Improved Single-Cell Proteome Coverage Using Narrow-Bore Packed NanoLC Columns and Ultrasensitive Mass Spectrometry.

Single-cell proteomics in complex tissues using microprobe capillary electrophoresis mass spectrometry.

Single-Cell Imaging

Addendum: Digestion and depletion of abundant proteins improves proteomic coverage.

We report on the quantitative proteomic analysis of single mammalian cells. Fluorescence-activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing chip, after which samples were analyzed by ultrasensitive nanoLC-MS. An average of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. We demonstrate that the single-cell proteomics platform can be used to differentiate cell types from enzyme-dissociated human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.

To better understand cell-to-cell heterogeneity, advanced analytical tools are in a growing demand for elucidating chemical compositions of each cell within a population. However, the progress of single-cell chemical analysis has been restrained by the limitations of small cell volumes and minute cellular concentrations. Here, we present a rapid and sensitive method for investigating the lipid profiles of isolated single cells using infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS). In this work, HeLa cells were dispersed onto a glass slide, and the cellular contents were ionized by IR-MALDESI and measured using a Q-Exactive HF-X mass spectrometer. Importantly, this approach does not require extraction and/or enrichment of analytes prior to MS analysis. Using this approach, 45 distinct lipid species, predominantly phospholipids, were detected and putatively annotated from the single HeLa cells. The proof-of-concept study demonstrates the feasibility and efficacy of IR-MALDESI-MS for rapid lipidomic profiling of single cells, which provides an important basis for future work on differentiation between normal and diseased cells at various developmental states, which can offer new insights into cellular metabolic pathways and pathological processes. Although not yet accomplished, we believe this approach can be readily used as an assessment tool to compare the number of identified species during source evolution and method optimization (intra-laboratory), and also disclose the complementary nature of different direct analytical approaches for the coverage of different types of endogenous analytes (inter-laboratory).Graphical abstract.

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.

With recent technological developments, protein biomarker discoveries directly from blood have regained interest due to elevated feasibility. Mass spectrometry (MS)-based proteomics can now characterize human plasma proteomes to a greater extent than has ever been possible before. Such deep proteome coverage comes, however, with important limitations in terms of analysis time which is a critical factor in the case of clinical studies. As a consequence, compromises still need to be made to balance the proteome coverage with realistic analysis time frame in clinical research. The analysis of a sufficient number of samples is compulsory to empower statistically robust candidate biomarker findings. We have, therefore, recently developed a scalable automated proteomic pipeline (ASAP2) to enable the proteomic analysis of large numbers of plasma and cerebrospinal fluid (CSF) samples, from dozens to a thousand of samples, with the latter number being currently processed in 15 weeks. A distinct characteristic of ASAP2 relies on the possibility to prepare samples in a highly automated way, mostly using 96-well plates. We describe herein a sample preparation procedure for human plasma that includes internal standard spiking, abundant protein removal, buffer exchange, reduction, alkylation, tryptic digestion, isobaric labeling, pooling, and sample purification. Other key elements of the pipeline (i.e., study design, sample tracking, liquid chromatography (LC) tandem MS (MS/MS), data processing, and data analysis) are also highlighted.

Protein glycosylation can be vital for changing the function or physiochemical properties of a protein. Abnormal glycosylation can lead to protein malfunction, resulting in severe diseases. Therefore, it is important to develop techniques for characterization of such modifications in proteins at a sensitivity level comparable with state-of-the-art proteomics. Whereas techniques exist for characterization of high abundance glycoproteins, no single method is presently capable of providing information on both site occupancy and glycan structure on a single band excised from an electrophoretic gel. We present a new technique that allows characterization of low amounts of glycoproteins separated by gel electrophoresis. The method takes advantage of sequential specific and nonspecific enzymatic treatment followed by selective purification and characterization of the glycopeptides using graphite powder microcolumns in combination with mass spectrometry. The method is faster and more sensitive than previous approaches and is compatible with proteomic studies.

We describe an atomic force microscope (AFM) manipulator that can detach a single, living adhesion cell from its substrate without compromising the cell's viability. The micrometer-scale cell scraper designed for this purpose was fabricated from an AFM micro cantilever using focused ion beam milling. The homemade AFM equipped with the scraper was compact and standalone and could be mounted on a sample stage of an inverted optical microscope. It was possible to move the scraper using selectable modes of operation, either a manual mode with a haptic device or a computer-controlled mode. The viability of the scraped single cells was evaluated using a fluorescence dye of calcein-acetoxymethl ester. Single cells detached from the substrate were collected by aspiration into a micropipette capillary glass using an electro-osmotic pump. As a demonstration, single HeLa cells were selectively detached from the substrate and collected by the micropipette. It was possible to recultivate HeLa cells from the single cells collected using the system.

Recent advances in the sensitivity and speed of mass spectrometers coupled with improved sample preparation methods have enabled the field of single cell proteomics to proliferate. While heavy development is occurring in the label free space, dramatic improvements in throughput are provided by multiplexing with tandem mass tags. Hundreds or thousands of single cells can be analyzed with this method, yielding large data sets which may contain poor data arising from loss of material during cell sorting or poor digestion, labeling, and lysis. To date, no tools have been described that can assess data quality prior to data processing. We present herein a lightweight python script and accompanying graphic user interface that can rapidly quantify reporter ion peaks within each MS/MS spectrum in a file. With simple summary reports, we can identify single cell samples that fail to pass a set quality threshold, thus reducing analysis time waste. In addition, this tool, Diagnostic Ion Data Analysis Reduction (DIDAR), will create reduced MGF files containing only spectra possessing a user-specified number of single cell reporter ions. By reducing the number of spectra that have excessive zero values, we can speed up sample processing with little loss in data completeness as these spectra are removed in later stages in data processing workflows. DIDAR and the DIDAR GUI are compatible with all modern operating systems and are available at: https://github.com/orsburn/DIDARSCPQC. All files described in this study are available at www.massive.ucsd.edu as accession MSV000088887.

The past half century has seen a tremendous progress in technology. The first hand held mobile phones began production in 1973[1] and today about 45% of the world's population owns a smart phone.[2] In 1983, the publicly available and practical version of the internet was invented allowing standard computer networks to communicate with one another.[3] Today, it is rare to find a stand-alone computer not connected to the world wide web. Science took a historical leap forward approximately a quarter of a century ago with the development of the human genome project (HGP).[4] While the HGP was the complete mapping of all the genes in the human genome, ultimately it changed how science was approached. The ushering in of genomics via the HGP quickly led to en masse measurement of gene transcripts (transcriptomics), proteins (proteomics), and finally metabolites (metabolomics). While the knowledge gained from these branches of omics will continue to increase as time passes, one thing we have learned for certain is that the human cell is an amazing feat of engineering. Its genome is made up of approximately 20 000 protein coding genes and produces an estimated 200 000 transcripts. Within the genomic code are blueprints for potentially producing upwards of a billion protein species when various splice variants and post-translational modifications are considered. The human metabolite database currently lists over 114 000 metabolites that have either been detected or are predicted to be present within human cells.[5] Compare these numbers to an automobile, which contains on the order of tens of thousands of moving parts. Just like an automobile engine, the components of the cell work together to create a functional entity. Also like an engine, the source of a dysfunctional component within a cell may not be directly connected to the component itself. For example, rapid cell proliferation in human epidermal growth factor receptor 2 (HER-2) breast cancer is caused by an excess of HER-2 receptors on the surface of the cells.[6] The source of this receptor expansion, however, can be traced to gene duplication events. Historically, studies would be designed to identify this connection molecule by molecule, specifically targeting the protein receptor and HER-2 gene. In today's omics-era it is possible to collect data on thousands of genes, transcripts, and proteins so that not only the source of the biological problem can be detected but also the peripheral damage it creates. The systems biology era has given rise to a plethora of different omics fields that aim to characterize various aspects of cells and organisms. The foundation of systems biology, however, remains the "big-four" omics: genomics, transcriptomics, proteomics, and metabolomics. The initial article in this edition provides a reflection on the progress that has been made in omics research beginning with the HGP, which arguably changed the strategy in how scientists design biological studies.[7] The article highlights the incredible progress that has been made in the big-four omic fields. Description of an article detailing a multi-omics study of plasma samples obtained from individuals infected with COVID-19 demonstrates how mainstream systems biology studies have become in just the past few years. While the article also highlights challenges in systems biology, it provides a glimpse into the many different databases and consortiums that have arisen to address the ambitious nature of attempting to characterize all the components within cells and organisms. While much effort has been spent in the past couple of decades identifying the entire complement of cellular biomolecules, the need to identify how these biomolecules interact to carry out the many functions that occur within these cells has become paramount. In their article, Basu et al., describe the use of functional proteomics and systems biology to generate protein-protein interaction networks in neurons, glia, and other cell types.[8] They summarize findings from recent studies that compare these interaction networks in normally functioning neurons, in developing neurons, and neurodegenerative diseases. The article goes on to demonstrate how these interaction networks can be used to decipher cellular functions that regulate neuronal activity and brain function. While the era of systems biology has increased the amount of data per study, most laboratories are not historically designed to accommodate this requirement. A complete systems biology view of a cell requires data from a variety of classes of biomolecules (e.g., DNA, RNA, proteins, etc.), however, most labs are designed to analyze only one (or possibly two) of these classes. Another barrier to limited lab resources is the ability to integrate data from multiple similar investigations to test the veracity of hypotheses that may be generated from omic studies. In their review, Mantini et al. describe various methods for integrating phosphoproteomic data particularly in the context of cancer research.[9] They proceed to present various methods that enable phosphoproteomic data to be integrated with data from other omic fields to provide a broader systems biology perspective. As briefly mentioned in the initial article of this special edition, a number of researchers are working towards generating omics data on single cell as a means of avoiding the inherent heterogeneity of cell populations. In an application of this area, Zhiyuan et al. developed a novel integrated microfluidic chip capable of isolating and characterizing the RNA expression profile of a single circulating cancer cells (CTCs).[10] While CTCs are effective for monitoring tumor metastasis, they are also heterogeneous. To validate their device, Zhiyuan et al. performed transcriptome profiling of single MCF-7 and white blood cells. When the transcriptomic profiles of the two cell types were compared significant genetic differences were observed based on three different measures. While proteomic diversity is mostly a result of cell-mediated post-translational modifications and the post-transcriptional occurrence of splice variants, there are many exogenous compounds that can modify proteins within the proteome.[11] In an article illustrating how dynamic the proteome can be, Tocmo et al. review how plant-derived natural products can covalently modify proteins.[13] In their article they describe how various proteomic techniques are used to identify various modifications derived from plant-based natural products can alter protein conformation, resulting in an alteration of their function and the biological activity that they modulate. One of the major benefits of the omics-era has been the discovery of the vast diversity of the biomolecules that make up cells. As scientists develop more creative uses of technologies, the number of known variations of biomolecules continues to increase. In their article, Muroski et al. build upon the observation that cyclized immonium ions that are observed in the tandem mass spectrometry (MS) spectra of acylated lysine residues could be used to identify acyl-lysine modifications.[14] They developed a stepped collision energy method that provides unique features in the tandem MS spectra of acyl-lysine containing peptides by optimizing the abundance of the immonium ion without sacrificing the information available to identify the modified peptide's sequence. Testing this novel MS method on a proteome extracted from Synotrophus aciditrophicus that had been spiked with three peptides that differed in their type and location of acetylation. This novel stepped collision energy method was able to successfully identify each of the three peptides within a complex background of peptides. In a further application, the researchers were able to identify, for the first time, a hydroxypimelyl-lysine residues within the Synotrophus aciditrophicus proteome. There is a basic paradigm in protein science that structure dictates function. But what about proteins that do not have a unique stable structure? So-called intrinsically disordered proteins (IDPs) are known to affect many cellular processes and have even been associated with diseases. The challenge, however, has been identifying and deciphering the mechanisms of this atypical class of proteins. Conventional techniques such as X-ray crystallography and nuclear magnetic resonance spectroscopy are designed to characterize stable structures within proteins but generally fail when dealing with unstructured domains. The article not only highlights the impact that MS has had on characterizing IDPs but discusses the future potential of this technique in uncovering further facets of this underexplored group of proteins.[15] Oxylipins are a family of oxygenated natural products derived from polyunsaturated fatty acids by the action of cyclooxygenase (COX) and lipoxygenase enzymes.[15] These highly potent molecules stimulate a variety of biological responses including inflammation, vasoconstriction, vasodilation, blood-clotting, etc. in response to tissue injury. In a combined targeted proteomics/metabolomics approach Scheb et al. developed a selected reaction monitoring-MS assay to measure the absolute abundance of the COX-2 enzyme and compared changes in its level to changes in the abundance of a panel of oxylipins.[16] They applied this method to study the association between COX-2 and oxylipin levels in the human colon carcinoma cell lines and lipopolysaccharide-stimulated macrophages. This combined, targeted omics approach has the potential to be expanded to include additional enzymes involved in oxylipin synthesis and can be used to further elucidate the biological mechanisms that modulate the response to tissue injury from exogenous and endogenous sources. I would like to express my deepest gratitude to all of the contributors who made this special issue possible. This past year is unlike any other that we have lived through. Even with all of the changes each of the contributors had to make to their labs and day-to-day duties because of the COVID-19 pandemic, they were generous enough to share their time to prepare an article for this issue. My hope as editor is that the reader will be impressed by the progress made in omics research in the recent past and be excited about what the next few years of continuing developments will reveal about the human organism. The author declares no conflict of interest.

Human cerebrospinal fluid (CSF) is a sample of choice in the study of brain disorders. This biological fluid circulates in the brain and the spinal cord and contains tissue-specific proteins, indicative of health and disease conditions. Despite its potential as a valid source of biological markers, CSF remains largely understudied as compared to blood, in particular due to its more invasive way of sampling.Challenges remain when performing proteomic analysis in clinical research studies. State-of-the-art mass spectrometry (MS) enables deep characterization of the human proteome. But some technical limitations are cardinal to be addressed, such as the capacity to routinely analyze large cohorts of samples. Importantly, a trade-off still needs to be made between the proteome coverage depth and the number of measured samples. In this context, we developed a scalable automated proteomic pipeline for the analysis of CSF. Because of its versatility, this workflow can be adapted to accommodate proteome coverage and/or sample throughput. It allows us to prepare and quantitatively analyze hundreds to thousands of CSF samples; it can also allow identification of more than 3000 proteins in a CSF sample when coupled with isoelectric focusing fractionation.In this chapter, we describe an end-to-end pipeline for the proteomic analysis of CSF. The main steps of the sample preparation comprise spiking of a standard, protein digestion, isobaric labeling, and purification; these are performed in a 96-well plate format enabling automation. Depending on the targeted depth of the CSF proteome, optional analytical steps can be included, such as the removal of abundant proteins and sample pre-fractionation. Liquid chromatography tandem MS as well as data processing and analysis complete the pipeline.

An analytical method based on liquid chromatography with quadrupole time-of-flight mass spectrometry has been developed for the simultaneous determination of six aminoglycoside antibiotics in honey. The sample pretreatment included extraction with aqueous trichloroacetic acid followed by solid-phase extraction on Strata-X polymeric reversed phase cartridges. Liquid chromatography separation was performed on an Obelisc R zwitterionic type mixed-mode column. An ionBooster™ heated electrospray source was used and showed enhanced ionization efficiency in comparison to a conventional electrospray source. The observed signal enhancement ranged from 3- (neomycin) to 16-fold (gentamicin C1). A data-dependent mass spectrometry acquisition approach was employed, in which the full mass spectrometry dataset provided quantification and a scheduled precursor list was used to trigger an alternating data-dependent acquisition of MS2 spectra for confirmation purposes. The described method was validated in accordance to CD 2002/657/EC. Decision limit values were in the range 11.2-33.6ng/g, and satisfactory performance characteristics were obtained for recovery (65-76%), repeatability (3.8-7.3%), and linearity (≥0.995). The method was applied to the analysis of 49 real honey samples from the country of Georgia. Streptomycin was detected in two samples at 117 and 35ng/g, and gentamicin C1 was detected in one sample at 32ng/g.

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 <6 nL of cytoplasm. The method was found to be robust in obtaining reproducible protein quantifications from single cells spanning the 1-128-cell stages of development. Furthermore, we used micropipette sampling to study intercellular heterogeneity within cells in a single embryo and investigated embryonic asymmetry along both animal-vegetal and dorsal-ventral axes during early stages of development. Investigation of the animal-vegetal axis led to discovery of various asymmetrically distributed proteins along the animal-vegetal axis. We have further compared the hits found from our proteomic data sets with other studies and validated a few hits using an orthogonal imaging technique. This study forms the first report of vegetal enrichment of the germ plasm associated protein DDX4/VASA in Xenopus embyos. Overall, the method and data presented here holds promise to enable important leads in developmental biology.

Edoxaban is an anti-coagulant medication and a director factor Xa inhibitor. A novel reverse phase liquid chromatography-mass spectrometry compatible method developed for separation and identification of new oxidative degradation impurities in edoxaban tosylate hydrate drug substance. The separation of three oxidative degradation impurities was achieved by using YMC Triart phenyl (250 × 4.6) mm, 5 µm column with mobile phase containing gradient elution of mobile phase-A as 10 mM ammonium acetate and mobile phase-B as acetonitrile:methanol (1:1)% (v/v). The flow rate of the mobile phase is 0.7 mL/min with a column temperature of 40 °C and detection wavelength of 290 nm. Edoxaban tosylate hydrate shows significant degradation in oxidative stress conditions and forms three oxidative degradation products. The degradation products were identified and characterized by using a high-resolution mass spectrometry quadrupole-time of flight mass detector. The three oxidative degradation impurities of Edoxaban drug substance were well resolved with each other and along with Edoxaban drug substance peak. Among the three oxidative degradation impurities di-N-oxide impurity was the new oxidative degradation impurity identified for the first time and a novel reverse-phase high-performance liquid chromatography method was developed for separation of the three oxidative degradation impurities.