The challenge of the proteome dynamic range and its implications for in-depth proteomics

Elucidating the Cell Surfaceome to Accelerate Cancer Drug Development

SUMMARY

Cell surface proteins represent ideal therapeutic targets because of their accessibility to antibodies, T cell-directed therapies, and radiotherapies, but there are only 25 therapeutically relevant cell surface targets for which cancer therapies are approved in the United States or European Union. This commentary calls for intensified research into mapping the universe of cell surface proteins - the cell surfaceome - in order to accelerate cancer drug development.

iRhom2 regulates ectodomain shedding and surface expression of the major histocompatibility complex (MHC) class I

Proteolytic release of transmembrane proteins from the cell surface, the so called ectodomain shedding, is a key process in inflammation. Inactive rhomboid 2 (iRhom2) plays a crucial role in this context, in that it guides maturation and function of the sheddase ADAM17 (a disintegrin and metalloproteinase 17) in immune cells, and, ultimately, its ability to release inflammatory mediators such as tumor necrosis factor α (TNFα). Yet, the macrophage sheddome of iRhom2/ADAM17, which is the collection of substrates that are released by the proteolytic complex, is only partly known. In this study, we applied high-resolution proteomics to murine and human iRhom2-deficient macrophages for a systematic identification of substrates, and therefore functions, of the iRhom2/ADAM17 proteolytic complex. We found that iRhom2 loss suppressed the release of a group of transmembrane proteins, including known (e.g. CSF1R) and putative novel ADAM17 substrates. In the latter group, shedding of major histocompatibility complex class I molecules (MHC-I) was consistently reduced in both murine and human macrophages when iRhom2 was ablated. Intriguingly, it emerged that in addition to its shedding, iRhom2 could also control surface expression of MHC-I by an undefined mechanism. We have demonstrated the biological significance of this process by using an in vitro model of CD8+ T-cell (CTL) activation. In this model, iRhom2 loss and consequent reduction of MHC-I expression on the cell surface of an Epstein-Barr virus (EBV)-transformed lymphoblastoid cell line dampened activation of autologous CTLs and their cell-mediated cytotoxicity. Taken together, this study uncovers a new role for iRhom2 in controlling cell surface levels of MHC-I by a dual mechanism that involves regulation of their surface expression and ectodomain shedding.

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients, into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 μg/ml phosphatidylcholine could singularly increase the number of unique proteins within the protein corona (897 proteins). This specific concentration of phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing concentration dynamic range of plasma proteome and boosting LC-MS/MS sensitivity for detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in plasma. This significant achievement is made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in proteomic depth of the plasma sample. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems

The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.

Data-Independent Acquisition and Label-Free Quantification for Quantitative Proteomics Analysis of Human Cerebrospinal Fluid

This article presents a practical guide to mass spectrometry-based data-independent acquisition and label-free quantification for proteomics analysis applied to cerebrospinal fluid, offering a robust and scalable approach to probing the proteomic composition of the central nervous system. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Cerebrospinal fluid sample collection and preparation for mass spectrometry analysis Basic Protocol 2: Mass spectrometry sample analysis with data-independent acquisition Support Protocol: Data-dependent mass spectrometry and spectral library construction Basic Protocol 3: Analysis of mass spectrometry data.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

A uniform data processing pipeline enables harmonized nanoparticle protein corona analysis across proteomics core facilities

Protein corona, a layer of biomolecules primarily comprising proteins, forms dynamically on nanoparticles in biological fluids and is crucial for predicting nanomedicine safety and efficacy. The protein composition of the corona layer is typically analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Our recent study, involving identical samples analyzed by 17 proteomics facilities, highlighted significant data variability, with only 1.8% of proteins consistently identified across these centers. Here, we implement an aggregated database search unifying parameters such as variable modifications, enzyme specificity, number of allowed missed cleavages and a stringent 1% false discovery rate at the protein and peptide levels. Such uniform search dramatically harmonizes the proteomics data, increasing the reproducibility and the percentage of consistency-identified unique proteins across distinct cores. Specifically, out of the 717 quantified proteins, 253 (35.3%) are shared among the top 5 facilities (and 16.2% among top 11 facilities). Furthermore, we note that reduction and alkylation are important steps in protein corona sample processing and as expected, omitting these steps reduces the number of total quantified peptides by around 20%. These findings underscore the need for standardized procedures in protein corona analysis, which is vital for advancing clinical applications of nanoscale biotechnologies.

Global detection of human variants and isoforms by deep proteome sequencing

An average shotgun proteomics experiment detects approximately 10,000 human proteins from a single sample. However, individual proteins are typically identified by peptide sequences representing a small fraction of their total amino acids. Hence, an average shotgun experiment fails to distinguish different protein variants and isoforms. Deeper proteome sequencing is therefore required for the global discovery of protein isoforms. Using six different human cell lines, six proteases, deep fractionation and three tandem mass spectrometry fragmentation methods, we identify a million unique peptides from 17,717 protein groups, with a median sequence coverage of approximately 80%. Direct comparison with RNA expression data provides evidence for the translation of most nonsynonymous variants. We have also hypothesized that undetected variants likely arise from mutation-induced protein instability. We further observe comparable detection rates for exon-exon junction peptides representing constitutive and alternative splicing events. Our dataset represents a resource for proteoform discovery and provides direct evidence that most frame-preserving alternatively spliced isoforms are translated.

Characterizing citrullination by mass spectrometry-based proteomics

Citrullination is an important post-translational modification (PTM) of arginine, known to play a role in autoimmune disorders, innate immunity response and maintenance of stem cell potency. However, citrullination remains poorly characterized and not as comprehensively understood compared to other PTMs, such as phosphorylation and ubiquitylation. High-resolution mass spectrometry (MS)-based proteomics offers a valuable approach for studying citrullination in an unbiased manner, allowing confident identification of citrullination modification sites and distinction from deamidation events on asparagine and glutamine. MS efforts have already provided valuable insights into peptidyl arginine deaminase targeting along with site-specific information of citrullination in for example synovial fluids derived from rheumatoid arthritis patients. Still, there is unrealized potential for the wider citrullination field by applying MS-based mass spectrometry approaches for proteome-wide investigations. Here we will outline contemporary methods and current challenges for studying citrullination by MS, and discuss how the development of neoteric citrullination-specific proteomics approaches still may improve our understanding of citrullination networks. This article is part of the Theo Murphy meeting issue 'The virtues and vices of protein citrullination'.

Comparison of in-gel and in-solution proteolysis in the proteome profiling of organ perfusion solutions

PURPOSE

The organ perfusion solution (perfusate), collected at clinically and temporally significant stages of the organ preservation and transplantation process, provides a valuable insight into the biological status of an organ over time and prior to reperfusion (transplantation) in the recipient. The objective of this study was to assess two bottom-up proteomics workflows for the extraction of tryptic peptides from the perfusate.

EXPERIMENTAL DESIGN

Two different kinds of perfusate samples from kidney and liver trials were profiled using liquid chromatography-mass spectrometry (LC-MS/MS). The preparation of clean peptide mixtures for downstream analysis was performed considering different aspects of sample preparation; protein estimation, enrichment, in-gel and urea-based in-solution digestion.

RESULTS

In-solution digestion of perfusate allowed identification of the highest number of peptides and proteins with greater sequence coverage and higher confidence data in kidney and liver perfusate. Key pathways identified by gene ontology analysis included complement, coagulation and antioxidant pathways, and a number of biomarkers previously linked to ischemia-reperfusion injury were also observed in perfusate.

CONCLUSIONS

This study showed that in-solution digestion is a more efficient method for LC-MS/MS analysis of kidney and liver organ perfusion solutions. This method is also quicker and easier than in-gel digestion, allowing for greater sample throughput, with fewer opportunities for experimental error or peptide loss.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.

Engineering Biological Nanopore Approaches toward Protein Sequencing

Biotechnological innovations have vastly improved the capacity to perform large-scale protein studies, while the methods we have for identifying and quantifying individual proteins are still inadequate to perform protein sequencing at the single-molecule level. Nanopore-inspired systems devoted to understanding how single molecules behave have been extensively developed for applications in genome sequencing. These nanopore systems are emerging as prominent tools for protein identification, detection, and analysis, suggesting realistic prospects for novel protein sequencing. This review summarizes recent advances in biological nanopore sensors toward protein sequencing, from the identification of individual amino acids to the controlled translocation of peptides and proteins, with attention focused on device and algorithm development and the delineation of molecular mechanisms with the aid of simulations. Specifically, the review aims to offer recommendations for the advancement of nanopore-based protein sequencing from an engineering perspective, highlighting the need for collaborative efforts across multiple disciplines. These efforts should include chemical conjugation, protein engineering, molecular simulation, machine-learning-assisted identification, and electronic device fabrication to enable practical implementation in real-world scenarios.

Differential Precipitation of Proteins: A Simple Protein Fractionation Strategy to Gain Biological Insights with Proteomics

Differential precipitation of proteins (DiffPOP) is a simple technique for fractionating complex protein mixtures. Using stepwise addition of acidified methanol, ten distinct subsets of proteins can be selectively precipitated by centrifugation and identified by mass spectrometry-based proteomics. We have previously shown that the ability of a protein to resist precipitation can be altered by drug binding, which enabled us to identify a novel drug-target interaction. Here, we show that the addition of DiffPOP to a standard LC-MS proteomics workflow results in a three-dimensional separation of peptides that increases protein coverage and peptide identifications. Importantly, DiffPOP reveals solubility differences between proteoforms, potentially providing valuable insights that are typically lost in bottom-up proteomics.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data Independent Acquisition

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data independent acquisition, the Thermo Scientific™ Orbitrap™ Astral™ mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific™ Orbitrap™ mass spectrometers, which have long been the gold standard for high resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high quality quantitative measurements across a wide dynamic range. We also use a newly developed extra-cellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5,000 plasma proteins in a 60-minute gradient with the Orbitrap Astral mass spectrometer.

Emergence of mass spectrometry detergents for membrane proteomics

Detergents enable the investigation of membrane proteins by mass spectrometry. Detergent designers aim to improve underlying methodologies and are confronted with the challenge to design detergents with optimal solution and gas-phase properties. Herein, we review literature related to the optimization of detergent chemistry and handling and identify an emerging research direction: the optimization of mass spectrometry detergents for individual applications in mass spectrometry-based membrane proteomics. We provide an overview about qualitative design aspects including their relevance for the optimization of detergents in bottom-up proteomics, top-down proteomics, native mass spectrometry, and Nativeomics. In addition to established design aspects, such as charge, concentration, degradability, detergent removal, and detergent exchange, it becomes apparent that detergent heterogeneity is a promising key driver for innovation. We anticipate that rationalizing the role of detergent structures in membrane proteomics will serve as an enabling step for the analysis of challenging biological systems.

Expanded Proteomic Survey of the Human Parasite Leishmania major Focusing on Changes in Null Mutants of the Golgi GDP-Mannose/Fucose/Arabinopyranose Transporter LPG2 and of the Mitochondrial Fucosyltransferase FUT1

The trypanosomatid protozoan parasite Leishmania has a significant impact on human health globally. Understanding the pathways associated with virulence within this significant pathogen is critical for identifying novel vaccination and chemotherapy targets. Within this study we leverage an ultradeep proteomic approach to improve our understanding of two virulence-associated genes in Leishmania, encoding the Golgi mannose/arabinopyranose/fucose nucleotide-sugar transporter (LPG2) and the mitochondrial fucosyltransferase (FUT1). Using deep peptide fractionation followed by complementary fragmentation approaches with higher-energy collisional dissociation (HCD) and electron transfer dissociation (ETD) allowed the identification of over 6,500 proteins, nearly doubling the experimentally known Leishmania major proteome. This deep proteomic analysis revealed significant quantitative differences in both Δlpg2- and Δfut1s mutants with FUT1-dependent changes linked to marked alterations within mitochondrion-associated proteins, while LPG2-dependent changes impacted many pathways, including the secretory pathway. While the FUT1 enzyme has been shown to fucosylate peptides in vitro, no evidence for protein fucosylation was identified within our ultradeep analysis, nor did we observe fucosylated glycans within Leishmania glycopeptides isolated using hydrophilic interaction liquid chromatography (HILIC) enrichment. This work provides a critical resource for the community on the observable Leishmania proteome as well as highlighting phenotypic changes associated with LPG2 or FUT1, ablation of which may guide the development of future therapeutics. IMPORTANCE Leishmania is a widespread trypanosomatid protozoan parasite of humans, with ~12 million cases currently, ranging from mild to fatal, and hundreds of millions asymptomatically infected. This work advances knowledge of the experimental proteome by nearly 2-fold, to more than 6,500 proteins and thus provides a great resource to investigators seeking to decode how this parasite is transmitted and causes disease and to identify new targets for therapeutic intervention. The ultradeep proteomics approach identified potential proteins underlying the "persistence-without-pathology" phenotype of mutants with deletion of the Golgi nucleotide transporter LPG2, showing many alterations and several candidates. Studies of a rare mutant with deletion of the mitochondrial fucosyltransferase FUT1 revealed changes underlying its strong mitochondrial dysfunction but did not reveal examples of fucosylation of either peptides or N-glycans. This suggests that this vital protein's elusive target(s) may be more complex than the methods used could detect or that this target may not be a protein but perhaps another glycoconjugate or glycolipid.

Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry

Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRAS^G12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRAS^G12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

A liver secretome gene signature-based approach for determining circulating biomarkers of NAFLD severity

Non-invasive biomarkers of non-alcoholic fatty liver disease (NAFLD) supporting diagnosis and monitoring disease progression are urgently needed. The present study aimed to establish a bioinformatics pipeline capable of defining and validating NAFLD biomarker candidates based on paired hepatic global gene expression and plasma bioanalysis from individuals representing different stages of histologically confirmed NAFLD (no/mild, moderate, more advanced NAFLD). Liver secretome gene signatures were generated in a patient cohort of 26 severely obese individuals with the majority having no or mild fibrosis. To this end, global gene expression changes were compared between individuals with no/mild NAFLD and moderate/advanced NAFLD with subsequent filtering for candidate gene products with liver-selective expression and secretion. Four candidate genes, including LPA (lipoprotein A), IGFBP-1 (insulin-like growth factor-binding protein 1), SERPINF2 (serpin family F member 2) and MAT1A (methionine adenosyltransferase 1A), were differentially expressed in moderate/advanced NAFLD, which was confirmed in three independent RNA sequencing datasets from large, publicly available NAFLD studies. The corresponding gene products were quantified in plasma samples but could not discriminate among different grades of NAFLD based on NAFLD activity score. Conclusion: We demonstrate a novel approach based on the liver transcriptome allowing for identification of secreted hepatic gene products as potential circulating diagnostic biomarkers of NAFLD. Using this approach in larger NAFLD patient cohorts may yield potential circulating biomarkers for NAFLD severity.

Mass spectrometry and the cellular surfaceome

The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.

Activation of non-classical NMDA receptors by glycine impairs barrier function of brain endothelial cells

Blood–brain barrier (BBB) integrity is necessary to maintain homeostasis of the central nervous system (CNS). NMDA receptor (NMDAR) function and expression have been implicated in BBB integrity. However, as evidenced in neuroinflammatory conditions, BBB disruption contributes to immune cell infiltration and propagation of inflammatory pathways. Currently, our understanding of the pathophysiological role of NMDAR signaling on endothelial cells remains incomplete. Thus, we investigated NMDAR function on primary mouse brain microvascular endothelial cells (MBMECs). We detected glycine-responsive NMDAR channels, composed of functional GluN1, GluN2A and GluN3A subunits. Importantly, application of glycine alone, but not glutamate, was sufficient to induce NMDAR-mediated currents and an increase in intracellular Ca²⁺ concentrations. Functionally, glycine-mediated NMDAR activation leads to loss of BBB integrity and changes in actin distribution. Treatment of oocytes that express NMDARs composed of different subunits, with GluN1 and GluN3A binding site inhibitors, resulted in abrogation of NMDAR signaling as measured by two-electrode voltage clamp (TEVC). This effect was only detected in the presence of the GluN2A subunits, suggesting the latter as prerequisite for pharmacological modulation of NMDARs on brain endothelial cells. Taken together, our findings argue for a novel role of glycine as NMDAR ligand on endothelial cells shaping BBB integrity.

The emerging landscape of spatial profiling technologies

Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.

Quantification of Protein Glycosylation Using Nanopores

Although nanopores can be used for single-molecule sequencing of nucleic acids using low-cost portable devices, the characterization of proteins and their modifications has yet to be established. Here, we show that hydrophilic or glycosylated peptides translocate too quickly across FraC nanopores to be recognized. However, high ionic strengths (i.e., 3 M LiCl) and low pH (i.e., pH 3) together with using a nanopore with a phenylalanine at its constriction allows the recognition of hydrophilic peptides, and to distinguish between mono- and diglycosylated peptides. Using these conditions, we devise a nanopore method to detect, characterize, and quantify post-translational modifications in generic proteins, which is one of the pressing challenges in proteomic analysis.

New Views of Old Proteins: Clarifying the Enigmatic Proteome

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

Single-Cell Chemical Proteomics (SCCP) Interrogates the Timing and Heterogeneity of Cancer Cell Commitment to Death

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

Protein Sequencing, One Molecule at a Time

Despite tremendous gains over the past decade, methods for characterizing proteins have generally lagged behind those for nucleic acids, which are characterized by extremely high sensitivity, dynamic range, and throughput. However, the ability to directly characterize proteins at nucleic acid levels would address critical biological challenges such as more sensitive medical diagnostics, deeper protein quantification, large-scale measurement, and discovery of alternate protein isoforms and modifications and would open new paths to single-cell proteomics. In response to this need, there has been a push to radically improve protein sequencing technologies by taking inspiration from high-throughput nucleic acid sequencing, with a particular focus on developing practical methods for single-molecule protein sequencing (SMPS). SMPS technologies fall generally into three categories: sequencing by degradation (e.g., mass spectrometry or fluorosequencing), sequencing by transit (e.g., nanopores or quantum tunneling), and sequencing by affinity (as in DNA hybridization-based approaches). We describe these diverse approaches, which range from those that are already experimentally well-supported to the merely speculative, in this nascent field striving to reformulate proteomics.

Obtaining Complete Human Proteomes

Proteins are the molecular effectors of the information encoded in the genome. Proteomics aims at understanding the molecular functions of proteins in their biological context. In contrast to transcriptomics and genomics, the study of proteomes provides deeper insight into the dynamic regulatory layers encoded at the protein level, such as posttranslational modifications, subcellular localization, cell signaling, and protein-protein interactions. Currently, mass spectrometry (MS)-based proteomics is the technology of choice for studying proteomes at a system-wide scale, contributing to clinical biomarker discovery and fundamental molecular biology. MS technologies are continuously being developed to fulfill the requirements of speed, resolution, and quantitative accuracy, enabling the acquisition of comprehensive proteomes. In this review, we present how MS technology and acquisition methods have evolved to meet the requirements of cutting-edge proteomics research, which is describing the human proteome and its dynamic posttranslational modifications with unprecedented depth. Finally, we provide a perspective on studying proteomes at single-cell resolution. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Opportunity of Proteomics to Advance the Understanding of Intra- and Extracellular Regulation of Malignant Hematopoiesis

Fetal and adult hematopoiesis are regulated by largely distinct sets of cell-intrinsic gene regulatory networks as well as extracellular cues in their respective microenvironment. These ontogeny-specific programs drive hematopoietic stem and progenitor cells (HSPCs) in fetus and adult to divergent susceptibility to initiation and progression of hematological malignancies, such as leukemia. Elucidating how leukemogenic hits disturb the intra- and extracellular programs in HSPCs along ontogeny will provide a better understanding of the causes for age-associated differences in malignant hematopoiesis and facilitate the improvement of strategies for prevention and treatment of pediatric and adult acute leukemia. Here, we review current knowledge of the intrinsic and extrinsic programs regulating normal and malignant hematopoiesis, with a particular focus on the differences between infant and adult acute leukemia. We discuss the recent advances in mass spectrometry-based proteomics and its opportunity for resolving the interplay of cell-intrinsic and niche-associated factors in regulating malignant hematopoiesis.

Landscape of surfaceome and endocytome in human glioma is divergent and depends on cellular spatial organization

Cancer immunotherapies, including checkpoint inhibitor blocking antibodies and antibody drug conjugates, currently revolutionize cancer treatment. However, a remaining challenge is the identification of tumor surfaceome (TS) targets for the design of more rational, individualized treatments. We have developed a procedure for unbiased mapping of TS targets in glioblastoma (GBM), i.e., the most common primary malignant brain tumor that remains among the most aggressive forms of cancer, and for which attempts to find effective treatments have failed so far. The present study provides additional layers of understanding fundamental to the future development of immunotherapy strategies, as well as procedures for proteomics-based target identification aimed at a better understanding of how to harness the TS for personalized immunotherapy.

Therapeutic strategies directed at the tumor surfaceome (TS), including checkpoint inhibitor blocking antibodies, antibody drug conjugates (ADCs), and chimeric antigen receptor T (CAR-T) cells, provide a new armament to fight cancer. However, a remaining bottleneck is the lack of strategies to comprehensively interrogate patient tumors for potential TS targets. Here, we have developed a platform (tumor surfaceome mapping [TS-MAP]) integrated with a newly curated TS classifier (SURFME) that allows profiling of primary 3D cultures and intact patient glioma tumors with preserved tissue architecture. Moreover, TS-MAP specifically identifies proteins capable of endocytosis as tractable targets for ADCs and other modalities requiring toxic payload internalization. In high-grade gliomas that remain among the most aggressive forms of cancer, we show that cellular spatial organization (2D vs. 3D) fundamentally transforms the surfaceome and endocytome (e.g., integrins, proteoglycans, semaphorins, and cancer stem cell markers) with general implications for target screening approaches, as exemplified by an ADC targeting EGFR. The TS-MAP platform was further applied to profile the surfaceome and endocytome landscape in a cohort of freshly resected gliomas. We found a highly diverse TS repertoire between patient tumors, not directly associated with grade and histology, which highlights the need for individualized approaches. Our data provide additional layers of understanding fundamental to the future development of immunotherapy strategies, as well as procedures for proteomics-based target identification and selection. The TS-MAP platform should be widely applicable in efforts aiming at a better understanding of how to harness the TS for personalized immunotherapy.

Generation of Peptides for Highly Efficient Proximity Utilizing Site-Specific Biotinylation in Cells

Protein tags are peptide sequences genetically embedded into a recombinant protein for various purposes, such as affinity purification, Western blotting, and immunofluorescence. Another recent application of peptide tags is in vivo labeling and analysis of protein–protein interactions (PPI) by proteomics methods. One of the common workflows involves site-specific in vivo biotinylation of an AviTag-fused protein in the presence of the biotin ligase BirA. However, due to the rapid kinetics of labeling, this tag is not ideal for analysis of PPI. Here we describe the rationale, design, and protocol for the new biotin acceptor peptides BAP1070 and BAP1108 using modular assembling of biotin acceptor fragments, DNA sequencing, transient expression of proteins in cells, and Western blotting methods. These tags were used in the Proximity Utilizing Biotinylation (PUB) method, which is based on coexpression of BAP-X and BirA-Y in mammalian cells, where X or Y are candidate interacting proteins of interest. By changing the sequence of these peptides, a low level of background biotinylation is achieved, which occurs due to random collisions of proteins in cells. Over 100 plasmid constructs, containing genes of transcription factors, histones, gene repressors, and other nuclear proteins were obtained during implementation of projects related to this method.

Impact of hemolysis on multi-OMIC pancreatic biomarker discovery to derisk biomarker development in precision medicine studies

Cancer biomarker discovery is critically dependent on the integrity of biofluid and tissue samples acquired from study participants. Multi-omic profiling of candidate protein, lipid, and metabolite biomarkers is confounded by timing and fasting status of sample collection, participant demographics and treatment exposures of the study population. Contamination by hemoglobin, whether caused by hemolysis during sample preparation or underlying red cell fragility, contributes 0–10 g/L of extraneous protein to plasma, serum, and Buffy coat samples and may interfere with biomarker detection and validation. We analyzed 617 plasma, 701 serum, and 657 buffy coat samples from a 7-year longitudinal multi-omic biomarker discovery program evaluating 400+ participants with or at risk for pancreatic cancer, known as Project Survival. Hemolysis was undetectable in 93.1% of plasma and 95.0% of serum samples, whereas only 37.1% of buffy coat samples were free of contamination by hemoglobin. Regression analysis of multi-omic data demonstrated a statistically significant correlation between hemoglobin concentration and the resulting pattern of analyte detection and concentration. Although hemolysis had the greatest impact on identification and quantitation of the proteome, distinct differentials in metabolomics and lipidomics were also observed and correlated with severity. We conclude that quality control is vital to accurate detection of informative molecular differentials using OMIC technologies and that caution must be exercised to minimize the impact of hemolysis as a factor driving false discovery in large cancer biomarker studies.

Ultrasensitive Trace Sample Proteomics Unraveled the Protein Remodeling during Mesenchymal-Amoeboid Transition

Deep mining the proteome of trace biological samples is critical for biomedical applications. However, it remains a challenge due to the loss of analytes caused by current sample preparation procedures. To address this, we recently developed a single-pot and miniaturized in-solution digestion (SMID) method for minute sample handling with three streamlined steps and completed within 3 h. The SMID approach outperformed the traditional workflow in substantially saving time, reducing sample loss, and exhibiting extensive applicability for 10-100 000 cell analysis. This user-friendly and high-sensitivity strategy enables ∼5300 proteins and 53 000 peptides to be confidently identified within 1 h of mass spectrometry (MS) time from a small amount of 1000 HeLa cells. In addition, we accurately and robustly detected proteomes in 10 mouse oocytes with excellent reproducibility. We further adopted SMID for the proteome analysis in cell migration under confinement, which induced cells to undergo a mesenchymal-amoeboid transition (MAT). During the MAT, a systematic quantitative proteome map of 1000 HeLa cells was constructed with seven expression profile clusters, which illustrated the application of SMID and provided a fundamental resource to investigate the mechanism of MAT.

Evaluation of FRET X for single-molecule protein fingerprinting

Single-molecule protein identification is an unrealized concept with potentially ground-breaking applications in biological research. We propose a method called FRET X (Förster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands and accumulated FRET efficiencies constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines, and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

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We propose a FRET-based single-molecule protein identification method

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Peptides are experimentally distinguishable by their fingerprints

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Our approach can classify the constituents of complex samples with 95% accuracy

Secretome Analysis of Rabbit and Human Mesenchymal Stem and Endothelial Progenitor Cells: A Comparative Study

Human adipose tissue-derived mesenchymal stem cells (AT-MSCs) have been studied several years for their immunomodulatory effect through the paracrine mechanism and cytokine secretion. In combination with endothelial progenitor cells (EPCs), MSCs have great therapeutical potential for the repair of endothelium and wound healing. However, little is known about the cytokine profile of rabbit AT-MSCs or even EPCs. The aim of this study was to analyze the secretomes of these rabbit stem/progenitor cells. A large-scale human cytokine array (up to 80 cytokines) was used to identify and compare cytokines secreted into conditioned media of human and rabbit AT-MSCs as well as HUVECs and rabbit EPCs. Few cytokines were highly expressed by human AT-MSCs (TIMP-2, TIMP-1), HUVECs (MCP-1, TIMP-2, GRO, Angiogenin, IL-8, TIMP-1), or by rabbit EPCs (TIMP-2). Several cytokines have moderate expression by human (MCP-1, GRO, Angiogenin, TGF-β 2, IL-8, LIF, IL-6, Osteopontin, Osteoprotegerin) and rabbit AT-MSCs (TIMP-2, TGF-β 2, LIF, Osteopontin, IL-8, IL-5, IL-3) or by HUVECs (IL-6, MIF, TGF-β 2, GCP-2, IGFBP-2, Osteoprotegerin, EGF, LIF, PDGF-BB, MCP-3, Osteopontin, Leptin, IL-5, ENA-78, TNF-β) and rabbit EPCs (TGF-β 2, Osteopontin, GRO, LIF, IL-8, IL-5, IL-3). In conclusion, the proposed method seems to be useful for the secretome analysis of rabbit stem/progenitor cells.

A conjoint multi metal-ion iminodiacetic acid monolith microfluidic chip for structural-based protein pre-fractionation

PDMS-based multichannel microfluidic chip was designed and fabricated in a simple approach using readily available tools. UV-initiated in situ polymerization of poly(2-hydroxy ethyl methacrylate-co-di(ethylene glycol) diacrylate-co-N,N'-diallyl l-tartardiamide) in an Eppendorf tube was achieved within 40 min. This polymerization process was successfully translated to a microfluidic chip format without any further modifications. Iminodiacetic acid was successfully immobilized on aldehyde functional monoliths via Schiff base reaction and confirmed by FT-IR spectroscopy. Four transition metal ions (Co (II), Zn (II), Ni (II), and Cu (II)) were chelated individually on four IDA-monolith microfluidic chips. The conjoint metal-ion monolith microfluidic chip has displayed high permeability (9.40 × 10^-13  m² ) and a porosity of 32.8%. This affinity microfluidic chip has pre-fractioned four human plasma proteins (fibrinogen, immunoglobulin, transferrin, and human serum albumin) based on their surface-exposed histidine surface topography. A protein recovery of approximately 95% (Bradford assay data) was achieved. The multimonolith microchip can be reusable even after three protein adsorption-desorption cycles.

Herding cats: Label-based approaches in protein translocation through nanopore sensors for single-molecule protein sequence analysis

Proteins carry out life's essential functions. Comprehensive proteome analysis technologies are thus required for a full understanding of the operating principles of biological systems. While current proteomics techniques suffer from limitations in sensitivity and/or throughput, nanopore technology has the potential to enable de novo protein identification through single-molecule sequencing. However, a significant barrier to achieving this goal is controlling protein/peptide translocation through the nanopore sensor for processive strand analysis. Here, we review recent approaches that use a range of techniques, from oligonucleotide conjugation to molecular motors, aimed at driving protein strands and peptides through protein nanopores. We further discuss site-specific protein conjugation chemistry that could be combined with these translocation approaches as future directions to achieve single-molecule protein detection and sequencing of native proteins.

Characterizing Endogenous Protein Complexes with Biological Mass Spectrometry

Biological mass spectrometry (MS) encompasses a range of methods for characterizing proteins and other biomolecules. MS is uniquely powerful for the structural analysis of endogenous protein complexes, which are often heterogeneous, poorly abundant, and refractive to characterization by other methods. Here, we focus on how biological MS can contribute to the study of endogenous protein complexes, which we define as complexes expressed in the physiological host and purified intact, as opposed to reconstituted complexes assembled from heterologously expressed components. Biological MS can yield information on complex stoichiometry, heterogeneity, topology, stability, activity, modes of regulation, and even structural dynamics. We begin with a review of methods for isolating endogenous complexes. We then describe the various biological MS approaches, focusing on the type of information that each method yields. We end with future directions and challenges for these MS-based methods.

Quantitative Top-Down Proteomics by Isobaric Labeling with Thiol-Directed Tandem Mass Tags

While identification-centric (qualitative) top-down proteomics (TDP) has seen rapid progress in the recent past, the quantification of intact proteoforms within complex proteomes is still challenging. The by far mostly applied approach is label-free quantification, which, however, provides limited multiplexing capacity, and its use in combination with multidimensional separation is encountered with a number of problems. Isobaric labeling, which is a standard quantification approach in bottom-up proteomics, circumvents these limitations. Here, we introduce the application of thiol-directed isobaric labeling for quantitative TDP. For this purpose, we analyzed the labeling efficiency and optimized tandem mass spectrometry parameters for optimal backbone fragmentation for identification and reporter ion formation for quantification. Two different separation schemes, gel-eluted liquid fraction entrapment electrophoresis × liquid chromatography-mass spectrometry (LC-MS) and high/low-pH LC-MS, were employed for the analyses of either Escherichia coli (E. coli) proteomes or combined E. coli/yeast samples (two-proteome interference model) to study potential ratio compression. While the thiol-directed labeling introduces a bias in the quantifiable proteoforms, being restricted to Cys-containing proteoforms, our approach showed excellent accuracy in quantification, which is similar to that achievable in bottom-up proteomics. For example, 876 proteoforms could be quantified with high accuracy in an E. coli lysate. The LC-MS data were deposited to the ProteomeXchange with the dataset identifier PXD026310.

Proteomics in Liver Transplantation: A Systematic Review

Background

Although proteomics has been employed in the study of several models of liver injury, proteomic methods have only recently been applied not only to biomarker discovery and validation but also to improve understanding of the molecular mechanisms involved in transplantation.

Methods

The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and the guidelines for performing systematic literature reviews in bioinformatics (BiSLR). The PubMed, ScienceDirect, and Scopus databases were searched for publications through April 2020. Proteomics studies designed to understand liver transplant outcomes, including ischemia-reperfusion injury (IRI), rejection, or operational tolerance in human or rat samples that applied methodologies for differential expression analysis were considered.

Results

The analysis included 22 studies after application of the inclusion and exclusion criteria. Among the 497 proteins annotated, 68 were shared between species and 10 were shared between sample sources. Among the types of studies analyzed, IRI and rejection shared a higher number of proteins. The most enriched pathway for liver biopsy samples, IRI, and rejection was metabolism, compared to cytokine-cytokine receptor interactions for tolerance.

Conclusions

Proteomics is a promising technique to detect large numbers of proteins. However, our study shows that several technical issues such as the identification of proteoforms or the dynamic range of protein concentration in clinical samples hinder the successful identification of biomarkers in liver transplantation. In addition, there is a need to minimize the experimental variability between studies, increase the sample size and remove high-abundance plasma proteins.

Nanoparticle-Aided Nanoreactor for Nanoproteomics

Large-scale bottom-up proteomics of few even single cells is crucial for a better understanding of the roles played by cell-to-cell heterogeneity in disease and development. Novel proteomic methodologies with extremely high sensitivity are required for few even single-cell proteomics. Sample processing with high recovery and no contaminants is one key step. Here we developed a nanoparticle-aided nanoreactor for nanoproteomics (Nano3) technique for processing low-nanograms of mammalian cell proteins for proteome profiling. The Nano3 technique employed nanoparticles packed in a capillary channel to form a nanoreactor (≤30 nL) for concentrating, cleaning, and digesting proteins originally in a lysis buffer containing sodium dodecyl sulfate (SDS), followed by nanoRPLC-MS/MS analysis. The Nano3 method identified a 40-times higher number of proteins based on MS/MS from 2-ng mouse brain protein samples compared to the SP3 (single-pot solid-phase-enhanced sample preparation) method, which performed the sample processing using the nanoparticles in a 10 μL solution in an Eppendorf tube. The data indicates a drastically higher sample recovery of the Nano3 compared to the SP3 method for processing mass-limited proteome samples. In this pilot study, the Nano3 method was further applied in processing 10-1000 HeLa cells for bottom-up proteomics, producing 441 ± 263 (n = 4) (MS/MS) and 983 ± 292 (n = 4) [match between runs (MBR)+MS/MS] protein identifications from only 10 HeLa cells using a Q-Exactive HF mass spectrometer. The preliminary results render the Nano3 method a useful approach for processing few mammalian cells for proteome profiling.

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

Proteomic profiling of bladder cancer for precision medicine in the clinical setting: A review for the busy urologist

At present, proteomic methods have successfully identified potential biomarkers of urological malignancies, such as prostate cancer (PC), bladder cancer (BC), and renal cell carcinoma (RCC), reflecting different numbers of key cellular processes, including extracellular environment modification, invasion and metastasis, chemotaxis, differentiation, metabolite transport, and apoptosis. The potential application of proteomics in the detection of clinical markers of urological malignancies can help improve patient assessment through early cancer detection, prognosis, and treatment response prediction. A variety of proteomic studies have already been carried out to find prognostic BC biomarkers, and a large number of potential biomarkers have been reported. It is worth noting that proteomics research has not been applied to the study of predictive markers; this may be due to the incompatibility between the number of measured variables and the available sample size, which has become particularly evident in the study of therapeutic response. On the contrary, prognostic correlation is more common, which is also reflected in existing research. We are now entering an era of clinical proteomics. Driven by proteomic-based workflows, computing tools, and the applicability of cross-correlation of proteomic data, it is now feasible to use proteomic analysis to support personalized medicine. In this paper, we will summarize the current emerging technologies for advanced discovery, targeted proteomics, and proteomic applications in BC, particularly in discovery of human-based biomarkers.

Recent technical progress in sample preparation and liquid-phase separation-mass spectrometry for proteomic analysis of mass-limited samples

Mass spectrometry (MS)-based proteomics has enabled the identification and quantification of thousands of proteins from complex proteomes in a single experiment. However, its performance for mass-limited proteome samples (e.g., single cells and tissue samples from laser capture microdissection) is still not satisfying. The development of novel proteomic methodologies with better overall sensitivity is vital. During the last several years, substantial technical progress has been achieved for the preparation and liquid-phase separation-MS characterization of mass-limited proteome samples. In this review, we summarize recent technological progress of sample preparation, liquid chromatography (LC)-MS, capillary zone electrophoresis (CZE)-MS and MS instrumentation for bottom-up proteomics of trace biological samples, highlight some exciting applications of the novel techniques for single-cell proteomics, and provide a very brief perspective about the field at the end.

RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool

Every laboratory performing mass-spectrometry-based proteomics strives to generate high-quality data. Among the many factors that impact the outcome of any experiment in proteomics is the LC–MS system performance, which should be monitored within each specific experiment and also long term. This process is termed quality control (QC). We present an easy-to-use tool that rapidly produces a visual, HTML-based report that includes the key parameters needed to monitor the LC–MS system performance, with a focus on monitoring the performance within an experiment. The tool, named RawBeans, generates a report for individual files or for a set of samples from a whole experiment. We anticipate that it will help proteomics users and experts evaluate raw data quality independent of data processing. The tool is available at https://bitbucket.org/incpm/prot-qc/downloads. The mass-spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD022816.

Instrumental analysis of RNA modifications

Organisms from all domains of life invest a substantial amount of energy for the introduction of RNA modifications into nearly all transcripts studied to date. Instrumental analysis of RNA can focus on the modified residues and reveal the function of these epitranscriptomic marks. Here, we will review recent advances and breakthroughs achieved by NMR spectroscopy, sequencing, and mass spectrometry of the epitranscriptome.