Using topic modeling to detect cellular crosstalk in scRNA-seq

• Published in: PLoS computational biology Vol. 18; no. 4; p. e1009975
• Main Authors: Pancheva, Alexandrina, Wheadon, Helen, Rogers, Simon, Otto, Thomas D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-04-2022; Public Library of Science (PLoS)
• Subjects: Biological activity; Biology and Life Sciences; Bone marrow; Cell adhesion & migration; Cell interaction; Cell interactions; Cell Physiological Phenomena; Change detection; Cluster Analysis; Clustering; Crosstalk; Datasets; Dirichlet problem; Exome Sequencing; Gene expression; Gene Expression Profiling - methods; Genes; Genetic aspects; Ligands; Medicine and Health Sciences; Physical Sciences; Population; Regression analysis; RNA sequencing; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Subpopulations; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009975

Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently, most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured, and as such we have the potential to study interactions in a complemantary way without relying on prior knowledge. We introduce a new method based on Latent Dirichlet Allocation (LDA) for detecting genes that change as a result of interaction. We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells to identify the genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to the genes discussed in the original publications, we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet-based protocol not designed to capture interacting cells, and discuss its suitability for analysing interactions. We present a method that streamlines detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression.