Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH‐MS is a specific variant of data‐independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH‐MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH‐MS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH‐MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH‐MS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATH‐MS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed.     

A novel approach and protocol for discovering extremely low-abundance proteins in serum

The proteomic analysis of serum (plasma) has been a major approach to determining biomarkers essential for early disease diagnoses and drug discoveries. The determination of these biomarkers, however, is analytically challenging since the dynamic concentration range of serum proteins/peptides is extremely wide (more than 10 orders of magnitude). Thus, the reduction in sample complexity prior to proteomic analyses is essential, particularly in analyzing low-abundance protein biomarkers. Here, we demonstrate a novel approach to the proteomic analyses of human serum that uses an originally developed serum protein separation device and a sequentially linked 3-D-LC-MS/MS system. Our hollow-fiber-membrane-based serum pretreatment device can efficiently deplete high-molecular weight proteins and concentrate low-molecular weight proteins/peptides automatically within 1 h. Four independent analyses of healthy human sera pretreated using this unique device, followed by the 3-D-LC-MS/MS successfully produced 12 000-13 000 MS/MS spectra and hit around 1800 proteins (>95% reliability) and 2300 proteins (>80% reliability). We believe that the unique serum pretreatment device and proteomic analysis protocol reported here could be a powerful tool for searching physiological biomarkers by its high throughput (3.7 days per one sample analysis) and high performance of finding low abundant proteins from serum or plasma samples.     

SWATH enables precise label‐free quantification on proteome scale

MS‐based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data‐dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole‐cell lysate). The quantitative proteomics uses stable isotopes or label‐free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label‐free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data‐independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label‐free quantification in an MRM‐like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked‐in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse‐cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label‐free quantification on proteome scale.     

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).     

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.     

Unrestricted modification search reveals lysine methylation as major modification induced by tissue formalin fixation and paraffin embedding

Formalin‐fixed paraffin‐embedded (FFPE) tissue is considered as an appropriate alternative to frozen/fresh tissue for proteomic analysis. Here we study formalin‐induced alternations on a proteome‐wide level. We compared LC‐MS/MS data of FFPE and frozen human kidney tissues by two methods. First, clustering analysis revealed that the biological variation is higher than the variation introduced by the two sample processing techniques and clusters formed in accordance with the biological tissue origin and not with the sample preservation method. Second, we combined open modification search and spectral counting to find modifications that are more abundant in FFPE samples compared to frozen samples. This analysis revealed lysine methylation (+14 Da) as the most frequent modification induced by FFPE preservation. We also detected a slight increase in methylene (+12 Da) and methylol (+30 Da) adducts as well as a putative modification of +58 Da, but they contribute less to the overall modification count. Subsequent SEQUEST analysis and X!Tandem searches of different datasets confirmed these trends. However, the modifications due to FFPE sample processing are a minor disturbance affecting 2–6% of all peptide‐spectrum matches and the peptides lists identified in FFPE and frozen tissues are still highly similar.     

Development and evaluation of a micro- and nanoscale proteomic sample preparation method

Challenges associated with the efficient and effective preparation of micro- and nanoscale (micro- and nanogram) clinical specimens for proteomic applications include the unmitigated sample losses that occur during the processing steps. Herein, we describe a simple "single-tube" preparation protocol appropriate for small proteomic samples using the organic cosolvent, trifluoroethanol (TFE) that circumvents the loss of sample by facilitating both protein extraction and protein denaturation without requiring a separate cleanup step. The performance of the TFE-based method was initially evaluated by comparisons to traditional detergent-based methods on relatively large scale sample processing using human breast cancer cells and mouse brain tissue. The results demonstrated that the TFE-based protocol provided comparable results to the traditional detergent-based protocols for larger, conventionally sized proteomic samples (>100 microg protein content), based on both sample recovery and numbers of peptide/protein identifications. The effectiveness of this protocol for micro- and nanoscale sample processing was then evaluated for the extraction of proteins/peptides and shown effective for small mouse brain tissue samples (approximately 30 microg total protein content) and also for samples of approximately 5000 MCF-7 human breast cancer cells (approximately 500 ng total protein content), where the detergent-based methods were ineffective due to losses during cleanup and transfer steps.     

Characterization of the mouse brain proteome using global proteomic analysis complemented with cysteinyl-peptide enrichment

We report a global proteomic approach for analyzing brain tissue and for the first time a comprehensive characterization of the whole mouse brain proteome. Preparation of the whole brain sample incorporated a highly efficient cysteinyl-peptide enrichment (CPE) technique to complement a global enzymatic digestion method. Both the global and the cysteinyl-enriched peptide samples were analyzed by SCX fractionation coupled with reversed phase LC-MS/MS analysis. A total of 48,328 different peptides were confidently identified (>98% confidence level), covering 7792 nonredundant proteins ( approximately 34% of the predicted mouse proteome). A total of 1564 and 1859 proteins were identified exclusively from the cysteinyl-peptide and the global peptide samples, respectively, corresponding to 25% and 31% improvements in proteome coverage compared to analysis of only the global peptide or cysteinyl-peptide samples. The identified proteins provide a broad representation of the mouse proteome with little bias evident due to protein pI, molecular weight, and/or cellular localization. Approximately 26% of the identified proteins with gene ontology (GO) annotations were membrane proteins, with 1447 proteins predicted to have transmembrane domains, and many of the membrane proteins were found to be involved in transport and cell signaling. The MS/MS spectrum count information for the identified proteins was used to provide a measure of relative protein abundances. The mouse brain peptide/protein database generated from this study represents the most comprehensive proteome coverage for the mammalian brain to date, and the basis for future quantitative brain proteomic studies using mouse models. The proteomic approach presented here may have broad applications for rapid proteomic analyses of various mouse models of human brain diseases.     

Comparison of fractionation proteomics for local SWATH library building

For data‐independent acquisition by means of sequential window acquisition of all theoretical fragment ion spectra (SWATH), a reference library of data‐dependent acquisition (DDA) runs is typically used to correlate the quantitative data from the fragment ion spectra with peptide identifications. The quality and coverage of such a reference library is therefore essential when processing SWATH data. In general, library sizes can be increased by reducing the impact of DDA precursor selection with replicate runs or fractionation. However, these strategies can affect the match between the library and SWATH measurement, and thus larger library sizes do not necessarily correspond to improved SWATH quantification. Here, three fractionation strategies to increase local library size were compared to standard library building using replicate DDA injection: protein SDS‐PAGE fractionation, peptide high‐pH RP‐HPLC fractionation and MS‐acquisition gas phase fractionation. The impact of these libraries on SWATH performance was evaluated in terms of the number of extracted peptides and proteins, the match quality of the peptides and the extraction reproducibility of the transitions. These analyses were conducted using the hydrophilic proteome of differentiating human embryonic stem cells. Our results show that SWATH quantitative results and interpretations are affected by choice of fractionation technique. Data are available via ProteomeXchange with identifier PXD006190.     

The Equine PeptideAtlas: A resource for developing proteomics‐based veterinary research

Progress in MS‐based methods for veterinary research and diagnostics is lagging behind compared to the human research, and proteome data of domestic animals is still not well represented in open source data repositories. This is particularly true for the equine species. Here we present a first Equine PeptideAtlas encompassing high‐resolution tandem MS analyses of 51 samples representing a selection of equine tissues and body fluids from healthy and diseased animals. The raw data were processed through the Trans‐Proteomic Pipeline to yield high quality identification of proteins and peptides. The current release comprises 24 131 distinct peptides representing 2636 canonical proteins observed at false discovery rates of 0.2% at the peptide level and 1.4% at the protein level. Data from the Equine PeptideAtlas are available for experimental planning, validation of new datasets, and as a proteomic data mining resource. The advantages of the Equine PeptideAtlas are demonstrated by examples of mining the contents for information on potential and well‐known equine acute phase proteins, which have extensive general interest in the veterinary clinic. The extracted information will support further analyses, and emphasizes the value of the Equine PeptideAtlas as a resource for the design of targeted quantitative proteomic studies.     

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.     

Enhancing Proteomic Throughput in Capillary Electrophoresis–Mass Spectrometry by Sequential Sample Injection

In this study we demonstrate the potential of sequential injection of samples in capillary electrophoresis–mass spectrometry for rapid and sensitive proteome characterization of human lymphoblastic T‐cells (line CCRF–CEM). Proteins were extracted, enzymatically digested, and the resulting peptides fractionated by RP–HPLC. Twenty fractions were thereafter analyzed by CE–MS within a single MS analysis. The CE–MS method was designed so that every 10 min a new fraction was injected into the CE system. Without any rinsing or equilibration steps we were able to generate a continuous stream of peptides feeding the mass analyzer. In 250 min, the total analysis time of a single sequential injection experiment, we were able to identify roughly 28 000 peptide sequences counting for 4800 proteins. These numbers could be increased to 62 000 peptides and more than 6100 proteins identified, when performing three experiments analyzing a total of 60 fractions, all within 12.5 h. We found that the electrophoretic mobility of peptides can be used to trace back peptides and assign them to the fraction they originate from.     

Highly Infectious CJD Particles Lack Prion Protein but Contain Many Viral‐Linked Peptides by LC‐MS/MS

It is widely believed that host prion protein (PrP), without nucleic acid, converts itself into an infectious form (PrP‐res) that causes transmissible encephalopathies (TSEs), such as human sporadic CJD (sCJD), endemic sheep scrapie, and epidemic BSE. There are many detailed investigations of PrP, but proteomic studies of other proteins in verified infectious TSE particles have not been pursued, even though brain homogenates without PrP retain their complete infectious titer. To define proteins that may be integral to, process, or protect an agent genome, we developed a streamlined, high‐yield purification of infectious FU‐CJD mouse brain particles with minimal PrP. Proteinase K (PK) abolished all residual particle PrP, but did not reduce infectivity, and viral‐size particles lacking PrP were ～70S (vs. 90–120S without PK). Furthermore, over 1,500 non‐PrP proteins were still present and positively identified in high titer FU‐CJD particles without detectable PrP by mass spectrometry (LC‐MS/MS); 114 of these peptides were linked to viral motifs in the environmental–viral database, and not evident in parallel uninfected controls. Host components were also identified in both PK and non‐PK treated particles from FU‐CJD mouse brain and human sCJD brain. This abundant cellular data had several surprises, including finding Huntingtin in the sCJD but not normal human brain samples. Similarly, the neural Wiskott–Aldrich sequence and multivesicular and endosome components associated with retromer APP (Alzheimer amyloid) processing were only in sCJD. These cellular findings suggest that new therapies directed at retromer–vesicular trafficking in other neurodegenerative diseases may also counteract late‐onset sCJD PrP amyloid pathology. J. Cell. Biochem. 115: 2012–2021, 2014. © 2014 Wiley Periodicals, Inc.

     

Tissue proteomics using chemical immobilization and mass spectrometry

Proteomics analysis is important for characterizing tissues to gain biological and pathological insights, which could lead to the identification of disease-associated proteins for disease diagnostics or targeted therapy. However, tissues are commonly embedded in optimal cutting temperature medium (OCT) or are formalin-fixed and paraffin-embedded (FFPE) in order to maintain tissue morphology for histology evaluation. Although several tissue proteomic analyses have been performed on FFPE tissues using advanced mass spectrometry (MS) technologies, high-throughput proteomic analysis of OCT-embedded tissues has been difficult due to the interference of OCT in the MS analysis. In addition, molecules other than proteins present in tissues further complicate tissue proteomic analysis. Here, we report the development of a method using chemical immobilization of proteins for peptide extraction (CIPPE). In this method, proteins are chemically immobilized onto a solid support; interferences from tissues and OCT embedding are removed by extensive washing of proteins conjugated on the solid support. Peptides are then released from the solid phase by proteolysis, enabling MS analysis. This method was first validated by eliminating OCT interference from a standard protein, human serum albumin, where all of the unique peaks contributed by OCT contamination were eradicated. Finally, this method was applied for the proteomic analysis of frozen and OCT-embedded tissues using iTRAQ (isobaric tag for relative and absolute quantitation) labeling and two-dimensional liquid chromatography tandem mass spectrometry. The data showed reproducible extraction and quantitation of 10,284 proteins from 3996 protein groups and a minimal impact of OCT embedding on the analysis of the global proteome of the stored tissue samples.     

Proteomic differences in recombinant CHO cells producing two similar antibody fragments

Chinese hamster ovary (CHO) cells are the most commonly used mammalian hosts for the production of biopharmaceuticals. To overcome unfavorable features of CHO cells, a lot of effort is put into cell engineering to improve phenotype. “Omics” studies investigating elevated growth rate and specific productivities as well as extracellular stimulus have already revealed many interesting engineering targets. However, it remains largely unknown how physicochemical properties of the recombinant product itself influence the host cell. In this study, we used quantitative label‐free LC‐MS proteomic analyses to investigate product‐specific proteome differences in CHO cells producing two similar antibody fragments. We established recombinant CHO cells producing the two antibodies, 3D6 and 2F5, both as single‐chain Fv‐Fc homodimeric antibody fragments (scFv‐Fc). We applied three different vector strategies for transgene delivery (i.e., plasmid, bacterial artificial chromosome, recombinase‐mediated cassette exchange), selected two best performing clones from transgene variants and transgene delivery methods and investigated three consecutively passaged cell samples by label‐free proteomic analysis. LC‐MS‐MS profiles were compared in several sample combinations to gain insights into different aspects of proteomic changes caused by overexpression of two different heterologous proteins. This study suggests that not only the levels of specific product secretion but the product itself has a large impact on the proteome of the cell. Biotechnol. Bioeng. 2016;113: 1902–1912. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.     

Standardization of a sample preparation and analytical workflow for proteomics of archival endometrial cancer tissue

The goal of the present study was to establish a standard operating procedure for mass spectrometry (MS)-based proteomic analysis of laser microdissected (LMD) formalin-fixed, paraffin-embedded (FFPE) uterine tissue. High resolution bioimage analysis of a large endometrial cancer tissue microarray immunostained for the breast cancer type 1 susceptibility protein enabled precise counting of cells to establish that there is an average of 600 cells/nL of endometrial cancer tissue. We sought to characterize the peptide recovery from various volumes of tissue gathered by LMD and processed/digested using the present methodology. We observed a nearly linear increase in peptide recovery amount with increasing tissue volume dissected. There was little discernible difference in the peptide recovery from stromal versus malignant epithelium, and there was no apparent difference in the day-to-day recovery. This methodology reproducibly results in 100 ng of digested peptides per nL of endometrial tissue, or ～25 pg peptides/endometrial cancer cell. Results from liquid chromatography (LC)-MS/MS experiments to assess the impact of total peptide load on column on the total number of peptides and proteins identified from FFPE tissue digests prepared with the present methodology indicate a demonstrable increase in the total number of peptides identified up to 1000 ng, beyond which diminishing returns were observed. Furthermore, we observed no impact on the peptide identification rates from analyses of equivalent peptide amounts derived from lower volume LMD samples. These results show that this single-tube collection-to-injection proteomics (CTIP) workflow represents a straightforward, scalable, and highly reliable methodology for sample preparation to enable high throughput LMD-MS analysis of tissues derived from biopsy or surgery.     

DeepQuanTR: MALDI‐MS‐based label‐free quantification of proteins in complex biological samples

The quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and quantification of differentially expressed proteins using LC‐MALDI‐MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI‐MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter‐sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative quantification of proteins derived from a wide variety of different samples.     

Proteome wide screening using peptide affinity capture

MS‐based strategies are key technologies for identifying proteins in proteomic research. Despite significant improvements in recent years efficient fractionation processes of target analytes remain major bottlenecks in MS‐based protein analysis. Immunoaffinity‐based sample fractionation strategies have shown their potential for the enrichment of analyte peptides of interest, but only small numbers of analytes can be quantified in one experiment. The lack of appropriate capture reagents limits the application of immunoaffinity‐based approaches and only biased biomarker discovery approaches are possible. This perspective discusses the current status of immunoaffinity MS‐based approaches and introduces a novel concept that uses group specific anti‐peptide antibodies – Triple X Proteomics Antibodies – for the enrichment of signature peptides. Classes of peptides with identical termini can be fractionated based on TXP immunoaffinity enrichment steps and can subsequently be identified using established tandem MS procedures. Based on bioinformatic algorithms minimal sets of TXP epitopes can be specified, that cover a wide range of given proteome landscapes of one or even several different species. This opens the possibility to use a minimal number of TXP antibodies as a universal toolbox for general immunoaffinity‐based approaches in proteome analysis.     

Data‐Independent Acquisition of HLA Class I Peptidomes on the Q Exactive Mass Spectrometer Platform

The characterization of peptides presented by human leukocyte antigen (HLA) class I molecules is crucial for understanding immune processes, biomarker discovery, and the development of novel immunotherapies or vaccines. Mass spectrometry allows the direct identification of thousands of HLA‐bound peptides from cell lines, blood, or tissue. In recent years, data‐independent acquisition (DIA) mass spectrometry methods have evolved, promising to increase reproducibility and sensitivity over classical data‐dependent acquisition (DDA) workflows. Here, we describe a DIA setup on the Q Exactive mass spectrometer, optimized regarding the unique properties of HLA class I peptides. The methodology enables sensitive and highly reproducible characterization of HLA peptidomes from individual cell lines. From up to 16 DDA analyses of 100 million human cells, more than 10 000 peptides could be confidently identified, serving as basis for the generation of spectral libraries. This knowledge enabled the subsequent interrogation of DIA data, leading to the identification of peptide sets with >90% overlap between replicate samples, a prerequisite for the comparative study of closely related specimens. Furthermore, >3000 peptides could be identified from just one million cells after DIA analysis using a library generated from 300 million cells. The reduction in sample quantity and the high reproducibility of DIA‐based HLA peptidome analysis should facilitate personalized medicine applications.     

Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan

The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of ‘omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer ‘omics data, will accelerate cancer research.