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Related Experiment Video

Updated: Jun 19, 2025

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Published on: December 16, 2014

Single-cell omics: experimental workflow, data analyses and applications.

Fengying Sun¹, Haoyan Li², Dongqing Sun^3,4

¹Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.

Science China. Life Sciences

|July 26, 2024

Summary

This summary is machine-generated.

Single-cell omics technologies rapidly advance, offering deep insights into cellular functions and complex diseases like cancer. This review guides researchers in selecting appropriate single-cell sequencing methods and data analysis techniques.

Keywords:

CRISPR screening epigenome genome metabolomics multimodal proteomics single-cell sequencing spatial transcriptomics

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Area of Science:

Biomedical Science
Genomics
Molecular Biology

Background:

Cells are fundamental biological units with unique developmental paths and molecular characteristics.
Understanding genome's role in cell formation, maintenance, and phenotype control is complex.
Rapid advancements in single-cell sequencing technologies have emerged since the first single-cell RNA sequencing.

Purpose of the Study:

To comprehensively summarize recent advances in single-cell omics technologies.
To focus specifically on the methodology of these advanced techniques.
To guide researchers in selecting appropriate single-cell sequencing methods and data analysis.

Main Methods:

Expansion of single-cell sequencing to include genome, epigenome, proteome, and metabolome.
Vertical integration of multiple omics data.
Incorporation of spatial single-cell RNA sequencing (scRNA-seq) and CRISPR screening.

Main Results:

Single-cell omics provide profound insights into complex diseases, particularly cancers.
Methodological advancements enable deeper understanding of cellular heterogeneity.
Integration of multi-omics and spatial data enhances biological discovery.

Conclusions:

Single-cell omics represent a groundbreaking advancement in biomedicine.
These technologies are crucial for understanding disease mechanisms.
This review serves as a guide for researchers navigating the field of single-cell omics.