Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Genome Biol. 2023 Dec 14;24(1):288. doi: 10.1186/s13059-023-03123-4.

Abstract

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.

Keywords: Cell sizes; Deconvolution; Single-cell RNA-sequencing; Single-nucleus RNA-sequencing.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Cell Size
  • Gene Expression Profiling* / methods
  • Humans
  • RNA, Messenger
  • Sequence Analysis, RNA / methods
  • Single-Cell Analysis
  • Transcriptome*

Substances

  • RNA, Messenger