Identifying potential ligand-receptor interactions by integrating LSTM network and the attention mechanism for cell-cell communication prediction.

BACKGROUND: Cell-cell communication (CCC) mediated by ligand-receptor (L-R) interactions is fundamental to deciphering tissue development and disease mechanisms. While single-cell RNA sequencing (scRNA-seq) has advanced this field, existing computational methods for inferring CCC often suffer from limitations such as dependence on static databases and a failure to capture the sequential dependency of amino acids within proteins, which restricts their generalizability and predictive accuracy. Therefore, the primary objective of this study was to develop a robust computational framework capable of identifying potential L-R interactions directly from protein sequence data, thereby overcoming the reliance on static databases and enabling the discovery of novel signaling pairs.

METHODS: To achieve this objective, we introduce CellAL, a deep learning-based framework for predicting potential interacting L-R pairs and decoding cellular communication. The CellAL pipeline consists of two main stages: (1) L-R Pair Identification, which extracts sequence features using BioTriangle, selects informative features via XGBoost to reduce dimensionality, and classifies interactions using a Long Short-Term Memory (LSTM) network integrated with an attention mechanism specifically designed to capture long-range sequence dependencies that characterize structural binding affinities; and (2) CCC Inference, which filters identified pairs using scRNA-seq data and quantifies crosstalk intensity through a comprehensive scoring strategy that combines expression thresholding, expression product, and specific expression metrics.

RESULTS: Performance evaluations on four standard L-R interaction datasets demonstrated that CellAL significantly surpassed classical protein-protein interaction prediction methods and achieved competitive performance against state-of-the-art ensemble models, achieving the highest AUPR values on three datasets. The identified To achieve L-R pairs showed a high degree of overlap with existing databases such as CellChat and Connectome. Furthermore, when applied to human melanoma scRNA-seq data, CellAL successfully inferred critical signaling networks, revealing strong bidirectional crosstalk between melanoma cells and cancer-associated fibroblasts (CAFs), macrophages, and endothelial cells. These findings were consistent with results from three other representative CCC prediction tools.

CONCLUSIONS: CellAL effectively overcomes the limitations of database dependence by leveraging sequence-level biochemical modeling to predict structural L-R interactions. By integrating deep learning predictions with transcriptomic data, CellAL provides a robust and valuable tool for dissecting complex CCC networks at single-cell resolution, particularly within the tumor microenvironment.