Awesome Single-Cell Proteomics

A curated list of resources, tools, databases, and publications for single-cell proteomics (SCP) research.

Single-cell proteomics (SCP) enables the quantification of proteins at single-cell resolution, revealing cellular heterogeneity that bulk measurements obscure. Recent advances in mass spectrometry sensitivity, sample preparation miniaturization, and computational methods have made it possible to quantify thousands of proteins from individual cells.

Contents

• Sample Preparation Methods
• Data Acquisition Strategies
• Mass Spectrometry Platforms
• Software & Tools
  • Data Processing
  • Quality Control
  • Data Analysis
• Databases & Datasets
• Reviews & Tutorials
• Key Publications
• Contributing

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Sample Preparation Methods

Isobaric Labeling-Based

• SCoPE2 - Single Cell ProtEomics by Mass Spectrometry. Uses TMT/TMTpro isobaric labeling with carrier cells for enhanced peptide identification. Cost-effective and scalable to thousands of cells.
• pSCoPE - Prioritized SCoPE using targeted MS acquisition for increased proteome coverage.

Label-Free & Multiplexed DIA

• plexDIA - Multiplexed data-independent acquisition using non-isobaric mass tags (mTRAQ, dimethyl, PSMtags). Achieves ~1,000 proteins per cell with 98% data completeness.
• nDIA - Narrow-window DIA (4 Th, 6 ms) optimized for the Orbitrap Astral. Achieves >5,000 proteins per HeLa cell with PTM detection capability.
• diaPASEF - Combines parallel accumulation–serial fragmentation with DIA for enhanced sensitivity.
• SC-pSILAC - Single-cell pulsed SILAC. Simultaneously analyzes protein abundance and turnover dynamics in single cells.

Miniaturized Sample Preparation

• Chip-Tip - High-sensitivity "one-pot" workflow using the proteoCHIP EVO 96. Enables direct transfer to Evotips, identifying >5,000 proteins per HeLa cell.
• nPOP - Nano-ProteOmic sample Preparation. Enables parallel preparation of thousands of cells in nanoliter droplets on glass slides. Quantifies 3,000-3,700 proteins per human cell.
• nanoPOTS - Nanodroplet Processing in One pot for Trace Samples. Chip-based platform using nanoliter reaction volumes.
• Nested NanoPOTS (N2) - Enhanced version accommodating 243 cells per chip with 10-fold improvement in throughput.
• mPOP - Minimal ProteOmic sample Preparation for cell lysis and processing.

Automated Platforms

• CellenONE - Single-cell isolation and dispensing platform widely used for SCP workflows (e.g., Chip-Tip, nPOP).
• cellenONE + Evosep - Integrated workflow combining cell dispensing with robust LC separation.

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Data Acquisition Strategies

Strategy	Description	Throughput	Proteome Depth
DDA	Data-Dependent Acquisition	Moderate	High
DIA	Data-Independent Acquisition	High	Very High
nDIA	Narrow-window DIA (Orbitrap Astral)	High	Very High
plexDIA	Multiplexed DIA	Very High	High
pSCoPE	Targeted/Prioritized	Moderate	Focused
diaPASEF	Ion mobility-enhanced DIA	High	Very High

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Mass Spectrometry Platforms

Optimized for Single-Cell

• Bruker timsTOF SCP - Purpose-built for single-cell proteomics with trapped ion mobility.
• Bruker timsTOF Ultra 2 - Next-generation platform with enhanced sensitivity.
• Thermo Orbitrap Astral - High-resolution mass analyzer with exceptional sensitivity, key to recent deep SCP methods (Chip-Tip, SC-pSILAC).
• Thermo Exploris Series - Versatile Orbitrap platforms for SCP applications.

LC Systems

• Evosep One - Robust LC platform with standardized gradients (e.g., Whisper methods).
• IonOpticks Aurora Series - High-performance columns optimized for low-flow proteomics.

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Software & Tools

Data Processing

Tool	Platform	Description	Link
MaxQuant	Windows	Quantitative proteomics with LFQ and TMT support	Link
DIA-NN	Cross-platform	Deep learning-based DIA analysis, supports plexDIA	Link
Spectronaut	Windows	Leading vendor software for DIA analysis (directDIA+)	Link
Proteome Discoverer	Windows	Comprehensive MS data analysis	Link
FragPipe	Cross-platform	Fast peptide/protein identification	Link
AlphaPeptDeep	Python	Modular deep learning framework for peptide property prediction	GitHub

Quality Control

Tool	Description	Link
DO-MS	Interactive QC and optimization for LC-MS/MS	do-ms.slavovlab.net
QuantQC	QC reports for nPOP experiments	GitHub
RawDiag	Diagnostic plots for raw MS data	GitHub

Data Analysis

R Packages

Package	Description	Link
scp	Bioconductor package for MS-based SCP analysis	Bioconductor
scpdata	Curated SCP datasets in scp format	Bioconductor
QFeatures	Quantitative proteomics data management	Bioconductor
SCPDA	Benchmarking framework for DIA-based SCP analysis	GitHub/Link
SCPline	Interactive Shiny framework for SCP preprocessing	Oxford Academic

Python Packages

Package	Description	Link
SCeptre	Processing pipeline for isobaric carrier SCP	GitHub
scProtVelo	Modeling translation dynamics and cell velocity	Science
pyteomics	MS data parsing and analysis	GitHub
alphapept	DIA-NN integration for Python workflows	GitHub

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Databases & Datasets

Comprehensive Databases

• SPDB - Single-cell Proteomic DataBase. 143 datasets, >300 million cells, 8000+ proteins across 4 species. Unified format with visualization tools.
• SingPro - Database for MS-based and flow cytometry-based SCP data with detailed experimental metadata.
• Slavov Lab Data Repository - Curated datasets organized by publication with linked protocols.

Benchmark Datasets

• scpdata (Bioconductor) - 21 MS-based SCP datasets from 13 publications in standardized format.
• SCoPE2 Macrophage Dataset - 3,042 proteins quantified in 1,490 single monocytes/macrophages.

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Reviews & Tutorials

Review Articles

• Recent methodological advances towards single-cell proteomics (2024) - Comprehensive overview of technological developments.
• Single-Cell Proteomics using Mass Spectrometry (2025) - State-of-the-art methods and applications.
• Trends in Mass Spectrometry-Based Single-Cell Proteomics (2025) - Current trends and future directions.
• Data acquisition approaches for single cell proteomics (2025) - Comparison of DDA, DIA, and hybrid approaches.

Tutorials & Workshops

• scp Bioconductor Workshop - Hands-on tutorial for SCP data analysis.
• Slavov Lab Protocols - Detailed experimental protocols for SCoPE2 and plexDIA.
• scp Package Vignette - Step-by-step data processing guide.
• Single cell proteomics Rescoring and comparison to single cell transcriptomics-Advanced Practical Course Bioinformatics - 2026 - Advancing single-cell proteomics by foundational tools
• Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry-Comprehensive Overview of Bottom-Up Proteomics using Mass Spectrometry

Guidelines

• Performing, benchmarking, and reporting single-cell proteomics experiments - Community guidelines (Nat Methods 2023).

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Key Publications

Foundational Methods & Workflows

Year	Title	Method	Journal
2018	SCoPE-MS: mass spectrometry of single mammalian cells	SCoPE-MS	Genome Biol
2021	Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity...	SCoPE2	Genome Biol
2022	Increasing the throughput of sensitive proteomics by plexDIA	plexDIA	Nat Biotech
2025	Global analysis of protein turnover dynamics in single cells	SC-pSILAC	Cell
2025	Enhanced sensitivity and scalability with a Chip-Tip workflow...	Chip-Tip	Nat Methods

Computational Methods

Year	Title	Focus	Journal
2022	AlphaPeptDeep: a modular deep learning framework...	Peptide Prediction	Nat Comm
2024	Standardized Workflow for Mass-Spectrometry-Based SCP Data Processing	scp workflow	JPR
2025	Benchmarking informatics workflows for data-independent acquisition SCP	SCPDA / Benchmarking	Nat Comm
2025	Mapping early human blood cell differentiation using single-cell proteomics...	scProtVelo	Science

Applications

Year	Title	Application	Journal
2021	Quantitative single-cell proteomics as a tool to characterize cellular hierarchies	Differentiation	Nat Comm
2025	Single cell proteomic analysis defines discrete neutrophil functional states...	Glioblastoma/Neutrophils	Nat Comm

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Emerging Technologies

• Single-molecule protein sequencing - Nanopore-based approaches for PTM identification
• Spatial proteomics - Combining SCP with spatial information
• Protein Turnover Analysis - Measuring synthesis and degradation rates (e.g., SC-pSILAC)
• Multi-omics integration - Linking SCP with scRNA-seq (CITE-seq, etc.)
• AI/ML applications - Deep learning for peptide identification (AlphaPeptDeep) and data imputation

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Obsidian Vault Resources

This repository doubles as an Obsidian knowledge base with interconnected notes, visual canvases, and dynamic views.

Structure

awesome_SCP/
├── canvas/                      # Visual knowledge maps
│   ├── SCP Overview.canvas      # High-level field overview
│   ├── Methods Workflow.canvas  # Sample-to-data pipeline
│   └── Tools and Software.canvas
├── notes/
│   ├── methods/                 # SCoPE2, plexDIA, Chip-Tip, SC-pSILAC, nDIA
│   ├── tools/                   # MaxQuant, DIA-NN, SCPDA, AlphaPeptDeep
│   ├── databases/               # SPDB, SingPro
│   ├── applications/            # Cancer, differentiation
│   ├── literature/              # Detailed paper notes
│   └── research_group/          # Leading scientists & research groups
├── templates/                   # Templater templates
└── Project Overview.base        # Dynamic Bases view

Templater Templates

Use with the Templater plugin:

Template	Purpose
Method Template	New experimental methods
Tool Template	Software & analysis tools
Database Template	Data resources
Application Template	Research applications
Literature Note	Paper annotations with citation
Quick Note	Simple notes

Features

• Wikilinks: [[note]] for cross-referencing
• Callouts: > [!cite] for publications with DOIs
• YAML Frontmatter: Structured metadata (title, tags, category, url)
• Canvas: Visual flowcharts and concept maps
• Bases: Dynamic queries and views

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Contributing

Contributions are welcome! Please read the contribution guidelines before submitting a pull request.

How to Contribute

1. Fork the repository
2. Create a new branch (git checkout -b add-new-resource)
3. Add your resource with a brief description
4. Commit your changes (git commit -am 'Add new resource')
5. Push to the branch (git push origin add-new-resource)
6. Create a Pull Request

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License

This work is licensed under CC0 1.0 Universal.

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Acknowledgments

This list is inspired by the awesome movement and the pioneering work of the single-cell proteomics community, particularly the Slavov Laboratory.