R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies

Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

A novel mistranslating tRNA model in Drosophila melanogaster has diverse, sexually dimorphic effects

Transfer RNAs (tRNAs) are the adaptor molecules required for reading the genetic code and producing proteins. Transfer RNA variants can lead to genome-wide mistranslation, the misincorporation of amino acids not specified by the standard genetic code into nascent proteins. While genome sequencing has identified putative mistranslating transfer RNA variants in human populations, little is known regarding how mistranslation affects multicellular organisms. Here, we create a multicellular model of mistranslation by integrating a serine transfer RNA variant that mistranslates serine for proline (tRNAUGG,G26ASer) into the Drosophila melanogaster genome. We confirm mistranslation via mass spectrometry and find that tRNAUGG,G26ASer misincorporates serine for proline at a frequency of ∼0.6% per codon. tRNAUGG,G26ASer extends development time and decreases the number of flies that reach adulthood. While both sexes of adult flies containing tRNAUGG,G26ASer present with morphological deformities and poor climbing performance, these effects are more pronounced in female flies and the impact on climbing performance is exacerbated by age. This model will enable studies into the synergistic effects of mistranslating transfer RNA variants and disease-causing alleles.

Humanized mice reveal a macrophage-enriched gene signature defining human lung tissue protection during SARS-CoV-2 infection

The human immunological mechanisms defining the clinical outcome of SARS-CoV-2 infection remain elusive. This knowledge gap is mostly driven by the lack of appropriate experimental platforms recapitulating human immune responses in a controlled human lung environment. Here, we report a mouse model (i.e., HNFL mice) co-engrafted with human fetal lung xenografts (fLX) and a myeloid-enhanced human immune system to identify cellular and molecular correlates of lung protection during SARS-CoV-2 infection. Unlike mice solely engrafted with human fLX, HNFL mice are protected against infection, severe inflammation, and histopathological phenotypes. Lung tissue protection from infection and severe histopathology associates with macrophage infiltration and differentiation and the upregulation of a macrophage-enriched signature composed of 11 specific genes mainly associated with the type I interferon signaling pathway. Our work highlights the HNFL model as a transformative platform to investigate, in controlled experimental settings, human myeloid immune mechanisms governing lung tissue protection during SARS-CoV-2 infection.

A Non-Hazardous Deparaffinization Protocol Enables Quantitative Proteomics of Core Needle Biopsy-Sized Formalin-Fixed and Paraffin-Embedded (FFPE) Tissue Specimens

Most human tumor tissues that are obtained for pathology and diagnostic purposes are formalin-fixed and paraffin-embedded (FFPE). To perform quantitative proteomics of FFPE samples, paraffin has to be removed and formalin-induced crosslinks have to be reversed prior to proteolytic digestion. A central component of almost all deparaffinization protocols is xylene, a toxic and highly flammable solvent that has been reported to negatively affect protein extraction and quantitative proteome analysis. Here, we present a 'green' xylene-free protocol for accelerated sample preparation of FFPE tissues based on paraffin-removal with hot water. Combined with tissue homogenization using disposable micropestles and a modified protein aggregation capture (PAC) digestion protocol, our workflow enables streamlined and reproducible quantitative proteomic profiling of FFPE tissue. Label-free quantitation of FFPE cores from human ductal breast carcinoma in situ (DCIS) xenografts with a volume of only 0.79 mm³ showed a high correlation between replicates (r² = 0.992) with a median %CV of 16.9%. Importantly, this small volume is already compatible with tissue micro array (TMA) cores and core needle biopsies, while our results and its ease-of-use indicate that further downsizing is feasible. Finally, our FFPE workflow does not require costly equipment and can be established in every standard clinical laboratory.

Fe3+-NTA magnetic beads as an alternative to spin column-based phosphopeptide enrichment

Protein phosphorylation is a central mechanism of cellular signal transduction in living organisms. Phosphoproteomic studies systematically catalogue and characterize alterations in phosphorylation states across multiple cellular conditions and are often incorporated into global proteomics experiments. Previously, we found that spin column-based Fe³⁺-NTA enrichment integrated well with our workflow but remained a bottleneck for methods that require higher throughput or a scale that is beyond the capacity of these columns. Here, we compare our well-established spin column-based enrichment strategy with one encompassing magnetic beads. Our data show little difference when using either method in terms of the number of identified phosphopeptides as well as their physicochemical properties. In all, we illustrate how the potentially scalable and automation-friendly magnetic Fe³⁺-NTA beads can seamlessly substitute spin column-based Fe³⁺-NTA agarose beads for global phosphoproteome profiling. SIGNIFICANCE: Protein phosphorylation plays a key role in regulating a multitude of biological processes and can lead to insights into disease pathogenesis. Methodologies which can efficiently enrich phosphopeptides in a scalable and high-throughput manner are essential for profiling dynamic phosphoproteomes. Here we compare two phosphopeptide enrichment workflows, a well-established spin column-based strategy with agarose Fe³⁺-NTA beads and a strategy using magnetic Fe³⁺-NTA beads. Our data suggest that the scalable and automation-friendly magnetic bead-based workflow is an equivalent, but more flexible, enrichment strategy for phosphoproteome profiling experiments.

Decoupling astrocytes in adult mice impairs synaptic plasticity and spatial learning

The mechanisms by which astrocytes modulate neural homeostasis, synaptic plasticity, and memory are still poorly explored. Astrocytes form large intercellular networks by gap junction coupling, mainly composed of two gap junction channel proteins, connexin 30 (Cx30) and connexin 43 (Cx43). To circumvent developmental perturbations and to test whether astrocytic gap junction coupling is required for hippocampal neural circuit function and behavior, we generate and study inducible, astrocyte-specific Cx30 and Cx43 double knockouts. Surprisingly, disrupting astrocytic coupling in adult mice results in broad activation of astrocytes and microglia, without obvious signs of pathology. We show that hippocampal CA1 neuron excitability, excitatory synaptic transmission, and long-term potentiation are significantly affected. Moreover, behavioral inspection reveals deficits in sensorimotor performance and a complete lack of spatial learning and memory. Together, our findings establish that astrocytic connexins and an intact astroglial network in the adult brain are vital for neural homeostasis, plasticity, and spatial cognition.

Proximity labeling for investigating protein-protein interactions

The study of protein complexes and protein-protein interactions is of great importance due to their fundamental roles in cellular function. Proximity labeling, often coupled with mass spectrometry, has become a powerful and versatile tool for studying protein-protein interactions by enriching and identifying proteins in the vicinity of a specified protein-of-interest. Here, we describe and compare traditional approaches to investigate protein-protein interactions to current day state-of-the-art proximity labeling methods. We focus on the wide array of proximity labeling strategies and underscore studies using diverse model systems to address numerous biological questions. In addition, we highlight current advances in mass spectrometry-based technology that exhibit promise in improving the depth and breadth of the data acquired in proximity labeling experiments. In all, we show the diversity of proximity labeling strategies and emphasize the broad range of applications and biological inquiries that can be addressed using this technology.

A Proteome-Level Investigation Into Plasmodiophora brassicae Resistance in Brassica napus Canola

Clubroot of Brassicaceae, an economically important soil borne disease, is caused by Plasmodiophora brassicae Woronin, an obligate, biotrophic protist. This disease poses a serious threat to canola and related crops in Canada and around the globe causing significant losses. The pathogen is continuously evolving and new pathotypes are emerging, which necessitates the development of novel resistant canola cultivars to manage the disease. Proteins play a crucial role in many biological functions and the identification of differentially abundant proteins (DAP) using proteomics is a suitable approach to understand plant-pathogen interactions to assist in the development of gene specific markers for developing clubroot resistant (CR) cultivars. In this study, P. brassicae pathotype 3 (P3H) was used to challenge CR and clubroot susceptible (CS) canola lines. Root samples were collected at three distinct stages of pathogenesis, 7-, 14-, and 21-days post inoculation (DPI), protein samples were isolated, digested with trypsin and subjected to liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis. A total of 937 proteins demonstrated a significant (q-value < 0.05) change in abundance in at least in one of the time points when compared between control and inoculated CR-parent, CR-progeny, CS-parent, CS-progeny and 784 proteins were significantly (q < 0.05) changed in abundance in at least in one of the time points when compared between the inoculated- CR and CS root proteomes of parent and progeny across the three time points tested. Functional annotation of differentially abundant proteins (DAPs) revealed several proteins related to calcium dependent signaling pathways. In addition, proteins related to reactive oxygen species (ROS) biochemistry, dehydrins, lignin, thaumatin, and phytohormones were identified. Among the DAPs, 73 putative proteins orthologous to CR proteins and quantitative trait loci (QTL) associated with eight CR loci in different chromosomes including chromosomes A3 and A8 were identified. Proteins including BnaA02T0335400WE, BnaA03T0374600WE, BnaA03T0262200WE, and BnaA03T0464700WE are orthologous to identified CR loci with possible roles in mediating clubroot responses. In conclusion, these results have contributed to an improved understanding of the mechanisms involved in mediating response to P. brassicae in canola at the protein level.

Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution

Dynamic change in subcellular localization of signaling proteins is a general concept that eukaryotic cells evolved for eliciting a coordinated response to stimuli. Mass spectrometry-based proteomics in combination with subcellular fractionation can provide comprehensive maps of spatio-temporal regulation of protein networks in cells, but involves laborious workflows that does not cover the phospho-proteome level. Here we present a high-throughput workflow based on sequential cell fractionation to profile the global proteome and phospho-proteome dynamics across six distinct subcellular fractions. We benchmark the workflow by studying spatio-temporal EGFR phospho-signaling dynamics in vitro in HeLa cells and in vivo in mouse tissues. Finally, we investigate the spatio-temporal stress signaling, revealing cellular relocation of ribosomal proteins in response to hypertonicity and muscle contraction. Proteomics data generated in this study can be explored through https://SpatialProteoDynamics.github.io .

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Regulating Expression of Mistranslating tRNAs by Readthrough RNA Polymerase II Transcription

Transfer RNA (tRNA) variants that alter the genetic code increase protein diversity and have many applications in synthetic biology. Since the tRNA variants can cause a loss of proteostasis, regulating their expression is necessary to achieve high levels of novel protein. Mechanisms to positively regulate transcription with exogenous activator proteins like those often used to regulate RNA polymerase II (RNAP II)-transcribed genes are not applicable to tRNAs as their expression by RNA polymerase III requires elements internal to the tRNA. Here, we show that tRNA expression is repressed by overlapping transcription from an adjacent RNAP II promoter. Regulating the expression of the RNAP II promoter allows inverse regulation of the tRNA. Placing either Gal4- or TetR–VP16-activated promoters downstream of a mistranslating tRNA^Ser variant that misincorporates serine at proline codons in Saccharomyces cerevisiae allows mistranslation at a level not otherwise possible because of the toxicity of the unregulated tRNA. Using this inducible tRNA system, we explore the proteotoxic effects of mistranslation on yeast cells. High levels of mistranslation cause cells to arrest in the G1 phase. These cells are impermeable to propidium iodide, yet growth is not restored upon repressing tRNA expression. High levels of mistranslation increase cell size and alter cell morphology. This regulatable tRNA expression system can be applied to study how native tRNAs and tRNA variants affect the proteome and other biological processes. Variations of this inducible tRNA system should be applicable to other eukaryotic cell types.

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

Strategies for mass spectrometry-based phosphoproteomics using isobaric tagging

INTRODUCTION

Protein phosphorylation is a primary mechanism of signal transduction in cellular systems. Isobaric tagging can be used to investigate alterations in phosphorylation events in sample multiplexing experiments where quantification extends across all conditions. As such, innovations in tandem mass tag methods can facilitate the expansion of the depth and breadth of phosphoproteomic analyses.

AREAS COVERED

This review discusses the current state of tandem mass tag-centric phosphoproteomics and highlights advances in reagent chemistry, instrumentation, data acquisition, and data analysis. We stress that approaches for phosphoproteomic investigations require high-specificity enrichment, sensitive detection, and accurate phosphorylation site localization.

EXPERT OPINION

Tandem mass tag-centric phosphoproteomics will continue to be an important conduit for our understanding of signal transduction in living organisms. We anticipate that progress in phosphopeptide enrichment methodologies, enhancements in instrumentation and data acquisition technologies, and further refinements in analytical strategies will be key to the discovery of biologically relevant findings from phosphoproteomics studies.

The amino acid substitution affects cellular response to mistranslation

Mistranslation, the misincorporation of an amino acid not specified by the "standard" genetic code, occurs in all organisms. tRNA variants that increase mistranslation arise spontaneously and engineered tRNAs can achieve mistranslation frequencies approaching 10% in yeast and bacteria. Interestingly, human genomes contain tRNA variants with the potential to mistranslate. Cells cope with increased mistranslation through multiple mechanisms, though high levels cause proteotoxic stress. The goal of this study was to compare the genetic interactions and the impact on transcriptome and cellular growth of two tRNA variants that mistranslate at a similar frequency but create different amino acid substitutions in Saccharomyces cerevisiae. One tRNA variant inserts alanine at proline codons whereas the other inserts serine for arginine. Both tRNAs decreased growth rate, with the effect being greater for arginine to serine than for proline to alanine. The tRNA that substituted serine for arginine resulted in a heat shock response. In contrast, heat shock response was minimal for proline to alanine substitution. Further demonstrating the significance of the amino acid substitution, transcriptome analysis identified unique up- and down-regulated genes in response to each mistranslating tRNA. Number and extent of negative synthetic genetic interactions also differed depending upon type of mistranslation. Based on the unique responses observed for these mistranslating tRNAs, we predict that the potential of mistranslation to exacerbate diseases caused by proteotoxic stress depends on the tRNA variant. Furthermore, based on their unique transcriptomes and genetic interactions, different naturally occurring mistranslating tRNAs have the potential to negatively influence specific diseases.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Decoding post translational modification crosstalk with proteomics

Post-translational modification (PTM) of proteins allows cells to regulate protein functions, transduce signals and respond to perturbations. PTMs expand protein functionality and diversity, which leads to increased proteome complexity. PTM crosstalk describes the combinatorial action of multiple PTMs on the same or on different proteins for higher order regulation. Here we review how recent advances in proteomic technologies, mass spectrometry instrumentation, and bioinformatics spurred the proteome-wide identification of PTM crosstalk through measurements of PTM sites. We provide an overview of the basic modes of PTM crosstalk, the proteomic methods to elucidate PTM crosstalk, and approaches that can inform about the functional consequences of PTM crosstalk.

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Description of basic modules and different modes of PTM crosstalk.

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Overview of current proteomic methods to identify and infer PTM crosstalk.

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Discussion of large-scale approaches to characterize functional PTM crosstalk.

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Future directions and potential proteomic methods for elucidating PTM crosstalk.

University of Washington Nathan Shock Center: innovation to advance aging research

The University of Washington Nathan Shock Center of Excellence in the Basic Biology of Aging provides leadership and resources to support the geroscience community locally, nationally, and internationally. Services are provided through our Resource Cores and funds are available annually to support pilot projects by external investigators. Aging-related studies involving proteomics, metabolomics, invertebrate model organisms, and bioinformatics/artificial intelligence are supported by our Cores. The UW Nathan Shock Center also serves as the administrative home for a Geropathology Research Resource. In addition, the Center works in conjunction with the University of Washington Healthy Aging and Longevity Research Institute to organize and support an annual Seminar Series in the Biology of Aging, an annual 1-day Geroscience Symposium, didactic training for the Biological Mechanisms of Healthy Aging Training Program, and other strategic initiatives. Our Center also supports the American Aging Association Annual Meeting, and we have recently partnered with the American Aging Association and the JAX Aging Center to create a set of video lectures on select topics in geroscience as part of the AGE Presents Video Lecture Series.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

A Semiautomated Paramagnetic Bead-Based Platform for Isobaric Tag Sample Preparation

The development of streamlined and high-throughput sample processing workflows is important for capitalizing on emerging advances and innovations in mass spectrometry-based applications. While the adaptation of new technologies and improved methodologies is fast paced, automation of upstream sample processing often lags. Here we have developed and implemented a semiautomated paramagnetic bead-based platform for isobaric tag sample preparation. We benchmarked the robot-assisted platform by comparing the protein abundance profiles of six common parental laboratory yeast strains in triplicate TMTpro16-plex experiments against an identical set of experiments in which the samples were manually processed. Both sets of experiments quantified similar numbers of proteins and peptides with good reproducibility. Using these data, we constructed an interactive website to explore the proteome profiles of six yeast strains. We also provide the community with open-source templates for automating routine proteomics workflows on an opentrons OT-2 liquid handler. The robot-assisted platform offers a versatile and affordable option for reproducible sample processing for a wide range of protein profiling applications.

Process analytics 4.0: A paradigm shift in rapid analytics for biologics development

Analytical testing of product quality attributes and process parameters during the biologics development (Process analytics) has been challenging due to the rapid growth of biomolecules with complex modalities to support unmet therapeutic needs. Thus, the expansion of the process analytics tool box for rapid analytics with the deployment of cutting-edge technologies and cyber-physical systems is a necessity. We introduce the term, Process Analytics 4.0; which entails not only technology aspects such as process analytical technology (PAT), assay automation, and high-throughput analytics, but also cyber-physical systems that enable data management, visualization, augmented reality, and internet of things (IoT) infrastructure for real time analytics in process development environment. This review is exclusively focused on dissecting high-level features of PAT, automation, and data management with some insights into the business aspects of implementing during process analytical testing in biologics process development. Significant technological and business advantages can be gained with the implementation of digitalization, automation, and real time testing. A systematic development and employment of PAT in process development workflows enable real time analytics for better process understanding, agility, and sustainability. Robotics and liquid handling workstations allow rapid assay and sample preparation automation to facilitate high-throughput testing of attributes and molecular properties which are otherwise challenging to monitor with PAT tools due to technological and business constraints. Cyber-physical systems for data management, visualization, and repository must be established as part of Process Analytics 4.0 framework. Furthermore, we review some of the challenges in implementing these technologies based on our expertise in process analytics for biopharmaceutical drug substance development.

Discovery of Arginine Methylation, Phosphorylation, and Their Co-occurrence in Condensate-Associated Proteins in Saccharomyces cerevisiae

The formation of condensates in membraneless organelles is thought to be driven by protein phase separation. Arginine methylation and serine/threonine phosphorylation are important in the phase separation process; however, these post-translational modifications are often present in intrinsically disordered regions that are difficult to analyze with standard proteomic techniques. To understand their presence and co-occurrence in condensate-associated proteins, here, we use a multiprotease and multi-tandem mass spectrometry (MS/MS) fragmentation approach, coupled with heavy methyl stable isotope labeling of amino acids in cell culture (SILAC) and phospho- or methyl-peptide enrichment. For Saccharomyces cerevisiae, we report a 50% increase in the known arginine methylproteome, involving 15 proteins that are all condensate-associated. Importantly, some of these proteins have arginine methylation on all predicted sites-providing evidence that this modification can be pervasive. We explored whether arginine-methylated, condensate-associated proteins are also phosphorylated and found 12 such proteins to carry phosphorylated serine or threonine. In Npl3, Ded1, and Sbp1, single peptides were found to carry both modifications, indicating a co-occurrence in close proximity and on the same protein molecule. These co-modifications occur in regions of disorder, whereas arginine methylation is typically on regions of disorder that are also basic. For phosphorylation, its association with charged regions of condensate-associated proteins was less consistent, although some regions with multisite phosphorylation sites were strongly acidic. We conclude that arginine-methylated proteins associated with condensates are typically also modified with protein phosphorylation.

Advances in quantitative high-throughput phosphoproteomics with sample multiplexing

Eukaryotic protein phosphorylation modulates nearly every major biological process. Phosphorylation regulates protein activity, mediates cellular signal transduction, and manipulates cellular structure. Consequently, the dysregulation of kinase and phosphatase pathways has been linked to a multitude of diseases. Mass spectrometry-based proteomic techniques are increasingly used for the global interrogation of perturbations in phosphorylation-based cellular signaling. Strategies for studying phosphoproteomes require high-specificity enrichment, sensitive detection, and accurate localization of phosphorylation sites with advanced LC-MS/MS techniques and downstream informatics. Sample multiplexing with isobaric tags has also been integral to recent advancements in throughput and sensitivity for phosphoproteomic studies. Each of these facets of phosphoproteomics analysis present distinct challenges and thus opportunities for improvement and innovation. Here, we review current methodologies, explore persistent challenges, and discuss the outlook for isobaric tag-based quantitative phosphoproteomic analysis.

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice

Performing large-scale plasma proteome profiling is challenging due to limitations imposed by lengthy preparation and instrument time. We present a fully automated multiplexed proteome profiling platform (AutoMP3) using the Hamilton Vantage liquid handling robot capable of preparing hundreds to thousands of samples. To maximize protein depth in single-shot runs, we combined 16-plex Tandem Mass Tags (TMTpro) with high-field asymmetric waveform ion mobility spectrometry (FAIMS Pro) and real-time search (RTS). We quantified over 40 proteins/min/sample, doubling the previously published rates. We applied AutoMP3 to investigate the naked mole-rat plasma proteome both as a function of the circadian cycle and in response to ultraviolet (UV) treatment. In keeping with the lack of synchronized circadian rhythms in naked mole-rats, we find few circadian patterns in plasma proteins over the course of 48 h. Furthermore, we quantify many disparate changes between mice and naked mole-rats at both 48 h and one week after UV exposure. These species differences in plasma protein temporal responses could contribute to the pronounced cancer resistance observed in naked mole-rats. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD022891.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

Zirconium(IV)-IMAC Revisited: Improved Performance and Phosphoproteome Coverage by Magnetic Microparticles for Phosphopeptide Affinity Enrichment

Phosphopeptide enrichment is an essential step in large-scale, quantitative phosphoproteomics by mass spectrometry. Several phosphopeptide affinity enrichment techniques exist, such as immobilized metal-ion affinity chromatography (IMAC) and metal oxide affinity chromatography (MOAC). We compared zirconium(IV) IMAC (Zr-IMAC) magnetic microparticles to more commonly used titanium(IV) IMAC (Ti-IMAC) and TiO₂ magnetic microparticles for phosphopeptide enrichment from simple and complex protein samples prior to phosphopeptide sequencing and characterization by mass spectrometry (liquid chromatography-tandem mass spectrometry, LC-MS/MS). We optimized sample-loading conditions to increase phosphopeptide recovery for Zr-IMAC-, Ti-IMAC-, and TiO₂-based workflows by 22, 24, and 35%, respectively. The optimized protocol resulted in improved performance of Zr-IMAC over Ti-IMAC and TiO₂ as well as high-performance liquid chromatography-based Fe(III)-IMAC with up to 23% more identified phosphopeptides. The different enrichment chemistries showed a high degree of overlap but also differences in phosphopeptide selectivity and complementarity. We conclude that Zr-IMAC improves phosphoproteome coverage and recommend that this complementary and scalable affinity enrichment method is more widely used in biological and biomedical studies of cell signaling and the search for biomarkers. Data are available via ProteomeXchange with identifier PXD018273.

Insights from the First Phosphopeptide Challenge of the MS Resource Pillar of the HUPO Human Proteome Project

Mass spectrometry has greatly improved the analysis of phosphorylation events in complex biological systems and on a large scale. Despite considerable progress, the correct identification of phosphorylated sites, their quantification, and their interpretation regarding physiological relevance remain challenging. The MS Resource Pillar of the Human Proteome Organization (HUPO) Human Proteome Project (HPP) initiated the Phosphopeptide Challenge as a resource to help the community evaluate methods, learn procedures and data analysis routines, and establish their own workflows by comparing results obtained from a standard set of 94 phosphopeptides (serine, threonine, tyrosine) and their nonphosphorylated counterparts mixed at different ratios in a neat sample and a yeast background. Participants analyzed both samples with their method(s) of choice to report the identification and site localization of these peptides, determine their relative abundances, and enrich for the phosphorylated peptides in the yeast background. We discuss the results from 22 laboratories that used a range of different methods, instruments, and analysis software. We reanalyzed submitted data with a single software pipeline and highlight the successes and challenges in correct phosphosite localization. All of the data from this collaborative endeavor are shared as a resource to encourage the development of even better methods and tools for diverse phosphoproteomic applications. All submitted data and search results were uploaded to MassIVE (https://massive.ucsd.edu/) as data set MSV000085932 with ProteomeXchange identifier PXD020801.

Organoids Model Transcriptional Hallmarks of Oncogenic KRAS Activation in Lung Epithelial Progenitor Cells

Mutant KRAS is a common driver in epithelial cancers. Nevertheless, molecular changes occurring early after activation of oncogenic KRAS in epithelial cells remain poorly understood. We compared transcriptional changes at single-cell resolution after KRAS activation in four sample sets. In addition to patient samples and genetically engineered mouse models, we developed organoid systems from primary mouse and human induced pluripotent stem cell-derived lung epithelial cells to model early-stage lung adenocarcinoma. In all four settings, alveolar epithelial progenitor (AT2) cells expressing oncogenic KRAS had reduced expression of mature lineage identity genes. These findings demonstrate the utility of our in vitro organoid approaches for uncovering the early consequences of oncogenic KRAS expression. This resource provides an extensive collection of datasets and describes organoid tools to study the transcriptional and proteomic changes that distinguish normal epithelial progenitor cells from early-stage lung cancer, facilitating the search for targets for KRAS-driven tumors.

Addressing Cellular Heterogeneity in Cancer through Precision Proteomics

Cells exhibit a broad spectrum of functions driven by differences in molecular phenotype. Understanding the heterogeneity between and within cell types has led to advances in our ability to diagnose and manipulate biological systems. Heterogeneity within and between tumors still poses a challenge to the development and efficacy of therapeutics. In this Perspective we review the toolkit of protein-level experimental approaches for investigating cellular heterogeneity. We describe how innovative approaches and technical developments have supported the advent of bottom-up single-cell proteomic analysis and present opportunities and challenges within cancer research. Finally, we introduce the concept of "precision proteomics" and discuss how the advantages and limitations of various experimental approaches render them suitable for different biological systems and questions.

Importance of protein Ser/Thr/Tyr phosphorylation for bacterial pathogenesis

Protein phosphorylation regulates a large variety of biological processes in all living cells. In pathogenic bacteria, the study of serine, threonine, and tyrosine (Ser/Thr/Tyr) phosphorylation has shed light on the course of infectious diseases, from adherence to host cells to pathogen virulence, replication, and persistence. Mass spectrometry (MS)-based phosphoproteomics has provided global maps of Ser/Thr/Tyr phosphosites in bacterial pathogens. Despite recent developments, a quantitative and dynamic view of phosphorylation events that occur during bacterial pathogenesis is currently lacking. Temporal, spatial, and subpopulation resolution of phosphorylation data is required to identify key regulatory nodes underlying bacterial pathogenesis. Herein, we discuss how technological improvements in sample handling, MS instrumentation, data processing, and machine learning should improve bacterial phosphoproteomic datasets and the information extracted from them. Such information is expected to significantly extend the current knowledge of Ser/Thr/Tyr phosphorylation in pathogenic bacteria and should ultimately contribute to the design of novel strategies to combat bacterial infections.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Recent advances in robotic protein sample preparation for clinical analysis and other biomedical applications

Discovery of new protein biomarker candidates has become a major research goal in the areas of clinical chemistry, analytical chemistry, and biomedicine. These important species constitute the molecular target when it comes to diagnosis, prognosis, and further monitoring of disease. However, their analysis requires powerful, selective and high-throughput sample preparation and product (analyte) characterisation approaches. In general, manual sample processing is tedious, complex and time-consuming, especially when large numbers of samples have to be processed (e.g., in clinical studies). Automation via microtiter-plate platforms involving robotics has brought improvements in high-throughput performance while comparable or even better precisions and repeatability (intra-day, inter-day) were achieved. At the same time, waste production and exposure of laboratory personnel to hazards were reduced. In comprehensive protein analysis workflows (e.g., liquid chromatography-tandem mass spectrometry analysis), sample preparation is an unavoidable step. This review surveys the recent achievements in automation of bottom-up and top-down protein and/or proteomics approaches. Emphasis is put on high-end multi-well plate robotic platforms developed for clinical analysis and other biomedical applications. The literature from 2013 to date has been covered.

A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients

State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 min LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument, we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

Multiparametric Assays for Accelerating Early Drug Discovery

Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.

Automated sample preparation with SP3 for low-input clinical proteomics

High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phase-enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands-on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low-input samples, reproducibly quantifying 500-1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end-to-end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost-effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non-clinical applications.