In situ proteomic analysis of human breast cancer epithelial cells using laser capture microdissection: annotation by protein set enrichment analysis and gene ontology

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Differential Gene Set Enrichment Analysis: a statistical approach to quantify the relative enrichment of two gene sets

MOTIVATION

Gene Set Enrichment Analysis (GSEA) is an algorithm widely used to identify statistically enriched gene sets in transcriptomic data. However, GSEA cannot examine the enrichment of two gene sets or pathways relative to one another. Here we present Differential Gene Set Enrichment Analysis (DGSEA), an adaptation of GSEA that quantifies the relative enrichment of two gene sets.

RESULTS

After validating the method using synthetic data, we demonstrate that DGSEA accurately captures the hypoxia-induced coordinated upregulation of glycolysis and downregulation of oxidative phosphorylation. We also show that DGSEA is more predictive than GSEA of the metabolic state of cancer cell lines, including lactate secretion and intracellular concentrations of lactate and AMP. Finally, we demonstrate the application of DGSEA to generate hypotheses about differential metabolic pathway activity in cellular senescence. Together, these data demonstrate that DGSEA is a novel tool to examine the relative enrichment of gene sets in transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

DGSEA software and tutorials are available at https://jamesjoly.github.io/DGSEA/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteomic Analyses of Vitreous in Proliferative Diabetic Retinopathy: Prior Studies and Future Outlook

Vitreous fluid is becoming an increasingly popular medium for the study of retinal disease. Numerous studies have demonstrated that proteomic analysis of the vitreous from patients with proliferative diabetic retinopathy yields valuable molecular information regarding known and novel proteins and pathways involved in this disease. However, there is no standardized methodology for vitreous proteomic studies. Here, we share a suggested protocol for such studies and outline the various experimental and analytic methods that are currently available. We also review prior mass spectrometry-based proteomic studies of the vitreous from patients with proliferative diabetic retinopathy, discuss common pitfalls of these studies, and propose next steps for moving the field forward.

A proposal for score assignment to characterize biological processes from mass spectral analysis of serum

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Biological process-associated scores generated from mass spectrometry of serum.

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Scores demonstrated acceptable levels of reproducibility.

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Scores associated with biological processes and clinical outcome in cancer patients.

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Possible application to biomarker studies for treatment or monitoring of disease.

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Multiple biological processes assessed from 3 µL of serum.

Introduction

Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient’s disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.

Objectives

We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.

Methods

A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.

Results

We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.

Conclusion

These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

NETGE-PLUS: Standard and Network-Based Gene Enrichment Analysis in Human and Model Organisms

Omics techniques provide a spectrum of information at the genomic level, whose analysis can characterize complex traits at a molecular level. The relationship among genotype and phenotype implies that from genome information the molecular pathways and biological processes underlying a given phenotype are discovered. In dealing with this problem, gene enrichment analysis has become the most widely adopted strategy. Here we present NETGE-PLUS, a Web server for standard and network-based functional interpretation of gene sets of human and of model organisms, including Sus scrofa, Saccharomyces cerevisiae, Escherichia coli, and Arabidopsis thaliana. NETGE-PLUS enables the functional enrichment of both simple and ranked lists of genes, introducing also the possibility of exploring relationships among KEGG pathways. A Web interface makes data retrieval complete and user-friendly. NETGE-PLUS is publicly available at http://net-ge2.biocomp.unibo.it.

Proteins and Molecular Pathways Relevant for the Malignant Properties of Tumor-Initiating Pancreatic Cancer Cells

Cancer stem cells (CSCs), a small subset of the tumor bulk with highly malignant properties, are deemed responsible for tumor initiation, growth, metastasis, and relapse. In order to reveal molecular markers and determinants of their tumor-initiating properties, we enriched rare stem-like pancreatic tumor-initiating cells (TICs) by harnessing their clonogenic growth capacity in three-dimensional multicellular spheroid cultures. We compared pancreatic TICs isolated from three-dimensional tumor spheroid cultures with nontumor-initiating cells (non-TICs) enriched in planar cultures. Employing differential proteomics (PTX), we identified more than 400 proteins with significantly different expression in pancreatic TICs and the non-TIC population. By combining the unbiased PTX with mRNA expression analysis and literature-based predictions of pro-malignant functions, we nominated the two calcium-binding proteins S100A8 (MRP8) and S100A9 (MRP14) as well as galactin-3-binding protein LGALS3BP (MAC-2-BP) as putative determinants of pancreatic TICs. In silico pathway analysis followed by candidate-based RNA interference mediated loss-of-function analysis revealed a critical role of S100A8, S100A9, and LGALS3BP as molecular determinants of TIC proliferation, migration, and in vivo tumor growth. Our study highlights the power of combining unbiased proteomics with focused gene expression and functional analyses for the identification of novel key regulators of TICs, an approach that warrants further application to identify proteins and pathways amenable to drug targeting.

[Laser microdissection applications in histology: an open way to molecular studies]

One of the most fascinating aspects of the use of a laser beam in the field of biology has emerged with the development of devices able to perform fine dissections of biological tissues. Laser microdissection can collect phenotypically identical cells from tissue regions laid on a microscope slide in order to make differential molecular analyses on these microdissected cells. Laser microdissection can be used many areas including oncology to specify molecular mechanisms that enable to adapt a treatment related to diagnosis and research in biology, but also forensic science for tissue selection, neurology for post-mortem studies on patients with Alzheimer's disease, for clonality studies from cell cultures and cytogenetics to decipher chromosomal rearrangements. This technology represents the missing link between clinical observations and the intrinsic physiological mechanisms of biological tissues and its major applications will be addressed here.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.

Mining cancer biology through bioinformatic analysis of proteomic data

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

Protein biomarkers for subtyping breast cancer and implications for future research

INTRODUCTION

Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure. Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery. Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.

The long noncoding RNA H19 promotes cell proliferation via E2F-1 in pancreatic ductal adenocarcinoma

H19 is a long noncoding RNA differentially expressed in many tumors and participates in tumorigenesis. This study aimed to investigate the expression and function of H19 in pancreatic ductal adenocarcinoma (PDAC). Pure malignant cells were isolated from frozen sections of 25 PDAC cases by laser captured microdessection, and H19 expression level was detected by qRT-PCR. Knockdown and overexpression were employed to manipulate H19 levels in pancreatic cancer cells, then cell viability, proliferation, apoptosis and cell cycle, and the growth of xenografts were evaluated. E2F-1 levels in PDAC tissues were detected by Western blot and immunohistochemical analysis. We found that H19 was overexpressed in PDAC tissues and correlated to histological grade of PDAC. Knockdown of H19 in T3M4 and PANC-1 cells with high H19 endogenous level suppressed cell viability, proliferation and tumor growth, while H19 overexpression in COLO357 and CAPAN-1 with low H19 endogenous level enhanced cell viability, proliferation and tumor growth. Knockdown of H19 led to G0/G1 arrest, accompanied by decreased levels of E2F-1 and its downstream targets. E2F-1 was overexpressed in PDAC tissues with possible correlation with H19 expression level. In conclusion, H19 is overexpressed and plays oncogenic role in PDAC through promoting cancer cell proliferation via the upregulation of E2F-1.

A systems biology-led insight into the role of the proteome in neurodegenerative diseases

INTRODUCTION

Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases.

AREAS COVERED

Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.

Statistical Aspects in Proteomic Biomarker Discovery

In the pursuit of a personalized medicine, i.e., the individual treatment of a patient, many medical decision problems are desired to be supported by biomarkers that can help to make a diagnosis, prediction, or prognosis. Proteomic biomarkers are of special interest since they can not only be detected in tissue samples but can also often be easily detected in diverse body fluids. Statistical methods play an important role in the discovery and validation of proteomic biomarkers. They are necessary in the planning of experiments, in the processing of raw signals, and in the final data analysis. This review provides an overview on the most frequent experimental settings including sample size considerations, and focuses on exploratory data analysis and classifier development.

The extracellular matrix in breast cancer predicts prognosis through composition, splicing, and crosslinking

The extracellular matrix in the healthy breast has an important tumor suppressive role, whereas the abnormal ECM in tumors can promote aggressiveness, and has been linked to breast cancer relapse, survival and resistance to chemotherapy. This review article gives an overview of the elements of the ECM which have been linked to prognosis of breast cancers, including changes in ECM protein composition, splicing, and microstructure.

Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance

Breast cancer (BCa) molecular subtypes include luminal A, luminal B, normal-like, HER-2-enriched, and basal-like tumors, among which luminal B and basal-like cancers are highly aggressive. Biochemical pathways associated with patient survival or treatment response in these more aggressive subtypes are not well understood. With the limited availability of pathologically verified clinical specimens, cell line models are routinely used for pathway-centric studies. We measured the metabolome of luminal and basal-like BCa cell lines using mass spectrometry, linked metabolites to biochemical pathways using Gene Set Analysis, and developed a novel rank-based method to select pathways on the basis of their enrichment in patient-derived omics data sets and prognostic relevance. Key mediators of the pathway were then characterized for their role in disease progression. Pyrimidine metabolism was altered in luminal versus basal BCa, whereas the combined expression of its associated genes or expression of one key gene, ribonucleotide reductase subunit M2 (RRM2) alone, associated significantly with decreased survival across all BCa subtypes, as well as in luminal patients resistant to tamoxifen. Increased RRM2 expression in tamoxifen-resistant patients was verified using tissue microarrays, whereas the metabolic products of RRM2 were higher in tamoxifen-resistant cells and in xenograft tumors. Both genetic and pharmacological inhibition of this key enzyme in tamoxifen-resistant cells significantly decreased proliferation, reduced expression of cell cycle genes, and sensitized the cells to tamoxifen treatment. Our study suggests for evaluating RRM2-associated metabolites as noninvasive markers for tamoxifen resistance and its pharmacological inhibition as a novel approach to overcome tamoxifen resistance in BCa.

Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis

The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field.

Proteomic challenges: sample preparation techniques for microgram-quantity protein analysis from biological samples

Proteins regulate many cellular functions and analyzing the presence and abundance of proteins in biological samples are central focuses in proteomics. The discovery and validation of biomarkers, pathways, and drug targets for various diseases can be accomplished using mass spectrometry-based proteomics. However, with mass-limited samples like tumor biopsies, it can be challenging to obtain sufficient amounts of proteins to generate high-quality mass spectrometric data. Techniques developed for macroscale quantities recover sufficient amounts of protein from milligram quantities of starting material, but sample losses become crippling with these techniques when only microgram amounts of material are available. To combat this challenge, proteomicists have developed micro-scale techniques that are compatible with decreased sample size (100 μg or lower) and still enable excellent proteome coverage. Extraction, contaminant removal, protein quantitation, and sample handling techniques for the microgram protein range are reviewed here, with an emphasis on liquid chromatography and bottom-up mass spectrometry-compatible techniques. Also, a range of biological specimens, including mammalian tissues and model cell culture systems, are discussed.

Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

Background

The majority of genetic biomarkers for human cancers are defined by statistical screening of high-throughput genomics data. While a large number of genetic biomarkers have been proposed for diagnostic and prognostic applications, only a small number have been applied in the clinic. Similarly, the use of proteomics methods for the discovery of cancer biomarkers is increasing. The emerging field of proteogenomics seeks to enrich the value of genomics and proteomics approaches by studying the intersection of genomics and proteomics data. This task is challenging due to the complex nature of transcriptional and translation regulatory mechanisms and the disparities between genomic and proteomic data from the same samples. In this study, we have examined tumor antigens as potential biomarkers for breast cancer using genomics and proteomics data from previously reported laser capture microdissected ER+ tumor samples.

Results

We applied proteogenomic analyses to study the genetic aberrations of 32 tumor antigens determined in the proteomic data. We found that tumor antigens that are aberrantly expressed at the genetic level and expressed at the protein level, are likely involved in perturbing pathways directly linked to the hallmarks of cancer. The results found by proteogenomic analysis of the 32 tumor antigens studied here, capture largely the same pathway irregularities as those elucidated from large-scale screening of genomics analyses, where several thousands of genes are often found to be perturbed.

Conclusion

Tumor antigens are a group of proteins recognized by the cells of the immune system. Specifically, they are recognized in tumor cells where they are present in larger than usual amounts, or are physiochemically altered to a degree at which they no longer resemble native human proteins. This proteogenomic analysis of 32 tumor antigens suggests that tumor antigens have the potential to be highly specific biomarkers for different cancers.

PSEA-Quant: a protein set enrichment analysis on label-free and label-based protein quantification data

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

Workflow for combined proteomics and glycomics profiling from histological tissues

Extracellular matrixes comprise glycoproteins, glycosaminoglycans and proteoglycans that order the environment through which cells receive signals and communicate. Proteomic and glycomic molecular signatures from tissue surfaces can add diagnostic power to the immunohistochemistry workflows. Acquired in a spatially resolved manner, such proteomic and glycomic information can help characterize disease processes and be easily applied in a clinical setting. Our aim toward obtaining integrated omics datasets was to develop the first workflow applicable for simultaneous analysis of glycosaminoglycans, N-glycans and proteins/peptides from tissue surface areas as small as 1.5 mm in diameter. Targeting small areas is especially important in the case of glycans, as their distribution can be very heterogeneous between different tissue regions. We first established reliable and reproducible digestion protocols for the individual compound classes by applying standards on the tissue using microwave irradiation to achieve reduced digestion times. Next, we developed a multienzyme workflow suitable for analysis of the different compound classes. Applicability of the workflow was demonstrated on serial mouse brain and liver sections, both fresh frozen and formalin-fixed. The glycomics data from the 1.5 mm diameter tissue surface area was consistent with data published on bulk mouse liver and brain tissues, which demonstrates the power of the workflow in obtaining combined molecular signatures from very small tissue regions.

Ferritin heavy chain in triple negative breast cancer: a favorable prognostic marker that relates to a cluster of differentiation 8 positive (CD8+) effector T-cell response

Ferritin heavy chain (FTH1) is a 21-kDa subunit of the ferritin complex, known for its role in iron metabolism, and which has recently been identified as a favorable prognostic protein for triple negative breast cancer (TNBC) patients. Currently, it is not well understood how FTH1 contributes to an anti-tumor response. Here, we explored whether expression and cellular compartmentalization of FTH1 correlates to an effective immune response in TNBC patients. Analysis of the tumor tissue transcriptome, complemented with in silico pathway analysis, revealed that FTH1 was an integral part of an immunomodulatory network of cytokine signaling, adaptive immunity, and cell death. These findings were confirmed using mass spectrometry (MS)-derived proteomic data, and immunohistochemical staining of tissue microarrays. We observed that FTH1 is localized in both the cytoplasm and/or nucleus of cancer cells. However, high cytoplasmic (c) FTH1 was associated with favorable prognosis (Log-rank p = 0.001), whereas nuclear (n) FTH1 staining was associated with adverse prognosis (Log-rank p = 0.019). cFTH1 staining significantly correlated with total FTH1 expression in TNBC tissue samples, as measured by MS analysis (Rs = 0.473, p = 0.0007), but nFTH1 staining did not (Rs = 0.197, p = 0.1801). Notably, IFN γ-producing CD8+ effector T cells, but not CD4+ T cells, were preferentially enriched in tumors with high expression of cFTH1 (p = 0.02). Collectively, our data provide evidence toward new immune regulatory properties of FTH1 in TNBC, which may facilitate development of novel therapeutic targets.

Laser microdissection of paraffin embedded tissue as a tool to estimate the sialylation status of selected cell populations

In vertebrates, sialic acids occur at the terminal end of glycans mediating numerous biological processes like cell differentiation or tumor metastasis. Consequently, the cellular sialylation status under healthy and pathological conditions is of high interest. Existing analytical strategies to determine sialylation patterns are mostly applied to tissue samples consisting of a mixture of different cell types. Alterations in the sialylation status in a distinct area of tissues or in a specific cell population may, therefore, be easily overlooked. Likewise, estimated variations in sialylation in tissue homogenates might be simply the result of a changed cell composition. To overcome these limitations, we employed laser microdissection to isolate defined cell types or functional subunits and cell populations of paraffin embedded specimens which represent the most abundant supply of human tissue associated with clinical records. For qualitative and quantitative estimation of the sialylation status, sialic acids were released, fluorescently labeled, and analyzed by an online high-performance liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS) system. As a proof of principle, this strategy was successfully applied to characterize the sialylation of the apical region of epididymal epithelial cells. Furthermore, it was possible to detect an impaired sialylation during kidney maturation in a transgenic mouse model, which was restricted to glomeruli, whereas no differences in sialylation were observed when whole kidney homogenates were used. Thus, starting from paraffin embedded tissue samples, the outlined approach offers a sensitive method to detect and quantify sialic acids on defined cell populations, which may be useful to explore novel sialic acid dependent roles during physiological and pathological processes.

Protein expression profile of rat type two alveolar epithelial cells during hyperoxic stress and recovery

In rodent model systems, the sequential changes in lung morphology resulting from hyperoxic injury are well characterized and are similar to changes in human acute respiratory distress syndrome. In the injured lung, alveolar type two (AT2) epithelial cells play a critical role in restoring the normal alveolar structure. Thus characterizing the changes in AT2 cells will provide insights into the mechanisms underpinning the recovery from lung injury. We applied an unbiased systems-level proteomics approach to elucidate molecular mechanisms contributing to lung repair in a rat hyperoxic lung injury model. AT2 cells were isolated from rat lungs at predetermined intervals during hyperoxic injury and recovery. Protein expression profiles were determined by using iTRAQ with tandem mass spectrometry. Of the 959 distinct proteins identified, 183 significantly changed in abundance during the injury-recovery cycle. Gene ontology enrichment analysis identified cell cycle, cell differentiation, cell metabolism, ion homeostasis, programmed cell death, ubiquitination, and cell migration to be significantly enriched by these proteins. Gene set enrichment analysis of data acquired during lung repair revealed differential expression of gene sets that control multicellular organismal development, systems development, organ development, and chemical homeostasis. More detailed analysis identified activity in two regulatory pathways, JNK and miR 374. A novel short time-series expression miner algorithm identified protein clusters with coherent changes during injury and repair. We concluded that coherent changes occur in the AT2 cell proteome in response to hyperoxic stress. These findings offer guidance regarding the specific molecular mechanisms governing repair of the injured lung.

Systems biology in drug discovery and development

The complexity of human biology makes it challenging to develop safe and effective new medicines. Systems biology omics-based efforts have led to an explosion of high-throughput data and focus is now shifting to the integration of diverse data types to connect molecular and pathway information to predict disease outcomes. Better models of human disease biology, including more integrated network-based models that can accommodate multiple omics data types, as well as more relevant experimental systems, will help predict drug effects in patients, enabling personalized medicine, improvement of the success rate of new drugs in the clinic, and the finding of new uses for existing drugs.

Shotgun-proteomics-based clinical testing for diagnosis and classification of amyloidosis

Shotgun proteomics technology has matured in the research laboratories and is poised to enter clinical laboratories. However, the road to this transition is sprinkled with major technical unknowns such as long-term stability of the platform, reproducibility of the technology and clinical utility over traditional antibody-based platforms. Further, regulatory bodies that oversee the clinical laboratory operations are unfamiliar with this new technology. As a result, diagnostic laboratories have avoided using shotgun proteomics for routine diagnostics. In this perspectives article, we describe the clinical implementation of a shotgun proteomics assay for amyloid subtyping, with a special emphasis on standardizing the platform for better quality control and earning clinical acceptance. This assay is the first shotgun proteomics assay to receive regulatory approval for patient diagnosis. The blueprint of this assay can be utilized to develop novel proteomics assays for detecting numerous other disease pathologies.

Innovations in studying in vivo cell behavior and pharmacology in complex tissues--microvascular endothelial cells in the spotlight

Many studies on the molecular control underlying normal cell behavior and cellular responses to disease stimuli and pharmacological intervention are conducted in single-cell culture systems, while the read-out of cellular engagement in disease and responsiveness to drugs in vivo is often based on overall tissue responses. As the majority of drugs under development aim to specifically interact with molecular targets in subsets of cells in complex tissues, this approach poses a major experimental discrepancy that prevents successful development of new therapeutics. In this review, we address the shortcomings of the use of artificial (single) cell systems and of whole tissue analyses in creating a better understanding of cell engagement in disease and of the true effects of drugs. We focus on microvascular endothelial cells that actively engage in a wide range of physiological and pathological processes. We propose a new strategy in which in vivo molecular control of cells is studied directly in the diseased endothelium instead of at a (far) distance from the site where drugs have to act, thereby accounting for tissue-controlled cell responses. The strategy uses laser microdissection-based enrichment of microvascular endothelium which, when combined with transcriptome and (phospho)proteome analyses, provides a factual view on their status in their complex microenvironment. Combining this with miniaturized sample handling using microfluidic devices enables handling the minute sample input that results from this strategy. The multidisciplinary approach proposed will enable compartmentalized analysis of cell behavior and drug effects in complex tissue to become widely implemented in daily biomedical research and drug development practice.

Recent advances in the development of single cell analysis--a review

Development of techniques for the analysis of the content of individual cells represents an important direction in modern bioanalytical chemistry. While the analysis of chromosomes, organelles, or location of selected proteins has been traditionally the domain of microscopic techniques, the advances in miniaturized analytical systems bring new possibilities for separations and detections of molecules inside the individual cells including smaller molecules such as hormones or metabolites. It should be stressed that the field of single cell analysis is very broad, covering advanced optical, electrochemical and mass spectrometry instrumentation, sensor technology and separation techniques. The number of papers published on single cell analysis has reached several hundred in recent years. Thus a complete literature coverage is beyond the limits of a journal article. The following text provides a critical overview of some of the latest developments with the main focus on mass spectrometry, microseparation methods, electrophoresis in capillaries and microfluidic devices and respective detection techniques for performing single cell analyses.

Breast cancer classification by proteomic technologies: current state of knowledge

Breast cancer is traditionally considered as a heterogeneous disease. Molecular profiling of breast cancer by gene expression studies has provided us an important tool to discriminate a number of subtypes. These breast cancer subtypes have been shown to be associated with clinical outcome and treatment response. In order to elucidate the functional consequences of altered gene expressions related to each breast cancer subtype, proteomic technologies can provide further insight by identifying quantitative differences at the protein level. In recent years, proteomic technologies have matured to an extent that they can provide proteome-wide expressions in different clinical materials. This technology can be applied for the identification of proteins or protein profiles to further refine breast cancer subtypes or for discovery of novel protein biomarkers pointing towards metastatic potential or therapy resistance in a specific subtype. In this review, we summarize the current state of knowledge of proteomic research on molecular breast cancer classification and discuss important aspects of the potential usefulness of proteomics for discovery of breast cancer-associated protein biomarkers in the clinic.

Immunohistochemical staining, laser capture microdissection, and filter-aided sample preparation-assisted proteomic analysis of target cell populations within tissue samples

An important problem involves isolating subpopulations of cells defined by protein markers in clinical tissue samples for proteomic studies. We describe a method termed Immunohistochemical staining, laser capture microdissection (LCM) and filter-aided sample preparation (FASP)-Assisted Proteomic analysis of Target cell populations within tissue samples (ILFAPT). The principle of ILFAPT is that a target cell population expressing a protein of interest can be lit up by immunohistochemical staining and isolated from tissue sections using LCM for FASP and proteomic analysis. Using this method, we isolated a small population of CD90(+) stem-like cells from glioblastoma multiforme tissue sections and identified 674 high-confidence (false discovery rate < 0.01) proteins from 32 nL of CD90(+) cells by LC-MS/MS using an Orbitrap Elite mass spectrometer. We further quantified the relative abundance of proteins identified from equal volumes of LCM-captured CD90(+) and CD90(-) cells, where 109 differentially expressed proteins were identified. The major group of these differentially expressed proteins was relevant to cell adhesion and cellular movement. This ILFAPT method has demonstrated the ability to provide in-depth proteome analysis of a very small specific cell population within tissues. It can be broadly applied to the study of target cell populations within clinical specimens.

Protein sets define disease states and predict in vivo effects of drug treatment

Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousands of proteins and observed significant changes in protein pathways that were (dys-) regulated in diet-induced obesity mice. Analysis of the expression and post-translational modifications of proteins in various peripheral metabolic target tissues including adipose, heart, and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high-fat diet feeding and medication with two antidiabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated, for example, early-on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates.

Differential expression analysis for pathways

Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.

The data deluge represents a growing challenge for life sciences. Within this sea of data surely lie many secrets to understanding important biological and medical systems. To quantify important patterns in this data, we present DEAP (Differential Expression Analysis for Pathways). DEAP amalgamates information about biological pathway structure and differential expression to identify important patterns of regulation. On both simulated and biological data, we show that DEAP is able to identify key mechanisms while making significant improvements over existing methodologies. For example, on the interferon study, DEAP uniquely identified both the interferon gamma signalling pathway and the JAK STAT signalling pathway.

Bioinformatics tools for secretome analysis

Over recent years, analyses of secretomes (complete sets of secreted proteins) have been reported in various organisms, cell types, and pathologies and such studies are quickly gaining popularity. Fungi secrete enzymes can break down potential food sources; plant secreted proteins are primarily parts of the cell wall proteome; and human secreted proteins are involved in cellular immunity and communication, and provide useful information for the discovery of novel biomarkers, such as for cancer diagnosis. Continuous development of methodologies supports the wide identification and quantification of secreted proteins in a given cellular state. The role of secreted factors is also investigated in the context of the regulation of major signaling events, and connectivity maps are built to describe the differential expression and dynamic changes of secretomes. Bioinformatics has become the bridge between secretome data and computational tasks for managing, mining, and retrieving information. Predictions can be made based on this information, contributing to the elucidation of a given organism's physiological state and the determination of the specific malfunction in disease states. Here we provide an overview of the available bioinformatics databases and software that are used to analyze the biological meaning of secretome data, including descriptions of the main functions and limitations of these tools. The important challenges of data analysis are mainly related to the integration of biological information from dissimilar sources. Improvements in databases and developments in software will likely substantially contribute to the usefulness and reliability of secretome studies. This article is part of a Special Issue entitled: An Updated Secretome.

Laser capture microdissection for protein and NanoString RNA analysis

Laser capture microdissection (LCM) allows the precise procurement of enriched cell populations from a heterogeneous tissue, or live cell culture, under direct microscopic visualization. Histologically enriched cell populations can be procured by harvesting cells of interest directly or isolating specific cells by ablating unwanted cells. The basic components of laser microdissection technology are (a) visualization of cells via light microscopy, (b) transfer of laser energy to a thermolabile polymer with either the formation of a polymer-cell composite (capture method) or transfer of laser energy via an ultraviolet laser to photovolatize a region of tissue (cutting method), and (c) removal of cells of interest from the heterogeneous tissue section. The capture and cutting methods (instruments) for laser microdissection differ in the manner by which cells of interest are removed from the heterogeneous sample. Laser energy in the capture method is infrared (810 nm), while in the cutting mode the laser is ultraviolet (355 nm). Infrared lasers melt a thermolabile polymer that adheres to the cells of interest, whereas ultraviolet lasers ablate cells for either removal of unwanted cells or excision of a defined area of cells. LCM technology is applicable to an array of applications including mass spectrometry, DNA genotyping and loss-of-heterozygosity analysis, RNA transcript profiling, cDNA library generation, proteomics discovery, and signal kinase pathway profiling. This chapter describes LCM using an Arcturus(XT) instrument for downstream protein sample analysis and using an mmi CellCut Plus® instrument for RNA analysis via NanoString technology.

Proteomic analysis of frozen tissue samples using laser capture microdissection

The discovery of effective cancer biomarkers is essential for the development of both advanced molecular diagnostics and new therapies/medications. Finding and exploiting useful clinical biomarkers for cancer patients is fundamentally linked to improving outcomes. Towards these aims, the heterogeneous nature of tumors represents a significant problem. Thus, methods establishing an effective functional linkage between laser capture microdissection (LCM) and mass spectrometry (MS) provides for an enhanced molecular profiling of homogenous, specifically targeted cell populations from solid tumors. Utilizing frozen tissue avoids molecular degradation and bias that can be induced by other preservation techniques. Since clinical samples are often of a small quantity, tissue losses must be minimized. Therefore, all steps are carried out in the same single tube. Proteins are identified through peptide sequencing and subsequent matching against a specific proteomic database. Using such an approach enhances clinical biomarker discovery in the following ways. First, LCM allows for the complexity of a solid tumor to be reduced. Second, MS provides for the profiling of proteins, which are the ultimate bio-effectors. Third, by selecting for tumor proper or microenvironment-specific cells from clinical samples, the heterogeneity of individual solid tumors is directly addressed. Finally, since proteins are the targets of most pharmaceuticals, the enriched protein data streams can then be further analyzed for potential biomarkers, drug targets, pathway elucidation, as well as an enhanced understanding of the various pathologic processes under study. Within this context, the following method illustrates in detail a synergy between LCM and MS for an enhanced molecular profiling of solid tumors and clinical biomarker discovery.

A quantitative proteomic workflow for characterization of frozen clinical biopsies: laser capture microdissection coupled with label-free mass spectrometry

This paper describes a simple, highly efficient and robust proteomic workflow for routine liquid-chromatography tandem mass spectrometry analysis of Laser Microdissection Pressure Catapulting (LMPC) isolates. Highly efficient protein recovery was achieved by optimization of a "one-pot" protein extraction and digestion allowing for reproducible proteomic analysis on as few as 500 LMPC isolated cells. The method was combined with label-free spectral count quantitation to characterize proteomic differences from 3000-10,000 LMPC isolated cells. Significance analysis of spectral count data was accomplished using the edgeR tag-count R package combined with hierarchical cluster analysis. To illustrate the capability of this robust workflow, two examples are presented: 1) analysis of keratinocytes from human punch biopsies of normal skin and a chronic diabetic wound and 2) comparison of glomeruli from needle biopsies of patients with kidney disease. Differentially expressed proteins were validated by use of immunohistochemistry. These examples illustrate that tissue proteomics carried out on limited clinical material can obtain informative proteomic signatures for disease pathogenesis and demonstrate the suitability of this approach for biomarker discovery.

Clinical proteomics and OMICS clues useful in translational medicine research

Since the advent of the new proteomics era more than a decade ago, large-scale studies of protein profiling have been used to identify distinctive molecular signatures in a wide array of biological systems, spanning areas of basic biological research, clinical diagnostics, and biomarker discovery directed toward therapeutic applications. Recent advances in protein separation and identification techniques have significantly improved proteomic approaches, leading to enhancement of the depth and breadth of proteome coverage.

Proteomic signatures, specific for multiple diseases, including cancer and pre-invasive lesions, are emerging. This article combines, in a simple manner, relevant proteomic and OMICS clues used in the discovery and development of diagnostic and prognostic biomarkers that are applicable to all clinical fields, thus helping to improve applications of clinical proteomic strategies for translational medicine research.

Relevant phosphoproteomic and mass spectrometry: approaches useful in clinical research

Background

"It's not what we do, it's the way that we do it". Never has this maxim been truer in proteomics than now. Mass Spectrometry-based proteomics/phosphoproteomics tools are critical to understand the structure and dynamics (spatial and temporal) of signalling that engages and migrates through the entire proteome. Approaches such as affinity purification followed by Mass Spectrometry (MS) have been used to elucidate relevant biological questions disease vs. health. Thousands of proteins interact via physical and chemical association. Moreover, certain proteins can covalently modify other proteins post-translationally. These post-translational modifications (PTMs) ultimately give rise to the emergent functions of cells in sequence, space and time.

Findings

Understanding the functions of phosphorylated proteins thus requires one to study proteomes as linked-systems rather than collections of individual protein molecules. Indeed, the interacting proteome or protein-network knowledge has recently received much attention, as network-systems (signalling pathways) are effective snapshots in time, of the proteome as a whole. MS approaches are clearly essential, in spite of the difficulties of some low abundance proteins for future clinical advances.

Conclusion

Clinical proteomics-MS has come a long way in the past decade in terms of technology/platform development, protein chemistry, and together with bioinformatics and other OMICS tools to identify molecular signatures of diseases based on protein pathways and signalling cascades. Hence, there is great promise for disease diagnosis, prognosis, and prediction of therapeutic outcome on an individualized basis. However, and as a general rule, without correct study design, strategy and implementation of robust analytical methodologies, the efforts, efficiency and expectations to make biomarkers (especially phosphorylated kinases) a useful reality in the near future, can easily be hampered.

Concordant release of glycolysis proteins into the plasma preceding a diagnosis of ER+ breast cancer

Although the identification of peripheral blood biomarkers would enhance early detection strategies for breast cancer, the discovery of protein markers has been challenging. In this study, we sought to identify coordinated changes in plasma proteins associated with breast cancer based on large-scale quantitative mass spectrometry. We analyzed plasma samples collected up to 74 weeks before diagnosis from 420 estrogen receptor (ER)(+) cases and matched controls enrolled in the Women's Health Initiative cohort. A gene set enrichment analysis was applied to 467 quantified proteins, linking their corresponding genes to particular biologic pathways. On the basis of differences in the concentration of individual proteins, glycolysis pathway proteins exhibited a statistically significant difference between cases and controls. In particular, the enrichment was observed among cases in which blood was drawn closer to diagnosis (effect size for the 0-38 weeks prediagnostic group, 1.91; P, 8.3E-05). Analysis of plasmas collected at the time of diagnosis from an independent set of cases and controls confirmed upregulated levels of glycolysis proteins among cases relative to controls. Together, our findings indicate that the concomitant release of glycolysis proteins into the plasma is a pathophysiologic event that precedes a diagnosis of ER(+) breast cancer.