A systems-based map of human brain cell-type enriched genes and malignancy-associated endothelial changes

Dusart, Philip; Hallström, Björn M.; Renné, Thomas; Odeberg, Jacob; Uhlén, Mathias; Butler, Lynn M.

doi:10.1101/528414

Cited by 1 publication

(4 citation statements)

References 91 publications

(137 reference statements)

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“…However, we previously showed that unbiased weighted network correlation analysis (WGCNA) (Langfelder and Horvath, 2008), where correlation coefficients between all transcripts are calculated and subsequently clustered into related groups (based on expression similarity), supports Ref.T. based analysis cell type enrichment predictions (Dusart et al, 2019;Norreen-Thorsen et al, 2022). Here, we performed WGNCA of lung and liver samples (Figure S2).…”

Section: Weighted Network Correlation Analysis Supports Cell Type Enr...mentioning

confidence: 83%

“…Bulk RNAseq datasets for 15 human tissue types were retrieved from Genotype-Tissue Expression (GTEx) V8 (www.gtexportal.org) (Consortium, 2015) (Figure 1A). To identify cell type-enriched transcript profiles, we performed an integrative correlation analysis on each dataset, using our previously published method (Butler et al, 2016;Dusart et al, 2019;Norreen-Thorsen et al, 2022).…”

Section: Cell Type Reference Transcripts Correlate Across Unfractiona...mentioning

confidence: 99%

“…This method was based on that we previously developed (Butler et al, 2016;Dusart et al, 2019;Norreen-Thorsen et al, 2022). Pairwise Spearman correlation coefficients between reference transcripts (Ref.T.…”

Section: Quantification and Statistical Analysis Reference Transcript...mentioning

confidence: 99%

“…We previously developed and validated an integrative correlation analysis method to identify cell type-enriched transcriptome profiles from unfractionated tissue RNAseq (Butler et al, 2016;Dusart et al, 2019;Norreen-Thorsen et al, 2022). Our method circumvents some limitations of scRNAseq; hundreds of samples are analysed concurrently to reduce the influence of biological variation and batch effects, cell types that are technically challenging to process can be analysed, and lowly expressed cell enriched transcripts classified (Norreen-Thorsen et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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A tissue centric atlas of cell type transcriptome enrichment signatures

Dusart

Öling

Norreen-Thorsen

et al. 2023

Preprint

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Genes with cell type specific expression typically encode for proteins that have cell type specific functions. Single cell RNAseq (scRNAseq) has facilitated the identification of such genes, but various challenges limit the analysis of certain cell types and lowly expressed genes. Here, we performed an integrative network analysis of over 6000 bulk RNAseq datasets from 15 human organs, to generate a tissue-by-tissue cell type enrichment prediction atlas for all protein coding genes. We profile all the major constituent cell types, including several that are fragile or difficult to process and thus absent from existing scRNAseq-based atlases. The stability and read depth of bulk RNAseq data, and the high number of biological replicates analysed, allowed us to identify lowly expressed cell type enriched genes that are difficult to classify using existing methods. We identify co-enriched gene panels shared by pancreatic alpha and beta cells, chart temporal changes in cell enrichment signatures during spermatogenesis, and reveal that cells in the hair root are a major source of skin enriched genes. In a cross-tissue analysis, we identify shared gene enrichment signatures between highly metabolic and motile cell types, and core identity profiles of cell types found in across tissue types. Our study provides the only cell type gene enrichment atlas generated independently of scRNAseq, representing a new addition to our existing toolbox of resources for the understanding of gene expression across human tissues.

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Section: Weighted Network Correlation Analysis Supports Cell Type Enr...mentioning

confidence: 83%

Section: Cell Type Reference Transcripts Correlate Across Unfractiona...mentioning

confidence: 99%

Section: Quantification and Statistical Analysis Reference Transcript...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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