Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 22, 2025

Abstract What features of transcription can be learnt by fitting mathematical models gene expression to mRNA count data? Given a suite models, data selects an optimal one, thus identifying probable transcriptional mechanism. Whilst attractive, the utility this methodology remains unclear. Here, we sample steady-state, single-cell distributions from parameters in physiological range, and show they cannot used confidently estimate number inactive states, i.e. rate-limiting steps initiation. Distributions over 99% parameter space generated using with 2, 3, or 4 states well fit one single state. However, that for many minutes following induction, eukaryotic cells increase mean obeys power law whose exponent equals sum visited initial active state post-transcriptional processing steps. Our study shows estimation sufficient determine lower bound on total regulatory initiation, splicing, nuclear export.

Language: Английский

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What can we learn when fitting a simple telegraph model to a complex gene expression model?

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(5), P. e1012118 - e1012118

Published: May 14, 2024

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to random telegraph model which includes synthesis and decay protein, switching gene between active inactive states. While commonly used, this does not describe how fluctuations influenced by crucial biological mechanisms such as feedback regulation, non-exponential inactivation durations, multiple activation pathways. Here we investigate dynamical properties four relatively complex expression models fitting their steady-state number simple model. We show that despite underlying mechanisms, with three effective parameters can accurately capture product distributions, well conditional state, models. Some reliable reflect realistic dynamic behaviors models, while others may deviate significantly from real values The also be applied characterize capability for a exhibit multimodality. Using additional information single-cell data at time points, provide an method distinguishing Furthermore, using measurements under varying experimental conditions, even reveal regulation effectiveness these methods is confirmed analysis E. coli mammalian cells. All results robust respect cooperative transcriptional extrinsic noise. particular, find faster relaxation speed steady state more precise parameter inference large

Language: Английский

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Deterministic patterns in single-cell transcriptomic data

npj Systems Biology and Applications, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 11, 2025

Abstract We report the existence of deterministic patterns in statistical plots single-cell transcriptomic data. develop a theory showing that are neither artifacts introduced by measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from finite sample size effects. The precisely predicts data multiplexed error-robust fluorescence situ hybridization and five different types sequencing platforms.

Language: Английский

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Trajectory inference from single-cell genomics data with a process time model

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(1), P. e1012752 - e1012752

Published: Jan. 21, 2025

Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across states. These have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according similarity. However, while single-cell sometimes offer valuable insights into processes, current methods for are limited descriptive notions “pseudotime” that lack intrinsic physical meaning. Instead pseudotime, we propose inference “process time” via a principled modeling approach formulating inferring latent variables corresponding timing subject biophysical process. Our implementation this approach, called Chronocell, provides formulation built on state transitions. The Chronocell model is identifiable, making parameter meaningful. Furthermore, can interpolate between trajectory inference, when states lie continuum, clustering, cluster discrete By using variety datasets ranging from cluster-like continuous, show enables us assess the suitability reveals distinct cellular distributions along process time consistent with biological times. We also compare our estimates degradation rates those derived metabolic labeling datasets, thereby showcasing utility Chronocell. Nevertheless, based performance characterization simulations, find be challenging, highlighting importance dataset quality careful assessment.

Language: Английский

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Incorporating spatial diffusion into models of bursty stochastic transcription

Journal of The Royal Society Interface, Journal Year: 2025, Volume and Issue: 22(225)

Published: April 1, 2025

The dynamics of gene expression are stochastic and spatial at the molecular scale, with messenger RNA (mRNA) transcribed specific nuclear locations then transported to boundary for export. Consequently, distributions these molecules encode their underlying dynamics. While mechanistic models counts have revealed numerous insights into expression, they largely neglected now-available subcellular resolution down individual molecules. Owing technical challenges inherent in processes, tools studying patterns still limited. Here, we introduce a model mRNA two-state (telegraph) transcriptional Observations can be concisely described as following Cox process driven by stochastically switching partial differential equation. We derive analytical solutions demographic moments validate them simulations. show that distribution accurately approximated Poisson-beta tractable parameters, even complex This observation allows efficient parameter inference demonstrated on synthetic data. Altogether, our work adds progress towards new frontier inferring from static snapshot

Language: Английский

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Correlation and distinction between stochastic gene transcription models with and without polymerase dynamics

Physical Review Research, Journal Year: 2025, Volume and Issue: 7(2)

Published: April 14, 2025

Polymerase dynamics (PD) is an important model for explaining transcriptional regulation in gene perturbation data. In this study, we conducted a detailed analysis of the dynamic behavior stochastic transcription models with PD. We first derived exact time-dependent formula mRNA distribution classical telegraph PD, then revealed different mechanism whereby PD simultaneously suppresses Fano factor and enhances bimodal distribution. For deeper insights into regulation, established optimal effective without to approximate steady-state Optimized parameters reliably captured input initiation production rates reflected parameter variations complex systems under biological perturbations. The also that introduced quantitatively distinct kurtosis values By fitting transcriptome-wide data from mouse fibroblast embryonic stem cells, found over 1000 sets may be better by integrating model. synthetic data, showed combinations cell sample size N number time points, n, required reliable selection between are N=103 n≥8, N=104 n≥2, or N=105 whereas estimation polymerase recruitment pause release N=104 n≥8 N=105 n≥4. Our proposed method can used determine regulatory roles other compounds. Published American Physical Society 2025

Language: Английский

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Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data

Nucleic Acids Research, Journal Year: 2025, Volume and Issue: 53(7)

Published: March 31, 2025

Bursty gene expression is characterized by two intuitive parameters, burst frequency and size, the cell-cycle dependence of which has not been extensively profiled at transcriptome level. In this study, we estimate parameters per allele in G1 G2/M phases for thousands mouse genes fitting mechanistic models to messenger RNA count data, obtained sequencing single cells whose position inferred using a deep-learning method. We find that upon DNA replication, median approximately halves, while size remains mostly unchanged. Genome-wide distributions parameter ratios between are broad, indicating substantial heterogeneity transcriptional regulation. also observe significant negative correlation ratios, suggesting regulatory processes do independently control parameters. show accurately must explicitly account copy number variation extrinsic noise due coupling transcription cell age across cycle, but corrections technical imperfect capture molecules experiments less critical.

Language: Английский

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Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 30, 2024

Common stochastic models of gene expression predict the analytical distribution total mRNA level per cell but not at subcellular resolution. Here, for a wide class transcription initiation, we obtain an exact steady-state solution joint nuclear and cytoplasmic levels cell. Correcting extrinsic noise fitting to single human data, precisely quantify extent bursty in thousands genes associate it with their biological functions.

Language: Английский

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Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing

Cell Systems, Journal Year: 2024, Volume and Issue: 15(8), P. 694 - 708.e12

Published: Aug. 1, 2024

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global single We present a stochastic model gene expression with cell size- and cycle-dependent rates growing dividing cells that harnesses temporal dimensions single-cell RNA sequencing through metabolic labeling protocols cel lcycle reporters. develop parallel highly scalable approximate Bayesian computation method corrects for technical variation accurately quantifies absolute burst frequency, size, degradation rate along the cycle at transcriptome-wide scale. Using selection, we reveal scaling between size unveil waves regulation transcriptome. Our study shows modeling dynamical correlations identifies regulation. A record this paper's transparent peer review process included supplemental information.

Language: Английский

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Transcriptional bursting dynamics in gene expression

Frontiers in Genetics, Journal Year: 2024, Volume and Issue: 15

Published: Sept. 13, 2024

Gene transcription is a stochastic process that occurs in all organisms. Transcriptional bursting, critical molecular dynamics mechanism, creates significant heterogeneity mRNA and protein levels. This drives cellular phenotypic diversity. Currently, the lack of comprehensive quantitative model limits research on transcriptional bursting. review examines various gene expression models compares their strengths weaknesses to guide researchers selecting most suitable for context. We also provide detailed summary key metrics related compared temporal bursting across species mechanisms influencing these bursts, highlighted spatiotemporal patterns differences by utilizing such as burst size frequency. summarized strategies modeling from both biostatistical biochemical reaction network perspectives. Single-cell sequencing data integrated multiomics approaches drive our exploration cutting-edge trends mechanisms. Moreover, we examined classical methods parameter estimation help capture dynamic parameters data, assessing merits limitations facilitate optimal estimation. Our current theories deeper insights promoting nature cell processes, fate determination, cancer diagnosis.

Language: Английский

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