Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF

A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.     

A prostate cancer tissue specific spectral library for targeted proteomic analysis

Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens.     

Proteomic analysis of formalin-fixed prostate cancer tissue

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue would enable retrospective biomarker investigations of this vast archive of pathologically characterized clinical samples that exist worldwide. These FFPE tissues are, however, refractory to proteomic investigations utilizing many state of the art methodologies largely due to the high level of covalently cross-linked proteins arising from formalin fixation. A novel tissue microdissection technique has been developed and combined with a method to extract soluble peptides directly from FFPE tissue for mass spectral analysis of prostate cancer (PCa) and benign prostate hyperplasia (BPH). Hundreds of proteins from PCa and BPH tissue were identified, including several known PCa markers such as prostate-specific antigen, prostatic acid phosphatase, and macrophage inhibitory cytokine-1. Quantitative proteomic profiling utilizing stable isotope labeling confirmed similar expression levels of prostate-specific antigen and prostatic acid phosphatase in BPH and PCa cells, whereas the expression of macrophage inhibitory cytokine-1 was found to be greater in PCa as compared with BPH cells.     

Evaluation of Somatostatin and CXCR4 Receptor Expression in a Large Set of Prostate Cancer Samples Using Tissue Microarrays and Well-Characterized Monoclonal Antibodies

BACKGROUND: Prostate cancer (PCa) is the most common type of cancer among men in Western countries. Despite numerous therapeutic options, few treatments are available for patients with end-stage disease. In the present study, different somatostatin receptors (SSTs) and the chemokine receptor CXCR4 were evaluated for their suitability as novel therapeutic targets in PCa. MATERIALS AND METHODS: The expression of SST subtypes 1, 2A, 3, and 5 and of CXCR4 was evaluated in 276 PCa tumor samples on a tissue microarray (TMA) in 23 whole-block tumor samples and in 3 PCa cell lines by immunohistochemistry using well-characterized monoclonal antibodies. RESULTS: Overall, the frequency and intensity of expression of SSTs and CXCR4 were very low among the PCa samples investigated. Specifically, SST5, SST2A, and SST3 were expressed, albeit at low intensity, in 10.5%, 9.1%, and 0.7% of the TMA samples, respectively. None of the TMA samples showed SST1 or CXCR4 expression. Only a single small-cell-type neuroendocrine carcinoma that was coincidentally included among the whole-block samples exhibited strong SST2A, SST5, and CXCR4 and moderate SST3 expression. Independent of the tumor cells, the tumor capillaries in many of the PCa samples were strongly positive for SST2A, SST3, SST5, or CXCR4 expression. SST expression in the tumor cells was associated with advanced tumor grade and stage. CONCLUSION: Overall, SST and CXCR4 expression levels are clearly of no therapeutic relevance in PCa. SST- or CXCR4-based therapy might be feasible, however, in rare cases of small-cell-type neuroendocrine carcinoma of the prostate.     

Search for potential markers for prostate cancer diagnosis, prognosis and treatment in clinical tissue specimens using amine-specific isobaric tagging (iTRAQ) with two-dimensional liquid chromatography and tandem mass spectrometry

This study aimed to identify candidate new diagnosis and prognosis markers and medicinal targets of prostate cancer (PCa), using state of the art proteomics. A total of 20 prostate tissue specimens from 10 patients with benign prostatic hyperplasia (BPH) and 10 with PCa (Tumour Node Metastasis [TNM] stage T1-T3) were analyzed by isobaric stable isotope labeling (iTRAQ) and two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) approaches using a hybrid quadrupole time-of-flight system (QqTOF). The study resulted in the reproducible identification of 825 nonredundant gene products (p < or = 0.05) of which 30 exhibited up-regulation (> or =2-fold) and another 35 exhibited down-regulation (< or =0.5-fold) between the BPH and PCa specimens constituting a major contribution toward their global proteomic assessment. Selected findings were confirmed by immunohistochemical analysis of prostate tissue specimens. The proteins determined support existing knowledge and uncover novel and promising PCa biomarkers. The PCa proteome found can serve as a useful aid for the identification of improved diagnostic and prognostic markers and ultimately novel chemopreventive and therapeutic targets.     

Differential ultracentrifugation enables deep plasma proteomics through enrichment of extracellular vesicles

Human plasma is a rich source of biomedical information and biomarkers. However, the enormous dynamic range of plasma proteins limits its accessibility to mass spectrometric (MS) analysis. Here, we show that enrichment of extracellular vesicles (EVs) by ultracentrifugation increases plasma proteome depth by an order of magnitude. With this approach, more than two thousand proteins are routinely and reproducibly quantified by label-free quantification and data independent acquisition (DIA) in single-shot liquid chromatography tandem mass spectrometry runs of less than one hour. We present an optimized plasma proteomics workflow that enables high-throughput with very short chromatographic gradients analyzing hundred samples per day with deep proteome coverage, especially when including a study-specific spectral library generated by repeated injection and gas-phase fractionation of pooled samples. Finally, we test the workflow on clinical biobank samples from malignant melanoma patients in immunotherapy to demonstrate the improved proteome coverage supporting the potential for future biomarker discovery.     

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

Introduction: While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways.

Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented.

Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.     

New targeted approaches for the quantification of data‐independent acquisition mass spectrometry

The use of data‐independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult‐to‐obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high‐quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data‐dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state‐of‐the‐art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.     

The Generation of a Comprehensive Spectral Library for the Analysis of the Guinea Pig Proteome by SWATH‐MS

Advances in liquid chromatography‐mass spectrometry have facilitated the incorporation of proteomic studies to many biology experimental workflows. Data‐independent acquisition platforms, such as sequential window acquisition of all theoretical mass spectra (SWATH‐MS), offer several advantages for label‐free quantitative assessment of complex proteomes over data‐dependent acquisition (DDA) approaches. However, SWATH data interpretation requires spectral libraries as a detailed reference resource. The guinea pig (Cavia porcellus) is an excellent experimental model for translation to many aspects of human physiology and disease, yet there is limited experimental information regarding its proteome. To overcome this knowledge gap, a comprehensive spectral library of the guinea pig proteome is generated. Homogenates and tryptic digests are prepared from 16 tissues and subjected to >200 DDA runs. Analysis of >250 000 peptide‐spectrum matches resulted in a library of 73 594 peptides from 7666 proteins. Library validation is provided by i) analyzing externally derived SWATH files ( https://doi.org/10.1016/j.jprot.2018.03.023 ) and comparing peptide intensity quantifications; ii) merging of externally derived data to the base library. This furnishes the research community with a comprehensive proteomic resource that will facilitate future molecular‐phenotypic studies using (re‐engaging) the guinea pig as an experimental model of relevance to human biology. The spectral library and raw data are freely accessible in the MassIVE repository (MSV000083199).     

基于数据非依赖采集的以肽为中心分析算法和软件的研究进展

数据非依赖采集(data-independent acquisition,DIA)是一种高通量、无偏性的质谱数据采集方法,具有定量结果重现性好,对低丰度蛋白质友好的特点,是近年来进行大队列蛋白质组研究的首选方法之一。由于DIA产生的二级谱是混合谱,包含了多个肽段的碎片离子信息,使得蛋白质鉴定和定量更加困难。目前,DIA数据分析方法分为两大类,即以肽为中心和以谱图为中心。其中,以肽为中心的分析方法鉴定更灵敏,定量更准确,已成为DIA数据解析的主流方法。其分析流程包括构建谱图库、提取色谱峰群、特征打分和结果质控4个关键步骤。本文综述了以肽为中心的DIA数据分析流程,介绍了基于此流程的数据分析软件及相关比较评估工作,进一步总结了已有的算法改进工作,最后对未来发展方向进行了展望。     

Automation of PRM‐dependent D3‐Leu tracer enrichment in HDL to study the metabolism of apoA‐I,LCAT and other apolipoproteins

We developed an automated quantification workflow for PRM‐enabled detection of D3‐Leu labeled apoA‐I in high‐density lipoprotein (HDL) isolated from humans. Subjects received a bolus injection of D3‐Leu and blood was drawn at eight time points over three days. HDL was isolated and separated into six size fractions for subsequent proteolysis and PRM analysis for the detection of D3‐Leu signal from ～0.03 to 0.6% enrichment. We implemented an intensity‐based quantification approach that takes advantage of high‐resolution/accurate mass PRM scans to identify the D3‐Leu 2HM3 ion from non‐specific peaks. Our workflow includes five modules for extracting the targeted PRM peak intensities (XPIs): Peak centroiding, noise removal, fragment ion matching using Δm/z windows, nine intensity quantification options, and validation and visualization outputs. We optimized the XPI workflow using in vitro synthesized and clinical samples of D0/D3‐Leu labeled apoA‐I. Three subjects’ apoA‐I enrichment curves in six HDL size fractions, and LCAT, apoA‐II and apoE from two size fractions were generated within a few hours. Our PRM strategy and automated quantification workflow will expedite the turnaround of HDL apoA‐I metabolism data in clinical studies that aim to understand and treat the mechanisms behind dyslipidemia.     

Impact of fixation time on GeLC-MS/MS proteomic profiling of formalin-fixed, paraffin-embedded tissues

Formalin-fixed, paraffin-embedded (FFPE) tissue banks represent an invaluable resource for biomarker discovery. Recently, the combination of full-length protein extraction, GeLC-MS/MS analysis, and spectral counting quantification has been successfully applied to mine proteomic information from these tissues. However, several sources of variability affect these samples; among these, the duration of the fixation process is one of the most important and most easily controllable ones. To assess its influence on quality of GeLC-MS/MS data, the impact of fixation time on efficiency of full-length protein extraction efficiency and on quality of label-free quantitative data was evaluated. As a result, although proteins were successfully extracted from FFPE liver samples fixed for up to eight days, fixation time appeared to negatively influence both protein extraction yield and GeLC-MS/MS quantitative proteomic data. Particularly, MS identification efficiency decreased with increasing fixation times. Moreover, amino acid modifications putatively induced by formaldehyde were detected and characterized. These results demonstrate that proteomic information can be achieved also from tissue samples fixed for relatively long times, but suggest that variations in fixation time need to be carefully taken into account when performing proteomic biomarker discovery studies on fixed tissue archives.     

Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer

In prostate cancer (PCa), prognostic (predictive) factors are particularly important given the marked heterogeneity of this disease at clinical, morphologic, and biomolecular levels. Blood contains a treasure of previously unstudied biomarkers that could reflect the ongoing physiological state of all tissue. The serum prostate-specific antigen (PSA) measurement is a very good biomarker for PCa, but the percentage of bad classification is somewhat high. The blood proteome mass spectra (MS) represent a potential tool for detection of diseases; however the identification of a single biomarker from the complex output from MS is often difficult. In this paper, we propose a general strategy, based on computational chemistry techniques, which should improve the predictive power of PSA. Our group adapted the square-spiral graph to represent human serum-plasma-proteome MS for healthy and PCa patients. These graphs were previously applied to DNA and/or protein sequences. In this work, we calculated different classes of connectivity indices (CIs), and created various models based on the spectral moments. The best QPDRs model found showed accuracy values ranging from 71.7% to 97.2%, and 70.4% to 99.2% of specificity. This methodology might be useful for several applications in computational chemistry.     

Combining Untargeted and Targeted Proteomic Strategies for Discrimination and Quantification of Cashmere Fibers

Cashmere is regarded as a specialty and luxury fiber due to its scarcity and high economic value. For fiber quality assessment, it is technically very challenging to distinguish and quantify the cashmere fiber from yak or wool fibers because of their highly similar physical appearance and substantial protein sequence homology. To address this issue, we propose a workflow combining untargeted and targeted proteomics strategies for selecting, verifying and quantifying biomarkers for cashmere textile authentication. Untargeted proteomic surveys were first applied to identify 174, 157, and 156 proteins from cashmere, wool and yak fibers, respectively. After marker selection at different levels, peptides turned out to afford much higher selectivity than proteins for fiber species discrimination. Subsequently, parallel reaction monitoring (PRM) methods were developed for ten selected peptide markers. The PRM-based targeted analysis of peptide markers enabled accurate determination of fiber species and cashmere percentages in different fiber mixtures. Furthermore, collective use of these peptide makers allowed us to discriminate and quantify cashmere fibers in commercial finished fabrics that have undergone heavy chemical treatments. Cashmere proportion measurement in fabric samples using our proteomic approach was in good agreement with results from traditional light microscopy, yet our method can be more readily standardized to become an objective and robust assay for assessing authenticity of fibers and textiles. We anticipate that the proteomic strategies presented in our study could be further implicated in discovery of quality trait markers for other products containing highly homologous proteomes.     

SWATH‐based proteomics reveals processes associated with immune evasion and metastasis in poor prognosis colorectal tumours

Newly emerged proteomic methodologies, particularly data‐independent acquisition (DIA) analysis–related approaches, would improve current gene expression–based classifications of colorectal cancer (CRC). Therefore, this study was aimed to identify protein expression signatures using SWATH‐MS DIA and targeted data extraction, to aid in the classification of molecular subtypes of CRC and advance in the diagnosis and development of new drugs. For this purpose, 40 human CRC samples and 7 samples of healthy tissue were subjected to proteomic and bioinformatic analysis. The proteomic analysis identified three different molecular CRC subtypes: P1, P2 and P3. Significantly, P3 subtype showed high agreement with the mesenchymal/stem‐like subtype defined by gene expression signatures and characterized by poor prognosis and survival. The P3 subtype was characterized by decreased expression of ribosomal proteins, the spliceosome, and histone deacetylase 2, as well as increased expression of osteopontin, SERPINA 1 and SERPINA 3, and proteins involved in wound healing, acute inflammation and complement pathway. This was also confirmed by immunodetection and gene expression analyses. Our results show that these tumours are characterized by altered expression of proteins involved in biological processes associated with immune evasion and metastasis, suggesting new therapeutic options in the treatment of this aggressive type of CRC.     

Advances in high-resolution quantitative proteomics: implications for clinical applications

The advances in high-resolution mass spectrometry instrumentation, capable of accurate mass measurement and fast acquisition, have enabled new approaches for targeted quantitative proteomics. More specifically, analyses performed on quadrupole-orbitrap mass spectrometers operated in parallel reaction monitoring (PRM) mode leverage the intrinsic high resolving power and trapping capabilities. The PRM technique offers unmatched degrees of selectivity and analytical sensitivity, typically required to analyze peptides in complex samples, such as those encountered in biomedical research or clinical studies. The features of PRM have provoked a paradigm change in targeted experiments, by decoupling acquisition and data processing. It has resulted in a new analytical workflow comprising distinct methods for each step, thus enabling much larger flexibility. The PRM technique was further enhanced by a new data acquisition scheme, allowing dynamic parameter settings. The potential of the technique may radically impact future quantitative proteomics studies.     

MiR-187 Targets the Androgen-Regulated Gene ALDH1A3 in Prostate Cancer

miRNAs are predicted to control the activity of approximately 60% of all protein-coding genes participating in the regulation of several cellular processes and diseases, including cancer. Recently, we have demonstrated that miR-187 is significantly downregulated in prostate cancer (PCa) and here we propose a proteomic approach to identify its potential targets. For this purpose, PC-3 cells were transiently transfected with miR-187 precursor and miRNA mimic negative control. Proteins were analyzed by a two-dimensional difference gel electrophoresis (2D-DIGE) and defined as differentially regulated if the observed fold change was ±1.06. Then, MALDI-TOF MS analysis was performed after protein digestion and low abundance proteins were identified by LC–MS/MS. Peptides were identified by searching against the Expasy SWISS PROT database, and target validation was performed both in vitro by western blot and qRT-PCR and in clinical samples by qRT-PCR, immunohistochemistry and ELISA. DIGE analysis showed 9 differentially expressed spots (p<0.05) and 7 showed a down-regulated expression upon miR-187 re-introduction. Among these targets we identified aldehyde dehydrogenase 1A3 (ALDH1A3). ALDH1A3 expression was significantly downregulated in PC3, LNCaP and DU-145 cells after miR-187 re-introduction. Supporting these data, the expression of ALDH1A3 was found significantly (p<0.0001) up-regulated in PCa samples and inversely correlated (p<0.0001) with miR-187 expression, its expression being directly associated with Gleason score (p = 0.05). The expression of ALDH1A3 was measured in urine samples to evaluate the predictive capability of this biomarker for the presence of PCa and, at a signification level of 10%, PSA and also ALDH1A3 were significantly associated with a positive biopsy of PCa. In conclusion, our data illustrate for the first time the role of ALDH1A3 as a miR-187 target in PCa and provide insights in the utility of using this protein as a new biomarker for PCa.     

In-depth analysis of the Sirtuin 5-regulated mouse brain malonylome and succinylome using library-free data-independent acquisitions

Post-translational modifications (PTMs) dynamically regulate proteins and biological pathways, typically through the combined effects of multiple PTMs. Lysine residues are targeted for various PTMs, including malonylation and succinylation. However, PTMs offer specific challenges to mass spectrometry-based proteomics during data acquisition and processing. Thus, novel and innovative workflows using data-independent acquisition (DIA) ensure confident PTM identification, precise site localization, and accurate and robust label-free quantification. In this study, we present a powerful approach that combines antibody-based enrichment with comprehensive DIA acquisitions and spectral library-free data processing using directDIA (Spectronaut). Identical DIA data can be used to generate spectral libraries and comprehensively identify and quantify PTMs, reducing the amount of enriched sample and acquisition time needed, while offering a fully automated workflow. We analyzed brains from wild-type and Sirtuin 5 (SIRT5)-knock-out mice, and discovered and quantified 466 malonylated and 2211 succinylated peptides. SIRT5 regulation remodeled the acylomes by targeting 164 malonylated and 578 succinylated sites. Affected pathways included carbohydrate and lipid metabolisms, synaptic vesicle cycle, and neurodegenerative diseases. We found 48 common SIRT5-regulated malonylation and succinylation sites, suggesting potential PTM crosstalk. This innovative and efficient workflow offers deeper insights into the mouse brain lysine malonylome and succinylome.     

Proteomic analysis of STEAP1 knockdown in human LNCaP prostate cancer cells

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. The six transmembrane epithelial antigen of the prostate 1 (STEAP1) protein is overexpressed in several types of human tumors, particularly in PCa. Our research group has demonstrated that STEAP1 overexpression is associated with PCa progression and aggressiveness. Therefore, understanding the cellular and molecular mechanisms triggered by STEAP1 overexpression will provide important insights to delineate new strategies for PCa treatment. In the present work, a proteomic strategy was used to characterize the intracellular signaling pathways and the molecular targets downstream of STEAP1 in PCa cells. A label-free approach was applied using an Orbitrap LC-MS/MS system to characterize the proteome of STEAP1-knockdown PCa cells. More than 6700 proteins were identified, of which a total of 526 proteins were found differentially expressed in scramble siRNA versus STEAP1 siRNA (234 proteins up-regulated and 292 proteins down-regulated). Bioinformatics analysis allowed us to explore the mechanism through which STEAP1 exerts influence on PCa, revealing that endocytosis, RNA transport, apoptosis, aminoacyl-tRNA biosynthesis, and metabolic pathways are the main biological processes where STEAP1 is involved. By immunoblotting, it was confirmed that STEAP1 silencing induced the up-regulation of cathepsin B, intersectin-1, and syntaxin 4, and the down-regulation of HRas, PIK3C2A, and DIS3. These findings suggested that blocking STEAP1 might be a suitable strategy to activate apoptosis and endocytosis, and diminish cellular metabolism and intercellular communication, leading to inhibition of PCa progression.