spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

Abstract

Extracting dynamical information from single‐cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory‐oriented, bottom‐up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single‐cell RNA sequencing (scRNA‐seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state‐specific gene–gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre‐existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

Finding meaningful and workable applied mathematics problems in science

“…Some proteins can affect the expression of other genes (mutual regulation) or their own corresponding genes (autoregulation). Since it is extremely difficult to directly determine gene regulatory relations through biochemical methods, there have been many methods to infer gene regulation [41,2]. I read some papers on gene regulation inference, and found that the settings are diverse: different methods use different models, which are based on different assumptions.…”

Three facets of mathematical cancer biology research

“…Since the gene expression and regulation are mostly confined within living cells, it is almost impossible to determine gene regulation with direct biochemical methods. Nevertheless, there have been many methods to infer the GRN structure, namely whether one gene regulates another, from experimental data [6,9,23]. Such inference methods use models based on different assumptions, and need different types of data.…”

Single-cell multi-omics and lineage tracing to dissect cell fate decision-making

“…One approach to reducing the complexity of a system is thus to both (1) restrict it to a group of its most relevant players (e.g., TFs as core regulators of cell fate at a particular differentiation stage) as well as (2) considering the regulatory interactions only in a certain context or neighborhood, for example specific cell types, as exemplified by two recent methods for inference of gene regulatory networks, CellOracle ( Kamimoto et al., 2020 ) and spliceJAC ( Bocci et al., 2022 ). Considering multiple sets of key players act throughout differentiation, multiple local or time-resolved consecutive regulatory networks would emerge.…”