Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.

Single cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all laboratories have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown effect on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single cell level, utilizing Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37 °C (control), (2) 4 °C overnight, and (3) −196 °C storage followed by liquid nitrogen preservation. Both cold and frozen storage induced significant alterations in the cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

The analysis of single cell proteomes has recently become a viable complement to transcript and genomics studies. Proteins are the main driver of cellular functionality and mRNA levels are often an unreliable proxy of such. Therefore, the global analysis of the proteome is essential to study cellular identities. Both multiplexed and label-free mass spectrometry-based approaches with single cell resolution have lately attributed surprising heterogeneity to believed homogenous cell populations. Even though specialized experimental designs and instrumentation have demonstrated remarkable advances, the efficient sample preparation of single cells still lacks behind. Here, we introduce the proteoCHIP, a universal option for single cell proteomics sample preparation at surprising sensitivity and throughput. The automated processing using a commercial system combining single cell isolation and picoliter dispensing, the cellenONE®, allows to reduce final sample volumes to low nanoliters submerged in a hexadecane layer simultaneously eliminating error prone manual sample handling and overcoming evaporation. With this specialized workflow we achieved around 1,000 protein groups per analytical run at remarkable reporter ion signal to noise while reducing or eliminating the carrier proteome. We identified close to 2,000 protein groups across 158 multiplexed single cells from two highly similar human cell types and clustered them based on their proteome. In-depth investigation of regulated proteins readily identified one of the main drivers for tumorigenicity in this cell type. Our workflow is compatible with all labeling reagents, can be easily adapted to custom workflows and is a viable option for label-free sample preparation. The specialized proteoCHIP design allows for the direct injection of label-free single cells via a standard autosampler resulting in the recovery of 30% more protein groups compared to samples transferred to PEG coated vials. We therefore are confident that our versatile, sensitive, and automated sample preparation workflow will be easily adoptable by non-specialized groups and will drive biological applications of single cell proteomics.

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in

Multiplexed quantitative mass spectrometry-based proteomics is shaped by numerous opposing propositions. With the emergence of multiplexed single-cell proteomics, studies increasingly present single cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e. <20x) and evaluate the elimination of such for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap, while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides through conceptualizing the optimal single-cell proteomics experiment.

The single-cell proteomics market is experiencing robust growth, projected to reach a substantial size driven by advancements in mass spectrometry, microfluidics, and bioinformatics. The market's Compound Annual Growth Rate (CAGR) of 9.8% from 2019 to 2033 signifies a significant expansion, indicating strong adoption across research and clinical applications. Key drivers include the increasing need for high-throughput, high-sensitivity proteomic analysis to understand cellular heterogeneity and disease mechanisms. This technology is crucial for unraveling complex biological processes like cancer progression, immune responses, and neurological disorders, demanding advanced tools to analyze individual cells. Furthermore, the development of novel technologies like single-cell multiplexed ion beam imaging (MIBI) further fuels market growth. The market is segmented by technology (e.g., mass spectrometry, antibody-based methods), application (e.g., drug discovery, biomarker discovery, diagnostics), and end-user (e.g., pharmaceutical companies, academic research institutions). While the market faces challenges like high instrument costs and the complexity of data analysis, the overall outlook remains positive given the transformative potential of single-cell proteomics in biomedical research and clinical translation. The significant presence of major players like Thermo Fisher Scientific, Bruker, and others underscores the market's maturity and competitiveness. These established companies are continually developing and refining their single-cell proteomics platforms, fostering innovation and driving down costs. The increasing involvement of smaller, specialized companies demonstrates a vibrant ecosystem with ample opportunities for technological advancements. The geographical distribution of the market is expected to be heavily concentrated in North America and Europe initially, due to established research infrastructures and regulatory frameworks, but is projected to expand rapidly into the Asia-Pacific region, driven by increasing research funding and growing awareness of single-cell proteomics' capabilities. The market's future growth hinges on continued technological innovation, streamlined data analysis pipelines, and increased regulatory clarity to accelerate clinical translation.

Data and scripts accompanying the paper Standardised workflow for mass spectrometry-based single-cell proteomics data analysis using scp.

• d.zip contains raw MS data from samples run on timsTOF SCP.
• raw.zip contains raw MS data from samples run on orbitrap mass spectrometers (Orbitrap Fusion Lumos Tribrid and Exploris 240).
• mzML.zip contains raw MS data in mzML format from all samples.
• sage.zip contains output results from the sage software (results.sage.tsv and quant.tsv) as well as configuration files (results.json) for both orbitrap (cbio) and timsTOF (giga) data.
• sample_annotation.zip contains csv files with samples annotation for each acquisition batch and used to build the colData.
• example_subset.zip contains csv files for short example datasets displayed in the paper.
• scp.rds file contains the initial QFeatures object of the full dataset with 56 PSM sets corresponding to the 56 MS runs.
• build_QF_dataset.Rmd file is the script used to build the scp.rds file described above from sage outputs and sample annotation.

These file descriptions are also available in the README.txt file.

We analyzed the proteomic data and DIDAR filtered/QC'ed proteomic data from a recent study of 270 single human cells divided between control and sotorasib treatments. The data included here is the processed results using Proteome Discoverer 2.4 using the same search parameters. This data is in support of Figure 2 of Jenkins and Orsburn 2022.

The single-cell proteomics market is experiencing robust growth, driven by advancements in mass spectrometry, microfluidics, and bioinformatics. This burgeoning field allows researchers to analyze the protein expression of individual cells, providing unprecedented insights into cellular heterogeneity and function. This granular level of analysis is revolutionizing various fields, including drug discovery, disease diagnostics, and personalized medicine. The market size in 2025 is estimated at $686.1 million. Considering the dynamic nature of this sector and assuming a conservative Compound Annual Growth Rate (CAGR) of 15% based on industry trends for similar innovative technologies, the market is projected to expand significantly over the forecast period (2025-2033). This growth is fueled by increasing investments in research and development, the rising prevalence of chronic diseases demanding more precise diagnostic tools, and the expanding application of single-cell proteomics in various research areas. The increased adoption of sophisticated techniques coupled with the decreasing cost of instrumentation further contribute to market expansion. Key players such as Thermo Fisher Scientific, Bruker, and others are actively driving innovation and market penetration through strategic partnerships, acquisitions, and the development of advanced technologies. However, challenges remain, including the high cost of instrumentation and specialized expertise required for data analysis. Despite these hurdles, the transformative potential of single-cell proteomics in addressing complex biological questions positions the market for sustained, high-growth trajectory, promising significant advancements in healthcare and biological research throughout the forecast period.

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here present DIA-ME, a data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We evaluate the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfers are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we observe co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics.

Chemical proteomics studies the effects of drugs upon a cellular proteome. Due to the complexity and diversity of tumors, the response of cancer cells to drugs is also heterogeneous, and thus, proteome analysis at the single-cell level is needed. Here, we demonstrate that single-cell proteomics techniques have become quantitative enough to tackle the drug effects on target proteins, enabling single-cell chemical proteomics (SCCP). Using SCCP, we studied here the time-resolved response of individual adenocarcinoma A549 cells to anticancer drugs methotrexate, camptothecin, and tomudex, revealing the early emergence of cellular subpopulations committed and uncommitted to death. As a novel and useful approach to exploring the heterogeneous response to drugs of cancer cells, SCCP may prove to be a breakthrough application for single-cell proteomics.

Fig 2

Bone marrow (Fig 2B, D, E, F, H, Supplementary Fig 1A, 2,3)

1. Fig 2/BM/Reference/ Fig2_BM_prepare_data.R: Prepare bone marrow for CellFuse

2. Fig 2/BM/ BM_CellFuse_Integration.R: Run CellFuse

3. Fig 2/BM/BM_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN)

4. Fig 2/BM/BM_scIB_Benchmarking.ipynb: evaluate performance of CellFuse and other benchmarking methods using scIB framework proposed by Luecken et al.

5. Fig 2/BM/ BM_scIB_prepare_figures.R: Visualize results of scIB framework

6. Fig 2/BM/Sequential_Feature_drop/Prepare_data.R: Prepare data for evaluating sequential feature drop

7. Fig 2/BM/Sequential_Feature_drop/Run_methods.R: Run CellFuse, Harmony, Seurat and FastMNN for sequential feature drop

8. Fig 2/BM/Sequential_Feature_drop/Evaluate_results.R: Evaluate results features drop and visualize data.

PBMC (Fig 2G,I, Supplementary Fig 1B and 4)

1. Fig 2/PBMC/Reference/ Fig2_PBMC_prepare_data.R: Prepare PBMC data for CellFuse

2. Fig 2/ PBMC / PBMC_CellFuse_Integration.R: Run CellFuse

3. Fig 2/ PBMC /PBMC_Running_Benchmark_Methods.R: Run benchmarking methods (Harmony, Seurat, FastMNN)

4. Fig 2/ PBMC /PBMC_scIB_Benchmarking.ipynb: evaluate performace of CellFuse and other benchmarking methods using scIB framework proposed by Luecken et al., 2021

5. Fig 2/ PBMC /PBMC_scIB_prepare_figures.R: Visualize results of scIB framework

6. Fig 2/ PBMC/ RunTime_benchmark/Run_Benchmark.R: Prepare data, run benchmarking method and evaluate results.

Fig 3 and Supplementary Fig 5

1. Fig 3/Reference/ Fig3_CyTOF_prepare_data.R: Prepare CyTOF and CITE-Seq data for CellFuse

2. Fig 3/CellFuse_Integration_CyTOF.R: Run CellFuse to remove batch effect and integrate CyTOF data from day 7 post-infusion

3. Fig 3/CellFuse_Integration_CITESeq.R: Run CellFuse to integrate CyTOF and CITE-Seq data

4. Fig 3/CART_Data_visualisation.R: Visualize data

Fig 4

HuBMAP CODEX data (Fig. 4A, B, C, D and Supplementary Fig 6)

1. Fig 4/CODEX_colorectal/Reference/ CODEX_HuBMAP_prepare_data.R: Prepare CODEX data from annotated and unannotated donor

2. Fig 4/ CODEX_colorectal/ CODEX_HuBMAP_CellFuse_Predict.R: Run CellFuse on cells from from annotated and unannotated donor

3. Fig 4/ CODEX_colorectal/CODEX_HuBMAP_Data_visualisation.R: Visualize data and prepare figures.

4. Fig 4/ CODEX_colorectal/ CODEX_HuBMAP_Benchmark.R: Benchmarking CellFuse against CELESTA, SVM and Seurat using cells from annotated donors and prepare figures.

a. Astir is python package so run following python notebook: Fig 4/ CODEX_colorectal/ Benchmarking/Astir/Astrir.ipynb

5. Fig 4/ CODEX_colorectal/CODEX_HuBMAP_Suppl_figure_heatmap.R: F1score calculation per celltype per Benchmarking methods and heatmap comparing celltypes from annotated and unannotated donors (Supplementary Fig 6)

IMC Breast cancer data (Fig. 4E,F, G and Supplementary Fig 7)

1. Fig 4/ IMC_Breast_Cancer/ IMC_prepare_data.R: Prepare CODEX data from annotated and unannotated donor

2. Fig 4/ IMC_Breast_Cancer/ IMC_CellFuse_Predict.R: Run CellFuse to predict cell types

3. Fig 4/ IMC_Breast_Cancer/ IMC_dat_visualization.R: Visualize data and prepare figures.

Fig 5

1. Fig5/ Reference/ Fig5_CyTOF_Data_prep.R: Prepare CyTOF data from healthy PBMC and healthy colon single cells

2. Fig5/ MIBI_CellFuse_Predict.R: Run CellFuse to predicte cells from colon cancer patients

3. Fig5/ MIBI_PostPrediction.R: Visualize data and prepare figures

4. Fig5/ Predicted_Data/ mask_generation.ipynb: Post CellFuse prediction annotated cell types in segmented images. This will generate Fig5C and D

Accurate measurements of the molecular composition of single cells will be necessary for understanding the relationship between gene expression and function in diverse cell types. One of the most important phenotypes that differs between cells is their size, which was recently shown to be an important determinant of proteome composition in populations of similarly sized cells. We, therefore, sought to test if the effects of the cell size on protein concentrations were also evident in single-cell proteomics data. Using the relative concentrations of a set of reference proteins to estimate a cell’s DNA-to-cell volume ratio, we found that differences in the cell size explain a significant amount of cell-to-cell variance in two published single-cell proteome data sets.

Mass cytometry data generated in the context of the characterization of prostate cancer samples

Techniques that allow single cell analysis are gaining widespread attention, and most of these studies utilize genomics-based approaches. While nanofluidic technologies have enabled mass spectrometric analysis of single cells, these measurements have been limited to metabolomics and lipidomic studies. Single cell proteomics has the potential to improve our understanding of intercellular heterogeneity. However, this approach has faced challenges including limited sample availability, as well as a requirement of highly sensitive methods for sample collection, cleanup, and detection. We present a technique to overcome these limitations by combining a micropipette (pulled glass capillary) based sample collection strategy with offline sample preparation and nanoLC-MS/MS to analyze proteins through a bottom-up proteomic strategy. This study explores two types of proteomics data acquisition strategies namely data-dependent (DDA) and data-independent acquisition (DIA). Results from the study indicate DIA to be more sensitive enabling analysis of >1600 proteins from ∼130 μm Xenopus laevis embryonic cells containing

The Single Cell Proteomics System market is experiencing robust growth, projected to reach a valuation of $113 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.7% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing prevalence of complex diseases like cancer necessitates more sophisticated diagnostic and therapeutic tools, driving demand for single-cell proteomics to unravel disease mechanisms at a granular level. Advancements in technology, particularly in high-throughput screening and automation (semi-automatic and fully automatic systems), are significantly lowering the cost and increasing the efficiency of single-cell proteomic analysis. Furthermore, the growing adoption of cancer immunotherapy and the ongoing research in oncology are creating substantial opportunities for the market. Major players like BD, Isoplexis, BICO, Evosep, Bruker, and several prominent Chinese companies are actively contributing to this growth through continuous innovation and strategic partnerships. The market segmentation by application (oncology being dominant) and by system type (with a shift towards fully automated systems) highlights the specialization and sophistication within the sector. Geographic distribution shows a strong presence in North America and Europe, although the Asia-Pacific region, particularly China and India, is expected to demonstrate significant growth in the coming years due to rising healthcare spending and increasing research activities. The market's restraints primarily involve the high initial investment costs associated with acquiring and maintaining sophisticated single-cell proteomics systems. However, this hurdle is gradually being overcome by the decreasing cost of technology and the development of more accessible, user-friendly platforms. Furthermore, the complexity of data analysis remains a challenge, requiring specialized expertise and powerful bioinformatics tools. Nevertheless, ongoing research and development efforts are focused on creating more streamlined analysis workflows and developing intuitive software solutions to address these limitations. The future of the single-cell proteomics market is bright, with the potential to revolutionize disease diagnosis, drug discovery, and personalized medicine. The continued investment in research and development, coupled with expanding applications and technological advancements, promises sustained market expansion in the coming decade.

This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput Data Independent Acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This optimized DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1,300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by integrating SCP and single-cell RNA-seq (scRNA-seq) data, we identified six upregulated and ten downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.

This resource contains data generated using the method DBS-Pro (Droplet Barcode Sequencing for Protein analysis) for single cells for the manuscript "Identification of Major Immune Cell Lineages with DBS-Pro". The data was generated using peripheral blood mononuclear cells (PBMCs) extracted from a single healthy donor from which two libraries were prepared (P29859_1001, P29859_1002). The data quantifies surface proteins on single cells using a panel of six marker antibodies (CD3, CD4, CD8, CD19, CD14, CD45) and one isotype control antibody to quantify background signal. The antibodies are conjugated to oligonucleotides containing an antibody barcode and a UMI sequence. The stained cells are barcoded in droplet emulsions and then sequenced to get out quantitative information about the cells.The data includes raw FASTQ files (*.fastq.gz). The FASTQs were also run through the DBS-Pro pipeline (https://github.com/FrickTobias/DBS-Pro, v0.4) to generate *data.tsv.gz and *counts.h5ad files. The TSV files *data.tsv.gz contain the following columns: Barcode: The corrected droplet barcode Target: The antibody target (marker) name. E.g. CD3UMI: The UMI sequenceReadCount: Number of reads with this droplet barcode, target and UMI combinationSample: Sample nameFor convenience, anndata h5ad files with count matrices are also generated for each sample. These can be used for downstream analysis using Scanpy. To import the data use the following code:import scanpy as scadata = sc.read_h5ad("mysample.h5ad")adata

Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ~4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Lastly, correlating the abundance of individual proteins from cells displaying a wide range of diameters show that histones and some proteins involved in the cell cycle do not scale with cell size confirming previous observations in yeast. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional post-translational measurements beyond protein abundance. This method not only distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner but also showcase the discovery potential of SCP in fundamental biology.

In order to identify the possible protein targets of cyanide in the regulation of root hair, we have carried out a single cell proteomic approach to characterize the protein atlas in wild type root hair cells compared to the cas-c1 mutant cells. Root hair specific cells were obtained from isolated protoplast from root tissues of Arabidopsis wt-pCOBL9:GFP and cas-c1-pCOBL9:GFP lines. To analyze the effect of the CAS-C1 mutation, we isolated 1 x 106 root hair cells by Fluorescence-activated cell sorting (FACS) from three independent replicate of wt-pCOBL9:GFP and cas-c1-pCOBL9:GFP roots. The extracted proteins from each sample were trypsin-digested and the digested peptides were analyzed by liquid chromatography-high resolution mass spectrometry (LC-MS/MS) for protein identification. In wild type samples, we have identified 3829 unique proteins at a false discovery rate below 1% (FDR<1%), that represent almost 10% of the total Arabidopsis proteome. In protein extract of the cas-c1-pCOBL9:GFP roots samples, we have identified 3972 proteins of which 3515 were commons in both cell types, 310 were only identified y wild type and 457 only in cas-c1 mutant (Fig. 5).