Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development

Gallivan, Cameron P.; Ren, Honglei; Read, Elizabeth L.

doi:10.3389/fgene.2019.01387

“…S2 ). Similar networks have been analyzed to provide insight into gene inhibition and activation ( Gallivan et al, 2020) , and into a diversity of biological systems, such as the lac-operon ( Ozbudak et al, 2004) and cell-cycle control ( Novak and Tyson, 1993) .…”

Section: Resultsmentioning

confidence: 99%

A dynamical systems treatment of transcriptomic trajectories in hematopoiesis

Freedman

¹

,

Xu

²

,

Goyal

³

et al. 2023

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Inspired by Waddington's illustration of an epigenetic landscape, cell-fate transitions have been envisioned as bifurcating dynamical systems, wherein exogenous signaling dynamics couple to a cell's enormously complex signaling and transcriptional machinery, to elicit qualitative transitions in the cell's collective state. Single-cell RNA sequencing (scRNA-seq), which measures the distributions of possible transcriptional states in large populations of differentiating cells, provides an alternate view, in which development is marked by the variations of a myriad of genes. Here, we present a mathematical formalism for rigorously evaluating, from a dynamical systems perspective, whether scRNA-seq trajectories display statistical signatures consistent with bifurcations and, as a case study, pinpoint regions of multistability along the neutrophil branch of hematopoeitic differentiation. Additionally, we leverage the geometric features of linear instability to identify the low-dimensional phase plane in gene expression space within which the multistability unfolds, highlighting novel genetic players crucial for neutrophil differentiation. Broadly, we show that a dynamical systems treatment of scRNA-seq data provides mechanistic insights into the high-dimensional processes of cellular differentiation, taking a step toward systematic construction of mathematical models for transcriptomic dynamics.

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“…Nevertheless, stochastic modeling of GRNs using SC gene expression data is still in its early stage [ 26 , 27 ] and has never been studied for GC B cells. Here, we apply a particular class of stochastic models combining deterministic dynamics and random jumps, called piecewise-deterministic Markov processes (PDMPs) [ 28 ], to the description of GC B cell differentiation.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

Koshkin,

Herbach,

Martínez

et al. 2024

PLoS ONE

1

0

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Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

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“…Nevertheless, stochastic modeling of GRNs using SC gene expression data is still in its early stage [26,27] and has never been studied for GC B cells. Here, we apply a particular class of stochastic models combining deterministic dynamics and random jumps, called 2/24 piecewise-deterministic Markov processes (PDMPs) [28], to the description of GC B cell di erentiation.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

Koshkin

¹

,

Herbach

²

,

Martínez

³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

Germinal centers (GCs) are the key histological structures of the adaptive immune system, responsible for the development and selection of B cells producing high-affinity antibodies against antigens. Due to their level of complexity, unexpected malfunctioning may lead to a range of pathologies, including various malignant formations. One promising way to improve the understanding of malignant transformation is to study the underlying gene regulatory networks (GRNs) associated with cell development and differentiation. Evaluation and inference of the GRN structure from gene expression data is a challenging task in systems biology: recent achievements in single-cell (SC) transcriptomics allow the generation of SC gene expression data, which can be used to sharpen the knowledge on GRN structure. In order to understand whether a particular network of three key gene regulators (BCL6, IRF4, BLIMP1), influenced by two external stimuli signals (surface receptors BCR and CD40), is able to describe GC B cell differentiation, we used a stochastic model to fit SC transcriptomic data from a human lymphoid organ dataset. The model is defined mathematically as a piecewise-deterministic Markov process. We showed that after parameter tuning, the model qualitatively recapitulates mRNA distributions corresponding to GC and plasmablast stages of B cell differentiation. Thus, the model can assist in validating the GRN structure and, in the future, could lead to better understanding of the different types of dysfunction of the regulatory mechanisms.

show abstract

Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development

Cited by 8 publications

References 56 publications

A dynamical systems treatment of transcriptomic trajectories in hematopoiesis

A dynamical systems treatment of transcriptomic trajectories in hematopoiesis

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

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