Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.     

Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry

Archived formalin-fixed paraffin-embedded (FFPE) tissue collections represent a valuable informational resource for proteomic studies. Multiple FFPE core biopsies can be assembled in a single block to form tissue microarrays (TMAs). We describe a protocol for analyzing protein in FFPE-TMAs using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). The workflow incorporates an antigen retrieval step following deparaffinization, in situ trypsin digestion, matrix application and then mass spectrometry signal acquisition. The direct analysis of FFPE-TMA tissue using IMS allows direct analysis of multiple tissue samples in a single experiment without extraction and purification of proteins. The advantages of high speed and throughput, easy sample handling and excellent reproducibility make this technology a favorable approach for the proteomic analysis of clinical research cohorts with large sample numbers. For example, TMA analysis of 300 FFPE cores would typically require 6 h of total time through data acquisition, not including data analysis.     

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.     

Liquid ultraviolet matrix-assisted laser desorption/ionization -- mass spectrometry for automated proteomic analysis

We have combined several key sample preparation steps for the use of a liquid matrix system to provide high analytical sensitivity in automated ultraviolet -- matrix-assisted laser desorption/ionisation -- mass spectrometry (UV-MALDI-MS). This new sample preparation protocol employs a matrix-mixture which is based on the glycerol matrix-mixture described by Sze et al. The low-femtomole sensitivity that is achievable with this new preparation protocol enables proteomic analysis of protein digests comparable to solid-state matrix systems. For automated data acquisition and analysis, the MALDI performance of this liquid matrix surpasses the conventional solid-state MALDI matrices. Besides the inherent general advantages of liquid samples for automated sample preparation and data acquisition the use of the presented liquid matrix significantly reduces the extent of unspecific ion signals in peptide mass fingerprints compared to typically used solid matrices, such as 2,5-dihydroxybenzoic acid (DHB) or alpha-cyano-hydroxycinnamic acid (CHCA). In particular, matrix and low-mass ion signals and ion signals resulting from cation adduct formation are dramatically reduced. Consequently, the confidence level of protein identification by peptide mass mapping of in-solution and in-gel digests is generally higher.     

MALDI Imaging Mass Spectrometry Profiling of N-Glycans in Formalin-Fixed Paraffin Embedded Clinical Tissue Blocks and Tissue Microarrays

A recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) method to spatially profile the location and distribution of multiple N-linked glycan species in frozen tissues has been extended and improved for the direct analysis of glycans in clinically derived formalin-fixed paraffin-embedded (FFPE) tissues. Formalin-fixed tissues from normal mouse kidney, human pancreatic and prostate cancers, and a human hepatocellular carcinoma tissue microarray were processed by antigen retrieval followed by on-tissue digestion with peptide N-glycosidase F. The released N-glycans were detected by MALDI-IMS analysis, and the structural composition of a subset of glycans could be verified directly by on-tissue collision-induced fragmentation. Other structural assignments were confirmed by off-tissue permethylation analysis combined with multiple database comparisons. Imaging of mouse kidney tissue sections demonstrates specific tissue distributions of major cellular N-linked glycoforms in the cortex and medulla. Differential tissue distribution of N-linked glycoforms was also observed in the other tissue types. The efficacy of using MALDI-IMS glycan profiling to distinguish tumor from non-tumor tissues in a tumor microarray format is also demonstrated. This MALDI-IMS workflow has the potential to be applied to any FFPE tissue block or tissue microarray to enable higher throughput analysis of the global changes in N-glycosylation associated with cancers.     

Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.     

Scalable transcriptome preparation for massive parallel sequencing

Background

The tremendous output of massive parallel sequencing technologies requires automated robust and scalable sample preparation methods to fully exploit the new sequence capacity.

Methodology

In this study, a method for automated library preparation of RNA prior to massively parallel sequencing is presented. The automated protocol uses precipitation onto carboxylic acid paramagnetic beads for purification and size selection of both RNA and DNA. The automated sample preparation was compared to the standard manual sample preparation.

Conclusion/Significance

The automated procedure was used to generate libraries for gene expression profiling on the Illumina HiSeq 2000 platform with the capacity of 12 samples per preparation with a significantly improved throughput compared to the standard manual preparation. The data analysis shows consistent gene expression profiles in terms of sensitivity and quantification of gene expression between the two library preparation methods.     

MALDI Profiling of Human Lung Cancer Subtypes

Background

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.

Methodology/Principal Findings

In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.

Conclusions/Significance

A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.     

Integrating intracellular and extracellular proteomic profiling for in-depth investigations of cellular communication in a model of prostate cancer

Cellular communication is essential for cell-cell interactions, maintaining homeostasis and progression of certain disease states. While many studies examine extracellular proteins, the holistic extracellular proteome is often left uncaptured, leaving gaps in our understanding of how all extracellular proteins may impact communication and interaction. We used a cellular-based proteomics approach to more holistically profile both the intracellular and extracellular proteome of prostate cancer. Our workflow was generated in such a manner that multiple experimental conditions can be observed with the opportunity for high throughput integration. Additionally, this workflow is not limited to a proteomic aspect, as metabolomic and lipidomic studies can be integrated for a multi-omics workflow. Our analysis showed coverage of over 8000 proteins while also garnering insights into cellular communication in the context of prostate cancer development and progression. Identified proteins covered a variety of cellular processes and pathways, allowing for the investigation of multiple aspects into cellular biology. This workflow demonstrates advantages for integrating intra- and extracellular proteomic analyses as well as potential for multi-omics researchers. This approach possesses great value for future investigations into the systems biology aspects of disease development and progression.     

A novel platform for automated high-throughput fluxome profiling of metabolic variants

Advances in metabolic engineering are enabling the creation of a large number of cell factories. However, high-throughput platforms do not yet exist for rapidly analyzing the metabolic network of the engineered cells. To fill the gap, we developed an integrated solution for fluxome profiling of large sets of biological systems and conditions. This platform combines a robotic system for ¹³C-labelling experiments and sampling of labelled material with NMR-based isotopic fingerprinting and automated data interpretation. As a proof-of-concept, this workflow was applied to discriminate between Escherichia coli mutants with gradual expression of the glucose-6-phosphate dehydrogenase. Metabolic variants were clearly discriminated while pathways that support metabolic flexibility towards modulation of a single enzyme were elucidating. By directly connecting the data flow between cell cultivation and flux quantification, considerable advances in throughput, robustness, release of resources and screening capacity were achieved. This will undoubtedly facilitate the development of efficient cell factories.     

Reconstructing protein networks of epithelial differentiation from histological sections

MOTIVATION: For systems biology of complex stratified epithelia like human epidermis, it will be of particular importance to reconstruct the spatiotemporal gene and protein networks regulating keratinocyte differentiation and homeostasis. RESULTS: Inside the epidermis, the differentiation state of individual keratinocytes is correlated with their respective distance from the connective tissue. We here present a novel method to profile this correlation for multiple epithelial protein biomarkers in the form of quantitative spatial profiles. Profiles were computed by applying image processing algorithms to histological sections stained with tri-color indirect immunofluorescence. From the quantitative spatial profiles, reflecting the spatiotemporal changes of protein expression during cellular differentiation, graphs of protein networks were reconstructed. CONCLUSION: Spatiotemporal networks can be used as a means for comparing and interpreting quantitative spatial protein expression profiles obtained from different tissue samples. In combination with automated microscopes, our new method supports the large-scale systems biological analysis of stratified epithelial tissues.     

Differentiation of tumours of ductal and lobular origin: II. Genomics of invasive ductal and lobular breast carcinomas

Breast cancer is considered to be a multifactorial disorder caused by both genetic and non-genetic factors. Different histological types of breast cancer differ in response to treatment and may have a divergent clinical course. Breast tissue is heterogeneous, with components of epithelial, mesenchymal, endothelial and lymphopoietic derivation. The genetic heterogeneity of invasive breast cancer is reflected by the wide spectrum of histological types and differentiation grades. Nevertheless, the influences of these cell types on the tumour's total pattern of gene expression can be estimated analytically. Microarrays permit total tissue analysis and provide a stable molecular portrait of tumours. Some investigations suggest differences in the gene expression profiling for ductal and lobular carcinomas. It has been reported that inactivating mutations of the E-cadherin gene are very frequent in infiltrating lobular breast carcinomas. Other than altered expression of E-cadherin, little is known about the underlying biology that distinguishes ductal and lobular tumour subtypes. However, about 8 genes have been identified differentially which are expressed in lobular and ductal cancers: E-CD, survivin, cathepsin B, TPI1, SPRY1, SCYA14, TFAP2B, and thrombospondin 4, osteopontin, HLA-G, and CHC1. Expression profiling of breast cancers can be used diagnostically to distinguish individual histologic subclassifications and may guide the selection of target therapeutics. However, future approaches will need to include methods for high throughput clinical validation and the ability to analyze microscopic samples.     

Antibody‐based profiling of cerebrospinal fluid within multiple sclerosis

Antibody suspension bead arrays have proven to enable multiplexed and high‐throughput protein profiling in unfractionated plasma and serum samples through a direct labeling approach. We here describe the development and application of an assay for protein profiling of cerebrospinal fluid (CSF). While setting up the assay, systematic intensity differences between sample groups were observed that reflected inherent sample specific total protein amounts. Supplementing the labeling reaction with BSA and IgG diminished these differences without impairing the apparent sensitivity of the assay. We also assessed the effects of heat treatment on the analysis of CSF proteins and applied the assay to profile 43 selected proteins by 101 antibodies in 339 CSF samples from a multiple sclerosis (MS) cohort. Two proteins, GAP43 and SERPINA3 were found to have a discriminating potential with altered intensity levels between sample groups. GAP43 was detected at significantly lower levels in secondary progressive MS compared to early stages of MS and the control group of other neurological diseases. SERPINA3 instead was detected at higher levels in all MS patients compared to controls. The developed assay procedure now offers new possibilities for broad‐scale protein profiling of CSF within neurological disorders.     

High-throughput immunoglobulin G N-glycan characterization using rapid resolution reverse-phase chromatography tandem mass spectrometry

We present an optimized high-throughput method for the characterization of 2-aminobenzamide (2-AB)-labeled N-glycans from recombinant immunoglobulin G (rIgG). This method includes an optimized sample preparation protocol involving microwave-assisted deglycosylation in conjunction with an automated sample cleanup strategy and a rapid resolution reverse-phase high-performance liquid chromatography (RRRP-HPLC) separation of labeled N-glycans. The RRRP-HPLC method permits generation of a comprehensive glycan profile using fluorescence detection in 45 min. In addition, the profiling method is directly compatible with electrospray ionization mass spectrometry (ESI-MS), allowing immediate and sensitive characterization of the glycan moiety by intact MS and tandem MS (MS/MS) fragmentation. We conservatively estimate an efficiency gain of fourfold with respect to the throughput capabilities of this optimized method as compared with traditional protocols (overnight deglycosylation, sample cleanup by graphitized carbon or cellulose cartridge, high-pH anion exchange chromatography, fraction collection, and processing for matrix-assisted laser desorption/ionization time-of-flight [MALDI-TOF] MS analysis) for a single sample. Even greater gains are achieved when processing of multiple samples is considered.     

A Hybrid DNA Extraction Method for the Qualitative and Quantitative Assessment of Bacterial Communities from Poultry Production Samples

The efficacy of DNA extraction protocols can be highly dependent upon both the type of sample being investigated and the types of downstream analyses performed. Considering that the use of new bacterial community analysis techniques (e.g., microbiomics, metagenomics) is becoming more prevalent in the agricultural and environmental sciences and many environmental samples within these disciplines can be physiochemically and microbiologically unique (e.g., fecal and litter/bedding samples from the poultry production spectrum), appropriate and effective DNA extraction methods need to be carefully chosen. Therefore, a novel semi-automated hybrid DNA extraction method was developed specifically for use with environmental poultry production samples. This method is a combination of the two major types of DNA extraction: mechanical and enzymatic. A two-step intense mechanical homogenization step (using bead-beating specifically formulated for environmental samples) was added to the beginning of the “gold standard” enzymatic DNA extraction method for fecal samples to enhance the removal of bacteria and DNA from the sample matrix and improve the recovery of Gram-positive bacterial community members. Once the enzymatic extraction portion of the hybrid method was initiated, the remaining purification process was automated using a robotic workstation to increase sample throughput and decrease sample processing error. In comparison to the strict mechanical and enzymatic DNA extraction methods, this novel hybrid method provided the best overall combined performance when considering quantitative (using 16S rRNA qPCR) and qualitative (using microbiomics) estimates of the total bacterial communities when processing poultry feces and litter samples.     

Nonredundant mass spectrometry: a strategy to integrate mass spectrometry acquisition and analysis

Protein identification using automated data-dependent tandem mass spectrometry (MS/MS) is now a standard procedure. However, in many cases data-dependent acquisition becomes redundant acquisition as many different peptides from the same protein are fragmented, whilst only a few are needed for unambiguous identification. To increase the quality of information but decrease the amount of information, a nonredundant MS (nrMS) strategy has been developed. With nrMS, data analysis is an integral part of the overall MS acquisition and analysis, and not an endpoint as typically performed. In this nrMS workflow a matrix assisted laser desorption/ionization-time of flight-time of flight (MALDI-TOF/TOF) instrument is used. MS and restricted MS/MS data are searched and identified proteins are used to generate an "exclusion list", after in silico digestion. Peptide fragmentation is then restricted to only the most intense ions not present in the exclusion list. This process is repeated until all peaks are accounted for or the sample is consumed. Compared to nanoLC-MS/MS, nrMS yielded similar results for the analysis of six pooled two-dimensional electrophoresis (2-DE) spots. In comparison to standard data-dependent MALDI-MS/MS for sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel band analysis, nrMS dramatically increased the number of identified proteins. It was also found that this new workflow significantly increased sequence coverage by identifying unexpected peptides, which can result from post-translational modifications.     

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Automated sample mounting and alignment system for biological crystallography at a synchrotron source

High-throughput data collection for macromolecular crystallography requires an automated sample mounting and alignment system for cryo-protected crystals that functions reliably when integrated into protein-crystallography beamlines at synchrotrons. Rapid mounting and dismounting of the samples increases the efficiency of the crystal screening and data collection processes, where many crystals can be tested for the quality of diffraction. The sample-mounting subsystem has random access to 112 samples, stored under liquid nitrogen. Results of extensive tests regarding the performance and reliability of the system are presented. To further increase throughput, we have also developed a sample transport/storage system based on "puck-shaped" cassettes, which can hold sixteen samples each. Seven cassettes fit into a standard dry shipping Dewar. The capabilities of a robotic crystal mounting and alignment system with instrumentation control software and a relational database allows for automated screening and data collection to be developed.