A Comprehensive Overview of RNA Deconvolution Methods and Their Application

Im, Yebin; Kim, Yongsoo

doi:10.14348/molcells.2023.2178

Cited by 28 publications

(22 citation statements)

References 45 publications

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“…To minimize limitations associated with deconvolution analysis, we used heterogenous data sets as basic matrices and a robust method, CIBERSORTx, for the deconvolution analyses. [61][62][63][64] Our study has limitations. Versatility/plasticity of T-cell subsets could lead to misassignment based on gene expression ascertained at a single time point.…”

Section: Discussionmentioning

confidence: 99%

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RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell–mediated Rejection

Mueller,

Yang,

et al. 2024

Transplantation

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Background. Delineation of T-cell genes, gene sets, pathways, and T-cell subtypes associated with acute T cell–mediated rejection (TCMR) may improve its management. Methods. We performed bulk RNA-sequencing of 34 kidney allograft biopsies (16 Banff TCMR and 18 no rejection [NR] biopsies) from 34 adult recipients of human kidneys. Computational analysis was performed to determine the differential intragraft expression of T-cell genes at the level of single-gene, gene set, and pathways. Results. T-cell signaling pathway gene sets for plenary T-cell activation were overrepresented in TCMR biopsies compared with NR biopsies. Heightened expression of T-cell signaling genes was validated using external TCMR biopsies. Pro- and anti-inflammatory immune gene sets were enriched, and metabolism gene sets were depleted in TCMR biopsies compared with NR biopsies. Gene signatures of regulatory T cells, Th1 cells, Th2 cells, Th17 cells, T follicular helper cells, CD4 tissue-resident memory T cells, and CD8 tissue-resident memory T cells were enriched in TCMR biopsies compared with NR biopsies. T-cell exhaustion and anergy were also molecular attributes of TCMR. Gene sets associated with antigen processing and presentation, and leukocyte transendothelial migration were overexpressed in TCMR biopsies compared with NR biopsies. Cellular deconvolution of graft infiltrating cells by gene expression patterns identified CD8 T cell to be the most abundant T-cell subtype infiltrating the allograft during TCMR. Conclusions. Our delineation of intragraft T-cell gene expression patterns, in addition to yielding new biological insights, may help prioritize T-cell genes and T-cell subtypes for therapeutic targeting.

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Section: Discussionmentioning

confidence: 99%

“…To minimize limitations associated with deconvolution analysis, we used heterogenous data sets as basic matrices and a robust method, CIBERSORTx, for the deconvolution analyses. 61-64…”

Section: Discussionmentioning

confidence: 99%

RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell–mediated Rejection

Mueller,

Yang,

et al. 2024

Transplantation

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“…We report the cellular composition and cell type-specific gene expression in lung tissue associated with disease severity in COPD and IPF subjects, extending the single-cell experiment discoveries from a modest sample size (<100 subjects) to a large population cohort (>1000 subjects). We trained a well-established and widely implemented computational RNA-seq deconvolution algorithm, CIBERSORTx [13,19,20], using publicly available scRNA-seq data from control, COPD, and IPF subjects [9]. We found that IPF lung tissues showed the most divergence from control lungs in cellular composition, with eighteen cell types whose abundance score was different from the controls, adjusting for covariates.…”

Section: Discussionmentioning

confidence: 99%

Computational Deconvolution of Cell Type-Specific Gene Expression in COPD and IPF Lungs Reveals Disease Severity Associations

Ryu,

Yun,

Kim

et al. 2024

Preprint

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Rationale: Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are debilitating diseases associated with divergent histopathological changes in the lungs. At present, due to cost and technical limitations, profiling cell types is not practical in large epidemiology cohorts (n>1000). Here, we used computational deconvolution to identify cell types in COPD and IPF lungs whose abundances and cell type-specific gene expression are associated with disease diagnosis and severity. Methods: We analyzed lung tissue RNA-seq data from 1026 subjects (COPD, n=465; IPF, n=213; control, n=348) from the Lung Tissue Research Consortium. We performed RNA-seq deconvolution, querying thirty-eight discrete cell-type varieties in the lungs. We tested whether deconvoluted cell-type abundance and cell type-specific gene expression were associated with disease severity. Results: The abundance score of twenty cell types significantly differed between IPF and control lungs. In IPF subjects, eleven and nine cell types were significantly associated with forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), respectively. Aberrant basaloid cells, a rare cells found in fibrotic lungs, were associated with worse FVC and DLCO in IPF subjects, indicating that this aberrant epithelial population increased with disease severity. Alveolar type 1 and vascular endothelial (VE) capillary A were decreased in COPD lungs compared to controls. An increase in macrophages and classical monocytes was associated with lower DLCO in IPF and COPD subjects. In both diseases, lower non-classical monocytes and VE capillary A cells were associated with increased disease severity. Alveolar type 2 cells and alveolar macrophages had the highest number of genes with cell type-specific differential expression by disease severity in COPD and IPF. In IPF, genes implicated in the pathogenesis of IPF, such as matrix metallopeptidase 7, growth differentiation factor 15, and eph receptor B2, were associated with disease severity in a cell type-specific manner. Conclusion: Utilization of RNA-seq deconvolution enabled us to pinpoint cell types present in the lungs that are associated with the severity of COPD and IPF. This knowledge offers valuable insight into the alterations within tissues in more advanced illness, ultimately providing a better understanding of the underlying pathological processes that drive disease progression.

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“…Deconvolution is a data analysis method that can be applied to estimate the ratio of cells in a specimen from bulk transcriptome data (Abbas et al ., 2009; Im and Kim, 2023). Bulk transcriptome is affected by distinctive sample conditions (e.g., gene knock out), individual or technical variations, and relative cell subset proportions.…”

Section: Introductionmentioning

confidence: 99%

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases

Morita

Mizuno

Azuma

et al. 2023

Preprint

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Toxicogenomics databases are useful for understanding biological responses in individuals because they are derived from well-controlled experiments and include a diverse spectrum of biological responses. Although these database contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could add information regarding immune cell trafficking to the database. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats and established a methodology to obtain information regarding immune cells from toxicogenomics databases. The contributions of this work are three-fold. First, we obtained the gene expression profiles of various rat immune cells necessary for deconvolution and constructed a dataset; second, we compared the accuracy of models based on human and mouse datasets and showed the impact of species differences on deconvolution; third, we showed that rat deconvolution could retrieve information regarding immune cell trafficking from toxicogenomics databases.

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A Comprehensive Overview of RNA Deconvolution Methods and Their Application

Cited by 28 publications

References 45 publications

RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell–mediated Rejection

RNA-sequencing of Human Kidney Allografts and Delineation of T-Cell Genes, Gene Sets, and Pathways Associated With Acute T Cell–mediated Rejection

Computational Deconvolution of Cell Type-Specific Gene Expression in COPD and IPF Lungs Reveals Disease Severity Associations

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases

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