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

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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.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (32130020, 32025009, 82030099, 30700397, 31970638, 61572361, 81973701, U23A20513, 32222026, 82373446), the National Key Research and Development Program of China (2021YFF1201200, 2021YFF1200900, 2022YFA1106000), the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), the Fundamental Research Funds for the Central Universities (20002150110, 22120230292). Beihang University & Capital Medical University Plan (BHME-201904), the Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals Authority (XTCX201809), the Cooperative Research Fund of the Affiliated Wuhu Hospital of East China Normal University (40500-20104-222400), the Fundamental Research Funds for the Central Universities (226-2024-00001), the Shanghai Municipal Science and Technology Commission “Science and Technology Innovation Action Plan” technical standard project (21DZ2201700), the Shanghai Municipal Science and Technology Commission “Science and Technology Innovation Action Plan” natural science foundation project (23ZR1435800), the Shanghai Natural Science Foundation Program (17ZR1449400), the Shanghai Artificial Intelligence Technology Standard Project (19DZ2200900), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, ECNU, Key Laboratory of MEA, Ministry of Education, ECNU. We would like to express our sincerest gratitude to the members of the expert panel of the Program on Single-Cell Omics.

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Correspondence to Minmin Xiao, Fangqing Zhao, Jing-Dong J. Han, Qi Liu, Xiaohui Fan, Chen Li, Chenfei Wang or Tieliu Shi.

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Table S1

Key features summary of different scRNA-seq/snRNA-seq technologies

Table S2

Overview of single cell isolation technologies

Table S3

Additional information for common single cell analytical frameworks in the text

Table S4

Supplementary information for extended downstream single cell analysis frameworks for gene regulatory network analysis, immune analysis, cell cycle assignment analysis, gene variants exploration analysis, alternative splicing analysis and other common single cell analysis

Table S5

Comparison of high-through scWGS methods

Table S6

Overview of Single-cell Epigenomic Sequencing Techniques

Table S7

Overview of Computational Methods for Single-cell Epigenomic Sequencing Techniques

Table S8

Examples of single-cell metabolomics studies based on MS (2012–2022)

Table S9

List of resources available for the analysis of single-cell metabolomics (Reproduced from Misra et al., 2020 (Open Access), PMID: 31565776)

Table S10

Computational algorithms for different types of single-cell multi-modal integration

Table S11

The characteristics of computational methods for spatial transcriptomics

Table S12

The tools to analyze scCRISPR-seq data

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Sun, F., Li, H., Sun, D. et al. Single-cell omics: experimental workflow, data analyses and applications. Sci. China Life Sci. 68, 5–102 (2025). https://doi.org/10.1007/s11427-023-2561-0

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