Year: 2018
Type: article
Abstract: We introduce a microfluidic platform that enables single-cell mass and growth rate measurements upstream of single-cell RNA-sequencing (scRNA-seq) to generate paired single-cell biophysical and transcriptional data sets. Biophysical measurements are collected with a serial suspended microchannel resonator platform (sSMR) that utilizes automated fluidic state switching to load individual cells at fixed intervals, achieving a throughput of 120 cells per hour. Each single-cell is subsequently captured downstream for linked molecular analysis using an automated collection system. From linked measurements of a murine leukemia (L1210) and pro-B cell line (FL5.12), we identify gene expression signatures that correlate significantly with cell mass and growth rate. In particular, we find that both cell lines display a cell-cycle signature that correlates with cell mass, with early and late cell-cycle signatures significantly enriched amongst genes with negative and positive correlations with mass, respectively. FL5.12 cells also show a significant correlation between single-cell growth efficiency and a G1-S transition signature, providing additional transcriptional evidence for a phenomenon previously observed through biophysical measurements alone. Importantly, the throughput and speed of our platform allows for the characterization of phenotypes in dynamic cellular systems. As a proof-of-principle, we apply our system to characterize activated murine CD8+ T cells and uncover two unique features of CD8+ T cells as they become proliferative in response to activation: i) the level of coordination between cell cycle gene expression and cell mass increases, and ii) translation-related gene expression increases and shows a correlation with single-cell growth efficiency. Overall, our approach provides a new means of characterizing the transcriptional mechanisms of normal and dysfunctional cellular mass and growth rate regulation across a range of biological contexts.
Source: Genome biology
Institutions: Koch Institute for Integrative Cancer Research At MIT, Massachusetts Institute of Technology, Broad Institute, Ragon Institute of MGH, MIT and Harvard, Harvard University, Dana-Farber Cancer Institute, KU Leuven, Massachusetts General Hospital, Harvard–MIT Division of Health Sciences and Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Funders: Pew Charitable Trusts, Alfred P. Sloan Foundation, Searle Scholars Program, National Institutes of Health, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases
Awards: G1083602967, G1129124601, Center for Cell Circuits, Graduate Training in Computational and Systems Biology, G2125592388, G2155643243, G2488319905, G3073373828, G3419788186, G3556728958, G3571043358, G3822927299, G4186406324, G4553958427, G4774430914, G6540252496, G7036091179, Pre-Doctoral Training in Biological Sciences, G7640925840, G7888124300, "Bottom - Up" Profiling of Interacting Cellular Systems, G7981823491, G8197466613, A systems biology approach to fingerprint HIV immune defense in Elite Controllers, G9000015422, G9000074672, G9000155476, G9000165427, G9000203899, Costimulatory Mechanisms of Autoimmunity, Cancer Center Support (Core) Grant, Single cell growth assay for residual cells in acute lymphoblastic leukemia, G941982536
OpenAlex ID: W2951871640