Studying stochastic systems biology of the cell with single-cell genomics data

Gorin, Gennady; Vastola, John J.; Pachter, Lior

doi:10.1101/2023.05.17.541250

2023

DOI: 10.1101/2023.05.17.541250

|View full text |Cite

Preprint

Studying stochastic systems biology of the cell with single-cell genomics data

Gennady Gorin

John J. Vastola

Lior Pachter

Abstract: Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We model a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and show ho… Show more

Help me understand this report

View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Preprint3

Relationship

Self Cite1

Independent2

Authors

Journals

Cited by 3 publications

References 182 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

Grima,

Esmenjaud

2023

Preprint

View full text Add to dashboard Cite

The snapshot distribution of mRNA counts per cell can be readily measured using single molecule FISH or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: the rate of transcription, the rate of switching to the active transcriptional state and the rate of switching to the inactive state. These parameters are often understood as being reflective of the average cell in a population. However it is currently unclear to what extent does the cell-to-cell variation of parameters (extrinsic noise) affect the accuracy of parameter estimation. Here we develop an analytical approach to understand the factors determining the size and sign of estimation bias. We find bias signatures that depend on the source of extrinsic noise and the mode of transcriptional activity. We estimate the size of extrinsic noise from the covariance matrix of scRNA-seq data which we use to correct published estimates of parameters for mammalian genes.

show abstract

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

Grima,

Esmenjaud

2023

Preprint

View full text Add to dashboard Cite

show abstract

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

Fang,

Gorin,

Pachter

2024

Preprint

Self Cite

View full text Add to dashboard Cite

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

show abstract

Fokker-Planck diffusion maps of multiple single cell microglial transcriptomes reveals radial differentiation into substates associated with Alzheimer’s pathology

Baumgartner,

Robinson,

Golde

et al. 2024

Preprint

View full text Add to dashboard Cite

The identification of microglia subtypes is important for understanding the role of innate immunity in neu-rodegenerative diseases. Current methods of unsupervised cell type identification assume a small noise-to-signal ratio of transcriptome measurements that would produce well-separated cell clusters. However, identification of subtypes is obscured by gene expression noise, diminishing the distances in transcriptome space between distinct cell types and blurring boundaries. Here we use Fokker-Planck (FP) diffusion maps to model cellular differentiation as a stochastic process whereby cells settle into local minima, corresponding to cell subtypes, in a potential landscape constructed from transcriptome data using a nearest neighbor graph approach. By applying critical transition fields, we identify individual cells on the verge of transitioning between subtypes, revealing microglial cells in inactivated, homeostatic state before radially transitioning into various specialized subtypes. Specifically, we show that cells from Alzheimer’s disease patients are enriched in a microglia subtype associated to antigen presentation and T-cell recruitment.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Studying stochastic systems biology of the cell with single-cell genomics data

Cited by 3 publications

References 182 publications

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

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

Fokker-Planck diffusion maps of multiple single cell microglial transcriptomes reveals radial differentiation into substates associated with Alzheimer’s pathology

Contact Info

Product

Resources

About