Integration of Multiple, Diverse Methods to Identify Biologically Significant Marker Genes. Issue 19 (15th October 2022)

• Record Type: Journal Article
• Authors: Chaney, Christopher P.; Drake, Keri A.; Carroll, Thomas J.
• Is Part Of: Journal of molecular biology. Volume 434:Issue 19(2022)
• Publication Date: 2022-10-15
• Subjects: Transcriptomics -- Ensemble -- Cellular heterogeneity -- Marker gene -- Cellular identity; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2022.167754
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: Identification of genes that reliably mark distinct cell states or types is key to leveraging single-cell RNA sequencing to better understand organismal biology. Statistically significant differences in expression of genes between groups of cells in single cell RNA sequencing experiments do not generally predict the ability to demonstrate distinct expression patterns with conventional molecular localization techniques. The assimilation of results from multiple diverse methods for detection of differential gene expression outperforms the individual methods in identification of genes whose expression is correlated with cell state and that demonstrate spatially restricted expression on analysis with mRNA in situ hybridization and immunohistochemistry. Abstract: Identification of genes that reliably mark distinct cell types is key to leveraging single-cell RNA sequencing to better understand organismal biology. Such genes are usually chosen by measurement of differential expression between groups of cells and selecting those with the greatest magnitude or most statistically significant change. Many methods have been developed for performing such analyses, but no single, best method has emerged. Validating the results of these analyses is costly in terms of time, effort and resources. We demonstrate that applying an ensemble of such methods robustly identifies genes that mark cells that cluster together and that show restricted expression assessed by antisense mRNA in situ and immunofluorescence. This technique is easily extensible to any number of differential expression methods and the inclusion of additional methods is expected to result in further improvement in performance.