Interpreting single cell transcriptomes in the pathway space and its applications in cancer

Abstract

Single-cell transcriptomics is a powerful technique that has revolutionized our approach to dissect cellular phenotypes and diversity in complex tissues at an unprecedented res- olution. The emergence of this groundbreaking technology has dramatically enhanced our understanding of cellular heterogeneity, interactions, and cell fate decisions during the development and progression of cancer. These new technologies have shown to be promising in the field of cancer genomics. Despite all the goodness, many computa- tional challenges remain. Human cells express about 20,000 genes, which dynamically carry out a multitude of biophysical activities. Statistical and machine learning-based methods treat genes as independent variables in the process of characterizing intra-tumoral heterogeneity and developing insights into cancer progression, pathogenesis, and clinical outcomes. This approach is quite limiting since constantly accumulating somatic genomic alter- ations are often manifested through the dysregulation of molecular pathways or cancer- relevant gene signatures. Thus, exploiting gene set and pathway scores to decipher heterogeneity in the single-cell will aid in many applications in cancer genomics. We propose a statistically robust method called UniPath to represent single cells in terms of pathway or gene set enrichment scores. UniPath projects gene expression readouts and single-cell ATAC-seq profiles into pathway scores while accounting for dropouts and sequencing depth. Further, it allows pseudotemporal ordering of single cells in pathway space. Visualization of gradients and distribution of pathways on a pseudotemporally ordered tree helps understand the lineage potency of cells. Another application of UniPath is that it helps enumerate differences in two cell populations through the exploitation of pathway co-occurrences. In a connected work, we introduce, Precily, deep learning framework that leverages pathway scores of gene expression pro- files and drug descriptors for anti-cancer drug response predictions. We thoroughly val- idated our proposed approach using bulk and single-cell gene expression profiles. We also assessed the performance of our approach on several in-house generated prostate cancer datasets. Finally, we interrogated the transcriptomic profile of triple-negative breast cancer tumor and Natural killer cell doublets and their physical distance cap- tured at single-cell resolution. We discovered that physical distances are governed by activities of regulatory modules, pinpointing the presence of transcriptional memory. In addition, our investigation into ligand-protein pairs interactions that are responsible for conveying messages into cells by activating signaling pathways revealed inflated activities of some of the specific pairs in NK-immune cell doublets. We concluded that intercellular communications in tumors play an essential role in deciphering the underlying mechanism operating in cancer. Our approach of capturing and profiling single-cell doublets will aid in the understanding of complex tumor microenvironment and cellular interactions.