Frontiers in immunology · 2026
Single-cell Raman profiling of B cell differentiation and leukemic transformation with transcriptome inference.
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Cheng Xuelian, Chen Ming, Li Qing, Dai Linxin, Liu Jing, Wang Haoyu, Zhou Yuan
Аннотация
INTRODUCTION: Label-free profiling of cellular transcriptional and metabolic states during B cell differentiation and malignant transformation remains technically challenging. Raman spectroscopy offers a non-destructive alternative for single-cell biochemical characterization, yet its ability to infer transcriptomic profiles and distinguish leukemic cells has been limited.
METHODS: We established a Raman spectroscopy-based platform to profile B cell differentiation stages (HSC, pro-B, pre-B, naive B) and leukemic B-ALL cells at the single-cell level. Raman spectra were acquired from fixed cells and analyzed using principal component-linear discriminant analysis (PC-LDA) classification. An adversarial autoencoder (AAE) framework was employed to align Raman measurements with reference single-cell RNA-seq data, generating Raman-inferred transcriptomic profiles in the reference expression space. Cytochrome c expression was validated by flow cytometry, and metabolic pathways were examined via GSEA.
RESULTS: PC-LDA resolved four B cell differentiation stages with 96.48% accuracy, identifying Raman features consistent with cytochrome c as potential spectral markers of differentiation status, validated by flow cytometry and mitochondrial membrane potential measurements. The AAE-based model generated Raman-inferred profiles that preserved major cell-type-associated transcriptomic patterns, with a mean classification accuracy of 91.78% ± 8.13% across repeated partitions. Performance varied considerably across repeated splits for pro-B (52.48-100%) and pre-B cells (34.4-100%), indicating that distinguishing closely related stages remains challenging. Applied to B-ALL, the method distinguished cord-blood-derived normal B cells from bone-marrow B-ALL cells (96.62% accuracy) and revealed reprogramming of glucose metabolism, consistent with transcriptomic enrichment analysis.
CONCLUSIONS: This study presents a proof-of-concept framework demonstrating that Raman spectroscopy, integrated with machine learning and transcriptomic alignment, enables non-destructive, fixed-cell-based omic profiling of B cell development and leukemia. The approach bridges label-free optical readouts with transcriptome-informed profiling, opening new avenues for hematopoietic research, analysis of archived specimens, and future clinical exploration. However, these findings are based on a limited number of donors and unmatched tissue sources, and require validation in larger cohorts before clinical translation.
METHODS: We established a Raman spectroscopy-based platform to profile B cell differentiation stages (HSC, pro-B, pre-B, naive B) and leukemic B-ALL cells at the single-cell level. Raman spectra were acquired from fixed cells and analyzed using principal component-linear discriminant analysis (PC-LDA) classification. An adversarial autoencoder (AAE) framework was employed to align Raman measurements with reference single-cell RNA-seq data, generating Raman-inferred transcriptomic profiles in the reference expression space. Cytochrome c expression was validated by flow cytometry, and metabolic pathways were examined via GSEA.
RESULTS: PC-LDA resolved four B cell differentiation stages with 96.48% accuracy, identifying Raman features consistent with cytochrome c as potential spectral markers of differentiation status, validated by flow cytometry and mitochondrial membrane potential measurements. The AAE-based model generated Raman-inferred profiles that preserved major cell-type-associated transcriptomic patterns, with a mean classification accuracy of 91.78% ± 8.13% across repeated partitions. Performance varied considerably across repeated splits for pro-B (52.48-100%) and pre-B cells (34.4-100%), indicating that distinguishing closely related stages remains challenging. Applied to B-ALL, the method distinguished cord-blood-derived normal B cells from bone-marrow B-ALL cells (96.62% accuracy) and revealed reprogramming of glucose metabolism, consistent with transcriptomic enrichment analysis.
CONCLUSIONS: This study presents a proof-of-concept framework demonstrating that Raman spectroscopy, integrated with machine learning and transcriptomic alignment, enables non-destructive, fixed-cell-based omic profiling of B cell development and leukemia. The approach bridges label-free optical readouts with transcriptome-informed profiling, opening new avenues for hematopoietic research, analysis of archived specimens, and future clinical exploration. However, these findings are based on a limited number of donors and unmatched tissue sources, and require validation in larger cohorts before clinical translation.