Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes

Although some cell types may be defined anatomically or by physiological function, a rigorous definition of cell state remains elusive. Here, we develop a quantitative, imaging-based platform for the systematic and automated classification of subcellular organization in single cells. We use this pla...

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Bibliographic Details
Published in:Cell systems 2021-06, Vol.12 (6), p.670-687.e10
Main Authors: Gerbin, Kaytlyn A., Grancharova, Tanya, Donovan-Maiye, Rory M., Hendershott, Melissa C., Anderson, Helen G., Brown, Jackson M., Chen, Jianxu, Dinh, Stephanie Q., Gehring, Jamie L., Johnson, Gregory R., Lee, HyeonWoo, Nath, Aditya, Nelson, Angelique M., Sluzewski, M. Filip, Viana, Matheus P., Yan, Calysta, Zaunbrecher, Rebecca J., Cordes Metzler, Kimberly R., Gaudreault, Nathalie, Knijnenburg, Theo A., Rafelski, Susanne M., Theriot, Julie A., Gunawardane, Ruwanthi N.
Format: Article
Language:English
Subjects:
cardiac differentiation
cardiomyocyte
cell organization
gene expression
hiPSC
imaging
RNA FISH
sarcomere
single cell
spatial transcriptomics
stem cell
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Summary:Although some cell types may be defined anatomically or by physiological function, a rigorous definition of cell state remains elusive. Here, we develop a quantitative, imaging-based platform for the systematic and automated classification of subcellular organization in single cells. We use this platform to quantify subcellular organization and gene expression in >30,000 individual human induced pluripotent stem cell-derived cardiomyocytes, producing a publicly available dataset that describes the population distributions of local and global sarcomere organization, mRNA abundance, and correlations between these traits. While the mRNA abundance of some phenotypically important genes correlates with subcellular organization (e.g., the beta-myosin heavy chain, MYH7), these two cellular metrics are heterogeneous and often uncorrelated, which suggests that gene expression alone is not sufficient to classify cell states. Instead, we posit that cell state should be defined by observing full distributions of quantitative, multidimensional traits in single cells that also account for space, time, and function. [Display omitted] •Automated image-based classification of subcellular organization in single cells•Integrated analysis of gene expression and cell structure to classify cell state•Curated dataset containing over 30,000 hiPSC-derived cardiomyocytes•Open-source images, analysis code, and quantitative tools This study establishes a framework for multidimensional analysis in single cells to study the relationship between gene expression and cell organization. The quantitative and automated image analysis tools developed in the study were applied to thousands of single cells, and the results suggest that gene expression alone is not sufficient to classify cell states.
ISSN:2405-4712
2405-4720
DOI:10.1016/j.cels.2021.05.001