bioRxivGeneticsPreprint
An atlas of TF driven gene programs across human cells
J. Patrick PettiD, Martin PreteiD, Duy PhamiD, Nick EnglandiD, Hao YuaniD, Elena PrigmoreiD, Elizabeth J. TuckiD, Ágnes OszláncziiD, Ken ToiD, Chuan XuiD, Chenqu SuoiD, Emma DanniD, Peng HeiD, Veronika R. KedlianiD, Kazumasa KanemaruiD, James J. CranleyiD, Ling YangiD, Rasa ElmentaiteiD, Amanda J. OliveriD, Ana-Maria CujbaiD, Batuhan ÇakıriD, Simon MurrayiD, Krishnaa T. A. MahbubaniiD, Kourosh Saeb‐ParsyiD, Laure GambardellaiD, , Kerstin B. MeyeriD
Abstract
Combinations of transcription factors (TFs) regulate gene expression and determine cell fate. Much effort has been devoted to understanding TF activity in different tissues and how tissue-specificity is achieved. However, ultimately gene regulation occurs at the single cell level and the recent explosion in the availability of single cell gene expression data now makes it possible to understand TF activity at this granular level of resolution. Here, we leverage a large collection of Human Cell Atlas (HCA) single cell data to explore TF activity by examining cell-type and tissue-specific sets of target genes, or regulons. We compile a regulon atlas, CellRegulon, and map the activity of TFs in an extensive set of healthy adult and foetal tissues spanning hundreds of cell types. Using CellRegulon, we describe dynamic patterns of co-regulation, associate TF-modules with different cellular functions and characterise the distribution of active TFs and TF families across cell types. We show that CellRegulon can link disease gene expression signatures to cell types and TFs relevant to the disease. Finally, using a newly generated multiome dataset of the adult lung, we show how CellRegulon can be extended into an enhancer-gene regulatory network (eGRN) to improve cell-type associations with genetic risk loci for diseases, such as childhood onset asthma, COPD and IPF, and to identify high risk gene modules. Our database for easy download and interactive exploration allows researchers to understand key gene modules activated at cell type transitions and will therefore be valuable for tasks such as cell type engineering (https://www.cellregulondb.org).
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: database / resource
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
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