Comprehensive Evaluation of Deconvolution Methods for Human Brain Gene Expression

Overview

Journal Nat Commun

Specialty Biology

Date 2022 Mar 16

PMID 35292647

Authors

Gavin J Sutton

Daniel Poppe

Rebecca K Simmons

Kieran Walsh

Urwah Nawaz

Ryan Lister

Johann A Gagnon-Bartsch

Irina Voineagu

Affiliations

Soon will be listed here.

Abstract

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

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References

Avila Cobos F, Vandesompele J, Mestdagh P, De Preter K . Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics. 2018; 34(11):1969-1979. DOI: 10.1093/bioinformatics/bty019. View

Pelvig D, Pakkenberg H, Stark A, Pakkenberg B . Neocortical glial cell numbers in human brains. Neurobiol Aging. 2007; 29(11):1754-62. DOI: 10.1016/j.neurobiolaging.2007.04.013. View

Frishberg A, Peshes-Yaloz N, Cohn O, Rosentul D, Steuerman Y, Valadarsky L . Cell composition analysis of bulk genomics using single-cell data. Nat Methods. 2019; 16(4):327-332. PMC: 6443043. DOI: 10.1038/s41592-019-0355-5. View

Yang L, Yang Y, Yuan J, Sun Y, Dai J, Su B . Transcriptomic Landscape of von Economo Neurons in Human Anterior Cingulate Cortex Revealed by Microdissected-Cell RNA Sequencing. Cereb Cortex. 2018; 29(2):838-851. PMC: 6319179. DOI: 10.1093/cercor/bhy286. View

Mendizabal I, Berto S, Usui N, Toriumi K, Chatterjee P, Douglas C . Cell type-specific epigenetic links to schizophrenia risk in the brain. Genome Biol. 2019; 20(1):135. PMC: 6617737. DOI: 10.1186/s13059-019-1747-7. View

Blodgett D, Nowosielska A, Afik S, Pechhold S, Cura A, Kennedy N . Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets. Diabetes. 2015; 64(9):3172-81. PMC: 4542439. DOI: 10.2337/db15-0039. View

Furuyama K, Chera S, van Gurp L, Oropeza D, Ghila L, Damond N . Diabetes relief in mice by glucose-sensing insulin-secreting human α-cells. Nature. 2019; 567(7746):43-48. PMC: 6624841. DOI: 10.1038/s41586-019-0942-8. View

Darmanis S, Sloan S, Zhang Y, Enge M, Caneda C, Shuer L . A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A. 2015; 112(23):7285-90. PMC: 4466750. DOI: 10.1073/pnas.1507125112. View

Miller J, Ding S, Sunkin S, Smith K, Ng L, Szafer A . Transcriptional landscape of the prenatal human brain. Nature. 2014; 508(7495):199-206. PMC: 4105188. DOI: 10.1038/nature13185. View

10.

Newman A, Liu C, Green M, Gentles A, Feng W, Xu Y . Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12(5):453-7. PMC: 4739640. DOI: 10.1038/nmeth.3337. View

11.

Sturm G, Finotello F, Petitprez F, Zhang J, Baumbach J, Fridman W . Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinformatics. 2019; 35(14):i436-i445. PMC: 6612828. DOI: 10.1093/bioinformatics/btz363. View

12.

Naumova O, Lee M, Rychkov S, Vlasova N, Grigorenko E . Gene expression in the human brain: the current state of the study of specificity and spatiotemporal dynamics. Child Dev. 2012; 84(1):76-88. PMC: 3557706. DOI: 10.1111/cdev.12014. View

13.

Yuste R, Hawrylycz M, Aalling N, Aguilar-Valles A, Arendt D, Armananzas R . A community-based transcriptomics classification and nomenclature of neocortical cell types. Nat Neurosci. 2020; 23(12):1456-1468. PMC: 7683348. DOI: 10.1038/s41593-020-0685-8. View

14.

Ramaker R, Bowling K, Lasseigne B, Hagenauer M, Hardigan A, Davis N . Post-mortem molecular profiling of three psychiatric disorders. Genome Med. 2017; 9(1):72. PMC: 5534072. DOI: 10.1186/s13073-017-0458-5. View

15.

Grove J, Ripke S, Als T, Mattheisen M, Walters R, Won H . Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019; 51(3):431-444. PMC: 6454898. DOI: 10.1038/s41588-019-0344-8. View

16.

Li Z, Del-Aguila J, Dube U, Budde J, Martinez R, Black K . Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure. Genome Med. 2018; 10(1):43. PMC: 5992755. DOI: 10.1186/s13073-018-0551-4. View

17.

McCoy M, Paul A, Victor M, Richner M, Gabel H, Gong H . LONGO: an R package for interactive gene length dependent analysis for neuronal identity. Bioinformatics. 2018; 34(13):i422-i428. PMC: 6022641. DOI: 10.1093/bioinformatics/bty243. View

18.

Parikshak N, Swarup V, Belgard T, Irimia M, Ramaswami G, Gandal M . Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature. 2016; 540(7633):423-427. PMC: 7102905. DOI: 10.1038/nature20612. View

19.

Gong T, Szustakowski J . DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013; 29(8):1083-5. DOI: 10.1093/bioinformatics/btt090. View

20.

Wang X, Park J, Susztak K, Zhang N, Li M . Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019; 10(1):380. PMC: 6342984. DOI: 10.1038/s41467-018-08023-x. View