Methods to analyze cell type-specific gene expression profiles from heterogeneous cell populations

Abstract

Recent technical progress in DNA and protein identification has made genome-wide survey of gene expression at tissue and animal levels a routine approach, such as microarray and RNA sequencing technologies to measure mRNA abundance and mass spectrometry to measure protein abundance. A key limitation in applying these genome-wide gene expression profiling methods at tissue and animal levels is that the contribution of a specific cell type to the total amount of measured gene expression cannot be determined. Here, we review currently available approaches to resolve this and discuss future directions of study to solve questions not addressable by state-ofthe-art techniques.ARTICLE HISTORY

Heterogeneous muscle gene expression patterns in patients with massive rotator cuff tears

“…Despite the fact that histological parameters other than muscle content are not linear predictors of gene expression, which may be explained by the chronicity of disease[ 23 , 39 ] relative to the phase of active remodeling[ 40 ], these data nonetheless demonstrate the importance of interpreting gene expression data in the context of biopsy composition[ 11 , 14 ]; without definitive evidence for the presence of muscle, there would be no basis for even the broad interpretations presented above. More generally, the disconnect between gene expression and histological findings (where even muscle presence and fat content are only modest predictors of expression levels and sample clustering) further complicates the interpretation of gene expression data not only in this and previous studies[ 7 , 8 ], but throughout the RC disease literature, where complex interactions of the mechanical and biological environment have a significant impact on several of the tissue types central to RC muscle pathology[ 22 ].…”

“…However, the latter, more targeted approach will allow for a deeper understanding of the processes that govern irreversible muscle loss in chronic musculoskeletal conditions, including a more definitive understanding of the phases that define RC disease. Only by separating the highly heterogeneous mix of cell and tissue types[ 11 , 14 ] will the cellular and molecular processes that govern RC disease progression from reversible, atrophic muscle loss to terminal muscle degeneration be elucidated. Ultimately, understanding the relationships between gene expression, disease state, and patient outcomes will aid in identifying optimal interventions on a more individualized basis, which will in turn lead to improved patient outcomes.…”

“…Given the gross changes in muscle composition observed across the spectrum of RC diseases[ 6 , 12 , 13 ], it is reasonable to hypothesize that gene expression changes are driven as much by the composition (e.g. muscle content) of the tissue as by changes in gene expression that occur within a given tissue type or cell population, a measurement which itself remains difficult[ 14 ]. Despite this common assumption, that gene expression patterns are influenced by changes in the underlying tissue composition, no previous study has included both gene expression and compositional data.…”

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

“…However, these techniques pose further technical and economic challenges. 88,89 Specifically, a large number of organoids must be sequenced to mitigate cellular complexity and batch heterogeneity and powerful, reproducible and accurate computational pipelines are required to analyse such data. 90 We predicted key regulators involved in differentiation or maintenance of Paneth cells and goblet cells in the enteroids: Cebpa, Jun, Nr1d1 and Rxra specific to Paneth cells, Gfi1b and Myc specific for goblet cells and Ets1, Nr3c1 and Vdr shared between them.…”

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

“…Extension of our workflow to single cell sequencing of enteroid cells could validate these findings by providing greater cell type specificity. However, these techniques pose further technical and economic challenges (88,89). Specifically, a large number of organoids must be sequenced to mitigate cellular complexity and batch heterogeneity and powerful, reproducible and accurate computational pipelines are required to analyse such data (90).…”