Comparison of Protein Quantification in a Complex Background by DIA and TMT Workflows with Fixed Instrument Time

MSLibrarian: Optimized Predicted Spectral Libraries for Data-Independent Acquisition Proteomics

Data-independent acquisition-mass spectrometry (DIA-MS) is the method of choice for deep, consistent, and accurate single-shot profiling in bottom-up proteomics. While classic workflows for targeted quantification from DIA-MS data require auxiliary data-dependent acquisition (DDA) MS analysis of subject samples to derive prior-knowledge spectral libraries, library-free approaches based on in silico prediction promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses are however limited, in part, by the large library size and persistent deviations from the experimental data. We present MSLibrarian, a new workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library and analysis approach. Predicted-vs-observed comparisons enabled optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups, and optimization of the library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded experiment of species-mixed proteins and quantitative ratio-validation confirmed gains of up to 13% on peptide and 8% on protein level at equivalent FDR control and validation criteria. MSLibrarian is made available as an open-source R software package, including step-by-step user instructions, at https://github.com/MarcIsak/MSLibrarian.

Multiplexed single-cell proteomics using SCoPE2

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.

Deep representation features from DreamDIAXMBD improve the analysis of data-independent acquisition proteomics

We developed DreamDIA^XMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.

Evaluation of the Sensitivity and Reproducibility of Targeted Proteomic Analysis Using Data-Independent Acquisition for Serum and Cerebrospinal Fluid Proteins

There is a need for targeted analysis of biological fluids for diagnosis, prognosis, or monitoring the progression of diseases. Cerebrospinal fluid (CSF) and serum have been widely used for the development of protein analysis for neurodegenerative diseases and other diseases, respectively. Recently, data-independent acquisition (DIA) mass spectrometry (MS) has been developed to increase the throughput over data-dependent acquisition (DDA) on screening of a large number of samples and discovery of candidate targets. When it comes to target validation, the analytical performance of targeted analysis is critical. However, the inter- and intralaboratory analytical performances of the DIA-MS for targeted proteomic analysis of CSF and serum samples have not yet been investigated. In this study, we showed that the DIA-MS approach allowed us to identify and quantify 1732 CSF and 424 serum proteins, with 90% of proteins identified and quantified in at least 50% of DIA-MS runs. To evaluate the sensitivity, linearity, and dynamic range of the DIA approach, we included the stable isotope-labeled (SI) peptides into CSF and serum samples with serial dilutions. The lower limit of quantification (LLOQ) of peptides was 0.1-0.5 fmol, and the dynamic range was over 3.53 orders of magnitude, with excellent linearity (r2 < 0.978) in CSF and serum samples. Finally, the reproducibility of the DIA-MS approach was evaluated using entire proteins identified in CSF and serum samples. The intralaboratory three replicate results showed reliable reproducibility with 12.5 and 17.3% of the median coefficient of variation (CV) in both CSF and serum matrices, whereas the median CVs of interlaboratory three replicates were 23.8 and 32.0% in CSF and serum samples, respectively. The comparison of the quantitative result between replicates showed close similarity at intra- and interlaboratories with a median Pearson correlation value of >0.98 in CSF and serum, respectively. In conclusion, we demonstrate the capability of the DIA approach as a targeted proteomic analysis for candidate proteins from CSF and serum samples.

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.

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

Quantification of Changes in Protein Expression Using SWATH Proteomics

Sequential Window Acquisition of all THeoretical fragment ion spectra (SWATH) is a data independent acquisition mode used to accurately quantify thousands of proteins in a biological sample in a single run. It exploits fast scanning hybrid mass spectrometers to combine accuracy, reproducibility and sensitivity. This method requires the use of ion libraries, a sort of databases of spectral and chromatographic information about the proteins to be quantified. In this chapter, a typical workflow of SWATH experiment is described, from the sample preparation to the analysis of proteomics data.

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.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

GMPPA defects cause a neuromuscular disorder with α-dystroglycan hyperglycosylation

GDP-mannose-pyrophosphorylase-B (GMPPB) facilitates the generation of GDP-mannose, a sugar donor required for glycosylation. GMPPB defects cause muscle disease due to hypoglycosylation of α-dystroglycan (α-DG). Alpha-DG is part of a protein complex, which links the extracellular matrix with the cytoskeleton, thus stabilizing myofibers. Mutations of the catalytically inactive homolog GMPPA cause alacrima, achalasia, and mental retardation syndrome (AAMR syndrome), which also involves muscle weakness. Here, we showed that Gmppa-KO mice recapitulated cognitive and motor deficits. As structural correlates, we found cortical layering defects, progressive neuron loss, and myopathic alterations. Increased GDP-mannose levels in skeletal muscle and in vitro assays identified GMPPA as an allosteric feedback inhibitor of GMPPB. Thus, its disruption enhanced mannose incorporation into glycoproteins, including α-DG in mice and humans. This increased α-DG turnover and thereby lowered α-DG abundance. In mice, dietary mannose restriction beginning after weaning corrected α-DG hyperglycosylation and abundance, normalized skeletal muscle morphology, and prevented neuron degeneration and the development of motor deficits. Cortical layering and cognitive performance, however, were not improved. We thus identified GMPPA defects as the first congenital disorder of glycosylation characterized by α-DG hyperglycosylation, to our knowledge, and we have unraveled underlying disease mechanisms and identified potential dietary treatment options.

Proteomic characterization of the human lens and Cataractogenesis

INTRODUCTION

The goal of this review is to highlight the triumphs and frontiers in measurement of the lens proteome as it relates to onset of age-related nuclear cataract. As global life expectancy increases, so too does the frequency of age-related nuclear cataracts. Molecular therapeutics do not exist for delay or relief of cataract onset in humans. Since lens fiber cells are incapable of protein synthesis after initial maturation, age-related changes in proteome composition and post-translational modification accumulation can be measured with various techniques. Several of these modifications have been associated with cataract onset.

AREAS COVERED

We discuss the impact of long-lived proteins on the lens proteome and lens homeostasis as well as proteomic techniques that may be used to measure proteomes at various levels of proteomic specificity and spatial resolution.

EXPERT OPINION

There is clear evidence that several proteome modifications are correlated with cataract formation. Past studies should be enhanced with cutting-edge, spatially resolved mass spectrometry techniques to enhance the specificity and sensitivity of modification detection as it relates to cataract formation.

The rise of single‐cell proteomics

Mass spectrometry‐based proteomics comprehensively defines proteome expression patterns in thousands of cells majorly contributing to our current understanding of many biological processes. More recently, single‐cell transcriptome and genome studies, however, have demonstrated overwhelming heterogeneity of tissues and cellular subpopulations. These studies have indicated different cellular functionality and identity, which are mainly driven by proteins and their posttranscriptional modifications. The rapidly emerging field of single‐cell proteomics aims at complementing transcriptome and genome data by generating comparative protein expression profiles from individual cells. Recent developments demonstrated tremendous improvements in sample preparation workflows and MS instrumentation, quantifying over 1000 proteins from a single cell. Efficient and reproducible sample processing in conjunction with sensitive MS acquisition strategies will allow to further increase the proteome coverage of tissues with single‐cell resolution. The required throughput and data reliability of such studies are still subject to further developments. Therefore, we herein discuss recent progress on specialized workflows and instrumentation next to advancements outside the field, which we expect to contribute to the development of comprehensive single‐cell proteomics.

Benchmarking Quantitative Performance in Label-Free Proteomics

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.

A critical comparison of three MS-based approaches for quantitative proteomics analysis

MS-based proteomics is expanding its role as a routine tool for biological discovery. Nevertheless, the task of accurately and precisely quantifying thousands of analytes in a single experiment remains challenging. In this study, the diagnostic accuracy of three popular data-dependent methods for protein relative quantification (label-free [LF], dimethyl labelling [DML] and tandem mass tags [TMT]) has been assessed using a mixed species proteome (three species) and five experimental replicates per condition. Data were produced using a quadrupole-Orbitrap mass spectrometer and analysed using a single platform (the MaxQuant/Perseus software suite). The whole comparative analysis was repeated three times over a period of 6 months, in order to assess the consistency of the reported findings. As expected, label-based methods reproducibly provided a lower false positives rate, whereas TMT and LF performed similarly, and significantly better than DML, in terms of proteome coverage using the same instrument time. Although parameters like proteome coverage and precision were consistent in between replicates, other parameters like sensitivity, intended as the capacity of correctly classifying true positives (regulated proteins), were found to be less reproducible, especially at challenging fold-changes (1.5). Collectively, data suggest that an increased interest in data reproducibility would be desirable in the quantitative proteomics field.

Blockade of High-Fat Diet Proteomic Phenotypes Using Exercise as Prevention or Treatment

The increasing consumption of high-fat foods combined with a lack of exercise is a major contributor to the burden of obesity in humans. Aerobic exercise such as running is known to provide metabolic benefits, but how the overconsumption of a high-fat diet (HFD) and exercise interact is not well characterized at the molecular level. Here, we examined the plasma proteome in mice for the effects of aerobic exercise as both a treatment and as a preventative regimen for animals on either a HFD or a healthy control diet. This analysis detected large changes in the plasma proteome induced by the HFD, such as increased abundance of SERPINA7, ALDOB, and downregulation of SERPINA1E and complement factor D (CFD; adipsin). Some of these changes were significantly reverted using exercise as a preventative measure but not as a treatment regimen. To determine if either the intensity or duration of exercise influenced the outcome, we compared high-intensity interval training and endurance running. Endurance running slightly outperformed high-intensity interval training exercise, but overall, both provided similar reversion in abundance of plasma proteins modulated by the HFD, including SERPINA7, apolipoprotein E, SERPINA1E, and CFD. Finally, we compared the changes induced by overconsumption of a HFD with previous data from mice fed on an isocaloric high-saturated fatty acid or polyunsaturated fatty acid diet. This identified several common changes, including not only increased apolipoprotein C-II and apolipoprotein E but also highlighted changes specific for overconsumption of a HFD (fructose-bisphosphate aldolase B, SERPINA7, and CFD), saturated fatty acid-based diets (SERPINA1E), or polyunsaturated fatty acid-based diets (haptoglobin). Together, these data highlight the importance of early intervention with exercise to revert HFD-induced phenotypes and suggest some of the molecular mechanisms leading to the changes in the plasma proteome generated by HFD consumption. Web-based interactive visualizations are provided for this dataset (larancelab.com/hfd-exercise), which give insight into diet and exercise phenotypic interactions on the plasma proteome.

Comparative Secretomics Gives Access to High Confident Secretome Data: Evaluation of Different Methods for the Determination of Bona Fide Secreted Proteins

Secretome analysis is broadly applied to understand the interplay between cells and their microenvironment. In particular, the unbiased analysis by mass spectrometry-based proteomics of conditioned medium has been successfully applied. In this context, several approaches have been developed allowing to distinguish proteins actively secreted by cells from proteins derived from culture medium or proteins released from dying cells. Here, three different methods comparing conditioned medium and lysate by quantitative mass spectrometry-based proteomics to identify bona fide secreted proteins are evaluated. Evaluation in three different human cell lines reveals that all three methods give access to a similar set of bona fide secreted proteins covering a broad abundance range. In the analyzed primary cells, that is, mesenchymal stromal cells and normal human dermal fibroblasts, more than 70% of the identified proteins are linked to classical secretion pathways. Furthermore, 4-12% are predicted to be released by unconventional secretion pathways. Interestingly, evidence of release by ectodomain shedding in a large number of the remaining candidate proteins is found. In summary, it is convinced that comparative secretomics is currently the method of choice to obtain high-confident secretome data and to identify novel candidates for unconventional protein secretion which have been neglected so far.

Single-cell Proteomics: Progress and Prospects

MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.

Enzymatic Dissociation Induces Transcriptional and Proteotype Bias in Brain Cell Populations

Different cell isolation techniques exist for transcriptomic and proteotype profiling of brain cells. Here, we provide a systematic investigation of the influence of different cell isolation protocols on transcriptional and proteotype profiles in mouse brain tissue by taking into account single-cell transcriptomics of brain cells, proteotypes of microglia and astrocytes, and flow cytometric analysis of microglia. We show that standard enzymatic digestion of brain tissue at 37 °C induces profound and consistent alterations in the transcriptome and proteotype of neuronal and glial cells, as compared to an optimized mechanical dissociation protocol at 4 °C. These findings emphasize the risk of introducing technical biases and biological artifacts when implementing enzymatic digestion-based isolation methods for brain cell analyses.

QuantPipe: A User-Friendly Pipeline Software Tool for DIA Data Analysis Based on the OpenSWATH-PyProphet-TRIC Workflow

Targeted analysis of data-independent acquisition (DIA) mass spectrometry data requires elegant software tools and strict statistical control. OpenSWATH-PyProphet-TRIC is a widely used DIA data analysis workflow. The OpenSWATH-PyProphet-TRIC workflow is typically executed by running command lines. Here, we present QuantPipe, which is a graphic interface software tool based on the OpenSWATH-PyProphet-TRIC workflow. In addition to OpenSWATH-PyProphet-TRIC functions, QuantPipe can convert the spectral library to the assay library and output peptides and protein intensities. We demonstrated that QuantPipe can be used to analyze SWATH-MS data from TripleTOF 5600 and TripleTOF 6600, phospho-SWATH-MS data, DIA data from Orbitrap instrument, and diaPASEF data from TimsTOF Pro instrument. The executable files, user manual, and source code of QuantPipe are freely available at https://github.com/tachengxmu/QuantPipe/releases.

Optimized Sample Preparation and Data Processing of Data-Independent Acquisition Methods for the Robust Quantification of Trace-Level Host Cell Protein Impurities in Antibody Drug Products

Host cell proteins (HCPs) are a major class of bioprocess-related impurities generated by the host organism and are generally present at low levels in purified biopharmaceutical products. The monitoring of these impurities is identified as an important critical quality attribute of monoclonal antibody (mAb) formulations not only due to the potential risk for the product stability and efficacy but also concerns linked to the immunogenicity of some of them. While overall HCP levels are usually monitored by enzyme-linked immunosorbent assay (ELISA), mass spectrometry (MS)-based approaches have been emerging as powerful and promising alternatives providing qualitative and quantitative information. However, a major challenge for liquid chromatography (LC)-MS-based methods is to deal with the wide dynamic range of drug products and the extreme sensitivity required to detect trace-level HCPs. In this study, we developed powerful and reproducible MS-based analytical workflows coupling optimized and efficient sample preparations, the library-free data-independent acquisition (DIA) method, and stringent validation criteria. The performances of several preparation protocols and DIA versus classical data-dependent acquisition (DDA) were evaluated using a series of four commercially available drug products. Depending on the selected protocols, the user has access to different information: on the one hand, a deep profiling of tens of identified HCPs and on the other hand, accurate and reproducible (coefficients of variation (CVs) < 12%) quantification of major HCPs. Overall, a final global HCP amount of a few tens of ng/mg mAb in these mAb samples was measured, while reaching a sensitivity down to the sub-ng/mg mAb level. Thus, this straightforward and robust approach can be intended as a routine quality control for any drug product analysis.

MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures

Tandem mass tag (TMT) is a multiplexing technology widely-used in proteomic research. It enables relative quantification of proteins from multiple biological samples in a single MS run with high efficiency and high throughput. However, experiments often require more biological replicates or conditions than can be accommodated by a single run, and involve multiple TMT mixtures and multiple runs. Such larger-scale experiments combine sources of biological and technical variation in patterns that are complex, unique to TMT-based workflows, and challenging for the downstream statistical analysis. These patterns cannot be adequately characterized by statistical methods designed for other technologies, such as label-free proteomics or transcriptomics. This manuscript proposes a general statistical approach for relative protein quantification in MS- based experiments with TMT labeling. It is applicable to experiments with multiple conditions, multiple biological replicate runs and multiple technical replicate runs, and unbalanced designs. It is based on a flexible family of linear mixed-effects models that handle complex patterns of technical artifacts and missing values. The approach is implemented in MSstatsTMT, a freely available open-source R/Bioconductor package compatible with data processing tools such as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine. Evaluation on a controlled mixture, simulated datasets, and three biological investigations with diverse designs demonstrated that MSstatsTMT balanced the sensitivity and the specificity of detecting differentially abundant proteins, in large-scale experiments with multiple biological mixtures.

Spatially resolved analysis of FFPE tissue proteomes by quantitative mass spectrometry

Bottom-up mass spectrometry-based proteomics relies on protein digestion and peptide purification. The application of such methods to broadly available clinical samples such as formalin-fixed and paraffin-embedded (FFPE) tissues requires reversal of chemical crosslinking and the removal of reagents that are incompatible with mass spectrometry. Here, we describe in detail a protocol that combines tissue disruption by ultrasonication, heat-induced antigen retrieval and two alternative methods for efficient detergent removal to enable quantitative proteomic analysis of limited amounts of FFPE material. To show the applicability of our approach, we used hepatocellular carcinoma (HCC) as a model system. By combining the described protocol with laser-capture microdissection, we were able to quantify the intra-tumor heterogeneity of a tumor specimen on the proteome level using a single slide with tissue of 10-µm thickness. We also demonstrate broader applicability to other tissues, including human gallbladder and heart. The procedure described in this protocol can be completed within 8 d.

Analyzing Ribosome Remodeling in Health and Disease

Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, evidence is reviewed demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. Such methods are reviewed, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. It is argued that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Impact of the Identification Strategy on the Reproducibility of the DDA and DIA Results

Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.

Integrated Proteomic and N-Glycoproteomic Analyses of Human Breast Cancer

Breast cancer is one of the most common cancers in women worldwide. In the past decades, many advances have been made in understanding and treating breast cancer. However, due to the highly heterogeneous nature of this disease, a precise characterization of breast cancer on the molecular level is of great importance but not yet readily available. In the present study, we systematically profiled proteomes and N-glycoproteomes of cancerous, paracancerous, and distal noncancerous tissues from patients with breast cancer. The data revealed distinct proteomic and N-glycoproteomic landscapes between different tissues, showing biological insights obtained from the two data sets were complementary. Specifically, the complement and angiogenesis pathways in the paracancerous tissues were activated. Taken together, the changes that occurred in paracancer tissue and N-glycoproteomics are important complements to the conventional proteomic analysis of cancer tissue. Their combination provides more precise and sensitive molecular correlates of breast cancer. Our data and strategy shed light on precisely defining breast cancer, providing valuable information for individual patient diagnosis and treatment. The MS data of this study have been deposited under the accession number IPX0001924000 at iProX.

Distinct and stage-specific contributions of TET1 and TET2 to stepwise cytosine oxidation in the transition from naive to primed pluripotency

Cytosine DNA bases can be methylated by DNA methyltransferases and subsequently oxidized by TET proteins. The resulting 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) are considered demethylation intermediates as well as stable epigenetic marks. To dissect the contributions of these cytosine modifying enzymes, we generated combinations of Tet knockout (KO) embryonic stem cells (ESCs) and systematically measured protein and DNA modification levels at the transition from naive to primed pluripotency. Whereas the increase of genomic 5-methylcytosine (5mC) levels during exit from pluripotency correlated with an upregulation of the de novo DNA methyltransferases DNMT3A and DNMT3B, the subsequent oxidation steps turned out to be far more complex. The strong increase of oxidized cytosine bases (5hmC, 5fC, and 5caC) was accompanied by a drop in TET2 levels, yet the analysis of KO cells suggested that TET2 is responsible for most 5fC formation. The comparison of modified cytosine and enzyme levels in Tet KO cells revealed distinct and differentiation-dependent contributions of TET1 and TET2 to 5hmC and 5fC formation arguing against a processive mechanism of 5mC oxidation. The apparent independent steps of 5hmC and 5fC formation suggest yet to be identified mechanisms regulating TET activity that may constitute another layer of epigenetic regulation.

A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.

Region-Specific Proteome Changes of the Intestinal Epithelium during Aging and Dietary Restriction

The small intestine is responsible for nutrient absorption and one of the most important interfaces between the environment and the body. During aging, changes of the epithelium lead to food malabsorption and reduced barrier function, thus increasing disease risk. The drivers of these alterations remain poorly understood. Here, we compare the proteomes of intestinal crypts from mice across different anatomical regions and ages. We find that aging alters epithelial immunity, metabolism, and cell proliferation and is accompanied by region-dependent skewing in the cellular composition of the epithelium. Of note, short-term dietary restriction followed by refeeding partially restores the epithelium by promoting stem cell differentiation toward the secretory lineage. We identify Hmgcs2 (3-hydroxy-3-methylglutaryl-coenzyme A [CoA] synthetase 2), the rate-limiting enzyme for ketogenesis, as a modulator of stem cell differentiation that responds to dietary changes, and we provide an atlas of region- and age-dependent proteome changes of the small intestine.

Metaproteomics characterizes human gut microbiome function in colorectal cancer

Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future.

Comparative Analysis of Quantitative Mass Spectrometric Methods for Subcellular Proteomics

Knowledge of intracellular location can provide important insights into the function of proteins and their respective organelles, and there is interest in combining classical subcellular fractionation with quantitative mass spectrometry to create global cellular maps. To evaluate mass spectrometric approaches specifically for this application, we analyzed rat liver differential centrifugation and Nycodenz density gradient subcellular fractions by tandem mass tag (TMT) isobaric labeling with reporter ion measurement at the MS2 and MS3 level and with two different label-free peak integration approaches, MS1 and data independent acquisition (DIA). TMT-MS2 provided the greatest proteome coverage, but ratio compression from contaminating background ions resulted in a narrower accurate dynamic range compared to TMT-MS3, MS1, and DIA, which were similar. Using a protein clustering approach to evaluate data quality by assignment of reference proteins to their correct compartments, all methods performed well, with isobaric labeling approaches providing the highest quality localization. Finally, TMT-MS2 gave the lowest percentage of missing quantifiable data when analyzing orthogonal fractionation methods containing overlapping proteomes. In summary, despite inaccuracies resulting from ratio compression, data obtained by TMT-MS2 assigned protein localization as well as other methods but achieved the highest proteome coverage with the lowest proportion of missing values.

Progress and pitfalls of using isobaric mass tags for proteome profiling

Introduction: Quantitative proteomics using mass spectrometry is performed via label-free or label-based approaches. Labeling strategies rely on the incorporation of stable heavy isotopes by metabolic, enzymatic, or chemical routes. Isobaric labeling uses chemical labels of identical masses but of different fragmentation behaviors to allow the relative quantitative comparison of peptide/protein abundances between biological samples.Areas covered: We have carried out a systematic review on the use of isobaric mass tags in proteomic research since their inception in 2003. We focused on their quantitative performances, their multiplexing evolution, as well as their broad use for relative quantification of proteins in pre-clinical models and clinical studies. Current limitations, primarily linked to the quantitative ratio distortion, as well as state-of-the-art and emerging solutions to improve their quantitative readouts are discussed.Expert opinion: The isobaric mass tag technology offers a unique opportunity to compare multiple protein samples simultaneously, allowing higher sample throughput and internal relative quantification for improved trueness and precision. Large studies can be performed when shared reference samples are introduced in multiple experiments. The technology is well suited for proteome profiling in the context of proteomic discovery studies.

Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition

In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Optimized Workflow for Multiplexed Phosphorylation Analysis of TMT-Labeled Peptides Using High-Field Asymmetric Waveform Ion Mobility Spectrometry

Phosphorylation is a post-translational modification with a vital role in cellular signaling. Isobaric labeling-based strategies, such as tandem mass tags (TMT), can measure the relative phosphorylation states of peptides in a multiplexed format. However, the low stoichiometry of protein phosphorylation constrains the depth of phosphopeptide analysis by mass spectrometry. As such, robust and sensitive workflows are required. Here we evaluate and optimize high-Field Asymmetric waveform Ion Mobility Spectrometry (FAIMS) coupled to Orbitrap Tribrid mass spectrometers for the analysis of TMT-labeled phosphopeptides. We determined that using FAIMS-MS3 with three compensation voltages (CV) in a single method (e.g., CV = -40/-60/-80 V) maximizes phosphopeptide coverage while minimizing inter-CV overlap. Furthermore, consecutive analyses using MSA-CID (multistage activation collision-induced dissociation) and HCD (higher-energy collisional dissociation) fragmentation at the MS2 stage increases the depth of phosphorylation analysis. The methodology and results outlined herein provide a template for tailoring optimized FAIMS-based methods.

Multibatch TMT Reveals False Positives, Batch Effects and Missing Values

Multiplexing strategies for large-scale proteomic analyses have become increasingly prevalent, tandem mass tags (TMT) in particular. Here we used a large iPSC proteomic experiment with twenty-four 10-plex TMT batches to evaluate the effect of integrating multiple TMT batches within a single analysis. We identified a significant inflation rate of protein missing values as multiple batches are integrated and show that this pattern is aggravated at the peptide level. We also show that without normalization strategies to address the batch effects, the high precision of quantitation within a single multiplexed TMT batch is not reproduced when data from multiple TMT batches are integrated.Further, the incidence of false positives was studied by using Y chromosome peptides as an internal control. The iPSC lines quantified in this data set were derived from both male and female donors, hence the peptides mapped to the Y chromosome should be absent from female lines. Nonetheless, these Y chromosome-specific peptides were consistently detected in the female channels of all TMT batches. We then used the same Y chromosome specific peptides to quantify the level of ion coisolation as well as the effect of primary and secondary reporter ion interference. These results were used to propose solutions to mitigate the limitations of multi-batch TMT analyses. We confirm that including a common reference line in every batch increases precision by facilitating normalization across the batches and we propose experimental designs that minimize the effect of cross population reporter ion interference.

Small-protein Enrichment Assay Enables the Rapid, Unbiased Analysis of Over 100 Low Abundance Factors from Human Plasma

Unbiased and sensitive quantification of low abundance small proteins in human plasma (e.g. hormones, immune factors, metabolic regulators) remains an unmet need. These small protein factors are typically analyzed individually and using antibodies that can lack specificity. Mass spectrometry (MS)-based proteomics has the potential to address these problems, however the analysis of plasma by MS is plagued by the extremely large dynamic range of this body fluid, with protein abundances spanning at least 13 orders of magnitude. Here we describe an enrichment assay (SPEA), that greatly simplifies the plasma dynamic range problem by enriching small-proteins of 2-10 kDa, enabling the rapid, specific and sensitive quantification of >100 small-protein factors in a single untargeted LC-MS/MS acquisition. Applying this method to perform deep-proteome profiling of human plasma we identify C5ORF46 as a previously uncharacterized human plasma protein. We further demonstrate the reproducibility of our workflow for low abundance protein analysis using a stable-isotope labeled protein standard of insulin spiked into human plasma. SPEA provides the ability to study numerous important hormones in a single rapid assay, which we applied to study the intermittent fasting response and observed several unexpected changes including decreased plasma abundance of the iron homeostasis regulator hepcidin.

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy

Optimization of chromatography and data analysis resulted in more than 10 000 proteins in a single shot at a validated FDR of 1% (two-species test) and revealed deep insights into the testis cancer physiology.