A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes

Abstract

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms.

Label-Free Quantitative Thermal Proteome Profiling Reveals Target Transcription Factors with Activities Modulated by MC3R Signaling

“…This approach enables the monitoring of the relationship between protein abundance and thermal stability, thereby identifying proteins that exhibit significant changes in both expression levels and thermal stability under different experimental conditions. Within this workflow, differential expression information plays a crucial role, particularly in the context of transcription factor analysis using BITFAM . Transcription factors serve as key regulators of gene expression and exert control over numerous downstream target genes.…”

“…Transcription factor activity was inferred from the combined data set of identified proteins with log-transformed intensity values using Bayesian inference transcription factor activity modeling as implemented in the Bayesian inference transcription factor activity modeling (BITFAM) R-package. 23 ■ RESULTS AND DISCUSSION 2D-TPP analysis of MC3R Interaction with Endogeneous Ligands. To characterize the systemic responses of MC3R activation and identify ligand-specific and concentration-dependent effects, we cultured HEK293 cells stably transfected with MC3R and stimulated them with the endogenous agonists ACTH, α-MSH, or γ-MSH at concentrations between 0 and 500 nM for 1 h (Figure 1A,B).…”

Theory and methodology of innovative education development in the National, European, and Global contexts

“…Scientists have built the Bayesian inference transcription factor activity model based on the fundamental biological principle that differences in single-cell DNA sequencing profiles reflect the underlying states of transcription factor activity. This model has been tested in lung, heart, and brain tissue cells (Gao, Dai, Rehman, 2021).…”

“…Gao and others, the proposed approach identifies not only significant actions of transcription factors but also provides valuable information on critical mechanisms for regulating transcription factors. Furthermore, by providing such data for each transcription factor in a cell, the model can give researchers a good idea of which ones to look for when studying new drug targets to work on this cell type (Gao, Dai, Rehman, 2021).…”

Joint inference of transcription factor activity and context-specific regulatory networks

“…However, these two methods have only been tested on data from model organisms. Recently, a single-cell TFA inference method – BITFAM [19] was developed to estimate regulatory network and TFA using prior network and scRNA-seq data. However, it does not distinguish between TF activation and inhibition and it was not validated using gold standard data (e.g.…”