Cell size control and gene expression homeostasis in single-cells

Vargas-García, César Augusto; Ghusinga, Khem Raj; Singh, Abhyudai

doi:10.1016/j.coisb.2018.01.002

Cited by 57 publications

(48 citation statements)

References 59 publications

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“…The maintenance of homeostasis requires that cells actively control their proliferation [14,16,18]. The necessary feedback can be exerted through e.g.…”

Section: Introductionmentioning

confidence: 99%

Cell volume distributions in exponentially growing populations

Bokes

Singh

2019

Preprint

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Stochastic effects in cell growth and division drive variability in cellular volumes both at the single-cell level and at the level of growing cell populations. Here we consider a simple and tractable model in which cell volumes grow exponentially, cell division is symmetric, and its rate is volume-dependent. Consistently with previous observations, the model is shown to sustain oscillatory behaviour with alternating phases of slow and fast growth. Exact simulation algorithms and largetime asymptotics are developed and cross-validated for the single-cell and whole-population formulations of the model. The two formulations are shown to provide similar results during the phases of slow growth, but differ during the fast-growth phases. Specifically, the single-cell formulation systematically underestimates the proportion of small cells. More generally, our results suggest that measurable characteristics of cells may follow different distributions depending on whether a single-cell lineage or an entire population is considered.

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“…The maintenance of homeostasis requires that cells actively control their proliferation [14,16,18]. The necessary feedback can be exerted through e.g.…”

Section: Introductionmentioning

confidence: 99%

Cell volume distributions in exponentially growing populations

Bokes

Singh

2019

Preprint

Self Cite

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“…For example, tissue size may be regulated by either controlling the sizes of individual cells, or the number of constituent cells [2][3][4][5][6]. How individual cells maintain size homeostasis has been extensively studied across organisms ranging from bacteria, animal and plant cells [7][8][9][10][11][12][13][14][15][16]. Interestingly, data reveals cell-autonomous control strategies that regulate cellular growth or timing of cell-cycle events to suppress aberrant deviations in cell size around an optimal size specific to that cell type [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimal feedback mechanisms for regulating cell numbers

Modi

Singh

2018

Preprint

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How living cells employ counting mechanisms to regulate their numbers or density is a long-standing problem in developmental biology that ties directly with organism or tissue size. Diverse cells types have been shown to regulate their numbers via secretion of factors in the extracellular space. These factors act as a proxy for the number of cells and function to reduce cellular proliferation rates creating a negative feedback. It is desirable that the production rate of such factors be kept as low as possible to minimize energy costs and detection by predators. Here we formulate a stochastic model of cell proliferation with feedback control via a secreted extracellular factor. Our results show that while low levels of feedback minimizes random fluctuations in cell numbers around a given set point, high levels of feedback amplify Poisson fluctuations in secreted-factor copy numbers. This trade-off results in an optimal feedback strength, and sets a fundamental limit to noise suppression in cell numbers. Intriguingly, this fundamental limit depends additively on two variables: relative half-life of the secreted factor with respect to the cell proliferation rate, and the average number of factors secreted in a cell's lifespan. We further expand the model to consider external disturbances in key physiological parameters, such as, proliferation and factor synthesis rates. Intriguingly, while negative feedback effectively mitigates disturbances in the proliferation rate, it amplifies disturbances in the synthesis rate. In summary, these results provide unique insights into the functioning of feedback-based counting mechanisms, and apply to organisms ranging from unicellular prokaryotes and eukaryotes to human cells.

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“…S9E). S. aureus cells may contain fewer mRNAs than E. coli cells, possibly due to their smaller cell size and genome 33 , though technical differences may also affect capture. We confirmed that optimized PETRI-seq continued to capture single cells with high purity (Fig.…”

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confidence: 99%

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

Blattman

Jiang

Oikonomou

et al. 2019

Preprint

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Despite longstanding appreciation of gene expression heterogeneity in isogenic bacterial populations, affordable and scalable technologies for studying single bacterial cells have been limited. While single-cell RNA sequencing (scRNA-seq) has revolutionized studies of transcriptional heterogeneity in diverse eukaryotic systems, application of scRNA-seq to prokaryotes has been hindered by their extremely low mRNA abundance, lack of mRNA polyadenylation, and thick cell walls. Here, we present Prokaryotic Expression-profiling by Tagging RNA In Situ and sequencing (PETRI-seq), a low-cost, high-throughput, prokaryotic scRNA-seq pipeline that overcomes these technical obstacles. PETRI-seq uses in situ combinatorial indexing to barcode transcripts from tens of thousands of cells in a single experiment. PETRI-seq captures single cell transcriptomes of Gram-negative and Gram-positive bacteria with high purity and low bias, with median capture rates >200 mRNAs/cell for exponentially growing E. coli. These characteristics enable robust discrimination of cell-states corresponding to different phases of growth. When applied to wild-type S. aureus, PETRI-seq revealed a rare sub-population of cells undergoing prophage induction. We anticipate broad utility of PETRI-seq in defining single-cell states and their dynamics in complex microbial communities. MainRecent developments in high-throughput single-cell RNA sequencing (scRNA-seq) technology have enabled rapid characterization of cellular diversity within complex eukaryotic tissues 1-13 . Despite these advances, comparable tools to study the transcriptomes of individual bacterial cells 14-16 have lagged behind significantly due to numerous technical challenges (Fig. S1). Current massively parallel eukaryotic scRNA-seq methods typically require custom microfluidics to co-encapsulate a single cell with a uniquely barcoded bead in a compartment, often a droplet 5,6,8 or microwell 4,7 . These approaches rely on two key properties of many eukaryotic cells, specifically that they are easily lysed with detergent to release their RNA and that their polyadenylated mRNAs can be effectively captured by beads coated with poly(dT) primers. Adaptation of these approaches for bacteria is thwarted by the presence of thick prokaryotic cell wall 17 , which makes lysis challenging, and the lack of poly-adenylated mRNAs for effective capture.Given these considerations, we identified in situ combinatorial indexing 18 as an alternative basis upon which to develop a method for high-throughput prokaryotic scRNA-seq. Two conceptually similar eukaryotic methods, single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) 11,13 and split-pool ligation-based transcriptome sequencing (SPLiT-seq) 12 , rely on cells themselves as compartments for barcoding, which abrogates the need for cell lysis in droplets or microwells. These methods are also amenable to reverse transcription (RT) with random hexamers instead of poly(dT) primers 12 . With just pipetting steps and no complex instruments, individual t...

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Cell size control and gene expression homeostasis in single-cells

Cited by 57 publications

References 59 publications

Cell volume distributions in exponentially growing populations

Cell volume distributions in exponentially growing populations

Optimal feedback mechanisms for regulating cell numbers

Prokaryotic Single-Cell RNA Sequencing by In Situ Combinatorial Indexing

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