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

  • Fengying Sun
  • , Haoyan Li
  • , Dongqing Sun
  • , Shaliu Fu
  • , Lei Gu
  • , Xin Shao
  • , Qinqin Wang
  • , Xin Dong
  • , Bin Duan
  • , Feiyang Xing
  • , Jun Wu
  • , Minmin Xiao*
  • , Fangqing Zhao*
  • , Jing Dong J. Han*
  • , Qi Liu*
  • , Xiaohui Fan*
  • , Chen Li*
  • , Chenfei Wang*
  • , Tieliu Shi*
  • *Corresponding author for this work
  • East China Normal University
  • Zhejiang University
  • Tongji University
  • Zhejiang Lab
  • Shanghai Research Institute for Intelligent Autonomous Systems
  • Shanghai Jiao Tong University
  • Zhejiang University
  • Chinese Academy of Sciences
  • Peking University

Research output: Contribution to journalReview articlepeer-review

Abstract

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Original languageEnglish
Pages (from-to)5-102
Number of pages98
JournalScience China Life Sciences
Volume68
Issue number1
DOIs
StatePublished - Jan 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

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

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