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

doi:10.1007/s11427-023-2561-0

Review

. 2025 Jan;68(1):5-102.

doi: 10.1007/s11427-023-2561-0. Epub 2024 Jul 23.

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

Fengying Sun^#¹, Haoyan Li^#², Dongqing Sun^#^{3

4}, Shaliu Fu^#^{3

5

6

7}, Lei Gu^#⁸, Xin Shao^#^{2

9}, Qinqin Wang^#⁸, Xin Dong^#^{3

4}, Bin Duan^#^{3

5

6

7}, Feiyang Xing^#^{3

4}, Jun Wu^#¹⁰, Minmin Xiao¹¹, Fangqing Zhao¹², Jing-Dong J Han¹³, Qi Liu^{14

15

16

17}, Xiaohui Fan^{18

19

20}, Chen Li²¹, Chenfei Wang^{22

23}, Tieliu Shi^{24

25

26}

Affiliations

¹ Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
² Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
³ Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
⁴ Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
⁵ Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
⁶ Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
⁷ Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
⁸ Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
⁹ National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
¹⁰ Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
¹¹ Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China. 1084173819@qq.com.
¹² Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China. zhfq@biols.ac.cn.
¹³ Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China. jackie.han@pku.edu.cn.
¹⁴ Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China. qiliu@tongji.edu.cn.
¹⁵ Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China. qiliu@tongji.edu.cn.
¹⁶ Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China. qiliu@tongji.edu.cn.
¹⁷ Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China. qiliu@tongji.edu.cn.
¹⁸ Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. fanxh@zju.edu.cn.
¹⁹ National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China. fanxh@zju.edu.cn.
²⁰ Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China. fanxh@zju.edu.cn.
²¹ Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. cli@shsmu.edu.cn.
²² Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China. 08chenfeiwang@tongji.edu.cn.
²³ Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China. 08chenfeiwang@tongji.edu.cn.
²⁴ Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China. tlshi@bio.ecnu.edu.cn.
²⁵ Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China. tlshi@bio.ecnu.edu.cn.
²⁶ Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China. tlshi@bio.ecnu.edu.cn.

^# Contributed equally.

PMID: 39060615
DOI: 10.1007/s11427-023-2561-0

Review

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

Fengying Sun et al. Sci China Life Sci. 2025 Jan.

. 2025 Jan;68(1):5-102.

doi: 10.1007/s11427-023-2561-0. Epub 2024 Jul 23.

Authors

Affiliations

¹ Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
² Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
³ Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
⁴ Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
⁵ Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
⁶ Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
⁷ Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
⁸ Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
⁹ National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
¹⁰ Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
¹¹ Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China. 1084173819@qq.com.
¹² Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China. zhfq@biols.ac.cn.
¹³ Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China. jackie.han@pku.edu.cn.
¹⁴ Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China. qiliu@tongji.edu.cn.
¹⁵ Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China. qiliu@tongji.edu.cn.
¹⁶ Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China. qiliu@tongji.edu.cn.
¹⁷ Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China. qiliu@tongji.edu.cn.
¹⁸ Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. fanxh@zju.edu.cn.
¹⁹ National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China. fanxh@zju.edu.cn.
²⁰ Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China. fanxh@zju.edu.cn.
²¹ Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. cli@shsmu.edu.cn.
²² Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China. 08chenfeiwang@tongji.edu.cn.
²³ Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China. 08chenfeiwang@tongji.edu.cn.
²⁴ Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China. tlshi@bio.ecnu.edu.cn.
²⁵ Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China. tlshi@bio.ecnu.edu.cn.
²⁶ Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China. tlshi@bio.ecnu.edu.cn.

^# Contributed equally.

PMID: 39060615
DOI: 10.1007/s11427-023-2561-0

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.

Keywords: CRISPR screening; epigenome; genome; metabolomics; multimodal; proteomics; single-cell sequencing; spatial transcriptomics.

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Conflict of interest statement

Compliance and ethics. The author(s) declare that they have no conflict of interest.

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References

1. Ascensión, A.M., Ibáñez-Solé, O., Inza, I., Izeta, A., and Araúzo-Bravo, M.J. (2022). Triku: a feature selection method based on nearest neighbors for single-cell data. Gigascience 11, giac017.
1. Abdelfattah, N., Kumar, P., Wang, C., Leu, J.S., Flynn, W.F., Gao, R., Baskin, D.S., Pichumani, K., Ijare, O.B., Wood, S.L., et al. (2022). Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat Commun 13, 767. - PubMed - PMC
1. Abid, A., Zhang, M.J., Bagaria, V.K., and Zou, J. (2018). Exploring patterns enriched in a dataset with contrastive principal component analysis. Nat Commun 9, 2134. - PubMed - PMC
1. Abouleila, Y., Onidani, K., Ali, A., Shoji, H., Kawai, T., Lim, C.T., Kumar, V., Okaya, S., Kato, K., Hiyama, E., et al. (2019). Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells. Cancer Sci 110, 697–706. - PubMed - PMC
1. Achim, K., Pettit, J.B., Saraiva, L.R., Gavriouchkina, D., Larsson, T., Arendt, D., and Marioni, J.C. (2015). High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat Biotechnol 33, 503–509. - PubMed

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[1] Ascensión, A.M., Ibáñez-Solé, O., Inza, I., Izeta, A., and Araúzo-Bravo, M.J. (2022). Triku: a feature selection method based on nearest neighbors for single-cell data. Gigascience 11, giac017.

[2] Ascensión, A.M., Ibáñez-Solé, O., Inza, I., Izeta, A., and Araúzo-Bravo, M.J. (2022). Triku: a feature selection method based on nearest neighbors for single-cell data. Gigascience 11, giac017.

[3] Abdelfattah, N., Kumar, P., Wang, C., Leu, J.S., Flynn, W.F., Gao, R., Baskin, D.S., Pichumani, K., Ijare, O.B., Wood, S.L., et al. (2022). Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat Commun 13, 767. - PubMed - PMC

[4] Abdelfattah, N., Kumar, P., Wang, C., Leu, J.S., Flynn, W.F., Gao, R., Baskin, D.S., Pichumani, K., Ijare, O.B., Wood, S.L., et al. (2022). Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat Commun 13, 767. - PubMed - PMC

[5] Abid, A., Zhang, M.J., Bagaria, V.K., and Zou, J. (2018). Exploring patterns enriched in a dataset with contrastive principal component analysis. Nat Commun 9, 2134. - PubMed - PMC

[6] Abid, A., Zhang, M.J., Bagaria, V.K., and Zou, J. (2018). Exploring patterns enriched in a dataset with contrastive principal component analysis. Nat Commun 9, 2134. - PubMed - PMC

[7] Abouleila, Y., Onidani, K., Ali, A., Shoji, H., Kawai, T., Lim, C.T., Kumar, V., Okaya, S., Kato, K., Hiyama, E., et al. (2019). Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells. Cancer Sci 110, 697–706. - PubMed - PMC

[8] Abouleila, Y., Onidani, K., Ali, A., Shoji, H., Kawai, T., Lim, C.T., Kumar, V., Okaya, S., Kato, K., Hiyama, E., et al. (2019). Live single cell mass spectrometry reveals cancer-specific metabolic profiles of circulating tumor cells. Cancer Sci 110, 697–706. - PubMed - PMC

[9] Achim, K., Pettit, J.B., Saraiva, L.R., Gavriouchkina, D., Larsson, T., Arendt, D., and Marioni, J.C. (2015). High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat Biotechnol 33, 503–509. - PubMed

[10] Achim, K., Pettit, J.B., Saraiva, L.R., Gavriouchkina, D., Larsson, T., Arendt, D., and Marioni, J.C. (2015). High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat Biotechnol 33, 503–509. - PubMed

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

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

Authors

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Abstract

Conflict of interest statement

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