Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes
Year: 2021
Type: article
Abstract: 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.
Source: Cell Systems
Authors: Kaytlyn A. Gerbin, Tanya Grancharova, Rory Donovan-Maiye, Melissa C. Hendershott, Jackson Brown, Stephanie Q. Dinh, Jamie L. Gehring, Matthew Hirano, Gregory R. Johnson, Aditya Nath, Angelique Nelson, Charles M. Roco, Alexander B. Rosenberg, M. Filip Sluzewski, Matheus P. Viana, Calysta Yan, Rebecca J. Zaunbrecher, Kimberly R. Cordes Metzler, Vilas Menon, Sean P. Palecek, Georg Seelig, Nathalie Gaudreault, Theo Knijnenburg, Susanne M. Rafelski, Julie A. Theriot, Ruwanthi N. Gunawardane
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $8,900
OpenAlex ID: W3164615498