509 (2016). 26718114 10.1002/stem.2265

53. Marupanthorn K Tantrawatpan C Kheolamai P Tantikanlayaporn D Manochantr S Bone morphogenetic protein-2 enhances the osteogenic differentiation capacity of mesenchymal stromal cells derived from human bone marrow and umbilical cord Int. J. Mol. Med. 2017 39 654 662 10.3892/ijmm.2017.2872 28204808 PMC5360390 Marupanthorn, K., Tantrawatpan, C., Kheolamai, P., Tantikanlayaporn, D. & Manochantr, S. Bone morphogenetic protein-2 enhances the osteogenic differentiation capacity of mesenchymal stromal cells derived from human bone marrow and umbilical cord. Int. J. Mol. Med. 39 , 654–662 (2017). 28204808 10.3892/ijmm.2017.2872 PMC5360390

54. Liu S Recombinant human BMP-2 accelerates the migration of bone marrow mesenchymal stem cells via the CDC42/PAK1/LIMK1 pathway in vitro and in vivo Biomater. Sci. 2018 7 362 372 10.1039/C8BM00846A 30484785 Liu, S. et al. Recombinant human BMP-2 accelerates the migration of bone marrow mesenchymal stem cells via the CDC42/PAK1/LIMK1 pathway in vitro and in vivo. Biomater. Sci. 7 , 362–372 (2018). 30484785 10.1039/c8bm00846a

55. Hettler F Osteoprogenitor SFRP1 prevents exhaustion of hematopoietic stem cells via PP2A-PR72/130-mediated regulation of p300 Haematologica 2023 108 490 501 10.3324/haematol.2022.280760 35950533 PMC9890018 Hettler, F. et al. Osteoprogenitor SFRP1 prevents exhaustion of hematopoietic stem cells via PP2A-PR72/130-mediated regulation of p300. Haematologica 108 , 490–501 (2023). 35950533 10.3324/haematol.2022.280760 PMC9890018

56. Álvarez-Viejo M Menéndez-Menéndez Y Otero-Hernández J CD271 as a marker to identify mesenchymal stem cells from diverse sources before culture World J. Stem Cells 2015 7 470 476 10.4252/wjsc.v7.i2.470 25815130 PMC4369502 Álvarez-Viejo, M., Menéndez-Menéndez, Y. & Otero-Hernández, J. CD271 as a marker to identify mesenchymal stem cells from diverse sources before culture. World J. Stem Cells 7 , 470–476 (2015). 25815130 10.4252/wjsc.v7.i2.470 PMC4369502

57. Quirici N Isolation of bone marrow mesenchymal stem cells by anti-nerve growth factor receptor antibodies Exp. Hematol. 2002 30 783 791 10.1016/S0301-472X(02)00812-3 12135677 Quirici, N. et al. Isolation of bone marrow mesenchymal stem cells by anti-nerve growth factor receptor antibodies. Exp. Hematol. 30 , 783–791 (2002). 12135677 10.1016/s0301-472x(02)00812-3

58. Triana, S. et al. Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states. Nat. Immunol. 22 , 1577–1589 (2021). 10.1038/s41590-021-01059-0 PMC8642243 34811546

59. Hay SB Ferchen K Chetal K Grimes HL Salomonis N The Human Cell Atlas bone marrow single-cell interactive web portal Exp. Hematol. 2018 68 51 61 10.1016/j.exphem.2018.09.004 30243574 PMC6296228 Hay, S. B., Ferchen, K., Chetal, K., Grimes, H. L. & Salomonis, N. The Human Cell Atlas bone marrow single-cell interactive web portal. Exp. Hematol. 68 , 51–61 (2018). 30243574 10.1016/j.exphem.2018.09.004 PMC6296228

60. Roy A Transitions in lineage specification and gene regulatory networks in hematopoietic stem/progenitor cells over human development Cell Rep. 2021 36 109698 10.1016/j.celrep.2021.109698 34525349 PMC8456780 Roy, A. et al. Transitions in lineage specification and gene regulatory networks in hematopoietic stem/progenitor cells over human development. Cell Rep. 36 , 109698 (2021). 34525349 10.1016/j.celrep.2021.109698 PMC8456780

61. Ainciburu, M. et al. Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution. eLife 12 , e79363 (2023). 10.7554/eLife.79363 PMC9904760 36629404

62. Xu C Automatic cell-type harmonization and integration across Human Cell Atlas datasets. Cell 2023 186 5876 5891.e20 38134877 10.1016/j.cell.2023.11.026 Xu, C. et al. Automatic cell-type harmonization and integration across Human. Cell Atlas datasets. Cell 186 , 5876–5891.e20 (2023). 38134877 10.1016/j.cell.2023.11.026

63. Szabo PA Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease Nat. Commun. 2019 10 4706 10.1038/s41467-019-12464-3 31624246 PMC6797728 Szabo, P. A. et al. Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease. Nat. Commun. 10 , 4706 (2019). 31624246 10.1038/s41467-019-12464-3 PMC6797728

64. Zhang X An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors Nat. Immunol. 2024 25 703 715 10.1038/s41590-024-01782-4 38514887 PMC11003869 Zhang, X. et al. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nat. Immunol. 25 , 703–715 (2024). 38514887 10.1038/s41590-024-01782-4 PMC11003869

65. Mathioudaki A The remission status of AML patients after allo-HCT is associated with a distinct single-cell bone marrow T-cell signature Blood 2024 143 1269 1281 10.1182/blood.2023021815 38197505 PMC10997908 Mathioudaki, A. et al. The remission status of AML patients after allo-HCT is associated with a distinct single-cell bone marrow T-cell signature. Blood 143 , 1269–1281 (2024). 38197505 10.1182/blood.2023021815 PMC10997908

66. Aibar S SCENIC: single-cell regulatory network inference and clustering Nat. Methods 2017 14 1083 1086 10.1038/nmeth.4463 28991892 PMC5937676 Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14 , 1083–1086 (2017). 28991892 10.1038/nmeth.4463 PMC5937676

67. Cros J Human CD14dim monocytes patrol and sense nucleic acids and viruses via TLR7 and TLR8 receptors Immunity 2010 33 375 386 10.1016/j.immuni.2010.08.012 20832340 PMC3063338 Cros, J. et al. Human CD14dim monocytes patrol and sense nucleic acids and viruses via TLR7 and TLR8 receptors. Immunity 33 , 375–386 (2010). 20832340 10.1016/j.immuni.2010.08.012 PMC3063338

68. Metcalf TU Human monocyte subsets are transcriptionally and functionally altered in aging in response to pattern recognition receptor agonists J. Immunol. 2017 199 1405 1417 10.4049/jimmunol.1700148 28696254 PMC5548610 Metcalf, T. U. et al. Human monocyte subsets are transcriptionally and functionally altered in aging in response to pattern recognition receptor agonists. J. Immunol. 199 , 1405–1417 (2017). 28696254 10.4049/jimmunol.1700148 PMC5548610

69. Witkowski MT Extensive remodeling of the immune microenvironment in B cell acute lymphoblastic leukemia Cancer Cell 2020 37 867 882.e12 10.1016/j.ccell.2020.04.015 32470390 PMC7341535 Witkowski, M. T. et al. Extensive remodeling of the immune microenvironment in B cell acute lymphoblastic leukemia. Cancer Cell 37 , 867–882.e12 (2020). 32470390 10.1016/j.ccell.2020.04.015 PMC7341535

70. Zhang L Lineage tracking reveals dynamic relationships of T cells in colorectal cancer Nature 2018 564 268 272 10.1038/s41586-018-0694-x 30479382 Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564 , 268–272 (2018). 30479382 10.1038/s41586-018-0694-x

71. Cano-Gamez E Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4 + T cells to cytokines Nat. Commun. 2020 11 1801 10.1038/s41467-020-15543-y 32286271 PMC7156481 Cano-Gamez, E. et al. Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4 + T cells to cytokines. Nat. Commun. 11 , 1801 (2020). 32286271 10.1038/s41467-020-15543-y PMC7156481

72. Cosgaya JM Chan JR & Shooter, E. M. The neurotrophin receptor p75NTR as a positive modulator of myelination Science 2002 298 1245 1248 10.1126/science.1076595 12424382 Cosgaya, J. M. & Chan, J. R. & Shooter, E. M. The neurotrophin receptor p75NTR as a positive modulator of myelination. Science 298 , 1245–1248 (2002). 12424382 10.1126/science.1076595

73. Leimkühler NB Heterogeneous bone-marrow stromal progenitors drive myelofibrosis via a druggable alarmin axis Cell Stem Cell 2021 28 637 652.e8 10.1016/j.stem.2020.11.004 33301706 PMC8024900 Leimkühler, N. B. et al. Heterogeneous bone-marrow stromal progenitors drive myelofibrosis via a druggable alarmin axis. Cell Stem Cell 28 , 637–652.e8 (2021). 33301706 10.1016/j.stem.2020.11.004 PMC8024900

74. de Jong MME The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape Nat. Immunol. 2021 22 769 780 10.1038/s41590-021-00931-3 34017122 de Jong, M. M. E. et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat. Immunol. 22 , 769–780 (2021). 34017122 10.1038/s41590-021-00931-3

75. Yang C Inducible formation of leader cells driven by CD44 switching gives rise to collective invasion and metastases in luminal breast carcinomas Oncogene 2019 38 7113 7132 10.1038/s41388-019-0899-y 31417182 Yang, C. et al. Inducible formation of leader cells driven by CD44 switching gives rise to collective invasion and metastases in luminal breast carcinomas. Oncogene 38 , 7113–7132 (2019). 31417182 10.1038/s41388-019-0899-y

76. Horny H-P Sotlar K Valent P Diagnostic value of histology and immunohistochemistry in myelodysplastic syndromes Leuk. Res. 2007 31 1609 1616 10.1016/j.leukres.2007.05.010 17604834 Horny, H.-P., Sotlar, K. & Valent, P. Diagnostic value of histology and immunohistochemistry in myelodysplastic syndromes. Leuk. Res. 31 , 1609–1616 (2007). 17604834 10.1016/j.leukres.2007.05.010

77. Flores-Figueroa E Gratzinger D Beyond the niche: myelodysplastic syndrome topobiology in the laboratory and in the clinic Int. J. Mol. Sci. 2016 17 553 10.3390/ijms17040553 27089321 PMC4849009 Flores-Figueroa, E. & Gratzinger, D. Beyond the niche: myelodysplastic syndrome topobiology in the laboratory and in the clinic. Int. J. Mol. Sci. 17 , 553 (2016). 27089321 10.3390/ijms17040553 PMC4849009

78. Boyd AL Acute myeloid leukaemia disrupts endogenous myelo-erythropoiesis by compromising the adipocyte bone marrow niche Nat. Cell Biol. 2017 19 1336 1347 10.1038/ncb3625 29035359 Boyd, A. L. et al. Acute myeloid leukaemia disrupts endogenous myelo-erythropoiesis by compromising the adipocyte bone marrow niche. Nat. Cell Biol. 19 , 1336–1347 (2017). 29035359 10.1038/ncb3625

79. Kawahara K Hohjoh H Inazumi T Tsuchiya S Sugimoto Y Prostaglandin E2-induced inflammation: Relevance of prostaglandin E receptors Biochim. Biophys. Acta 2015 1851 414 421 10.1016/j.bbalip.2014.07.008 25038274 Kawahara, K., Hohjoh, H., Inazumi, T., Tsuchiya, S. & Sugimoto, Y. Prostaglandin E2-induced inflammation: Relevance of prostaglandin E receptors. Biochim. Biophys. Acta 1851 , 414–421 (2015). 25038274 10.1016/j.bbalip.2014.07.008

80. Lu D Xu Y Liu Q Zhang Q Mesenchymal stem cell-macrophage crosstalk and maintenance of inflammatory microenvironment homeostasis Front. Cell Dev. Biol. 2021 9 681171 10.3389/fcell.2021.681171 34249933 PMC8267370 Lu, D., Xu, Y., Liu, Q. & Zhang, Q. Mesenchymal stem cell-macrophage crosstalk and maintenance of inflammatory microenvironment homeostasis. Front. Cell Dev. Biol. 9 , 681171 (2021). 34249933 10.3389/fcell.2021.681171 PMC8267370

81. Spaeth EL Mesenchymal CD44 expression contributes to the acquisition of an activated fibroblast phenotype via TWIST activation in the tumor microenvironment Cancer Res. 2013 73 5347 5359 10.1158/0008-5472.CAN-13-0087 23838935 PMC3767181 Spaeth, E. L. et al. Mesenchymal CD44 expression contributes to the acquisition of an activated fibroblast phenotype via TWIST activation in the tumor microenvironment. Cancer Res. 73 , 5347–5359 (2013). 23838935 10.1158/0008-5472.CAN-13-0087 PMC3767181

82. Li S Targeting mechanics-induced fibroblast activation through CD44-RhoA-YAP pathway ameliorates crystalline silica-induced silicosis Theranostics 2019 9 4993 5008 10.7150/thno.35665 31410197 PMC6691376 Li, S. et al. Targeting mechanics-induced fibroblast activation through CD44-RhoA-YAP pathway ameliorates crystalline silica-induced silicosis. Theranostics 9 , 4993–5008 (2019). 31410197 10.7150/thno.35665 PMC6691376

83. Chen L A single-cell taxonomy predicts inflammatory niche remodeling to drive tissue failure and outcome in human AML Blood Cancer Discov. 2023 4 394 417 10.1158/2643-3230.BCD-23-0043 37470778 PMC10472197 Chen, L. et al. A single-cell taxonomy predicts inflammatory niche remodeling to drive tissue failure and outcome in human AML. Blood Cancer Discov. 4 , 394–417 (2023). 37470778 10.1158/2643-3230.BCD-23-0043 PMC10472197

84. Luo H Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment Nat. Commun. 2022 13 6619 10.1038/s41467-022-34395-2 36333338 PMC9636408 Luo, H. et al. Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. Nat. Commun. 13 , 6619 (2022). 36333338 10.1038/s41467-022-34395-2 PMC9636408

85. Charbord P A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche Cell Stem Cell 2014 15 376 391 10.1016/j.stem.2014.06.005 25042701 Charbord, P. et al. A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche. Cell Stem Cell 15 , 376–391 (2014). 25042701 10.1016/j.stem.2014.06.005

86. Nakatani T Ebf3+ niche-derived CXCL12 is required for the localization and maintenance of hematopoietic stem cells Nat. Commun. 2023 14 6402 10.1038/s41467-023-42047-2 37880234 PMC10600098 Nakatani, T. et al. Ebf3+ niche-derived CXCL12 is required for the localization and maintenance of hematopoietic stem cells. Nat. Commun. 14 , 6402 (2023). 37880234 10.1038/s41467-023-42047-2 PMC10600098

87. Raredon, M. S. B. et al. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 39 , btac775 (2023). 10.1093/bioinformatics/btac775 PMC9825783 36458905

88. Trapnell C The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells Nat. Biotechnol. 2014 32 381 386 10.1038/nbt.2859 24658644 PMC4122333 Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32 , 381–386 (2014). 24658644 10.1038/nbt.2859 PMC4122333

89. Kholmatov, M. et al. SpliceUp: Predicting SF3B1 mutations in individual cells via aberrant splice site activation from scRNA-seq data. bioRxiv (2025).

90. Buck, M. C. et al. Progressive disruption of hematopoietic architecture from clonal hematopoiesis to MDS. iScience 26 , 107328 (2023). 10.1016/j.isci.2023.107328 PMC10382887 37520699

91. Pang WW Hematopoietic stem cell and progenitor cell mechanisms in myelodysplastic syndromes Proc. Natl. Acad. Sci. USA 2013 110 3011 3016 10.1073/pnas.1222861110 23388639 PMC3581956 Pang, W. W. et al. Hematopoietic stem cell and progenitor cell mechanisms in myelodysplastic syndromes. Proc. Natl. Acad. Sci. USA 110 , 3011–3016 (2013). 23388639 10.1073/pnas.1222861110 PMC3581956

92. Wang W Multiregion single-cell sequencing reveals the transcriptional landscape of the immune microenvironment of colorectal cancer Clin. Transl. Med. 2021 11 e253 10.1002/ctm2.253 33463049 PMC7775989 Wang, W. et al. Multiregion single-cell sequencing reveals the transcriptional landscape of the immune microenvironment of colorectal cancer. Clin. Transl. Med. 11 , e253 (2021). 33463049 10.1002/ctm2.253 PMC7775989

93. Jonsson AH Granzyme K + CD8 T cells form a core population in inflamed human tissue Sci. Transl. Med. 2022 14 eabo0686 10.1126/scitranslmed.abo0686 35704599 PMC9972878 Jonsson, A. H. et al. Granzyme K + CD8 T cells form a core population in inflamed human tissue. Sci. Transl. Med. 14 , eabo0686 (2022). 35704599 10.1126/scitranslmed.abo0686 PMC9972878

94. Zhang F Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry Nat. Immunol. 2019 20 928 942 10.1038/s41590-019-0378-1 31061532 PMC6602051 Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20 , 928–942 (2019). 31061532 10.1038/s41590-019-0378-1 PMC6602051

95. Wu X Single-cell sequencing of immune cells from anticitrullinated peptide antibody positive and negative rheumatoid arthritis Nat. Commun. 2021 12 4977 10.1038/s41467-021-25246-7 34404786 PMC8371160 Wu, X. et al. Single-cell sequencing of immune cells from anticitrullinated peptide antibody positive and negative rheumatoid arthritis. Nat. Commun. 12 , 4977 (2021). 34404786 10.1038/s41467-021-25246-7 PMC8371160

96. Zeng Z Overexpression of OASL upregulates TET1 to induce aberrant activation of CD4 + T cells in systemic sclerosis via IRF1 signaling Arthritis Res. Ther. 2022 24 50 10.1186/s13075-022-02741-w 35183246 PMC8857842 Zeng, Z. et al. Overexpression of OASL upregulates TET1 to induce aberrant activation of CD4 + T cells in systemic sclerosis via IRF1 signaling. Arthritis Res. Ther. 24 , 50 (2022). 35183246 10.1186/s13075-022-02741-w PMC8857842

97. Tewary P Granulysin activates antigen-presenting cells through TLR4 and acts as an immune alarmin Blood 2010 116 3465 3474 10.1182/blood-2010-03-273953 20660289 PMC2981473 Tewary, P. et al. Granulysin activates antigen-presenting cells through TLR4 and acts as an immune alarmin. Blood 116 , 3465–3474 (2010). 20660289 10.1182/blood-2010-03-273953 PMC2981473

98. Quintana FJ Mimran A Carmi P Mor F Cohen IR HSP60 as a target of anti-ergotypic regulatory T cells PLoS ONE 2008 3 e4026 10.1371/journal.pone.0004026 19107191 PMC2602852 Quintana, F. J., Mimran, A., Carmi, P., Mor, F. & Cohen, I. R. HSP60 as a target of anti-ergotypic regulatory T cells. PLoS ONE 3 , e4026 (2008). 19107191 10.1371/journal.pone.0004026 PMC2602852

99. Shi LZ Interdependent IL-7 and IFN-γ signalling in T-cell controls tumour eradication by combined α-CTLA-4+α-PD-1 therapy Nat. Commun. 2016 7 12335 10.1038/ncomms12335 27498556 PMC4979067 Shi, L. Z. et al. Interdependent IL-7 and IFN-γ signalling in T-cell controls tumour eradication by combined α-CTLA-4+α-PD-1 therapy. Nat. Commun. 7 , 12335 (2016). 27498556 10.1038/ncomms12335 PMC4979067

100. Padro T Increased angiogenesis in the bone marrow of patients with acute myeloid leukemia Blood 2000 95 2637 2644 10.1182/blood.V95.8.2637 10753845 Padro, T. et al. Increased angiogenesis in the bone marrow of patients with acute myeloid leukemia. Blood 95 , 2637–2644 (2000). 10753845

101. Korkolopoulou P Prognostic evaluation of the microvascular network in myelodysplastic syndromes Leukemia 2001 15 1369 1376 10.1038/sj.leu.2402220 11516097 Korkolopoulou, P. et al. Prognostic evaluation of the microvascular network in myelodysplastic syndromes. Leukemia 15 , 1369–1376 (2001). 11516097 10.1038/sj.leu.2402220

102. Wimazal F Immunohistochemical detection of vascular endothelial growth factor (VEGF) in the bone marrow in patients with myelodysplastic syndromes: correlation between VEGF expression and the FAB category Leuk. Lymphoma 2006 47 451 460 10.1080/10428190500353083 16396768 Wimazal, F. et al. Immunohistochemical detection of vascular endothelial growth factor (VEGF) in the bone marrow in patients with myelodysplastic syndromes: correlation between VEGF expression and the FAB category. Leuk. Lymphoma 47 , 451–460 (2006). 16396768 10.1080/10428190500353083

103. Weidenaar AC High acute myeloid leukemia derived VEGFA levels are associated with a specific vascular morphology in the leukemic bone marrow Cell Oncol. (Dordr.) 2011 34 289 296 10.1007/s13402-011-0017-9 21468688 PMC3162634 Weidenaar, A. C. et al. High acute myeloid leukemia derived VEGFA levels are associated with a specific vascular morphology in the leukemic bone marrow. Cell Oncol. (Dordr.) 34 , 289–296 (2011). 21468688 10.1007/s13402-011-0017-9 PMC3162634

104. Baccin C Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization Nat. Cell Biol. 2020 22 38 48 10.1038/s41556-019-0439-6 31871321 PMC7610809 Baccin, C. et al. Combined single-cell and spatial transcriptomics reveal the molecular, cellular and spatial bone marrow niche organization. Nat. Cell Biol. 22 , 38–48 (2020). 31871321 10.1038/s41556-019-0439-6 PMC7610809

105. Tikhonova AN The bone marrow microenvironment at single-cell resolution Nature 2019 569 222 228 10.1038/s41586-019-1104-8 30971824 PMC6607432 Tikhonova, A. N. et al. The bone marrow microenvironment at single-cell resolution. Nature 569 , 222–228 (2019). 30971824 10.1038/s41586-019-1104-8 PMC6607432

106. Zhong, L. et al. Single cell transcriptomics identifies a unique adipose lineage cell population that regulates bone marrow environment. eLife 9 , e54695 (2020). 10.7554/eLife.54695 PMC7220380 32286228

107. Kim, P. G. et al. Dnmt3a-mutated clonal hematopoiesis promotes osteoporosis. J. Exp. Med . 218 , e20211872 (2021). 10.1084/jem.20211872 PMC8552148 34698806

108. Wang H Clonal hematopoiesis driven by mutated DNMT3A promotes inflammatory bone loss Cell 2024 187 3690 3711.e19 10.1016/j.cell.2024.05.003 38838669 PMC11246233 Wang, H. et al. Clonal hematopoiesis driven by mutated DNMT3A promotes inflammatory bone loss. Cell 187 , 3690–3711.e19 (2024). 38838669 10.1016/j.cell.2024.05.003 PMC11246233

109. Loberg MA Sequentially inducible mouse models reveal that Npm1 mutation causes malignant transformation of Dnmt3a-mutant clonal hematopoiesis Leukemia 2019 33 1635 1649 10.1038/s41375-018-0368-6 30692594 PMC6609470 Loberg, M. A. et al. Sequentially inducible mouse models reveal that Npm1 mutation causes malignant transformation of Dnmt3a-mutant clonal hematopoiesis. Leukemia 33 , 1635–1649 (2019). 30692594 10.1038/s41375-018-0368-6 PMC6609470

110. Bell-Hensley A Skeletal abnormalities in mice with Dnmt3a missense mutations Bone 2024 183 117085 10.1016/j.bone.2024.117085 38522809 PMC11057337 Bell-Hensley, A. et al. Skeletal abnormalities in mice with Dnmt3a missense mutations. Bone 183 , 117085 (2024). 38522809 10.1016/j.bone.2024.117085 PMC11057337

111. Siegel G Phenotype, donor age and gender affect function of human bone marrow-derived mesenchymal stromal cells BMC Med. 2013 11 146 10.1186/1741-7015-11-146 23758701 PMC3694028 Siegel, G. et al. Phenotype, donor age and gender affect function of human bone marrow-derived mesenchymal stromal cells. BMC Med. 11 , 146 (2013). 23758701 10.1186/1741-7015-11-146 PMC3694028

112. Kizilay Mancini Ö A proinflammatory secretome mediates the impaired immunopotency of human mesenchymal stromal cells in elderly patients with atherosclerosis Stem Cells Transl. Med. 2017 6 1132 1140 10.1002/sctm.16-0221 28194905 PMC5442842 Kizilay Mancini, Ö et al. A proinflammatory secretome mediates the impaired immunopotency of human mesenchymal stromal cells in elderly patients with atherosclerosis. Stem Cells Transl. Med. 6 , 1132–1140 (2017). 28194905 10.1002/sctm.16-0221 PMC5442842

113. Gnani D An early-senescence state in aged mesenchymal stromal cells contributes to hematopoietic stem and progenitor cell clonogenic impairment through the activation of a pro-inflammatory program Aging Cell 2019 18 e12933 10.1111/acel.12933 30828977 PMC6516180 Gnani, D. et al. An early-senescence state in aged mesenchymal stromal cells contributes to hematopoietic stem and progenitor cell clonogenic impairment through the activation of a pro-inflammatory program. Aging Cell 18 , e12933 (2019). 30828977 10.1111/acel.12933 PMC6516180

114. Chen S Massive parallel RNA sequencing of highly purified mesenchymal elements in low-risk MDS reveals tissue-context-dependent activation of inflammatory programs Leukemia 2016 30 1938 1942 10.1038/leu.2016.91 27109510 PMC5240018 Chen, S. et al. Massive parallel RNA sequencing of highly purified mesenchymal elements in low-risk MDS reveals tissue-context-dependent activation of inflammatory programs. Leukemia 30 , 1938–1942 (2016). 27109510 10.1038/leu.2016.91 PMC5240018

115. Jann J-C Bone marrow derived stromal cells from myelodysplastic syndromes are altered but not clonally mutated in vivo Nat. Commun. 2021 12 6170 10.1038/s41467-021-26424-3 34697318 PMC8546146 Jann, J.-C. et al. Bone marrow derived stromal cells from myelodysplastic syndromes are altered but not clonally mutated in vivo. Nat. Commun. 12 , 6170 (2021). 34697318 10.1038/s41467-021-26424-3 PMC8546146

116. Jaiswal S Age-related clonal hematopoiesis associated with adverse outcomes N. Engl. J. Med. 2014 371 2488 2498 10.1056/NEJMoa1408617 25426837 PMC4306669 Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371 , 2488–2498 (2014). 25426837 10.1056/NEJMoa1408617 PMC4306669

117. Desai P Somatic mutations precede acute myeloid leukemia years before diagnosis Nat. Med. 2018 24 1015 1023 10.1038/s41591-018-0081-z 29988143 PMC6849383 Desai, P. et al. Somatic mutations precede acute myeloid leukemia years before diagnosis. Nat. Med. 24 , 1015–1023 (2018). 29988143 10.1038/s41591-018-0081-z PMC6849383

118. Hormaechea-Agulla D Chronic infection drives Dnmt3a-loss-of-function clonal hematopoiesis via IFNγ signaling Cell Stem Cell 2021 28 1428 1442.e6 10.1016/j.stem.2021.03.002 33743191 PMC8349829 Hormaechea-Agulla, D. et al. Chronic infection drives Dnmt3a-loss-of-function clonal hematopoiesis via IFNγ signaling. Cell Stem Cell 28 , 1428–1442.e6 (2021). 33743191 10.1016/j.stem.2021.03.002 PMC8349829

119. Obeng EA Physiologic expression of sf3b1(k700e) causes impaired erythropoiesis, aberrant splicing, and sensitivity to therapeutic spliceosome modulation Cancer Cell 2016 30 404 417 10.1016/j.ccell.2016.08.006 27622333 PMC5023069 Obeng, E. A. et al. Physiologic expression of sf3b1(k700e) causes impaired erythropoiesis, aberrant splicing, and sensitivity to therapeutic spliceosome modulation. Cancer Cell 30 , 404–417 (2016). 27622333 10.1016/j.ccell.2016.08.006 PMC5023069

120. Shiozawa Y Aberrant splicing and defective mRNA production induced by somatic spliceosome mutations in myelodysplasia Nat. Commun. 2018 9 3649 10.1038/s41467-018-06063-x 30194306 PMC6128865 Shiozawa, Y. et al. Aberrant splicing and defective mRNA production induced by somatic spliceosome mutations in myelodysplasia. Nat. Commun. 9 , 3649 (2018). 30194306 10.1038/s41467-018-06063-x PMC6128865

121. Pollyea DA MDS-associated SF3B1 mutations enhance proinflammatory gene expression in patient blast cells J. Leukoc. Biol. 2021 110 197 205 10.1002/JLB.6AB0520-318RR 33155727 PMC8612809 Pollyea, D. A. et al. MDS-associated SF3B1 mutations enhance proinflammatory gene expression in patient blast cells. J. Leukoc. Biol. 110 , 197–205 (2021). 33155727 10.1002/JLB.6AB0520-318RR PMC8612809

122. Choudhary, G. S. et al. Activation of targetable inflammatory immune signaling is seen in myelodysplastic syndromes with SF3B1 mutations. eLife 11 , e78136 (2022). 10.7554/eLife.78136 PMC9427103 36040792

123. Scherer M Clonal tracing with somatic epimutations reveals dynamics of blood ageing Nature 2025 643 478 487 10.1038/s41586-025-09041-8 40399669 PMC12240852 Scherer, M. et al. Clonal tracing with somatic epimutations reveals dynamics of blood ageing. Nature 643 , 478–487 (2025). 40399669 10.1038/s41586-025-09041-8 PMC12240852

124. Chen, L. et al. An inflammatory T-cell-stromal axis contributes to hematopoietic stem/progenitor cell failure and clonal evolution in human myelodysplastic syndrome. Nat. Commun. 10.1038/s41467-025-65802-z (2025). 10.1038/s41467-025-65802-z PMC12627551 41253768

125. Koncina E IL1R1+ cancer-associated fibroblasts drive tumor development and immunosuppression in colorectal cancer Nat. Commun. 2023 14 4251 10.1038/s41467-023-39953-w 37460545 PMC10352362 Koncina, E. et al. IL1R1+ cancer-associated fibroblasts drive tumor development and immunosuppression in colorectal cancer. Nat. Commun. 14 , 4251 (2023). 37460545 10.1038/s41467-023-39953-w PMC10352362

126. Kawano Y IL-1R1 and IL-18 signals regulate mesenchymal stromal cells in an aged murine model of myelodysplastic syndromes Blood 2025 145 1632 1644 10.1182/blood.2024024818 39841001 PMC12000655 Kawano, Y. et al. IL-1R1 and IL-18 signals regulate mesenchymal stromal cells in an aged murine model of myelodysplastic syndromes. Blood 145 , 1632–1644 (2025). 39841001 10.1182/blood.2024024818 PMC12000655

127. Croes M Proinflammatory mediators enhance the osteogenesis of human mesenchymal stem cells after lineage commitment PLoS ONE 2015 10 e0132781 10.1371/journal.pone.0132781 26176237 PMC4503569 Croes, M. et al. Proinflammatory mediators enhance the osteogenesis of human mesenchymal stem cells after lineage commitment. PLoS ONE 10 , e0132781 (2015). 26176237 10.1371/journal.pone.0132781 PMC4503569

128. Maruyama M Modulation of the inflammatory response and bone healing Front Endocrinol. (Lausanne) 2020 11 386 10.3389/fendo.2020.00386 32655495 PMC7325942 Maruyama, M. et al. Modulation of the inflammatory response and bone healing. Front Endocrinol. (Lausanne) 11 , 386 (2020). 32655495 10.3389/fendo.2020.00386 PMC7325942

129. Jann, J.-C. et al. Instructive interaction between myelodysplastic hematopoiesis and the bone marrow microenvironment at single cell level. Blood Neoplasia 1 , 100021 (2024). 10.1016/j.bneo.2024.100021 PMC12082128 40453063

130. Hauet-Broere F Heat shock proteins induce T cell regulation of chronic inflammation Ann. Rheum. Dis. 2006 65 iii65 iii68 10.1136/ard.2006.058495 17038477 PMC1798372 Hauet-Broere, F. et al. Heat shock proteins induce T cell regulation of chronic inflammation. Ann. Rheum. Dis. 65 , iii65–iii68 (2006). 17038477 10.1136/ard.2006.058495 PMC1798372

131. Zhou X HSPA6 is correlated with the malignant progression and immune microenvironment of gliomas Front. Cell Dev. Biol. 2022 10 833938 10.3389/fcell.2022.833938 35281087 PMC8904718 Zhou, X. et al. HSPA6 is correlated with the malignant progression and immune microenvironment of gliomas. Front. Cell Dev. Biol. 10 , 833938 (2022). 35281087 10.3389/fcell.2022.833938 PMC8904718

132. Kumar BV Connors TJ Farber DL Human T cell development, localization, and function throughout life Immunity 2018 48 202 213 10.1016/j.immuni.2018.01.007 29466753 PMC5826622 Kumar, B. V., Connors, T. J. & Farber, D. L. Human T cell development, localization, and function throughout life. Immunity 48 , 202–213 (2018). 29466753 10.1016/j.immuni.2018.01.007 PMC5826622

133. Muroyama, Y. & Wherry, E. J. Memory T-cell heterogeneity and terminology. Cold Spring Harb. Perspect. Biol . 13 , a037929 (2021). 10.1101/cshperspect.a037929 PMC8485749 33782027

134. Wobus, M. et al. Preclinical efficacy of tasquinimod in myelodysplastic neoplasms: restoring erythropoiesis and mitigating bone loss. Preprint BioRxiv 10.1101/2025.07.01.660147 (2025). 10.1002/hem3.70352 PMC13077779 41987903

135. Cenzano, I. et al. Transcriptional remodeling of the stromal and endothelial microenvironment in MGUS to multiple myeloma progression. Preprint BioRxiv 10.1101/2024.04.24.589777 (2024).

136. Ghamlouch H A proinflammatory response and polarized differentiation of stromal elements characterizes the murine myeloma bone marrow niche Exp. Hematol. Oncol. 2025 14 22 10.1186/s40164-025-00606-x 40011943 PMC11866767 Ghamlouch, H. et al. A proinflammatory response and polarized differentiation of stromal elements characterizes the murine myeloma bone marrow niche. Exp. Hematol. Oncol. 14 , 22 (2025). 40011943 10.1186/s40164-025-00606-x PMC11866767

137. Oelschlaegel U Comparison of five diagnostic flow cytometry scores in patients with myelodysplastic syndromes: Diagnostic power and prognostic impact Cytom. B Clin. Cytom. 2023 104 141 150 10.1002/cyto.b.22030 34390327 Oelschlaegel, U. et al. Comparison of five diagnostic flow cytometry scores in patients with myelodysplastic syndromes: Diagnostic power and prognostic impact. Cytom. B Clin. Cytom. 104 , 141–150 (2023). 10.1002/cyto.b.22030 34390327

138. Porwit A Multiparameter flow cytometry in the evaluation of myelodysplasia: analytical issues: Recommendations from the European leukemianet/international myelodysplastic syndrome flow cytometry working group Cytom. B Clin. Cytom. 2023 104 27 50 10.1002/cyto.b.22108 PMC10107708 36537621 Porwit, A. et al. Multiparameter flow cytometry in the evaluation of myelodysplasia: analytical issues: Recommendations from the European leukemianet/international myelodysplastic syndrome flow cytometry working group. Cytom. B Clin. Cytom. 104 , 27–50 (2023). 10.1002/cyto.b.22108 PMC10107708 36537621

139. Schindelin J Fiji: an open-source platform for biological-image analysis Nat. Methods 2012 9 676 682 10.1038/nmeth.2019 22743772 PMC3855844 Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9 , 676–682 (2012). 22743772 10.1038/nmeth.2019 PMC3855844

140. Tratwal J Marrowquant across aging and aplasia: a digital pathology workflow for quantification of bone marrow compartments in histological sections Front Endocrinol. (Lausanne) 2020 11 480 10.3389/fendo.2020.00480 33071956 PMC7542184 Tratwal, J. et al. Marrowquant across aging and aplasia: a digital pathology workflow for quantification of bone marrow compartments in histological sections. Front Endocrinol. (Lausanne) 11 , 480 (2020). 33071956 10.3389/fendo.2020.00480 PMC7542184

141. Tohyama K Tsutani H Ueda T Nakamura T Yoshida Y Establishment and characterization of a novel myeloid cell line from the bone marrow of a patient with the myelodysplastic syndrome Br. J. Haematol. 1994 87 235 242 10.1111/j.1365-2141.1994.tb04904.x 7947263 Tohyama, K., Tsutani, H., Ueda, T., Nakamura, T. & Yoshida, Y. Establishment and characterization of a novel myeloid cell line from the bone marrow of a patient with the myelodysplastic syndrome. Br. J. Haematol. 87 , 235–242 (1994). 7947263 10.1111/j.1365-2141.1994.tb04904.x

142. Ritchie ME limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res. 2015 43 e47 10.1093/nar/gkv007 25605792 PMC4402510 Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 , e47 (2015). 25605792 10.1093/nar/gkv007 PMC4402510

143. Xu S Using clusterProfiler to characterize multiomics data Nat. Protoc. 2024 19 3292 3320 10.1038/s41596-024-01020-z 39019974 Xu, S. et al. Using clusterProfiler to characterize multiomics data. Nat. Protoc. 19 , 3292–3320 (2024). 39019974 10.1038/s41596-024-01020-z

144. Mootha VK PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes Nat. Genet. 2003 34 267 273 10.1038/ng1180 12808457 Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34 , 267–273 (2003). 12808457 10.1038/ng1180

145. Subramanian A Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proc. Natl. Acad. Sci. USA 2005 102 15545 15550 10.1073/pnas.0506580102 16199517 PMC1239896 Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102 , 15545–15550 (2005). 16199517 10.1073/pnas.0506580102 PMC1239896

146. Gu Z Complex heatmap Vis. iMeta 2022 1 e43 10.1002/imt2.43 PMC10989952 38868715 Gu, Z. Complex heatmap Vis. iMeta 1 , e43 (2022). 10.1002/imt2.43 PMC10989952 38868715

147. Muraro MJ A single-cell transcriptome atlas of the human pancreas Cell Syst. 2016 3 385 394.e3 10.1016/j.cels.2016.09.002 27693023 PMC5092539 Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3 , 385–394.e3 (2016). 27693023 10.1016/j.cels.2016.09.002 PMC5092539

148. Hashimshony T CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq Genome Biol. 2016 17 77 10.1186/s13059-016-0938-8 27121950 PMC4848782 Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17 , 77 (2016). 27121950 10.1186/s13059-016-0938-8 PMC4848782

149. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10 , giab008 2021). 10.1093/gigascience/giab008 PMC7931819 33590861

150. Das S Next-generation genotype imputation service and methods Nat. Genet. 2016 48 1284 1287 10.1038/ng.3656 27571263 PMC5157836 Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48 , 1284–1287 (2016). 27571263 10.1038/ng.3656 PMC5157836

151. McCarthy S A reference panel of 64,976 haplotypes for genotype imputation Nat. Genet. 2016 48 1279 1283 10.1038/ng.3643 27548312 PMC5388176 McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48 , 1279–1283 (2016). 27548312 10.1038/ng.3643 PMC5388176

152. Loh P-R Reference-based phasing using the haplotype reference consortium panel Nat. Genet. 2016 48 1443 1448 10.1038/ng.3679 27694958 PMC5096458 Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48 , 1443–1448 (2016). 27694958 10.1038/ng.3679 PMC5096458

153. Heaton H Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes Nat. Methods 2020 17 615 620 10.1038/s41592-020-0820-1 32366989 PMC7617080 Heaton, H. et al. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. Methods 17 , 615–620 (2020). 32366989 10.1038/s41592-020-0820-1 PMC7617080

154. 1000 Genomes Project Consortium A global reference for human genetic variation Nature 2015 526 68 74 10.1038/nature15393 26432245 PMC4750478 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526 , 68–74 (2015). 26432245 10.1038/nature15393 PMC4750478

155. Young MD Behjati S SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data Gigascience 2020 9 giaa151 10.1093/gigascience/giaa151 33367645 PMC7763177 Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9 , giaa151 (2020). 33367645 10.1093/gigascience/giaa151 PMC7763177

156. Germain P-L Lun A Garcia Meixide C Macnair W Robinson MD Doublet identification in single-cell sequencing data using scDblFinder F1000Res. 2021 10 979 10.12688/f1000research.73600.1 35814628 PMC9204188 Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res. 10 , 979 (2021). 35814628 10.12688/f1000research.73600.1 PMC9204188

157. Stuart T Comprehensive Integration of Single- Cell Data. Cell 2019 177 1888 1902.e21 31178118 10.1016/j.cell.2019.05.031 PMC6687398 Stuart, T. et al. Comprehensive Integration of Single-. Cell Data. Cell 177 , 1888–1902.e21 (2019). 31178118 10.1016/j.cell.2019.05.031 PMC6687398

158. Mölder F Sustainable data analysis with Snakemake F1000Res. 2021 10 33 10.12688/f1000research.29032.2 34035898 PMC8114187 Mölder, F. et al. Sustainable data analysis with Snakemake. F1000Res. 10 , 33 (2021). 34035898 10.12688/f1000research.29032.1 PMC8114187

159. Dobin A STAR: ultrafast universal RNA-seq aligner Bioinformatics 2013 29 15 21 10.1093/bioinformatics/bts635 23104886 PMC3530905 Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29 , 15–21 (2013). 23104886 10.1093/bioinformatics/bts635 PMC3530905

160. Petukhov V dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments Genome Biol. 2018 19 78 10.1186/s13059-018-1449-6 29921301 PMC6010209 Petukhov, V. et al. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol. 19 , 78 (2018). 29921301 10.1186/s13059-018-1449-6 PMC6010209

161. Choudhary S Satija R Comparison and evaluation of statistical error models for scRNA-seq Genome Biol. 2022 23 27 10.1186/s13059-021-02584-9 35042561 PMC8764781 Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23 , 27 (2022). 35042561 10.1186/s13059-021-02584-9 PMC8764781

162. Pearson K On lines and planes of closest fit to systems of points in space Philos. Mag. Ser. 1901 6 559 572 10.1080/14786440109462720 Pearson, K. On lines and planes of closest fit to systems of points in space. Philos. Mag. Ser. 6 , 559–572 (1901).

163. McInnes L Healy J Saul N Großberger L UMAP: uniform manifold approximation and projection JOSS 2018 3 861 10.21105/joss.00861 McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. JOSS 3 , 861 (2018).

164. Traag VA Waltman L van Eck NJ From Louvain to Leiden: guaranteeing well-connected communities Sci. Rep. 2019 9 5233 10.1038/s41598-019-41695-z 30914743 PMC6435756 Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9 , 5233 (2019). 30914743 10.1038/s41598-019-41695-z PMC6435756

165. Zappia L Oshlack A Clustering trees: a visualization for evaluating clusterings at multiple resolutions Gigascience 2018 7 giy083 10.1093/gigascience/giy083 30010766 PMC6057528 Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7 , giy083 (2018). 30010766 10.1093/gigascience/giy083 PMC6057528

166. Van de Sande B A scalable SCENIC workflow for single-cell gene regulatory network analysis Nat. Protoc. 2020 15 2247 2276 10.1038/s41596-020-0336-2 32561888 Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15 , 2247–2276 (2020). 32561888 10.1038/s41596-020-0336-2

167. Zeng, A. G. X. et al. Single-cell Transcriptional Atlas of Human Hematopoiesis Reveals Genetic and Hierarchy-Based Determinants of Aberrant AML Differentiation. Blood Cancer Discov. 6 , 307–324 (2025). 10.1158/2643-3230.BCD-24-0342 PMC12209776 40294241

168. Love MI Huber W Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol. 2014 15 550 10.1186/s13059-014-0550-8 25516281 PMC4302049 Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550 (2014). 25516281 10.1186/s13059-014-0550-8 PMC4302049

169. Stephens M False discovery rates: a new deal Biostatistics 2017 18 275 294 27756721 10.1093/biostatistics/kxw041 PMC5379932 Stephens, M. False discovery rates: a new deal. Biostatistics 18 , 275–294 (2017). 27756721 10.1093/biostatistics/kxw041 PMC5379932

170. Liberzon A The Molecular Signatures Database (MSigDB) hallmark gene set collection Cell Syst. 2015 1 417 425 10.1016/j.cels.2015.12.004 26771021 PMC4707969 Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1 , 417–425 (2015). 26771021 10.1016/j.cels.2015.12.004 PMC4707969

171. Liberzon A Molecular signatures database (MSigDB) 3.0 Bioinformatics 2011 27 1739 1740 10.1093/bioinformatics/btr260 21546393 PMC3106198 Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27 , 1739–1740 (2011). 21546393 10.1093/bioinformatics/btr260 PMC3106198

172. Wu T clusterProfiler 4.0: a universal enrichment tool for interpreting omics data Innov. (Camb.) 2021 2 100141 10.1016/j.xinn.2021.100141 PMC8454663 34557778 Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov. (Camb.) 2 , 100141 (2021). 10.1016/j.xinn.2021.100141 PMC8454663 34557778

173. Türei, D. et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol . 17 , e9923 (2021). 10.15252/msb.20209923 PMC7983032 33749993

174. Qiu X Reversed graph embedding resolves complex single-cell trajectories Nat. Methods 2017 14 979 982 10.1038/nmeth.4402 28825705 PMC5764547 Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14 , 979–982 (2017). 28825705 10.1038/nmeth.4402 PMC5764547

175. Cao J The single-cell transcriptional landscape of mammalian organogenesis Nature 2019 566 496 502 10.1038/s41586-019-0969-x 30787437 PMC6434952 Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566 , 496–502 (2019). 30787437 10.1038/s41586-019-0969-x PMC6434952

176. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv 10.48550/arxiv.1802.03426 (2018).

177. Korsunsky I Fast, sensitive and accurate integration of single-cell data with Harmony Nat. Methods 2019 16 1289 1296 10.1038/s41592-019-0619-0 31740819 PMC6884693 Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16 , 1289–1296 (2019). 31740819 10.1038/s41592-019-0619-0 PMC6884693

178. Mishra PJ Carcinoma-associated fibroblast-like differentiation of human mesenchymal stem cells Cancer Res. 2008 68 4331 4339 10.1158/0008-5472.CAN-08-0943 18519693 PMC2725025 Mishra, P. J. et al. Carcinoma-associated fibroblast-like differentiation of human mesenchymal stem cells. Cancer Res. 68 , 4331–4339 (2008). 18519693 10.1158/0008-5472.CAN-08-0943 PMC2725025

179. Tirosh I Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq Science 2016 352 189 196 10.1126/science.aad0501 27124452 PMC4944528 Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352 , 189–196 (2016). 27124452 10.1126/science.aad0501 PMC4944528

180. Ouyang JF Kamaraj US Cao EY Rackham OJL ShinyCell: simple and sharable visualization of single-cell gene expression data Bioinformatics 2021 37 3374 3376 10.1093/bioinformatics/btab209 33774659 Ouyang, J. F., Kamaraj, U. S., Cao, E. Y. & Rackham, O. J. L. ShinyCell: simple and sharable visualization of single-cell gene expression data. Bioinformatics 37 , 3374–3376 (2021). 33774659 10.1093/bioinformatics/btab209