Bridging Simplicity and Depth in Single-Cell Proteomics: A Cost-Effective Workflow and an Expanded Framework for Data Evaluation.

Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.

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.

SUMMARYSingle-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low-abundance proteins. Herein, we describe an ion-mobility-enhanced mass spectrometry acquisition and peptide identification method, transferring identification based on FAIMS filtering (TIFF), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells, with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes. A record of this paper’s transparent peer review process is included in the supplemental information.

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

Study question Can single-cell transcriptomics and proteomics contribute to a better understanding uterine of leiomyoma tumorigenesis? Summary answer We demonstrate the significant involvement of the MAPK, PI3K-Akt, and proteoglycan pathways in smooth muscle, endothelial and perivascular cells in leiomyoma tumorigenesis. What is known already Uterine leiomyomas (LM), also known as fibroids, are benign tumors of the uterus that arise from the myometrium. Previous studies have shown cellular heterogeneity in both myometrium and uterine leiomyomas, although cell spatial location within the tissue has not been shown. Further, no data describing single-cell-level proteomic differences and key pathways involved in uterine LM tumorigenesis have yet been reported. Study design, size, duration A prospective, observational, and biomedical study of cohorts was conducted at Hospital La Fe (Valencia, Spain) for one year. Single-cell RNAseq (scRNA-seq; n = 16) and single-cell proteomic (scP; n = 16) analyses were performed on eight sample pairs of LM and matched myometrium (MM), to generate a high-resolution transcriptomic and proteomic map decoupled from cell type, state, and spatial location. Participants/materials, setting, methods After obtaining informed consent, LM and MM samples were collected from eight patients between 35-50 years undergoing hysterectomies. Part of the samples were preserved in paraffin for spatial transcriptomics using VISIUM (10x Genomics). While the remaining tissues were dissociated into single-cell suspensions and subjected to Chromium Controller and Orbitrap Eclipse Tribid mass spectrometry for scRNA-seq and scP, respectively. All data were analyzed using publicly available R/Python tools. Main results and the role of chance After restrictive quality control filtering, we analyzed a total of 52,599 and 5,909 cells by scRNA-seq and scP, respectively. While LM and MM possessed similarities in terms of cellular composition, they displayed differential expression of genes and proteins across all the cell populations studied, particularly in smooth muscle, endothelial and perivascular cells. In LM samples, these cell populations displayed impaired MAPK signaling, which acts as a signal integrator for growth factors, estrogen, and vitamin D. We also observed alterations in the PI3K-Akt and proteoglycan pathways in LM smooth muscle and perivascular clusters, which relate to cell proliferation and tumor growth. Additionally, we encountered a subset of consistently dysregulated genes in all LM populations, which may suggest the existence of a shared tumorigenic pathway independent of cell type. Spatial transcriptomics further demonstrated relationships between cells and their relative locations within the tissue, which we validated by immunofluorescence. Together, our results highlight the relevance of specific cell populations in LM tumorigenesis. Limitations, reasons for caution This study involved a sample cohort limited to Caucasian women; therefore, further studies including more patients, and addressing racial disparities will help to generalize these findings to a broader population. Wider implications of the findings Our work describes an unprecedented transcriptomic and proteomic analysis of LM and MM at single-cell resolution, which supports a novel understanding of myometrial tumorigenesis. Trial registration number NCT04214457

Although single cell RNA-seq has had a tremendous impact on biological research, a corresponding technology for unbiased mass spectrometric analysis of single cells has only recently become available. Significant technological breakthroughs including miniaturized sample handling have enabled proteome profiling of single cells. Furthermore, trapped ion mobility spectrometry (TIMS) in combination with parallel accumulation-serial fragmentation operated in data-dependent acquisition mode (DDA-PASEF) allowed improved proteome coverage from low-input samples. It has been demonstrated that modulating the ion flux in TIMS affects the overall performance of proteome profiling. However, the effect of TIMS settings on the analysis of low-input samples has been less investigated. Thus, we sought to optimize the conditions of TIMS with regard to ion accumulation/ramp times and ion mobility range for low-input samples. We observed that an ion accumulation time of 180 ms and monitoring a narrower ion mobility range from 0.7 to 1.3 V s cm-2 resulted in a substantial gain in the depth of proteome coverage and in detecting proteins with low abundance. We used these optimized conditions for proteome profiling of sorted human primary T cells, which yielded an average of 365, 804, 1116, and 1651 proteins from single, five, ten, and forty T cells, respectively. Notably, we demonstrated that the depth of proteome coverage from a low number of cells was sufficient to delineate several essential metabolic pathways and the T cell receptor signaling pathway. Finally, we showed the feasibility of detecting post-translational modifications including phosphorylation and acetylation from single cells. We believe that such an approach could be applied to label-free analysis of single cells obtained from clinically relevant samples.

Trapped ion-mobility spectrometry (TIMS) was used to fractionate ions in the gas phase based on their ion mobility (V s/cm2), followed by parallel accumulation-serial fragmentation (PASEF) using a quadrupole time-of-flight instrument to determine the effect on the depth of proteome coverage. TIMS fractionation (up to four gas-phase fractions) coupled to data-dependent acquisition (DDA)-PASEF resulted in the detection of ∼7000 proteins and over 70,000 peptides overall from 200 ng of human (HeLa) cell lysate per injection using a commercial 25 cm ultra high performance liquid chromatography (UHPLC) column with a 90 min gradient. This result corresponded to ∼19 and 30% increases in protein and peptide identifications, respectively, when compared to a default, single-range TIMS DDA-PASEF analysis. Quantitation precision was not affected by TIMS fractionation as demonstrated by the average and median coefficient of variation values that were less than 4% upon label-free quantitation of technical replicates. TIMS fractionation was utilized to generate a DDA-based spectral library for downstream data-independent acquisition (DIA) analysis of lower sample input using a shorter LC gradient. The TIMS-fractionated library, consisting of over 7600 proteins and 82,000 peptides, enabled the identification of ∼4000 and 6600 proteins from 10 and 200 ng of human (HeLa) cell lysate input, respectively, with a 20 min gradient, single-shot DIA analysis. Data are available in ProteomeXchange: identifier PXD033129.

Mass spectrometry (MS)-based single-cell proteomics (SCP) enables proteome-wide analysis at single-cell resolution, offering insights into cellular heterogeneity, biological processes, and disease mechanisms. However, conventional nanoLC-MS workflows are constrained by long gradients and low throughput, limiting their application to large cohorts. Here, we present a multicolumn nanoLC–MS platform that achieves 5-minute separation windows with 100% duty cycles at ~100 nL/min, enabling the analysis of up to 288 single cells per day with minimal additional hardware. The system provides stable peptide separation, negligible carryover, and robust retention-time reproducibility across 2,000 consecutive injections. Over the course of this study, we successfully analyzed more than 4,000 samples at nearly 288 samples per day (SPD) throughput. The platform identified ~4,400 proteins per 250 pg digest injection and an average of ~3,200 proteins per single HeLa cell with maxima exceeding 4,300, which is comparable to state-of-the-art longer-gradient workflows. Quantitative benchmarking with mixed-species standards confirmed accurate measurements across ~6,000 proteins. The workflow distinguished proteome profiles across hundreds of single cells, and profiling of RAW264.7 macrophages revealed LPS-induced markers and macrophage activation pathways. Together, these results establish a robust and scalable platform for high-throughput SCP, demonstrating the feasibility of thousands of single-cell analyses within a single study while maintaining deep proteome coverage and biological interpretability.

Proteomics, the large-scale study of proteins in biological systems has emerged as a pivotal tool in the field of animal and veterinary sciences, mainly for investigating local and rustic breeds. Proteomics provides valuable insights into biological processes underlying animal growth, reproduction, health, and disease. In this review, we highlight the key proteomics technologies, methodologies, and their applications in domestic animals, particularly in the tropical context. We also discuss advances in proteomics research, including integration of multi-omics data, single-cell proteomics, and proteogenomics, all of which are promising for improving animal health, adaptation, welfare, and productivity. However, proteomics research in domestic animals faces challenges, such as sample preparation variation, data quality control, privacy and ethical considerations relating to animal welfare. We also provide recommendations for overcoming these challenges, emphasizing the importance of following best practices in sample preparation, data quality control, and ethical compliance. We therefore aim for this review to harness the full potential of proteomics in advancing our understanding of animal biology and ultimately improve animal health and productivity in local breeds of diverse animal species in a tropical context.

Single-cell proteomics (SCP) is transforming our understanding of biologicalcomplexity by shifting from bulk proteomics, where signals are averaged overthousands of cells, to the proteome analysis of individual cells. This granularperspective reveals distinct cell states, population heterogeneity, and theunderpinnings of disease pathogenesis that bulk approaches may obscure.However, SCP demands exceptional sensitivity, precise cell handling, and robustdata processing to overcome the inherent challenges of analyzing picogram-levelprotein samples without amplification. Recent innovations in samplepreparation, separations, data acquisition strategies, and specialized massspectrometry instrumentation have substantially improved proteome coverage andthroughput. Approaches that integrate complementary omics, streamlinemulti-step sample processing, and automate workflows through microfluidics andspecialized platforms promise to further push SCP boundaries. Advances incomputational methods, especially for data normalization and imputation,address the pervasive issue of missing values, enabling more reliabledownstream biological interpretations. Despite these strides, higherthroughput, reproducibility, and consensus best practices remain pressing needsin the field. This mini review summarizes the latest progress in SCP technologyand software solutions, highlighting how closer integration of analytical,computational, and experimental strategies will facilitate deeper and broadercoverage of single-cell proteomes.

Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

Trapped ion mobility spectrometry (TIMS) using parallel accumulation serial fragmentation (PASEF®) is an advanced analytical technique that offers several advantages in mass spectrometry (MS)-based lipidomics. TIMS provides an additional dimension of separation to mass spectrometry and accurate collision cross-section (CCS) measurements for ions, aiding in the structural characterization of molecules. This is especially valuable in lipidomics for identifying and distinguishing isomeric or structurally similar compounds. On the other hand, PASEF technology allows for fast and efficient data acquisition by accumulating ions in parallel and then serially fragmenting them. This accelerates the analysis process and improves throughput, making it suitable for high-throughput applications. Moreover, the combination of TIMS and PASEF reduces co-elution and ion coalescence issues, leading to cleaner and more interpretable mass spectra. This results in higher data quality and more confident identifications. In this chapter, a data-dependent TIMS-PASEF® workflow for lipidomics analysis is presented.

Unravelling molecular determinants of ruxolitinib treatment response in myelofibrosis using single cell multiomics

Protocol for untargeted lipidomics of human serum using LC-TIMS-PASEF