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Related Experiment Video

Updated: Jan 14, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

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

Huaicheng Sun^1,2, Yuan Zhou¹, Ruoyu Jiang^1,3

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

Nature Protocols

|October 16, 2025

Summary

This summary is machine-generated.

ANPELA is a new tool that screens thousands of proteomic data processing workflows to find the best ones for single-cell proteomics (SCP) studies. It helps researchers identify cell subpopulations and trajectories, making complex biological data analysis easier.

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Last Updated: Jan 14, 2026

Sample Preparation for Mass Cytometry Analysis

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Published on: April 29, 2017

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Isolation and Staining of Mouse Skin Keratinocytes for Cell Cycle Specific Analysis of Cellular Protein Expression by Mass Cytometry

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Published on: May 9, 2019

Area of Science:

Proteomics
Computational Biology
Data Science

Background:

Single-cell proteomics (SCP) offers high-resolution biological insights.
Processing SCP data involves numerous workflows, making optimal selection challenging.
Identifying effective data processing strategies is crucial for robust SCP analysis.

Purpose of the Study:

To develop ANPELA, a novel method for large-scale screening of SCP data processing workflows.
To enable comparison of thousands of workflows for identifying cell subpopulations and pseudo-time trajectories.
To provide a user-friendly, accessible tool for optimizing SCP data analysis.

Main Methods:

ANPELA employs machine learning for large-scale screening of proteomic data processing workflows.
It compares workflow performance in cell subpopulation identification and pseudo-time trajectory inference.
A package is deployed for multi-scenario usability (desktop, R, online) and data security.

Main Results:

ANPELA successfully identifies optimal data processing workflows for cytometry-based SCP studies.
The tool facilitates the comparison of thousands of processing pipelines.
Case studies demonstrate ANPELA's effectiveness in navigating complex proteomic data.

Conclusions:

ANPELA provides an out-of-the-box solution for navigating proteomic data processing.
It is accessible to a broad audience, including non-coders, enhancing SCP research.
The tool is freely available, promoting wider adoption and data-driven biological discovery.