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Related Experiment Video

Updated: Jul 31, 2025

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Published on: December 9, 2022

Single-Cell Multiomics.

Emily Flynn¹, Ana Almonte-Loya^1,2, Gabriela K Fragiadakis^1,3

¹CoLabs, University of California, San Francisco, California, USA;

Annual Review of Biomedical Data Science

|May 9, 2023

Summary

This summary is machine-generated.

New single-cell technologies measure multiple cell features, including genomics and proteomics. Computational methods integrate this multimodal data for deeper biological insights into cell identity and interactions.

Keywords:

computation integration multimodal multiomics next-generation sequencing single-cell

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Last Updated: Jul 31, 2025

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity.
Emerging single-cell technologies assay multiple biological modalities (genomic, epigenomic, proteomic, spatial).

Purpose of the Study:

To review single-cell technologies for multimodal data generation.
To describe computational methods for integrating multimodal single-cell data.
To highlight leveraging multimodal data for enhanced biological insight.

Main Methods:

Discussion of various single-cell technologies for assaying multiple modalities.
Characterization of computational integration strategies for paired and unpaired multimodal data.
Review of methods enabling combined analysis of genetic variation and transcription.

Main Results:

Multimodal single-cell assays provide unprecedented cellular insights.
Computational integration of multimodal data reveals cell identities and interactions.
Integrated data analysis links genetic variation to transcriptional states.

Conclusions:

Multimodal single-cell analysis significantly advances understanding of cellular biology.
Computational integration is key to unlocking the potential of multimodal single-cell data.
This approach deepens insights into complex biological systems and cell-cell interactions.