Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data DOI Creative Commons
Tara Chari, Gennady Gorin, Lior Pachter

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 17, 2023

Multimodal, single-cell genomics technologies enable simultaneous capture of multiple facets DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies cellular heterogeneous cell types, with applications ranging from inferring kinetic differences between cells, to role stochasticity driving heterogeneity. However, current methods determining types or 'clusters' present multimodal data often rely on ad hoc independent treatment modalities, assumptions ignoring inherent properties count data. To interpretable consistent cluster determination data, we meK-Means (mechanistic K-Means) which integrates modalities learns underlying, shared biophysical states through a unifying model transcription. In particular, demonstrate how can be used cells unspliced spliced mRNA modalities. By utilizing causal, physical relationships underlying these identify transcriptional kinetics across induce observed gene expression profiles, provide an alternative definition governing parameters processes.

Language: Английский

Quantifying and correcting bias in transcriptional parameter inference from single-cell data DOI Creative Commons
Ramon Grima,

Pierre-Marie Esmenjaud

Biophysical Journal, Journal Year: 2023, Volume and Issue: 123(1), P. 4 - 30

Published: Oct. 27, 2023

The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state two-state telegraph model estimate three transcriptional parameters for a gene interest: synthesis rate, switching on rate (the state being active state), and off rate. This assumes no extrinsic noise, i.e., do not vary between cells, thus estimated understood as approximating average values population. accuracy this approximation is currently unclear. Here, we develop theory that explains size sign estimation bias when inferring from data standard model. We find specific signatures depending source noise (which parameter most variable across cells) mode activity. If expression bursty then population averages all overestimated if rate; underestimation occurs both overestimation occur some tend infinity approaches critical threshold. In contrast bursty, cases mean burst (ratio rate) while frequency underestimated. covariance matrix sequencing use together with our correct published estimates mammalian genes.

Language: Английский

Citations

15
Biophysically interpretable inference of cell types from multimodal sequencing data DOI
Tara Chari, Gennady Gorin, Lior Pachter

et al.

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(9), P. 677 - 689

Published: Sept. 20, 2024

Language: Английский

Citations

6
Dissection and integration of bursty transcriptional dynamics for complex systems DOI
Cheng Gao,

Suriyanarayanan Vaikuntanathan,

Samantha J. Riesenfeld

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(18)

Published: April 26, 2024

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach,

Language: Английский

Citations

5
Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data DOI Creative Commons
Maria Carilli, Gennady Gorin, Yongin Choi

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 14, 2023

Abstract We motivate and present biVI , which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent mature RNA distributions. While previous approaches to integrate bimodal data via ignore causal relationship between measurements, biophysical processes that give rise observations. demonstrate through simulated benchmarking captures cell type structure in a low-dimensional space accurately recapitulates parameter values copy number On biological data, provides scalable route identifying mechanisms underlying gene expression. This analytical approach outlines generalizable strateg treating multimodal datasets generated by high-throughput, single-cell genomic assays.

Language: Английский

Citations

12
Balanced implicit Patankar–Euler methods for positive solutions of stochastic differential equations of biological regulatory systems DOI Creative Commons
Aimin Chen, Quanwei Ren, Tianshou Zhou

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(6)

Published: Feb. 14, 2024

Stochastic differential equations (SDEs) are a powerful tool to model fluctuations and uncertainty in complex systems. Although numerical methods have been designed simulate SDEs effectively, it is still problematic when solutions may be negative, but application problems require positive simulations. To address this issue, we propose balanced implicit Patankar-Euler ensure simulations of SDEs. Instead considering the addition terms explicit existing methods, attempt deletion possible negative from maintain positivity The include negative-valued drift potential diffusion terms. proposed method successfully addresses issue divisions with very small denominators our recently stochastic Patankar method. Stability analysis shows that has much better stability properties than composite Four SDE systems used examine effectiveness, accuracy, convergence methods. Numerical results suggest an effective efficient approach any appropriate stepsize simulating biological regulatory

Language: Английский

Citations

4
Incorporating spatial diffusion into models of bursty stochastic transcription DOI Creative Commons
Christopher E. Miles

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

Citations

0
Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data DOI Creative Commons

Augustinas Sukys,

Ramon Grima

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

Citations

0
Qualitative behaviors of the Fenton reaction system DOI Open Access

Jiaqi Teng,

Tianshou Zhou

Discrete and Continuous Dynamical Systems - B, Journal Year: 2025, Volume and Issue: 0(0), P. 0 - 0

Published: Jan. 1, 2025

Language: Английский

Citations

0
Distinguishing biophysical stochasticity from technical noise in single-cell RNA sequencing usingMonod DOI Creative Commons
Gennady Gorin, Lior Pachter

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: June 12, 2022

Abstract We present the Python package Monod for analysis of single-cell RNA sequencing count data through biophysical modeling. naturally “integrates” unspliced and spliced matrices, provides a route to identifying studying differential expression patterns that do not cause changes in average gene expression. The framework is open-source modular, may be extended more sophisticated models variation further experimental observables. can installed from command line using pip install monod. source code available maintained at https://github.com/pachterlab/monod . A separate repository, which contains sample notebooks with , accessible https://github.com/pachterlab/monod_examples/ Structured documentation tutorials are hosted https://monod-examples.readthedocs.io/

Language: Английский

Citations

15
Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions DOI
Christopher E. Miles, Scott A. McKinley, Fangyuan Ding

et al.

Bulletin of Mathematical Biology, Journal Year: 2024, Volume and Issue: 86(6)

Published: May 13, 2024

Language: Английский

Citations

3