Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis

Simultaneous characterization of proteomic and metabolomic profiles at the single-cell level is crucial for deciphering cellular heterogeneity and elucidating disease mechanisms. However, it is still a great challenge to achieve high-depth dual-omics analysis in the same single cell. Here, we propose a unified strategy called one-shot hybrid-mode single-cell proteome and metabolome analysis (hybrid-scPMA), in which the mass spectrometry (MS) detection mode of data-independent acquisition (DIA) is utilized for the analysis of peptides from protein digestion, and the data-dependent acquisition (DDA) mode is used for metabolite analysis in a single liquid chromatography-mass spectrometry (LC-MS) analysis run, enabling deep analysis of the proteome and metabolome in single-cell samples. Building upon the strategy, we established an improved single-cell multiomics analysis workflow that integrated automated single-cell capture, simplified sample pretreatment, LC injection and separation, and DIA-DDA hybrid-mode MS detection. With this approach, we achieved an average identification of 3510 protein groups and 255 metabolites from single HepG2 cells, representing a substantial increase in identification depth over previous approaches. We also performed time-resolved proteomic and metabolomic profiling of HepG2 single cells undergoing sorafenib drug intervention, resolving drug response characteristics at the single-cell level and providing multiomics insights into drug mechanisms from a proteo-metabolomic perspective.

Authors

Institutions

Publication Details

Journal
Analytical Chemistry
Published
2026-04-27
DOI
https://doi.org/10.1021/acs.analchem.5c08090
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis

Xiao Liang, Jianmin Wu, Qun Fang, Fang-Hua Lu et al.
Analytical Chemistry
Single-cell and spatial transcriptomics
article

Simultaneous In-Depth Single-Cell Proteomic and Metabolomic Analysis

Xiao Liang, Jianmin Wu, Qun Fang, Fang-Hua Lu, Jian‐Zhang Pan, Yi Yang, Yu Wang, L Y Tao, Yi Huang, Ya-Li Wang
article en

Abstract

Simultaneous characterization of proteomic and metabolomic profiles at the single-cell level is crucial for deciphering cellular heterogeneity and elucidating disease mechanisms. However, it is still a great challenge to achieve high-depth dual-omics analysis in the same single cell. Here, we propose a unified strategy called one-shot hybrid-mode single-cell proteome and metabolome analysis (hybrid-scPMA), in which the mass spectrometry (MS) detection mode of data-independent acquisition (DIA) is utilized for the analysis of peptides from protein digestion, and the data-dependent acquisition (DDA) mode is used for metabolite analysis in a single liquid chromatography-mass spectrometry (LC-MS) analysis run, enabling deep analysis of the proteome and metabolome in single-cell samples. Building upon the strategy, we established an improved single-cell multiomics analysis workflow that integrated automated single-cell capture, simplified sample pretreatment, LC injection and separation, and DIA-DDA hybrid-mode MS detection. With this approach, we achieved an average identification of 3510 protein groups and 255 metabolites from single HepG2 cells, representing a substantial increase in identification depth over previous approaches. We also performed time-resolved proteomic and metabolomic profiling of HepG2 single cells undergoing sorafenib drug intervention, resolving drug response characteristics at the single-cell level and providing multiomics insights into drug mechanisms from a proteo-metabolomic perspective.

Analytical Chemistry
Zhejiang University of Science and Technology (CN), Sir Run Run Shaw Hospital (CN), Bioanalytica (Switzerland) (CH), Precision for Medicine (United States) (US), Institute of Bioinformatics and Applied Biotechnology (IN), Zhejiang University (CN)
National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China
Good health and well-being
Openalex Percentile: Top 12%
Single-cell and spatial transcriptomics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.