Literature DB >> 32735851

Memory Sequencing Reveals Heritable Single-Cell Gene Expression Programs Associated with Distinct Cellular Behaviors.

Sydney M Shaffer1, Benjamin L Emert2, Raúl A Reyes Hueros3, Christopher Cote4, Guillaume Harmange5, Dylan L Schaff6, Ann E Sizemore6, Rohit Gupte6, Eduardo Torre3, Abhyudai Singh7, Danielle S Bassett8, Arjun Raj9.   

Abstract

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. It remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we combined Luria and Delbrück's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several divisions. MemorySeq revealed multiple gene modules that expressed together in rare cells within otherwise homogeneous clonal populations. These rare cell subpopulations were associated with biologically distinct behaviors like proliferation in the face of anti-cancer therapeutics. The identification of non-genetic, multigenerational fluctuations can reveal new forms of biological memory in single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  cancer drug resistance; gene expression memory; single-cell

Mesh:

Year:  2020        PMID: 32735851      PMCID: PMC7496637          DOI: 10.1016/j.cell.2020.07.003

Source DB:  PubMed          Journal:  Cell        ISSN: 0092-8674            Impact factor:   41.582


  49 in total

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Authors:  S E Luria; M Delbrück
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5.  Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements.

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6.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

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8.  Memory and relatedness of transcriptional activity in mammalian cell lineages.

Authors:  Nicholas E Phillips; Aleksandra Mandic; Saeed Omidi; Felix Naef; David M Suter
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9.  Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis.

Authors:  Sabrina L Spencer; Suzanne Gaudet; John G Albeck; John M Burke; Peter K Sorger
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10.  Direct cell reprogramming is a stochastic process amenable to acceleration.

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3.  A heritable, non-genetic road to cancer evolution.

Authors:  Tamara Prieto; Dan A Landau
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Review 5.  Drug Repurposing by Tumor Tissue Editing.

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6.  An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.

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Review 7.  Drivers of dynamic intratumor heterogeneity and phenotypic plasticity.

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