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

Sean K Maden; Sang Ho Kwon; Louise A Huuki-Myers; Leonardo Collado-Torres; Stephanie C Hicks; Kristen R Maynard

doi:10.1186/s13059-023-03123-4

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.

Authors

Sean K Maden¹, Sang Ho Kwon^{2

3}, Louise A Huuki-Myers², Leonardo Collado-Torres^{1

2}, Stephanie C Hicks^#^{4

5

6

7}, Kristen R Maynard^#^{8

9

10}

Affiliations

¹ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
² Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
³ The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
⁴ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. [email protected].
⁵ Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. [email protected].
⁶ Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA. [email protected].
⁷ Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA. [email protected].
⁸ Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA. [email protected].
⁹ The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].
¹⁰ Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA. [email protected].

^# Contributed equally.

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

Abstract

Publication types

MeSH terms

Substances

Grants and funding