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Navigating the data processing for cytometry-based single-cell proteomics

Nature Protocols (2025)Cite this article

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Abstract

Cytometry-based single-cell proteomics (SCP) has emerged as a powerful technique that greatly advances our understanding of complex biological systems with a new level of granularity. Various methods have been developed to process cytometry-based SCP data. However, it remains extremely challenging to identify the well-performing processing workflows for specific datasets. Here, we develop ANPELA, an out-of-the-box method for navigating the proteomic data processing based on large-scale screening. It enables a comparison among the performances of thousands of the processing workflows in identifying cell subpopulations and inferring pseudo-time trajectories based on machine learning. Several cases are then analyzed, highlighting its ability to identify the optimal ways of data processing for cytometry-based SCP studies. A new package is also deployed to ensure multiscenario usability (such as desktop software, R package and online server), data security (enabling local and open-source execution) and a user-friendly interface (realizing interactive and visualizable applications). Overall, ANPELA can be utilized by a broad audience, including those without coding skills, and is freely accessible and downloadable at https://idrblab.org/anpela/. Its execution time may range from minutes to hours depending on the size of the analyzed data.

Key points

  • ANPELA is a tool for evaluating the utility of proteomic data processing workflows and is intended to facilitate the automatic selection of the most appropriate processing methods for single cell proteomic data.

  • ANPELA enables a systematic assessment of existing data processing methods for cell subpopulation identification and pseudo-time trajectory inference.

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Fig. 1: The schematic overview of ANPELA.
Fig. 2: Overview of the Protocol procedures.
Fig. 3: Assessment-driven navigation based on multiple criteria.
Fig. 4: Navigating data processing for CSI using ANPELA.
Fig. 5: Navigating data processing for PTI Using ANPELA.

Data availability

All datasets that were analyzed within this protocol had been made downloadable on the website https://idrblab.org/anpela/ANPELA_exampledata.zip. These datasets were also accessible in the SingPro database (https://idrblab.org/singpro/) through IDs SCP57021, SCP11272, SCP43132, SCP77365, SCP80719, SCP47065, SCP37430, SCP36391, SCP96723 and SCP93731.

Code availability

All source codes of this protocol are available for use under a GPL v3 license and can be acquired via GitHub at https://github.com/idrblab/ANPELA. ANPELA web platform is freely available for academic purposes at https://idrblab.org/anpela. ANPELA desktop software is available for academic use at https://idrblab.org/anpela/ANPELA-Setup.exe.

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Acknowledgements

We acknowledge the National Natural Science Foundation of China (grant nos. 22220102001, 82373790, and 82404511); Natural Science Foundation of Zhejiang (grant no. RG25H300001); National Key R&D Programs of China (grant no. 2024YFA1307503); Information Technology Center and State Key Lab of CAD&CG, Zhejiang University.

Author information

Author notes

  1. These authors contributed equally: Huaicheng Sun, Yuan Zhou, Ruoyu Jiang.

Authors and Affiliations

  1. College of Pharmaceutical Sciences, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou, China

    Huaicheng Sun, Yuan Zhou, Ruoyu Jiang, Ziqi Pan, Minjie Mou, Xichen Lian, Ying Zhang, Yintao Zhang, Xin Shen, Jiannan Deng, Tingting Fu & Feng Zhu

  2. Department of Pharmacy, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Huaicheng Sun & Feng Zhu

  3. Chu Kochen Honors College, Zhejiang University, Hangzhou, China

    Ruoyu Jiang & Siqi Liu

  4. Department of Otolaryngology Head and Neck Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Yuxuan Liu, Xiuna Sun & Mang Xiao

  5. Zhejiang University—University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, China

    Chengbin Gu

  6. College of Life Sciences, Zhejiang University, Hangzhou, China

    Bohan Chen

  7. School of Pharmacy, Hebei Medical University, Shijiazhuang, China

    Tianle Niu, Hao Yang & Yang Zhang

  8. Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China

    Baoliang Zhang & Wanqing Xie

  9. State Key Laboratory for Organic Electronics and Information Displays, Institute of Advanced Materials, Nanjing University of Posts and Telecommunications, Nanjing, China

    Yangbo Dai

  10. Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Qingxia Yang

Authors
  1. Huaicheng Sun
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  2. Yuan Zhou
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Contributions

F.Z. conceived the idea and designed the entire research. H.C.S., Y. Zhou., R.Y.J. and Y.X.L. wrote codes. H.C.S., Y. Zhou., C.B.G. and Z.Q.P. conducted benchmark studies. H.C.S., Y. Zhou., M.J.M., X.C.L., B.H.C., T.L.N., Y. Zhang., Y.T.Z., X.N.S., H.Y., X.S., W.Q.X and B.L.Z. finished statistical analysis. Y.B.D., J.N.D., S.Q.L., T.T.F., Y. Zhang., M.X. and Q.X.Y. visualized the results. F.Z. and T.T.F. wrote the manuscript.

Corresponding authors

Correspondence to Tingting Fu or Feng Zhu.

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The authors declare no competing interests.

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Nature Protocols thanks Florian Ingelfinger and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references

Zhang, Y. et al. Adv. Sci. 10, e2207061 (2023): https://doi.org/10.1002/advs.202207061

Jurburg, S. D. et al. Microbiome 10, 225 (2022): https://doi.org/10.1186/s40168-022-01423-8

Tang, J. et al. Brief. Bioinform. 21, 621–636 (2020): https://doi.org/10.1093/bib/bby127

Tang, J. et al. Mol. Cell. Proteomics 18, 1683–1699 (2019): https://doi.org/10.1074/mcp.RA118.001169

Cui, X. et al. Front. Pharmacol. 10, 127 (2019): https://doi.org/10.3389/fphar.2019.00127

Extended data

Extended Data Fig. 1 Graphical User Interface of ANPELA and Preparation of Required Data.

(a) The navigation bar of ANPELA software included ‘HOME’, ‘Single-cell Proteomics’, ‘Textual Tutorial’, and ‘Interactive Tutorial’. Clicking on ‘Single-cell Proteomics’ initiated data upload and processing. Clicking on ‘Textual Tutorial’ permitted downloading the textual tutorial. Clicking on ‘Interactive Tutorial’ opened a step-by-step interactive tutorial. (b) The essential data required by ANPELA included FCS files (i.e., raw data files) generated from cytometry-based SCP experiments and a metadata file describing the correlation between the raw data and experimental conditions. The metadata file was a user-created file named ‘metadata.csv’, containing key information in two columns.

Extended Data Fig. 2 GUI of Processing & Assessment and Outcomes of the Assessment.

(a) Simplified GUI for data processing and performance assessment in the desktop software of ANPELA. (b) Results of performance assessment consist of ‘Ranking_Table.csv’ and ‘Ranking_Figure.pdf’, which respectively recorded criteria values and performance levels for all executed data processing workflows.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Tables 1–3 and Methods 1–3.

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Sun, H., Zhou, Y., Jiang, R. et al. Navigating the data processing for cytometry-based single-cell proteomics. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01257-2

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