A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast

Jariani, Abbas; Vermeersch, Lieselotte; Cerulus, Bram; Perez-Samper, Gemma; Voordeckers, Karin; Brussel, Thomas Van; Thienpont, Bernard; Lambrechts, Diether; Verstrepen, Kevin J.

doi:10.7554/elife.55320

Cited by 53 publications

(60 citation statements)

References 60 publications

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“…Chlamydomonas cells, just like yeast cells, can present a significant cell wall that might be considered a physical barrier for RNA extraction from single cells. In yeast, this technical limitation was resolved by adding the cell wall digesting enzyme zymolyase before (Jackson et al, 2020) or during (Jariani et al, 2020) the reverse transcription step of the same 10X Chromium Single Cell 30 v2 protocol we followed here. However, it should be noted that the authors did not attempt to generate scRNA-seq libraries from walled (undigested) yeast cells.…”

Section: Discussionmentioning

confidence: 99%

Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

Salomé

Merchant

et al. 2020

Preprint

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The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of the facility with which it can be cultured in the laboratory. Genomic and systems biology approaches have previously been used to describe how the transcriptome responds to environmental changes, but this analysis has been limited to bulk data, representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing in the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample algae communities in the wild. Second, scRNA-seq successfully separated single cells into non-overlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest cell division to economize resources. Notably, these groups of cells recapitulated known patterns observed with bulk RNA-seq, but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We propose that bulk RNA-seq data represent an average of varied cell states that hides underappreciated heterogeneity.One-sentence summaryWe show that single-cell RNA-seq (scRNA-seq) can be applied to Chlamydomonas cultures to reveal the that heterogenity in bulk cultures is largely driven by diurnal cycle phasesThe author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Matteo Pellegrini (matteop@mcdb.ucla.edu)

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Section: Discussionmentioning

confidence: 99%

Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

Salomé

Merchant

et al. 2020

Preprint

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“…Our study also provides a unifying context for the interpretation of classic and recent reports of coincident recombination in yeasts, in mammalian experimental systems, and in human disease. The combination of whole genome analyses, modern lineage tracing and single cell transcriptomic approaches (50,51), and the double LOH selection approach described here offer a powerful experimental platform to further dissect the core mechanisms responsible for the SGI phenomenon.…”

Section: Possible Mechanisms Underlying Sgimentioning

confidence: 99%

Characterization of systemic genomic instability in budding yeast

Sampaio

Ajith

Watson

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Conventional models of genome evolution are centered around the principle that mutations form independently of each other and build up slowly over time. We characterized the occurrence of bursts of genome-wide loss-of-heterozygosity (LOH) in Saccharomyces cerevisiae, providing support for an additional nonindependent and faster mode of mutation accumulation. We initially characterized a yeast clone isolated for carrying an LOH event at a specific chromosome site, and surprisingly found that it also carried multiple unselected rearrangements elsewhere in its genome. Whole-genome analysis of over 100 additional clones selected for carrying primary LOH tracts revealed that they too contained unselected structural alterations more often than control clones obtained without any selection. We also measured the rates of coincident LOH at two different chromosomes and found that double LOH formed at rates 14- to 150-fold higher than expected if the two underlying single LOH events occurred independently of each other. These results were consistent across different strain backgrounds and in mutants incapable of entering meiosis. Our results indicate that a subset of mitotic cells within a population can experience discrete episodes of systemic genomic instability, when the entire genome becomes vulnerable and multiple chromosomal alterations can form over a narrow time window. They are reminiscent of early reports from the classic yeast genetics literature, as well as recent studies in humans, both in cancer and genomic disorder contexts. The experimental model we describe provides a system to further dissect the fundamental biological processes responsible for punctuated bursts of structural genomic variation.

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“…Single-cell data is undersampled and noisy, but large numbers of observations are collected in parallel and count data derived from unique molecular identifiers have some intrinsic advantages. In order to quantitatively evaluate network inference performance, we apply the Inferelator to Saccharomyces cerevisiae single-cell expression data [22, 47], and score the model performance based on a previously-defined yeast gold standard [40]. This data is split into 15 separate tasks, based on labels that correspond to experimental conditions from the original works (Figure 4A).…”

Section: Resultsmentioning

confidence: 99%

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

Gibbs

Jackson

Saldi

et al. 2021

Preprint

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Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator 3.0 reliably learns informative networks from the model organisms Bacillus subtilis and Saccharomyces cerevisiae. We demonstrate its capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression data set with paired single-cell chromatin accessibility data.

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A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast

Cited by 53 publications

References 60 publications

Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

Single-Cell RNA Sequencing of Batch Chlamydomonas Cultures Reveals Heterogeneity in their Diurnal Cycle Phase

Characterization of systemic genomic instability in budding yeast

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

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