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Updated: Oct 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Temporal modelling using single-cell transcriptomics.

Jun Ding¹, Nadav Sharon², Ziv Bar-Joseph^3,4

¹Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada.

Nature Reviews. Genetics

|February 1, 2022

Summary

This summary is machine-generated.

Time-series single-cell RNA sequencing (scRNA-seq) offers insights into dynamic biological processes. This review covers analysis methods for scRNA-seq data, addressing challenges in modeling cell state changes over time.

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Last Updated: Oct 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Transcriptome Analysis of Single Cells

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-cell gene profiling advances the study of development, differentiation, and disease.
Time-series analysis of cells reveals dynamic changes in cell states, gene expression, and regulatory networks.

Purpose of the Study:

To review analytical and modeling approaches for time-series single-cell RNA sequencing (scRNA-seq) data.
To highlight the steps, assumptions, and applicability of various scRNA-seq analysis methods.

Main Methods:

Discussion of computational methods for analyzing time-series scRNA-seq data.
Exploration of techniques for inferring continuous cellular trajectories.
Integration of bulk and single-cell data for dynamic network modeling.

Main Results:

Identification of key challenges in time-series scRNA-seq data analysis.
Overview of diverse methods for reconstructing dynamic biological processes.
Guidance on selecting appropriate methods based on data and biological questions.

Conclusions:

Time-series scRNA-seq is crucial for understanding dynamic biological systems.
Effective analysis requires careful consideration of methodological choices.
This review provides a framework for navigating the complexities of time-series scRNA-seq data.