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Updated: Jun 28, 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

Unique challenges and best practices for single cell transcriptomic analysis in toxicology.

David Filipovic¹, Omar Kana^1,2,3, Daniel Marri^1,4

¹Institute for Quantitative Health Science & Engineering, East Lansing, MI, 48824, USA.

Current Opinion in Toxicology

|April 22, 2024

Summary

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Dynamical systems theory as an organizing principle for single-cell biology.

NPJ systems biology and applications·2025

This review addresses challenges in single-cell transcriptomics for toxicology. It offers best practices for analyzing cell populations, gene expression, and cellular responses to chemical exposures.

Area of Science:

Toxicology
Genomics
Bioinformatics

Background:

Single-cell transcriptomics offers high resolution for toxicological studies.
Analyzing this data presents challenges in identifying sensitive cell types and interpreting dynamic shifts.

Purpose of the Study:

To review challenges in single-cell transcriptomics for toxicology.
To present best practices for critical single-cell analysis tasks.

Main Methods:

Review of existing literature and analytical approaches.
Focus on cell type identification, differential abundance, gene expression, and cellular trajectories.

Main Results:

Identified key challenges in applying single-cell transcriptomics to toxicology.

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Last Updated: Jun 28, 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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Outlined practical bioinformatic solutions and best practices for data analysis.

Conclusions:

Appropriate bioinformatic techniques are crucial for extracting insights from single-cell transcriptomic data in toxicology.
Enhancing analytical methods will improve understanding of cellular responses to toxic exposures.