Single-Cell Proteomics Analysis: Methods and Key Technologies
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Quantitative analysis: Determine relative or absolute protein expression levels at single-cell resolution;
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High-throughput detection: Enable parallel analysis of multiple single cells to construct comprehensive maps of cellular heterogeneity;
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Biological function reconstruction: Integrate protein expression profiles with cellular functions, states, and pathway networks.
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The carrier channel enhances signal intensity and typically consists of hundreds of cells of the same type;
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Single-cell samples are individually labeled and subsequently mixed with the carrier before co-injection;
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High-resolution mass spectrometry identifies peptides and decodes the tags, facilitating the simultaneous quantitative analysis of multiple samples.
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Nano-liquid chromatography (nanoLC): to minimize sample dilution;
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Ion mobility spectrometry (IMS): to improve peptide separation;
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Advanced mass spectrometry platforms: such as ZenoTOF or timsTOF.
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CyTOF (metal-labeled mass cytometry): employing rare metal-tagged antibodies to simultaneously detect surface and intracellular proteins across multiple channels;
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CITE-seq: linking antibody barcodes with single-cell RNA sequencing to simultaneously capture proteomic and transcriptomic information.
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FACS (Fluorescence-Activated Cell Sorting): Enables high-throughput sorting, suitable for suspension cells.
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Microfluidic platforms: For example, Fluidigm C1, which allows automated processing.
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Micromanipulation or automated pipetting platforms: Appropriate for adherent cells and the isolation of rare cell populations.
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Low-adsorption consumables: Such as silanized reaction tubes and microreaction plates, to minimize protein loss.
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Magnetic bead-assisted enzymatic digestion: Enhances the efficiency of proteolytic reactions.
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Combined use of heat treatment and denaturants: Accelerates the lysis process.
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Thermo Orbitrap Eclipse/Fusion Lumos: Offers high resolution and robust stability.
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Sciex ZenoTOF 7600: Provides strong signal enhancement capabilities.
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Bruker timsTOF SCP: Supports the combined PASEF+DIA mode, enabling ultra-high sensitivity and extensive proteome coverage.
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Parsing raw mass spectrometry data and identifying peptides
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Assigning proteins and constructing quantitative matrices
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Performing batch normalization and addressing missing values
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Conducting differential expression analysis, functional enrichment, and clustering-based classification
In modern life science research, understanding cellular heterogeneity is crucial for elucidating disease mechanisms, developing precision therapies, and reconstructing tissue developmental trajectories. While single-cell RNA sequencing has provided abundant insights at the transcriptional level, proteins, as the direct executors of cellular functions, more accurately capture the physiological states of cells. Consequently, Single-Cell Proteomics (SCP) has emerged as a key technology for investigating cell fate, states, and regulatory mechanisms. The primary objective of Single-Cell Proteomics is to accurately detect and quantify protein expression, post-translational modifications, and regulatory networks at single-cell resolution.
Analytical Framework of Single-Cell Proteomics
1. Technical Objectives
Single-Cell Proteomics is designed to achieve the following objectives:
2. Basic Experimental Workflow
SCP generally consists of the following steps:
Single-cell isolation, sample lysis and enzymatic digestion, protein or peptide enrichment, labeling and loading (e.g., using TMT), mass spectrometry analysis, data interpretation, and functional annotation. Each step must be optimized to accommodate ultra-low input sample processing, high-sensitivity detection, and high-accuracy quantification.
Mainstream Analysis Methods and Strategies
1. Tandem Mass Tag (TMT) Labeling Strategy
The Tandem Mass Tag (TMT) labeling strategy employs chemical reagents that enable the differentiation of peptides from distinct samples through isotope-based tags. This approach is extensively utilized in single-cell proteomics, particularly when integrated with the “carrier channel” technique:
The TMT strategy offers high throughput, robust quantitative reproducibility, and strong compatibility, establishing it as the predominant methodology for mass spectrometry-based single-cell proteomics (SCP).
2. Label-Free Quantification
Label-free quantification avoids the use of chemical tags and instead quantifies peptides by measuring ion peak intensities. This method features simplified sample preparation and is particularly suitable for exploratory studies or low-throughput applications. To enhance sensitivity, it is commonly paired with:
Although label-free quantification generally exhibits lower reproducibility and quantitative precision, ongoing algorithmic advancements and improvements in instrumentation are progressively expanding its applicability.
3. Antibody-Based Strategies
Beyond mass spectrometry, antibody-based strategies provide an alternative for protein detection, with notable technologies including:
These approaches are particularly advantageous for high-throughput detection of predefined targets and are widely applied in immunology and tumor microenvironment studies.
Detailed Analysis of Key Technical Components
1. Single-Cell Sorting and Sample Loading Technologies
The quality of single-cell samples directly determines the reliability of subsequent analyses. The primary approaches for single-cell sorting include:
Following sorting, cells must be rapidly transferred into a low-volume reaction system (typically in the nanoliter to picoliter range) to minimize protein degradation or adsorption losses.
2. Protein Lysis and Enzymatic Digestion
Because the total protein content of a single cell is extremely low, several optimized strategies are widely adopted, including:
Certain platforms, such as nPOP, integrate protein lysis, enzymatic digestion, and labeling within microdroplets, significantly improving sample recovery and standardization.
3. High-Sensitivity Mass Spectrometry Platforms
Mass spectrometry is the core tool in Single-Cell Proteomics (SCP), and its performance dictates both the depth and accuracy of the resulting data. Commonly employed platforms include:
When combined with nano-flow liquid chromatography and automated sample loading systems, current SCP workflows can quantify thousands of proteins per cell, encompassing most of the cell’s major functional pathways.
4. Data Analysis and Annotation
Typical SCP data processing workflows comprise the following steps:
To address the inherent data sparsity and high variability in single-cell datasets, dimensionality reduction and subpopulation identification are often achieved by applying machine learning algorithms such as PCA, t-SNE, UMAP, or clustering methods.
Single-cell proteomics transitions research from a paradigm of "population averaging" to a new era of "individual precision." Accurate reconstruction of cellular states at the protein level is emerging as a powerful approach for elucidating biological regulatory mechanisms, decoding disease heterogeneity, and advancing precision therapeutic strategies. As technological workflows become increasingly standardized and analytical platforms continue to evolve, single-cell proteomics is poised to assume a more central role in both basic research and clinical studies. MtoZ Biolabs remains committed to advancing the frontiers of this field by delivering high-quality single-cell proteomics analysis services, enabling researchers to accurately acquire, interpret, and apply proteomics data, and supporting efforts to uncover the fundamental complexity of cells.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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