Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Marginalized measures: The harmonization of diversity in precision medicine research

The production of large, shareable datasets is increasingly prioritized for a wide range of research purposes. In biomedicine, especially in the United States, calls to enhance representation of historically underrepresented populations in databases that integrate genomic, health history, demographic and lifestyle data have also increased in order to support the goals of precision medicine. Understanding the assumptions and values that shape the design of such datasets and the practices through which they are constructed are a pressing area of social inquiry. We examine how diversity is conceptualized in U.S. precision medicine research initiatives, specifically attending to how measures of diversity, including race, ethnicity, and medically underserved status, are constructed and harmonized to build commensurate datasets. In three case studies, we show how symbolic embrace of both diversity and harmonization efforts can compromise the utility of diversity data. Although big data and diverse population representation are heralded as the keys to unlocking the promises of precision medicine research, these cases reveal core tensions between what kinds of data are seen as central to 'the science' and which are marginalized.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Targeted detection of endogenous LINE-1 proteins and ORF2p interactions

BACKGROUND

Both the expression and activities of LINE-1 (L1) retrotransposons are known to occur in numerous cell-types and are implicated in pathobiological contexts such as aging-related inflammation, autoimmunity, and in cancers. L1s encode two proteins that are translated from bicistronic transcripts. The translation product of ORF1 (ORF1p) has been robustly detected by immunoassays and shotgun mass spectrometry (MS). Yet, more sensitive detection methods would enhance the use of ORF1p as a clinical biomarker. In contrast, until now, no direct evidence of endogenous L1 ORF2 translation to protein (ORF2p) has been shown. Instead, assays for ORF2p have been limited to ectopic L1 ORF over-expression contexts and to indirect detection of endogenous ORF2p enzymatic activity, such as by the sequencing of de novo genomic insertions. Immunoassays for endogenous ORF2p have been problematic, producing apparent false positives due to cross-reactivities, and shotgun MS has not yielded reliable evidence of ORF2p peptides in biological samples.

RESULTS

Here we present targeted mass spectrometry assays, selected and parallel reaction monitoring (SRM and PRM, respectively) to detect and quantify L1 ORF1p and ORF2p at their endogenous abundances. We were able to quantify ORF1p and ORF2p present in our samples down to a range in the low attomoles. Confident in our ability to affinity enrich ORF2p, we describe an interactome associated with endogenous ORF2-containing macromolecular assemblies.

CONCLUSIONS

This is the first assay to demonstrate sensitive and robust quantitation of endogenous ORF2p. The ability to assay ORF2p directly and quantitatively will improve our understanding of the developmental and diseased cell states where L1 expression and its activity naturally occur. The ability to simultaneously assay endogenous L1 ORF1p and ORF2p is an important step forward for L1 analytical biochemistry. Endogenous ORF2p interactomes can now be presented with confidence that ORF2p is among the enriched proteins.

Standard operating procedure combined with comprehensive quality control system for multiple LC-MS platforms urinary proteomics

Urinary proteomics is emerging as a potent tool for detecting sensitive and non-invasive biomarkers. At present, the comparability of urinary proteomics data across diverse liquid chromatography-mass spectrometry (LC-MS) platforms remains an area that requires investigation. In this study, we conduct a comprehensive evaluation of urinary proteome across multiple LC-MS platforms. To systematically analyze and assess the quality of large-scale urinary proteomics data, we develop a comprehensive quality control (QC) system named MSCohort, which extracted 81 metrics for individual experiment and the whole cohort quality evaluation. Additionally, we present a standard operating procedure (SOP) for high-throughput urinary proteome analysis based on MSCohort QC system. Our study involves 20 LC-MS platforms and reveals that, when combined with a comprehensive QC system and a unified SOP, the data generated by data-independent acquisition (DIA) workflow in urine QC samples exhibit high robustness, sensitivity, and reproducibility across multiple LC-MS platforms. Furthermore, we apply this SOP to hybrid benchmarking samples and clinical colorectal cancer (CRC) urinary proteome including 527 experiments. Across three different LC-MS platforms, the analyses report high quantitative reproducibility and consistent disease patterns. This work lays the groundwork for large-scale clinical urinary proteomics studies spanning multiple platforms, paving the way for precision medicine research.

iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control

Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrate that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then prioritize 15 metrics and invite 21 experts to manually assess the quality of 2754 DIA files based on those metrics. We develop an AI model for DIA-based QC using 2110 training files. It achieves AUCs of 0.91 (LC) and 0.97 (MS) in the first validation dataset (n = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (n = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology.

Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia

Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics

Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42485-024-00166-4.

Proteomic analysis of serum in a population-based cohort did not reveal a biomarker for Modic changes

Introduction

Modic changes (MC) are bone marrow lesions of vertebral bones, which can be detected with magnetic resonance imaging (MRI) adjacent to degenerated intervertebral discs. Defined by their appearance on T1 and T2 weighted images, there are three interconvertible types: MC1, MC2, and MC3. The inter-observer variability of the MRI diagnosis is high, therefore a diagnostic serum biomarker complementing the MRI to facilitate diagnosis and follow-up would be of great value.

Methods

We used a highly sensitive and reproducible proteomics approach: DIA/SWATH-MS to find serum biomarkers in a subset of the Northern Finland Birth Cohort 1966. Separately, we measured a panel of factors involved in inflammation and angiogenesis to confirm some potential biomarkers published before with an ELISA-based method called V-Plex.

Results

We found neither an association between the serum concentrations of the proteins detected with DIA/SWATH-MS with the presence of MC, nor a correlation with the size of the MC lesions. We did not find any association between the factors measured with the V-Plex and the presence of MC or their size.

Conclusion

Altogether, our study suggests that a robust and generally usable biomarker to facilitate the diagnosis of MC cannot readily be found in serum.

HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson's disease classification

Purpose

Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts. Although data harmonization can mitigate this issue, current methods typically rely on large or paired datasets, which can be impractical to obtain in distributed setups.

Approach

We introduced HarmonyTM, a data harmonization method tailored for the TM. HarmonyTM effectively mitigates bias in the model's feature representation while retaining crucial disease-related information, all without requiring extensive datasets. Specifically, we employed adversarial training to "unlearn" bias from the features used in the model for classifying Parkinson's disease (PD). We evaluated HarmonyTM using multi-center three-dimensional (3D) neuroimaging datasets from 83 centers using 23 different scanners.

Results

Our results show that HarmonyTM improved PD classification accuracy from 72% to 76% and reduced (unwanted) scanner classification accuracy from 53% to 30% in the TM setup.

Conclusion

HarmonyTM is a method tailored for harmonizing 3D neuroimaging data within the TM approach, aiming to minimize shortcut learning in distributed setups. This prevents the disease classifier from leveraging scanner-specific details to classify patients with or without PD-a key aspect for deploying ML models for clinical applications.

Multi-omics analysis reveals the impact of influenza a virus host adaptation on immune signatures in pig tracheal tissue

Introduction

Influenza A virus (IAV) infection is a global respiratory disease, which annually leads to 3-5 million cases of severe illness, resulting in 290,000-650,000 deaths. Additionally, during the past century, four global IAV pandemics have claimed millions of human lives. The epithelial lining of the trachea plays a vital role during IAV infection, both as point of viral entry and replication as well as in the antiviral immune response. Tracheal tissue is generally inaccessible from human patients, which makes animal models crucial for the study of the tracheal host immune response.

Method

In this study, pigs were inoculated with swine- or human-adapted H1N1 IAV to gain insight into how host adaptation of IAV shapes the innate immune response during infection. In-depth multi-omics analysis (global proteomics and RNA sequencing) of the host response in upper and lower tracheal tissue was conducted, and results were validated by microfluidic qPCR. Additionally, a subset of samples was selected for histopathological examination.

Results

A classical innate antiviral immune response was induced in both upper and lower trachea after infection with either swine- or human-adapted IAV with upregulation of genes and higher abundance of proteins associated with viral infection and recognition, accompanied by a significant induction of interferon stimulated genes with corresponding higher proteins concentrations. Infection with the swine-adapted virus induced a much stronger immune response compared to infection with a human-adapted IAV strain in the lower trachea, which could be a consequence of a higher viral load and a higher degree of inflammation.

Discussion

Central components of the JAK-STAT pathway, apoptosis, pyrimidine metabolism, and the cytoskeleton were significantly altered depending on infection with swine- or human-adapted virus and might be relevant mechanisms in relation to antiviral immunity against putative zoonotic IAV. Based on our findings, we hypothesize that during host adaptation, IAV evolve to modulate important host cell elements to favor viral infectivity and replication.

Salmonella-driven intestinal edema in mice is characterized by tensed fibronectin fibers

Intestinal edema is a common manifestation of numerous gastrointestinal diseases and is characterized by the accumulation of fluid in the interstitial space of the intestinal wall. Technical advances in laser capture microdissection and low-biomass proteomics now allow us to specifically characterize the intestinal edema proteome. Using advanced proteomics, we identify peptides derived from antimicrobial factors with high signal intensity, but also highlight major contributions from the blood clotting system, extracellular matrix (ECM) and protease-protease inhibitor networks. The ECM is a complex fibrillar network of macromolecules that provides structural and mechanical support to the intestinal tissue. One abundant component of the ECM observed in Salmonella-driven intestinal edema is the glycoprotein fibronectin, recognized for its structure-function interplay regulated by mechanical forces. Using mechanosensitive staining of fibronectin fibers reveals that they are tensed in the edema, despite the high abundance of proteases able to cleave fibronectin. In contrast, fibronectin fibers increasingly relax in other cecal tissue areas as the infection progresses. Co-staining for fibrin(ogen) indicates the formation of a provisional matrix in the edema, similar to what is observed in response to skin injury, while collagen staining reveals a sparse and disrupted collagen fiber network. These observations plus the absence of low tensional fibronectin fibers and the additional finding of a high number of protease inhibitors in the edema proteome could indicate a critical role of stretched fibronectin fibers in maintaining tissue integrity in the severely inflamed cecum. Understanding these processes may also provide valuable functional diagnostic markers of intestinal disease progression in the future.

Probabilistic pathway-based multimodal factor analysis

MOTIVATION

Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations.

RESULTS

We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts.

AVAILABILITY AND IMPLEMENTATION

The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA

BACKGROUND

Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring.

METHODS

Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides.

RESULTS

We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making.

CONCLUSIONS

Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis

Myelofibrosis is a hematopoietic stem cell disorder belonging to the myeloproliferative neoplasms. Myelofibrosis patients frequently carry driver mutations in either JAK2 or Calreticulin (CALR) and have limited therapeutic options. Here, we integrate ex vivo drug response and proteotype analyses across myelofibrosis patient cohorts to discover targetable vulnerabilities and associated therapeutic strategies. Drug sensitivities of mutated and progenitor cells were measured in patient blood using high-content imaging and single-cell deep learning-based analyses. Integration with matched molecular profiling revealed three targetable vulnerabilities. First, CALR mutations drive BET and HDAC inhibitor sensitivity, particularly in the absence of high Ras pathway protein levels. Second, an MCM complex-high proliferative signature corresponds to advanced disease and sensitivity to drugs targeting pro-survival signaling and DNA replication. Third, homozygous CALR mutations result in high endoplasmic reticulum (ER) stress, responding to ER stressors and unfolded protein response inhibition. Overall, our integrated analyses provide a molecularly motivated roadmap for individualized myelofibrosis patient treatment.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Serial and multi-level proteome analysis for microscale protein samples

Post-translational modifications (PTMs), such as phosphorylation and ubiquitination, play key roles in signal transduction and protein homeostasis. The crosstalk of PTMs greatly expands the components of proteome and protein functions. Multi-level proteome analysis, which involves proteome investigations of total lysate and PTMs in this context, provides a comprehensive approach to explore the PTM crosstalk of a biological system under diverse disturbances. However, multi-level proteome practice remains technically challenging. Here we intended to build a strategy for multi-level proteome analysis, in which we focus on the serial profiling the total proteome, ubiquitinome and phosphoproteome from the microscale of starting material. We started by evaluating five common lysis buffers and found that the sodium deoxycholate buffer provided the best overall performance. We then developed an approach for serial enrichment and profiling of the multi-level proteome. To expand the depth of identification, we customized the variable windows to perform data-independent acquisition (DIA) sequencing for each proteome. In total, we identified 6465 proteins, ∼20,000 GlyGly sites (class 1), and ∼ 19,000 phosphosites (class 1) sequentially using 1 mg of HeLa digest by three DIA measurements. We applied this strategy to analyze MG132-treated HeLa cells and observed the crosstalk between ubiquitination and phosphorylation. Our method can be referenced for other multi-level proteome studies with microscale samples. SIGNIFICANCE: Lysis buffer containing sodium deoxycholate provided the best overall performance in multi-level proteome analysis. One step of ubiquitination enrichment before phosphorylation enrichment does not reduce the reproducibility of phosphoproteome. Customized isolation windows were established for DIA analysis on each level of proteome. Combined the serial enrichment approach and the customized single-shot DIA method enabled the multi-level proteome of microscale protein samples.

Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of mass spectrometry methods (DO-MS). The DO-MS app v2.0 ( do-ms.slavovlab.net ) allows to optimize and evaluate results from both label free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant for single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication quality figures, that can be easily shared. The source code is available at: github.com/SlavovLab/DO-MS .

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

A multi-omics dataset of human transcriptome and proteome stable reference

The development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits their application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. The transcriptome and proteome of most cell lines shift during culturing, which limits their applicability as standard samples. In this study, we demonstrated that the human hepatocellular cell line MHCC97H has a very stable transcriptome (r = 0.983~0.997) and proteome (r = 0.966~0.988 for data-dependent acquisition, r = 0.970~0.994 for data-independent acquisition) after 9 subculturing generations, which allows this steady standard sample to be consistently produced on an industrial scale in long term. Moreover, this stability was maintained across labs and platforms. In sum, our study provides omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM.

Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA

Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Recent advances in the field of single-cell proteomics

The field of single-cell omics is rapidly progressing. Although DNA and RNA sequencing-based methods have dominated the field to date, global proteome profiling has also entered the main stage. Single-cell proteomics was facilitated by advancements in different aspects of mass spectrometry (MS)-based proteomics, such as instrument design, sample preparation, chromatography and ion mobility. Single-cell proteomics by mass spectrometry (scp-MS) has moved beyond being a mere technical development, and is now able to deliver actual biological application and has been successfully applied to characterize different cell states. Here, we review some key developments of scp-MS, provide a background to the field, discuss the various available methods and foresee possible future directions.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Proteotype coevolution and quantitative diversity across 11 mammalian species

Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.

High-throughput proteomic sample preparation using pressure cycling technology

High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. Here we describe a detailed protocol of pressure cycling technology (PCT)-assisted sample preparation for proteomic analysis of biopsy tissues. A piece of fresh frozen or formalin-fixed paraffin-embedded tissue weighing ~0.1–2 mg is placed in a 150 μL pressure-resistant tube called a PCT-MicroTube with proper lysis buffer. After closing with a PCT-MicroPestle, a batch of 16 PCT-MicroTubes are placed in a Barocycler, which imposes oscillating pressure to the samples from one atmosphere to up to ~3,000 times atmospheric pressure. The pressure cycling schemes are optimized for tissue lysis and protein digestion, and can be programmed in the Barocycler to allow reproducible, robust and efficient protein extraction and proteolysis digestion for mass spectrometry-based proteomics. This method allows effective preparation of not only fresh frozen and formalin-fixed paraffin-embedded tissue, but also cells, feces and tear strips. It takes ~3 h to process 16 samples in one batch. The resulting peptides can be analyzed by various mass spectrometry-based proteomics methods. We demonstrate the applications of this protocol with mouse kidney tissue and eight types of human tumors.

High-throughput lysis and proteolytic digestion of biopsy-level tissue specimens is a major bottleneck for clinical proteomics. This protocol describes pressure cycling technology (PCT)-assisted sample preparation of biopsy tissues.

A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

Study of Dimorphism Transition Mechanism of Tremella fuciformis Based on Comparative Proteomics

Tremella fuciformis is a dimorphic fungus that can undertake a reversible transition between yeast-like conidia and hyphal forms. The transformation mechanism and proteomic differences between these two forms have not been reported. Therefore, in this study, we attempted to explore the differential protein profiles of dikaryotic yeast-like conidia from fruiting bodies and mycelia (FBMds) and dikaryotic mycelia (DM) by synthetically applying high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) full proteomics and parallel reaction monitoring (PRM) targeted proteomics. The results showed that a total of 5687 proteins were quantified, and 2220 of them (39.01%) showed more than a two-fold change in expression. The functional analysis of the differentially expressed proteins (DEPs) confirmed that the DEPs were mainly located in the membrane and nucleus. The FBMds tended to express proteins involved in biosynthesis, metabolism, DNA replication and transcription, and DNA damage repair. At the same time, DM exhibited an increased expression of proteins involved in signal transduction mechanisms such as the mitogen-activated protein kinase (MAPK) signaling pathway and the Ras signaling pathway. Further, phosphorylation analysis confirmed the importance of the MAPK signaling pathway in T. fuciformis dimorphism, and comparative metabolism analysis demonstrated the metabolic difference between FBMds and DM. The information obtained in the present study will provide new insights into the difference between FBMds and DM and lay a foundation for further research on the dimorphism formation mechanism of T. fuciformis.

ERK1/2 Activity Is Critical for the Outcome of Ischemic Stroke

Ischemic disorders are the leading cause of death worldwide. The extracellular signal-regulated kinases 1 and 2 (ERK1/2) are thought to affect the outcome of ischemic stroke. However, it is under debate whether activation or inhibition of ERK1/2 is beneficial. In this study, we report that the ubiquitous overexpression of wild-type ERK2 in mice (ERK2^wt) is detrimental after transient occlusion of the middle cerebral artery (tMCAO), as it led to a massive increase in infarct volume and neurological deficits by increasing blood–brain barrier (BBB) leakiness, inflammation, and the number of apoptotic neurons. To compare ERK1/2 activation and inhibition side-by-side, we also used mice with ubiquitous overexpression of the Raf-kinase inhibitor protein (RKIP^wt) and its phosphorylation-deficient mutant RKIP^S153A, known inhibitors of the ERK1/2 signaling cascade. RKIP^wt and RKIP^S153A attenuated ischemia-induced damages, in particular via anti-inflammatory signaling. Taken together, our data suggest that stimulation of the Raf/MEK/ERK1/2-cascade is severely detrimental and its inhibition is rather protective. Thus, a tight control of the ERK1/2 signaling is essential for the outcome in response to ischemic stroke.

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.

Reproducible Determination of High-Density Lipoprotein Proteotypes

High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know

Artificial intelligence [AI] techniques are quickly spreading across medicine as an analytical method to tackle challenging clinical questions. What were previously thought of as highly complex data sources, such as images or free text, are now becoming manageable. Novel analytical methods merge the latest developments in information technology infrastructure with advances in computer science. Once primarily associated with Silicon Valley, AI techniques are now making their way into medicine, including in the field of inflammatory bowel diseases [IBD]. Understanding potential applications and limitations of these techniques can be difficult, in particular for busy clinicians. In this article, we explain the basic terminologies and provide a particular focus on the foundations behind state-of-the-art AI methodologies in both imaging and text. We explore the growing applications of AI in medicine, with a specific focus on IBD to inform the practising gastroenterologist and IBD specialist. Finally, we outline possible future uses of these technologies in daily clinical practice.

Longitudinal stability of urinary extracellular vesicle protein patterns within and between individuals

The protein content of urinary extracellular vesicles (EVs) is considered to be an attractive non-invasive biomarker source. However, little is known about the consistency and variability of urinary EV proteins within and between individuals over a longer time-period. Here, we evaluated the stability of the urinary EV proteomes of 8 healthy individuals at 9 timepoints over 6 months using data-independent-acquisition mass spectrometry. The 1802 identified proteins had a high correlation amongst all samples, with 40% of the proteome detected in every sample and 90% detected in more than 1 individual at all timepoints. Unsupervised analysis of top 10% most variable proteins yielded person-specific profiles. The core EV-protein-interaction network of 516 proteins detected in all measured samples revealed sub-clusters involved in the biological processes of G-protein signaling, cytoskeletal transport, cellular energy metabolism and immunity. Furthermore, gender-specific expression patterns were detected in the urinary EV proteome. Our findings indicate that the urinary EV proteome is stable in longitudinal samples of healthy subjects over a prolonged time-period, further underscoring its potential for reliable non-invasive diagnostic/prognostic biomarkers.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

Integrated Graphene Oxide with Noble Metal Nanoparticles to Develop High-Sensitivity Fiber Optic Particle Plasmon Resonance (FOPPR) Biosensor for Biomolecules Determination

In this research, a direct, simple and ultrasensitive fiber optic particle plasmon resonance (FOPPR) biosensing platform for immunoglobulin G (IgG) detection was developed using a gold nanoparticle/graphene oxide (AuNP/GO) composite as signal amplification element. To obtain the best analytical performance of the sensor, experimental parameters including the surface concentration of GO on the AuNPs, formation time of the GO, the concentration of the anti-IgG and incubation time of anti-IgG were optimized. The calibration plots displayed a good linear relationship between the sensor response (ΔI/I₀) and the logarithm of the analyte concentrations over a linear range from 1.0 × 10⁻¹⁰ to 1.0 × 10⁻⁶ g/mL of IgG under the optimum conditions. A limit of detection (LOD) of 0.038 ng/mL for IgG was calculated from the standard calibration curve. The plot has a linear relationship (correlation coefficient, R = 0.9990). The analytical performance of present work’s biosensor was better than that of our previously reported mixed self-assembled monolayer of 11-mercaptoundecanoic acid/6-mercapto-1-hexanol (MUA/MCH = 1:4) method by about three orders of magnitude. The achieved good sensitivity may be attributed to the synergistic effect between GO and AuNPs in this study. In addition, GO could immobilize more antibodies due to the abundant carboxylic groups on its surface. Furthermore, we also demonstrated that the results from this sensor have good reproducibility, with coefficients of variation (CVs) < 8% for IgG. Therefore, the present strategy provides a novel and convenient method for chemical and biochemical quantification and determination.