Using Single Cell Atlas Data to Reconstruct Regulatory Networks

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2023 Feb 10

PMID 36762475

Authors

Qi Song

Matthew Ruffalo

Ziv Bar-Joseph

Affiliations

Soon will be listed here.

Abstract

Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.

Citing Articles

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Loers J, Vermeirssen V Brief Bioinform. 2024; 25(5).

PMID: 39207727 PMC: 11359808. DOI: 10.1093/bib/bbae382.

Recent advances in exploring transcriptional regulatory landscape of crops.

Huo Q, Song R, Ma Z Front Plant Sci. 2024; 15:1421503.

PMID: 38903438 PMC: 11188431. DOI: 10.3389/fpls.2024.1421503.

TFvelo: gene regulation inspired RNA velocity estimation.

Li J, Pan X, Yuan Y, Shen H Nat Commun. 2024; 15(1):1387.

PMID: 38360714 PMC: 11258302. DOI: 10.1038/s41467-024-45661-w.

Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks.

Gonzalez G, Lin X, Herath I, Veselkov K, Bronstein M, Zitnik M bioRxiv. 2024; .

PMID: 38260532 PMC: 10802439. DOI: 10.1101/2024.01.03.573985.

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.

Kim D, Tran A, Kim H, Lin Y, Yang J, Yang P NPJ Syst Biol Appl. 2023; 9(1):51.

PMID: 37857632 PMC: 10587078. DOI: 10.1038/s41540-023-00312-6.

References

Chen X, Gu J, Wang X, Jung J, Wang T, Hilakivi-Clarke L . CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data. Bioinformatics. 2017; 34(10):1733-1740. PMC: 5946876. DOI: 10.1093/bioinformatics/btx827. View

Glass K, Huttenhower C, Quackenbush J, Yuan G . Passing messages between biological networks to refine predicted interactions. PLoS One. 2013; 8(5):e64832. PMC: 3669401. DOI: 10.1371/journal.pone.0064832. View

de la Fuente A . From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. Trends Genet. 2010; 26(7):326-33. DOI: 10.1016/j.tig.2010.05.001. View

Liao W, Lin J, Leonard W . Interleukin-2 at the crossroads of effector responses, tolerance, and immunotherapy. Immunity. 2013; 38(1):13-25. PMC: 3610532. DOI: 10.1016/j.immuni.2013.01.004. View

Haury A, Mordelet F, Vera-Licona P, Vert J . TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol. 2012; 6:145. PMC: 3598250. DOI: 10.1186/1752-0509-6-145. View

Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View

Nguyen P, Braun R . Time-lagged Ordered Lasso for network inference. BMC Bioinformatics. 2018; 19(1):545. PMC: 6311035. DOI: 10.1186/s12859-018-2558-7. View

Yu J, Jiang Z, Ning L, Zhao Z, Yang N, Chen L . Protective HSP70 Induction by Z-Ligustilide against Oxygen-Glucose Deprivation Injury via Activation of the MAPK Pathway but Not of HSF1. Biol Pharm Bull. 2015; 38(10):1564-72. DOI: 10.1248/bpb.b15-00352. View

Stepniak E, Ricci R, Eferl R, Sumara G, Sumara I, Rath M . c-Jun/AP-1 controls liver regeneration by repressing p53/p21 and p38 MAPK activity. Genes Dev. 2006; 20(16):2306-14. PMC: 1553212. DOI: 10.1101/gad.390506. View

10.

Popp A, Hettich J, Gebhardt J . Altering transcription factor binding reveals comprehensive transcriptional kinetics of a basic gene. Nucleic Acids Res. 2021; 49(11):6249-6266. PMC: 8216454. DOI: 10.1093/nar/gkab443. View

11.

Butte A, Kohane I . Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000; :418-29. DOI: 10.1142/9789814447331_0040. View

12.

Uhlen M, Fagerberg L, Hallstrom B, Lindskog C, Oksvold P, Mardinoglu A . Proteomics. Tissue-based map of the human proteome. Science. 2015; 347(6220):1260419. DOI: 10.1126/science.1260419. View

13.

Stachtea X, Loughrey M, Salvucci M, Lindner A, Cho S, McDonough E . Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations. Mod Pathol. 2021; 35(4):564-576. PMC: 8964416. DOI: 10.1038/s41379-021-00953-0. View

14.

Matsumoto H, Kiryu H, Furusawa C, Ko M, Ko S, Gouda N . SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics. 2017; 33(15):2314-2321. PMC: 5860123. DOI: 10.1093/bioinformatics/btx194. View

15.

Hamey F, Nestorowa S, Kinston S, Kent D, Wilson N, Gottgens B . Reconstructing blood stem cell regulatory network models from single-cell molecular profiles. Proc Natl Acad Sci U S A. 2017; 114(23):5822-5829. PMC: 5468644. DOI: 10.1073/pnas.1610609114. View

16.

Fujita A, Sato J, Garay-Malpartida H, Yamaguchi R, Miyano S, Sogayar M . Modeling gene expression regulatory networks with the sparse vector autoregressive model. BMC Syst Biol. 2007; 1:39. PMC: 2048982. DOI: 10.1186/1752-0509-1-39. View

17.

Beenken A, Mohammadi M . The FGF family: biology, pathophysiology and therapy. Nat Rev Drug Discov. 2009; 8(3):235-53. PMC: 3684054. DOI: 10.1038/nrd2792. View

18.

Conde J, Otero M, Scotece M, Abella V, Gomez R, Lopez V . E74-Like Factor (ELF3) and Leptin, a Novel Loop Between Obesity and Inflammation Perpetuating a Pro-Catabolic State in Cartilage. Cell Physiol Biochem. 2018; 45(6):2401-2410. DOI: 10.1159/000488227. View

19.

Schaffter T, Marbach D, Floreano D . GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics. 2011; 27(16):2263-70. DOI: 10.1093/bioinformatics/btr373. View

20.

Bergen V, Lange M, Peidli S, Wolf F, Theis F . Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol. 2020; 38(12):1408-1414. DOI: 10.1038/s41587-020-0591-3. View