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Lineage Commitment01:21

Lineage Commitment

Commitment is the  process whereby stem cells:
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Real-time Bioluminescence Imaging of Notch Signaling Dynamics during Murine Neurogenesis
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Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease.

Jiawei Xing1, Stephen J Staklinski1, Zhihan Liu1

  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY.

Biorxiv : the Preprint Server for Biology
|July 3, 2026
PubMed
Summary

LaVOUS is a new computational framework that analyzes gene expression dynamics along cell lineages. It accurately identifies how gene expression changes during development and disease by integrating lineage tracing with single-cell RNA sequencing data.

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Area of Science:

  • Computational Biology
  • Genomics
  • Developmental Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) data is often treated as static snapshots, neglecting dynamic gene expression changes along cell lineages.
  • Existing methods struggle with the sparse and overdispersed nature of scRNA-seq counts and often use imprecise Gaussian assumptions.
  • Reconstructing cell lineage phylogenies offers a framework to study transcriptional changes during development, differentiation, and disease.

Purpose of the Study:

  • To develop a probabilistic framework, LaVOUS (Lineage-aware Variational Ornstein-Uhlenbeck Single-cell RNA-seq analysis), for analyzing gene expression dynamics in the context of cell lineage trees.
  • To enable likelihood-based testing of cellular heritability and branch-specific gene expression shifts.
  • To reconstruct latent expression histories phylogenetically.

Main Methods:

  • LaVOUS couples lineage-based models of latent dynamics (Brownian motion, Ornstein-Uhlenbeck processes) with negative-binomial observation models.
  • It employs scalable variational inference for efficient analysis of sparse count data.
  • The framework integrates cell lineage tracing with single-cell transcriptomic data.

Main Results:

  • In simulations, LaVOUS demonstrated superior performance over Gaussian methods in detecting lineage-associated expression changes and reconstructing expression histories.
  • LaVOUS successfully identified lineage-associated gene expression changes in metastatic lung cancer, class-switching B cells, and developing brain tissues.
  • Specific applications revealed insights into metastatic progression, B-cell isotype switching, and neuronal differentiation.

Conclusions:

  • LaVOUS provides an expressive and scalable framework for modeling sparse count data on lineage trees, advancing the study of single-cell expression dynamics.
  • It establishes a foundation for investigating gene expression changes in developmental and disease contexts.
  • Future extensions can incorporate multi-gene regulation, lineage uncertainty, and multi-modal data integration.