High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip

Single-Cell Proteomics Using the One-Step Droplet-in-Oil Digestion Method Reveals Proteins Important for Male Meiotic Progression

Different from DNA or RNA, proteins cannot be amplified. The performance of single-cell proteomics is limited by sample loss during sample preparation. Here, we present a one-step droplet-in-oil digestion (OSDO) method that involves one-step water-in-oil processing using cyclohexane or n-heptane, which can reduce the sample adsorption loss and sample volume change due to evaporation and increase the sensitivity and stability of single-cell proteomics. The OSDO is demonstrated to be compatible with tandem mass tag (TMT) labeling to improve the throughput. The OSDO, followed by data-independent acquisition (DIA), quantified more than 3700 proteins per cell during meiotic progression from pachytene to metaphase I, with no correlation between protein and mRNA levels. Inhibition of VPS34 and DNMT1, two proteins up-regulated in metaphase I, both affected metaphase I formation. The OSDO is an easy-to-operate method compatible with both subsequent labeled and unlabeled quantification to expand the depth, throughput, and applicability in single-cell proteomics.

Advances in Spatial Multi-Omics: A Review of Multi-Modal Mass Spectrometry Imaging and Laser Capture Microdissection-LCMS Integration

Mass spectrometry has long been utilized to characterize a variety of biomolecules such as proteins, metabolites, and lipids. Most MS-based omics studies rely on bulk analysis; however, bulk approaches often overlook low-abundance molecules that may exert critical biological effects. Recently, multi-omics analyses have been driving an explosion of knowledge about how biomolecules interact within biological systems. In particular, spatial multi-omics has emerged as a groundbreaking approach for implementing multi-omic and multi-modal analyses. Broadly defined, spatial omics has the ability to analyze biomolecules within their native spatial contexts, offering transformative insights. This review focuses on mass spectrometry-based spatial omics, specifically matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We will explore how MALDI-MSI, in combination with laser capture microdissection (LCM) and traditional liquid chromatography-mass spectrometry (LC-MS) workflow, is advancing spatially resolved multi-omics research.

Quantitative Label-Free Single-Cell Proteomics on the Orbitrap Astral MS

Single-cell proteomics by mass spectrometry (scp-MS) holds the potential to provide unprecedented insights into molecular features directly linked to the cellular phenotype, while deconvoluting complex organisms into their basic building blocks. Tailored sample preparation that maximizes the extracted amount of material that is introduced into the mass spectrometer has rapidly propelled the field forward. However, the measured signal is still at the lower edge of detection approaching the sensitivity boundary of current instrumentation. Here, we investigate the capacity of the enhanced sensitivity of the Orbitrap Astral mass spectrometer to facilitate deeper proteome profiles from low-input to single-cell samples. We carry out a comprehensive data acquisition method survey to pinpoint which parameters provide most sensitivity. Furthermore, we explore the quantitative accuracy of the obtained measurements to ensure that the obtained abundances are in line with expected ground truth values. We culminate our technical exploration by generating small datasets from two cultured cell lines and a primary bone marrow sample, to showcase obtainable proteome coverage differences from different source materials. Finally, as a proof of concept we explore protein covariation to showcase how information on known protein complexes is captured inherently in our scp-MS data.

Cell Population-resolved Multiomics Atlas of the Developing Lung

The lung is a vital organ that undergoes extensive morphological and functional changes during postnatal development. To disambiguate how different cell populations contribute to organ development, we performed proteomic and transcriptomic analyses of four sorted cell populations from the lung of human subjects 0-8 years of age with a focus on early life. The cell populations analyzed included epithelial, endothelial, mesenchymal, and immune cells. Our results revealed distinct molecular signatures for each of the sorted cell populations that enable the description of molecular shifts occurring in these populations during postnatal development. We confirmed that the proteome of the different cell populations was distinct regardless of age and identified functions specific to each population. We identified a series of cell population protein markers, including those located at the cell surface, that show differential expression and distribution on RNA in situ hybridization and immunofluorescence imaging. We validated the spatial distribution of alveolar type 1 and endothelial cell surface markers. Temporal analyses of the proteomes of the four populations revealed processes modulated during postnatal development and clarified the findings obtained from whole-tissue proteome studies. Finally, the proteome was compared with a transcriptomics survey performed on the same lung samples to evaluate processes under post-transcriptional control.

IS-SCP: enhanced single-cell proteomics using an in situ simplified strategy

Advances in single-cell proteomics have enabled the investigation of the distinctive proteomic makeup of individual cells, significantly impacting biomedical research. However, most existing approaches involve complex sample preparation workflows and are sensitive to potential sample loss, which limits their applicability. In this paper, we reported an advanced workflow for easy-to-use single-cell proteome analysis using an in situ simplified strategy, named "in situ simplified single-cell proteomics (IS-SCP)". This workflow was developed following a comprehensive evaluation of reagent mix, volume, and reaction conditions, notably including the utilization of a cleavable surfactant, n-decyl-disulfide-β-D-maltoside (DSSM). In comparison to previous workflows that require multiple steps in sample preparation, the IS-SCP workflow simplifies the single-cell proteome pretreatment to a single step of adding single-cell samples into a mixed reagent, which increases the repeatability and depth of single-cell proteome analysis. The IS-SCP workflow was applied to the proteomic analysis of single mammalian tumor cells, specifically HeLa and A549 cells, resulting in the quantification of an average of 3021 and 3289 protein groups, respectively. These results showed the potential of this workflow for investigating cellular heterogeneity at a deep single-cell level.

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

Offset Mass Carrier Proteome Improves Quantification of Multiplexed Single Cell Proteomics

Multiplexed single cell proteomics by mass spectrometry (scpMS) approaches currently offer the highest throughput as measured by cells analyzed per day. These methods employ isobaric labels and typically a carrier proteome, a sample added at 20 to 500× the single cell level that improves peptide sampling and identification. Peptides from the carrier and single cell proteomes exist within the same precursor isotopic cluster and are co-isolated for identification and quantification. This represents a challenge as high levels of carrier proteome limit the sampling of peptide ions from single cell samples and can potentially lead to decreased accuracy of quantitative measurements. Here, we address this limitation by introducing a triggered by offset mass acquisition method for scpMS (toma-scpMS) that utilizes a carrier proteome labeled with nonisobaric tags that have the same chemical composition but different mass as the labels used for quantitative multiplexing. Within toma-scpMS, the carrier proteome and single cell proteome are separated at the precursor level, enabling separate isolation, fragmentation, and quantitation of the single cell samples. To enable this workflow, we implemented a custom data acquisition scheme within inSeqAPI, an instrument application programming interface program that performed real-time identification of carrier proteome peptides and subsequent triggering of offset single cell quantification scans. We demonstrate that toma-scpMS is more robust to high levels of carrier proteome and offers superior quantitative accuracy as compared to traditional multiplexed scpMS approaches when similar carrier proteome levels are employed.

Single-Cell Proteomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

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 DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1300 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 conducting single-cell RNA-seq (scRNA-seq) analysis, we identified six upregulated and 10 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.

Large-scale acoustic single cell trapping and selective releasing

Recent advancements in single-cell analysis have underscored the need for precise isolation and manipulation of individual cells. Traditional techniques for single-cell manipulation are often limited by the number of cells that can be parallel trapped and processed and usually require complex devices or instruments to operate. Here, we introduce an acoustic microfluidic platform that efficiently traps and selectively releases individual cells using spherical air cavities embedded in a polydimethylsiloxane (PDMS) substrate for large scale manipulation. Our device utilizes the principle of acoustic impedance mismatches to generate near-field acoustic potential gradients that create trapping sites for single cells. These single cell traps can be selectively disabled by illuminating a near-infrared laser pulse, allowing targeted release of trapped cells. This method ensures minimal impact on cell viability and proliferation, making it ideal for downstream single-cell analysis. Experimental results demonstrate our platform's capability to trap and release synthetic microparticles and biological cells with high efficiency and biocompatibility. Our device can handle a wide range of cell sizes (8-30 μm) across a large active manipulation area of 1 cm2 with 20 000 single-cell traps, providing a versatile and robust platform for single-cell applications. This acoustic microfluidic platform offers a cost-effective and practical method for large scale single-cell trapping and selective releasing with potential applications in genomics, proteomics, and other fields requiring precise single-cell manipulation.

Graph neural networks for single-cell omics data: a review of approaches and applications

Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular signaling molecules within single-cell datasets, coupled with the complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges to conventional deep learning operations reliant on Euclidean geometries. Graph neural networks (GNNs) have extended deep learning to non-Euclidean data, allowing cells and their features in single-cell datasets to be modeled as nodes within a graph structure. GNNs have been successfully applied across a broad range of tasks in single-cell data analysis. In this survey, we systematically review 107 successful applications of GNNs and their six variants in various single-cell omics tasks. We begin by outlining the fundamental principles of GNNs and their six variants, followed by a systematic review of GNN-based models applied in single-cell epigenomics, transcriptomics, spatial transcriptomics, proteomics, and multi-omics. In each section dedicated to a specific omics type, we have summarized the publicly available single-cell datasets commonly utilized in the articles reviewed in that section, totaling 77 datasets. Finally, we summarize the potential shortcomings of current research and explore directions for future studies. We anticipate that this review will serve as a guiding resource for researchers to deepen the application of GNNs in single-cell omics.

Navigating the landscape of plant proteomics

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein-protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

Droplets in open microfluidics: generation, manipulation, and application in cell analysis

Open droplet microfluidics is an emerging technology that generates, manipulates, and analyzes droplets in open configuration systems. Droplets function as miniaturized reactors for high-throughput analysis due to their compartmentalization and parallelization, while openness enables addressing and accessing the targeted contents. The convergence of two technologies facilitates the localization and intricate manipulation of droplets using external tools, showing great potential in large-scale chemical and biological applications, particularly in cell analysis. In this review, we first introduce various methods of droplet generation and manipulation in open environments. Next, we summarize the typical applications of open droplet systems in cell culture. Then, a comprehensive overview of cell analysis is provided, including nucleic acids, proteins, metabolites, and behaviors. Finally, we present a discussion of current challenges and perspectives in this field.

Comparative proteomic landscapes elucidate human preimplantation development and failure

Understanding mammalian preimplantation development, particularly in humans, at the proteomic level remains limited. Here, we applied our comprehensive solution of ultrasensitive proteomic technology to measure the proteomic profiles of oocytes and early embryos and identified nearly 8,000 proteins in humans and over 6,300 proteins in mice. We observed distinct proteomic dynamics before and around zygotic genome activation (ZGA) between the two species. Integrative analysis with translatomic data revealed extensive divergence between translation activation and protein accumulation. Multi-omic analysis indicated that ZGA transcripts often contribute to protein accumulation in blastocysts. Using mouse embryos, we identified several transcriptional regulators critical for early development, thereby linking ZGA to the first lineage specification. Furthermore, single-embryo proteomics of poor-quality embryos from over 100 patient couples provided insights into preimplantation development failure. Our study may contribute to reshaping the framework of mammalian preimplantation development and opening avenues for addressing human infertility.

Single Cell Proteomics Reveals Specific Cellular Subtypes in Cardiomyocytes Derived from Human iPSCs and Adult Hearts

Single cell proteomics was performed on human induced pluripotent stem cells (iPSCs), iPSC-derived cardiomyocytes, and adult cardiomyocytes. Over 700 proteins could be simultaneously measured in each cell revealing unique subpopulations. A sub-set of iPSCs expressed higher levels of Lin28a and Tra-1-60 towards the outer edge of cell colonies. In the cardiomyocytes, two distinct populations were found that exhibited complementary metabolic profiles. Cardiomyocytes from iPSCs showed a glycolysis profile while adult cardiomyocytes were enriched in proteins involved with fatty acid metabolism. Interestingly, rare single cells also co-expressed markers of both cardiac and neuronal lineages, suggesting there maybe a novel hybrid cell type in the human heart.

Proteomic Insight Into Alzheimer's Disease Pathogenesis Pathways

Alzheimer's disease (AD) is a leading cause of dementia, but the pathogenesis mechanism is still elusive. Advances in proteomics have uncovered key molecular mechanisms underlying AD, revealing a complex network of dysregulated pathways, including amyloid metabolism, tau pathology, apolipoprotein E (APOE), protein degradation, neuroinflammation, RNA splicing, metabolic dysregulation, and cognitive resilience. This review examines recent proteomic findings from AD brain tissues and biological fluids, highlighting potential biomarkers and therapeutic targets. By examining the proteomic landscape of them, we aim to deepen our understanding of the disease and support developing precision medicine strategies for more effective interventions.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Review and Practical Guide for Getting Started With Single-Cell Proteomics

Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.

Functional Single-Cell Proteomics: Technology and Biological Applications

Single-cell proteomics has the potential to decipher tumor heterogeneity, and methods such as Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) allow profiling of several tens of single cells for more than 1,000 proteins per cell. Our laboratory has advanced state-of-the-art single-cell proteomics technology to enable linking proteomes of individual cells with phenotypes of interest. Here, we describe a microscopy-based functional single-cell proteomic profiling technology, called FUNpro, designed for general use in cultured cell lines. FUNpro enables high-throughput, real-time live-cell screening, identification, and isolation of single cells of interest, even when the phenotypes are dynamic or the cells are rare. This chapter outlines a generalized protocol for functional single-cell proteomic profiling and analysis of a standard cancer cell line, including an overview of the microscopy, cell culture, image analysis, photolabeling of cells of interest, fluorescence-activated cell sorting (FACS), and the single-cell proteomics procedures and analyses employed.

Advancements in Single-Cell Proteomics and Mass Spectrometry-Based Techniques for Unmasking Cellular Diversity in Triple Negative Breast Cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer characterized by a lack of targeted treatment options. Intratumoral heterogeneity significantly drives disease progression and complicates therapeutic responses, necessitating advanced analytical approaches to understand its underlying biology. This review aims to explore the advancements in single-cell proteomics and their application in uncovering cellular diversity in TNBC. It highlights innovations in sample preparation, mass spectrometry-based techniques, and the potential for integrating proteomics into multi-omics platforms.

METHODS

The review discusses the combination of improved sample preparation methods and cutting-edge mass spectrometry techniques in single-cell proteomics. It emphasizes the challenges associated with protein analysis, such as the inability to amplify proteins akin to transcripts, and examines strategies to overcome these limitations.

RESULTS

Single-cell proteomics provides a direct link to phenotype and cell behavior, complementing transcriptomic approaches and offering new insights into the mechanisms driving TNBC. The integration of advanced techniques has enabled deeper exploration of cellular heterogeneity and disease mechanisms.

CONCLUSION

Despite the challenges, single-cell proteomics holds immense potential to evolve into a high-throughput and scalable multi-omics platform. Addressing existing hurdles will enable deeper biological insights, ultimately enhancing the diagnosis and treatment of TNBC.

Digital Microfluidics for Sample Preparation in Low-Input Proteomics

Low-input proteomics, also referred to as micro- or nanoproteomics, has become increasingly popular as it allows one to elucidate molecular processes in rare biological materials. A major prerequisite for the analytics of minute protein amounts, e.g., derived from low cell numbers, down to single cells, is the availability of efficient sample preparation methods. Digital microfluidics (DMF), a technology allowing the handling and manipulation of low liquid volumes, has recently been shown to be a powerful and versatile tool to address the challenges in low-input proteomics. Here, an overview is provided on recent advances in proteomics sample preparation using DMF. In particular, the capability of DMF to isolate proteomes from cells and small model organisms, and to perform all necessary chemical sample preparation steps, such as protein denaturation and proteolytic digestion on-chip, are highlighted. Additionally, major prerequisites to making these steps compatible with follow-up analytical methods such as liquid chromatography-mass spectrometry will be discussed.

Mapping Nanoscale-To-Single-Cell Phosphoproteomic Landscape by Chip-DIA

Protein phosphorylation plays a crucial role in regulating disease phenotypes and serves as a key target for drug development. Mapping nanoscale-to-single-cell samples can unravel the heterogeneity of cellular signaling events. However, it remains a formidable analytical challenge due to the low detectability, abundance, and stoichiometry of phosphorylation sites. Here, we present a Chip-DIA strategy, integrating a microfluidic-based phosphoproteomic chip (iPhosChip) with data-independent acquisition mass spectrometry (DIA-MS) for ultrasensitive nanoscale-to-single-cell phosphoproteomic profiling. The iPhosChip operates as an all-in-one station that accommodates both quantifiable cell capture/imaging and the entire phosphoproteomic workflow in a highly streamlined and multiplexed manner. Coupled with a sample size-comparable library-based DIA-MS strategy, Chip-DIA achieved ultra-high sensitivity, detecting 1076±158 to 15869±1898 phosphopeptides from 10±0 to 1013±4 cells, and revealed the first single-cell phosphoproteomic landscape comprising druggable sites and basal phosphorylation-mediated networks in lung cancer. Notably, the sensitivity and coverage enabled the illumination of heterogeneous cytoskeleton remodeling and cytokeratin signatures in patient-derived cells resistant to third-generation EGFR therapy, stratifying mixed-lineage adenocarcinoma-squamous cell carcinoma subtypes, and identifying alternative targeted therapy for late-stage patients. With flexibility in module design and functionalization, Chip-DIA can be adapted to other PTM-omics to explore dysregulated PTM landscapes, thereby guiding therapeutic strategies toward precision oncology.

Unlocking the Heterogeneity in Acute Leukaemia: Dissection of Clonal Architecture and Metabolic Properties for Clinical Interventions

Genetic studies of haematological cancers have pointed out the heterogeneity of leukaemia in its different subpopulations, with distinct mutations and characteristics, impacting the treatment response. Next-generation sequencing (NGS) and genome-wide analyses, as well as single-cell technologies, have offered unprecedented insights into the clonal heterogeneity within the same tumour. A key component of this heterogeneity that remains unexplored is the intracellular metabolome, a dynamic network that determines cell functions, signalling, epigenome regulation, immunity and inflammation. Understanding the metabolic diversities among cancer cells and their surrounding environments is therefore essential in unravelling the complexities of leukaemia and improving therapeutic strategies. Here, we describe the currently available methodologies and approaches to addressing the dynamic heterogeneity of leukaemia progression. In the second section, we focus on metabolic leukaemic vulnerabilities in acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL). Lastly, we provide a comprehensive overview of the most interesting clinical trials designed to target these metabolic dependencies, highlighting their potential to advance therapeutic strategies in leukaemia treatment. The integration of multi-omics data for cancer identification with the metabolic states of tumour cells will enable a comprehensive "micro-to-macro" approach for the refinement of clinical practices and delivery of personalised therapies.

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics

The coefficient of variation (CV) is a measure that is frequently used to assess data dispersion for mass spectrometry-based proteomics. In the current era of burgeoning technical developments, there has been an increased focus on using CVs to measure the quantitative precision of new methods. Thus, it has also become important to define a set of guidelines on how to calculate and report the CVs. This perspective shows the effects that the CV equation, data normalization as well as software parameters, can have on data dispersion and CVs, highlighting the importance of reporting all these variables within the methods section. It also proposes a set of recommendations to calculate and report CVs for technical studies, where the main objective is to benchmark technical developments with a focus on precision. To assist in this process, a novel R package to calculate CVs (proteomicsCV) is also included.

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

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.

Parallel sample processing for mass spectrometry-based single cell proteomics

BACKGROUND

Single cell mass spectrometry (scMS) has shown great promise for label free proteomics analysis recently. To present single cell samples for proteomics analysis by MS is not a trivial task. Existing methods rely on robotic liquid handlers to scale up sample preparation throughput. The cost associated with specialized equipment hinders the broad adoption of these workflows, and the sequential sample processing nature limits the ultimate throughput.

RESULTS

In this work, we report a parallel sample processing workflow that can simultaneously process 10 single cells without the need of robotic liquid handlers for scMS. This method utilized 3D printed microfluidic devices to form reagent arrays on a glass slide, and a magnetic beads-based streamlined sample processing workflow to present peptides for LC-MS detection. We optimized key operational parameters of the method and demonstrated the quantification consistency among 10 parallel processed samples. Finally, the utility of the method in differentiating cell lines and studying the proteome change induced by drug treatment were demonstrated.

SIGNIFICANCE

The present method allows parallel sample processing for single cells without the need of expensive liquid handlers, which has great potential to further improve throughput and decrease the barrier for single cell proteomics.

Proteomic Characterization of 1000 Human and Murine Neutrophils Freshly Isolated From Blood and Sites of Sterile Inflammation

Neutrophils are indispensable for defense against pathogens. Injured tissue-infiltrated neutrophils can establish a niche of chronic inflammation and promote degeneration. Studies investigated transcriptome of single-infiltrated neutrophils which could misinterpret molecular states of these post mitotic cells. However, neutrophil proteome characterization has been challenging due to low harvests from affected tissues. Here, we present a workflow to obtain proteome of 1000 murine and human tissue-infiltrated neutrophils. We generated spectral libraries containing ∼6200 mouse and ∼5300 human proteins from circulating neutrophils. 4800 mouse and 3400 human proteins were recovered from 1000 cells with 102-108 copies/cell. Neutrophils from stroke-affected mouse brains adapted to the glucose-deprived environment with increased mitochondrial activity and ROS-production, while cells invading inflamed human oral cavities increased phagocytosis and granule release. We provide an extensive protein repository for resting human and mouse neutrophils, identify proteins lost in low input samples, thus enabling the proteomic characterization of limited tissue-infiltrated neutrophils.

Single-Cell Proteomic and Transcriptomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

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.

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.

Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples

Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1 µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50 µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.

A Zwitterionic Detergent and Catalyst-Based Single-Cell Proteomics Using a Loss-Free Microhole-Collection Disc

Recent advances in single-cell proteomics have solved many bottlenecks, such as throughput, sample recovery, and scalability via nanoscale sample handling. In this study, we aimed for a sensitive mass spectrometry (MS) analysis capable of handling single cells with a conventional mass spectrometry workflow without additional equipment. We achieved seamless cell lysis and TMT labeling in a micro-HOLe Disc (microHOLD) by developing a mass-compatible single solution based on a zwitterionic detergent and a catalyst for single-cell lysis and tandem mass tag labeling without a heat incubation step. This method was developed to avoid peptide loss by surface adsorption and buffer or tube changes by collecting tandem mass tag-labeled peptide through microholes placed in the liquid chromatography injection vials in a single solution. We successfully applied the microHOLD single-cell proteomics method for the analysis of proteome reprogramming in hormone-sensitive prostate cells to develop castration-resistant prostate cancer cells. This novel single-cell proteomics method is not limited by cutting-edge nanovolume handling equipment and achieves high throughput and ultrasensitive proteomics analysis of limited samples, such as single cells.

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer

Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

High-Throughput Proteomics Enabled by a Fully Automated Dual-Trap and Dual-Column LC-MS

This Technical Note describes a dual-column liquid chromatography system coupled to mass spectrometry (LC-MS) for high-throughput bottom-up proteomic analysis. This system made full use of two 2-position 10-port valves and a binary pump with an integrated loading pump of a commercial LC instrument to provide successive operation of two parallel subsystems. Each subsystem consisted of a set of trap columns and an analytical column. A T-junction union was used to split the mobile phase from the loading pump into two parts. This allowed one set of columns to be washed and equilibrated, followed by the injection of the next sample, while the previous sample was eluting and being analyzed on the other set of columns, thereby greatly increasing the analysis throughput. This approach showed high reproducibility for the analysis of HeLa tryptic digests with average relative standard deviation (RSD) values of 1.75%, 6.90%, and 5.19% for the identification number of proteins, peptides, and peptide-spectrum matches (PSMs), respectively, across 10 consecutive runs. The capacity for peptide and protein identification, as well as proteome depth, of the dual-column LC system was comparable to a conventional single-column system. Due to its simple equipment requirements and set up process, this method should be highly accessible for other laboratories.

Top-down proteomics

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

Top-Down Proteomics Analysis of Picogram-Level Complex Samples Using Spray-Capillary-Based Capillary Electrophoresis-Mass Spectrometry

Proteomics analysis of mass-limited samples has become increasingly important for understanding biological systems in physiologically relevant contexts such as patient samples, multicellular organoids, spheroids, and single cells. However, relatively low sensitivity in top-down proteomics methods makes their application to mass-limited samples challenging. Capillary electrophoresis (CE) has emerged as an ideal separation method for mass-limited samples due to its high separation resolution, ultralow detection limit, and minimal sample volume requirements. Recently, we developed "spray-capillary", an electrospray ionization (ESI)-assisted device, that is capable of quantitative ultralow-volume sampling (e.g., pL-nL level). Here, we developed a spray-capillary-CE-MS platform for ultrasensitive top-down proteomics analysis of intact proteins in mass-limited complex biological samples. Specifically, to improve the sensitivity of the spray-capillary platform, we incorporated a polyethylenimine (PEI)-coated capillary and optimized the spray-capillary inner diameter. Under optimized conditions, we successfully detected over 200 proteoforms from 50 pg of E. coli lysate. To our knowledge, the spray-capillary CE-MS platform developed here represents one of the most sensitive detection methods for top-down proteomics. Furthermore, in a proof-of-principle experiment, we detected 261 ± 65 and 174 ± 45 intact proteoforms from fewer than 50 HeLa and OVCAR-8 cells, respectively, by coupling nanodroplet-based sample preparation with our optimized CE-MS platform. Overall, our results demonstrate the capability of the modified spray-capillary CE-MS platform to perform top-down proteomics analysis on picogram amounts of samples. This advancement presents the possibility of meaningful top-down proteomics analysis of mass-limited samples down to the level of single mammalian cells.

AM-DMF-SCP: Integrated Single-Cell Proteomics Analysis on an Active Matrix Digital Microfluidic Chip

Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.

Pushing the Isotopic Envelope: When carrier channels pollute their neighbors' signals

Individual cells are the foundational unit of biology, and understanding their functions and interactions is critical to advancing our understanding of health and disease. Single cell proteomics has seen intense interest from mass spectrometrists, with a goal of quantifying the proteome of single cells by adapting current techniques used in bulk samples. To date, most method optimizations research has worked towards increasing the proteome coverage of single cells. One prominent technique multiplexes many individual cells into a single data acquisition event using isobaric labels. Accompanying the single cells, one label is typically used for a mixed set of many cells, called a carrier or boost channel. Although this improves peptide identification rates, several groups have examined the impact on quantitative accuracy as more cells are included in the carrier channel, e.g. 100x or 500x. This manuscript explores how impurities in the multiplexing reagent can lead to inaccurate quantification observed as a measurable signal in the wrong channel. We discover that the severe abundance differential between carrier and single cell, combined with the reagent impurities, can overshadow several channels typically used for single cells. For carrier amounts 100x and above, this contamination can be as abundant as true signal from a single cell. Therefore, we suggest limiting the carrier channel to a minimal amount and balance the goals of identification and quantification.

Optimal conditions for carrying out trypsin digestions on complex proteomes: From bulk samples to single cells

To identify proteins by the bottom-up mass spectrometry workflow, enzymatic digestion is essential to break down proteins into smaller peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity and generation of peptides with desirable positively charged N- and C-terminal amino acid residues that are amenable to reverse phase HPLC separation and MS/MS analyses. However, trypsin can yield variable digestion profiles and its protein cleavage activity is interdependent on trypsin source and quality, digestion time and temperature, pH, denaturant, trypsin and substrate concentrations, composition/complexity of the sample matrix, and other factors. There is therefore a need for a more standardized, general-purpose trypsin digestion protocol. Based on a review of the literature we delineate optimal conditions for carrying out trypsin digestions of complex proteomes from bulk samples to limiting amounts of protein extracts. Furthermore, we highlight recent developments and technological advances used in digestion protocols to quantify complex proteomes from single cells. SIGNIFICANCE: Currently, bottom-up MS-based proteomics is the method of choice for global proteome analysis. Since trypsin is the most utilized protease in bottom-up MS proteomics, delineating optimal conditions for carrying out trypsin digestions of complex proteomes in samples ranging from tissues to single cells should positively impact a broad range of biomedical research.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Universal Pretreatment Development for Low-input Proteomics Using Lauryl Maltose Neopentyl Glycol

In recent years, there has been a growing demand for low-input proteomics, particularly in the context of single-cell proteomics (SCP). In this study, we have developed a lauryl maltose neopentyl glycol (LMNG)-assisted sample preparation (LASP) method. This method effectively reduces protein and peptide loss in samples by incorporating LMNG, a surfactant, into the digestion solution and subsequently removing the LMNG simply via reversed phase solid-phase extraction. The advantage of removing LMNG during sample preparation for general proteomic analysis is the prevention of mass spectrometry (MS) contamination. When we applied the LASP method to the low-input SP3 method and on-bead digestion in coimmunoprecipitation-MS, we observed a significant improvement in the recovery of the digested peptides. Furthermore, we have established a simple and easy sample preparation method for SCP based on the LASP method and identified a median of 1175 proteins from a single HEK239F cell using liquid chromatography (LC)-MS/MS with a throughput of 80 samples per day.

The proteomic landscape of microglia in health and disease

Microglia are the resident immune cells of the central nervous system (CNS) and as such play crucial roles in regulating brain homeostasis. Their presence in neurodegenerative diseases is known, with neurodegeneration-associated risk genes heavily expressed in microglia, highlighting their importance in contributing to disease pathogenesis. Transcriptomics studies have uncovered the heterogeneous landscape of microglia in health and disease, identifying important disease-associated signatures such as DAM, and insight into both the regional and temporal diversity of microglia phenotypes. Quantitative mass spectrometry methods are ever increasing in the field of neurodegeneration, utilised as ways to identify disease biomarkers and to gain deeper understanding of disease pathology. Proteins are the main mechanistic indicators of cellular function, yet discordance between transcript and proteomic findings has highlighted the need for in-depth proteomic phenotypic and functional analysis to fully understand disease kinetics at the cellular and molecular level. This review details the current progress of using proteomics to define microglia biology, the relationship between gene and protein expression in microglia, and the future of proteomics and emerging methods aiming to resolve heterogeneous cell landscapes.

Research progress on the multi-omics and survival status of circulating tumor cells

In the dynamic process of metastasis, circulating tumor cells (CTCs) emanate from the primary solid tumor and subsequently acquire the capacity to disengage from the basement membrane, facilitating their infiltration into the vascular system via the interstitial tissue. Given the pivotal role of CTCs in the intricate hematogenous metastasis, they have emerged as an essential resource for a deeper comprehension of cancer metastasis while also serving as a cornerstone for the development of new indicators for early cancer screening and new therapeutic targets. In the epoch of precision medicine, as CTC enrichment and separation technologies continually advance and reach full fruition, the domain of CTC research has transcended the mere straightforward detection and quantification. The rapid advancement of CTC analysis platforms has presented a compelling opportunity for in-depth exploration of CTCs within the bloodstream. Here, we provide an overview of the current status and research significance of multi-omics studies on CTCs, including genomics, transcriptomics, proteomics, and metabolomics. These studies have contributed to uncovering the unique heterogeneity of CTCs and identifying potential metastatic targets as well as specific recognition sites. We also review the impact of various states of CTCs in the bloodstream on their metastatic potential, such as clustered CTCs, interactions with other blood components, and the phenotypic states of CTCs after undergoing epithelial-mesenchymal transition (EMT). Within this context, we also discuss the therapeutic implications and potential of CTCs.