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Updated: Jan 10, 2026

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

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Published on: January 19, 2017

Inferring differential dynamics from multi-lineage, multi-omic, and multi-sample single-cell data with MultiVeloVAE.

Chen Li¹, Yichen Gu², Maria C Virgilio^1,3

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Nature Communications

|November 20, 2025

Summary

This summary is machine-generated.

This study introduces MultiVeloVAE, a new computational tool for analyzing single-cell multi-omic data to understand cell differentiation dynamics. It offers novel insights into gene expression and chromatin accessibility during development.

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Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

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Published on: January 19, 2017

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Biomedical Science
Computational Biology
Genomics

Background:

Cell differentiation is crucial for understanding specialized cell fates.
Single-cell multi-omic profiling offers insights into dynamic molecular changes.
Existing RNA velocity methods struggle with multi-lineage, multi-sample, and multi-omic single-cell data.

Purpose of the Study:

To develop a computational framework for multi-sample RNA velocity inference using integrated single-cell RNA and multi-omic data.
To address limitations of previous methods in handling complex single-cell datasets.
To enable the identification of differential molecular dynamics during cell differentiation.

Main Methods:

Introduced MultiVeloVAE, a probabilistic framework for RNA velocity inference.
Integrated single-cell RNA and multi-omic data (gene expression and chromatin accessibility).
Modeled dynamics on a shared time scale, handling lineage bifurcations and multi-sample inference.

Main Results:

MultiVeloVAE successfully models gene expression and chromatin accessibility dynamics.
The framework supports multi-sample inference from datasets with partially overlapping modalities.
Demonstrated novel insights into chromatin accessibility and gene expression dynamics using human embryoid body and macrophage differentiation data.

Conclusions:

MultiVeloVAE overcomes limitations of previous RNA velocity methods for complex single-cell data.
The framework provides a powerful tool for analyzing multi-omic single-cell data to study cell differentiation.
Offers new understanding of molecular dynamics during human development and cell specialization.