ArticleNature communications2024
Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- ProFormer: generalizable classification of single-cell and plasma proteomes using deep learning.Nature communications · 2026Article
- Multi-mode droplet splitting on active-matrix digital microfluidics: quantitative boundaries and optimal sequential generation.Microsystems & nanoengineering · 2026Article
- Single-oocyte proteome-transcriptome co-profiling reveals a role of dysregulated lactate metabolism in oocyte aging.Nature communications · 2026Article
- Proteomic investigation of signaling dynamics: from static maps to network rewiring.Bioscience reports · 2026Review
- SoftHybrid: A Hybrid Imputation Algorithm Optimized for Single-Cell Proteomics Data.Journal of proteome research · 2026Article
- Single-Cell Proteomics Reveals Proteome Remodeling and Cellular Heterogeneity During NGF-Induced PC12 Neuronal Differentiation.ACS chemical neuroscience · 2026Article
- Integrating Low-Voltage Microfluidics With On-Chip Electrospray Ionization for Miniaturized Protein Analysis.Small methods · 2026Review
- Molecular profiling of the single-cell proteome via gel electrophoresis and 3D single-molecule imaging.Nature communications · 2026Article
- Dissecting autonomous enzymatic variability in single cells.Nature communications · 2026Article
- Integrating Single-Cell Sequencing and Proteomics to Unravel Stem Cell Heterogeneity: Implications for Precision Regenerative Medicine.Stem cell reviews and reports · 2026Review
- Single-Cell Proteomics Decodes the Cellular Response to Lysosomal Storage inInternational journal of molecular sciences · 2026Article
- Capturing Cardiomyocyte Cell-to-Cell Heterogeneity via Shotgun Single Cell Top-Down Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Convergent bioprinting and microfluidics: toward next-generation biomimetic tumor models.Hepatobiliary surgery and nutrition · 2026Article
- From articles to code: on-demand generation of core algorithms from scientific publications.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.Nature communications · 2025Article
- Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.Lab on a chip · 2025Review
- Integrative Omics and AI-Driven Systems Biology: Multilayer Networks DecodingJournal of proteome research · 2025Review
- Real-Time Eco-AI, Electrophoresis-Correlative Data-Dependent Acquisition with AI-Based Data Processing Broadens Access to Single-Cell Mass Spectrometry Proteomics.Angewandte Chemie (International ed. in English) · 2025Article
- Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment.Molecular cancer · 2025Review
- Mass Spectrometry-based Solutions for Single-cell Proteomics.Genomics, proteomics & bioinformatics · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449-3500, 2278-3257 and 1621-2904 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.
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Registered trials
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