Non-genetic inheritance restraint of cell-to-cell variation

Vashistha, Harsh; Kohram, Maryam; Salman, Hanna

doi:10.7554/elife.64779

Cited by 24 publications

(43 citation statements)

References 38 publications

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“…Such variables have been extensively used to infer statistical properties of bacterial physiology and variability (Amir 2014;Sauls, Li, and Jun 2016;Grilli et al 2017); they are illustrated in Fig 2A . One may identify the two fundamental time/rate variables in each cycle, the inter-division (or generation) time τ and the single-cycle exponential growth-rate α. Although growth deviates from an exact exponential when measured carefully (Kohram et al 2021;Nordholt, Heerden, and Bruggeman 2020;Vashistha, Kohram, and Salman 2021), the best fit exponential rate is still a useful approximation for growth over the cell cycle and will be used in what follows. Cell size is characterized by its length x 0 at the start and x τ at the end of each cycle, as well as their difference ∆ = x τ − x 0 .…”

Section: Sloppy and Stiff Variables In Trapped Bacteriamentioning

confidence: 99%

“…We utilize the experimental design of the sisters machine to follow two lineages in each channel of a single trap, which start growing at roughly the same time (Vashistha, Kohram, and Salman 2021). In this device, lineages stemming from two sister cells -"sister lineages"…”

Section: Sensitivity To Environment Underlies Sloppy Set-pointsmentioning

confidence: 99%

“…-were previously used to analyze the divergence in the physiological variables between two sisters after their separation (Vashistha, Kohram, and Salman 2021). Here, we examine pairs of unrelated lineages having flown into both sides of the same trap and grown in parallel in that trap -"neighbor lineages".…”

Section: Sensitivity To Environment Underlies Sloppy Set-pointsmentioning

confidence: 99%

“…It is nevertheless possible that homeostasis mechanisms act also on longer timescales. In support of this possibility, recent work revealed that bacterial cellular properties exhibit memory patterns that can extend up to ∼ 10 generations (Susman et al 2018;Vashistha, Kohram, and Salman 2021). Therefore long-term correlations between the different phenotypic variables should also be examined to identify possible slow homeostasis mechanisms.…”

Section: Introductionmentioning

confidence: 97%

“…Instead, we utilize uncontrolled variation across measurements performed under nominally identical conditions, to reveal the relative sensitivity of the different cellular variables. We analyze data from the recently developed "sisters machine" microfluidic device (Vashistha, Kohram, and Salman 2021); this unique experiment design allows us to distinguish effects of lineage history from micro-environment and to quantify their influence on the set-point variation.…”

Section: Introductionmentioning

confidence: 99%

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Multiple timescales in bacterial growth homeostasis

Stawsky

Vashistha

Salman

et al. 2021

Preprint

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Microbial growth and division are fundamental processes relevant to many areas of life science. Of particular interest are homeostasis mechanisms, which buffer growth and division from accumulating random fluctuations. This classic problem has seen recent advance in both theory and experiments, but still much remains unclear and debatable. Because there are many coupled processes inside the cell, it is not a-priori clear which variables are under regulation and which are stabilize by their coupling to regulated variables. Here, we address this question by distentangling homeostatic set-points -- estimated as temporal averages across individual lineages -- from fluctuations around them. Applying variance decomposition to individually trapped bacteria (mother machine data), we find that phenotypic variables estimated from cell-size measurements (inter-division time, growth rate, added size and more), exhibit a range of different behaviors. Some have flexible set-points that vary significantly between lineages - "sloppy" variables, while others are tightly fixed - "stiff" variables. Analyzing pairs of trapped lineages (sisters machine data), reveals that the primary source of sloppiness is a high sensitivity to the trap environment, while lineage-specific factors contribute only a small fraction. We find strong long-term correlations between sloppy set-points, and identify a control manifold in the space of growth and division variables. Cell size is a sloppy variable, whose set-point is uncoupled to growth variables; such correlations appear only on the short, single-cycle timescale. We discuss these results in light of recent models of cell size control, and point to new avenues of research.

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Section: Sloppy and Stiff Variables In Trapped Bacteriamentioning

confidence: 99%

Section: Sensitivity To Environment Underlies Sloppy Set-pointsmentioning

confidence: 99%

Section: Sensitivity To Environment Underlies Sloppy Set-pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Multiple timescales in bacterial growth homeostasis

Stawsky

Vashistha

Salman

et al. 2021

Preprint

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Connecting single-cell ATP dynamics to overflow metabolism, cell growth, and the cell cycle in Escherichia coli

Lin

Jacobs‐Wagner

2022

Current Biology

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Multiple timescales in bacterial growth homeostasis

Stawsky¹,

Vashistha²,

Salman³

et al. 2022

iScience

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Summary In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are “sloppy” with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define “stiff” combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.

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Non-genetic inheritance restraint of cell-to-cell variation

Cited by 24 publications

References 38 publications

Multiple timescales in bacterial growth homeostasis

Multiple timescales in bacterial growth homeostasis

Connecting single-cell ATP dynamics to overflow metabolism, cell growth, and the cell cycle in Escherichia coli

Multiple timescales in bacterial growth homeostasis

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