ICPL--isotope-coded protein label

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable of quantitatively comparing many thousands of proteins obtained from cells and organisms.

Bulked sample analysis in genetics, genomics and crop improvement

Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high-throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker-assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.

Identification of Predictive Markers for Response to Neoadjuvant Chemoradiation in Rectal Carcinomas by Proteomic Isotope Coded Protein Label (ICPL) Analysis

Neoadjuvant chemoradiation (nCRT) is an established procedure in stage union internationale contre le cancer (UICC) II/III rectal carcinomas. Around 53% of the tumours present with good tumor regression after nCRT, and 8%–15% are complete responders. Reliable selection markers would allow the identification of poor or non-responders prior to therapy. Tumor biopsies were harvested from 20 patients with rectal carcinomas, and stored in liquid nitrogen prior to therapy after obtaining patients’ informed consent (Erlangen-No.3784). Patients received standardized nCRT with 5-Fluoruracil (nCRT I) or 5-Fluoruracil ± Oxaliplatin (nCRT II) according to the CAO/ARO/AIO-04 protocol. After surgery, regression grading (Dworak) of the tumors was performed during histopathological examination of the specimens. Tumors were classified as poor (Dworak 1 + 2) or good (Dworak 3 + 4) responders. Laser capture microdissection (LCM) for tumor enrichment was performed on preoperative biopsies. Differences in expressed proteins between poor and good responders to nCRT I and II were identified by proteomic analysis (Isotope Coded Protein Label, ICPL™) and selected markers were validated by immunohistochemistry. Tumors of 10 patients were classified as histopathologically poor (Dworak 1 or 2) and the other 10 tumor samples as histopathologically good (Dworak 3 or 4) responders to nCRT after surgery. Sufficient material in good quality was harvested for ICPL analysis by LCM from all biopsies. We identified 140 differentially regulated proteins regarding the selection criteria and the response to nCRT. Fourteen of these proteins were synchronously up-regulated at least 1.5-fold after nCRT I or nCRT II (e.g., FLNB, TKT, PKM2, SERINB1, IGHG2). Thirty-five proteins showed a complete reciprocal regulation (up or down) after nCRT I or nCRT II and the rest was regulated either according to nCRT I or II. The protein expression of regulated proteins such as PLEC1, TKT, HADHA and TAGLN was validated successfully by immunohistochemistry. ICPL is a valid method to identify differentially expressed proteins in rectal carcinoma tissue between poor vs. good responders to nCRT. The identified protein markers may act as selection criteria for nCRT in the future, but our preliminary findings must be reproduced and validated in a prospective cohort.

Quantitative proteomics to study carbapenem resistance in Acinetobacter baumannii

Acinetobacter baumannii is an opportunistic pathogen causing pneumonia, respiratory infections and urinary tract infections. The prevalence of this lethal pathogen increases gradually in the clinical setup where it can grow on artificial surfaces, utilize ethanol as a carbon source. Moreover it resists desiccation. Carbapenems, a β-lactam, are the most commonly prescribed drugs against A. baumannii. Resistance against carbapenem has emerged in Acinetobacter baumannii which can create significant health problems and is responsible for high morbidity and mortality. With the development of quantitative proteomics, a considerable progress has been made in the study of carbapenem resistance of Acinetobacter baumannii. Recent updates showed that quantitative proteomics has now emerged as an important tool to understand the carbapenem resistance mechanism in Acinetobacter baumannii. Present review also highlights the complementary nature of different quantitative proteomic methods used to study carbapenem resistance and suggests to combine multiple proteomic methods for understanding the response to antibiotics by Acinetobacter baumannii.

Proteomic analysis of glioblastomas: what is the best brain control sample?

Glioblastoma (GB) is the most frequent and aggressive tumor of the central nervous system. There is currently growing interest in proteomic studies of GB, particularly with the aim of identifying new prognostic or therapeutic response markers. However, comparisons between different proteomic analyses of GB have revealed few common differentiated proteins. The types of control samples used to identify such proteins may in part explain the different results obtained. We therefore tried to determine which control samples would be most suitable for GB proteomic studies. We used an isotope-coded protein labeling (ICPL) method followed by mass spectrometry to reveal and compare the protein patterns of two commonly used types of control sample: GB peritumoral brain zone samples (PBZ) from six patients and epilepsy surgery brain samples (EB) pooled from three patients. The data obtained were processed using AMEN software for network analysis. We identified 197 non-redundant proteins and 35 of them were differentially expressed. Among these 35 differentially expressed proteins, six were over-expressed in PBZ and 29 in EB, showing different proteomic patterns between the two samples. Surprisingly, EB appeared to display a tumoral-like expression pattern in comparison to PBZ. In our opinion, PBZ may be more appropriate control sample for GB proteomic analysis.

BIOLOGICAL SIGNIFICANCE

This manuscript describes an original study in which we used an isotope-coded protein labeling method followed by mass spectrometry to identify and compare the protein patterns in two types of sample commonly used as control for glioblastoma (GB) proteomic analysis: peritumoral brain zone and brain samples obtained during surgery for epilepsy. The choice of control samples is critical for identifying new prognostic and/or diagnostic markers in GB.

Toxicogenomic approaches for understanding molecular mechanisms of heavy metal mutagenicity and carcinogenicity

Heavy metals that are harmful to humans include arsenic, cadmium, chromium, lead, mercury, and nickel. Some metals or their related compounds may even cause cancer. However, the mechanism underlying heavy metal-induced cancer remains unclear. Increasing data show a link between heavy metal exposure and aberrant changes in both genetic and epigenetic factors via non-targeted multiple toxicogenomic technologies of the transcriptome, proteome, metabolome, and epigenome. These modifications due to heavy metal exposure might provide a better understanding of environmental disorders. Such informative changes following heavy metal exposure might also be useful for screening of biomarker-monitored exposure to environmental pollutants and/or predicting the risk of disease. We summarize advances in high-throughput toxicogenomic-based technologies and studies related to exposure to individual heavy metal and/or mixtures and propose the underlying mechanism of action and toxicant signatures. Integrative multi-level expression analysis of the toxicity of heavy metals via system toxicology-based methodologies combined with statistical and computational tools might clarify the biological pathways involved in carcinogenic processes. Although standard in vitro and in vivo endpoint testing of mutagenicity and carcinogenicity are considered a complementary approach linked to disease, we also suggest that further evaluation of prominent biomarkers reflecting effects, responses, and disease susceptibility might be diagnostic. Furthermore, we discuss challenges in toxicogenomic applications for toxicological studies of metal mixtures and epidemiological research. Taken together, this review presents toxicogenomic data that will be useful for improvement of the knowledge of carcinogenesis and the development of better strategies for health risk assessment.

A practical guide to the ICPL_ESIQuant software for ICPL-based quantitative proteomics

ICPL_ESIQuant is a proteomics software tool for quantitatively analyzing large mass spectrometric datasets acquired from ICPL based proteomics experiments. It is able to process mass spectrometric data from various vendors and implements results from the Mascot search engine to generate protein and peptide result tables. This protocol briefly introduces ICPL_ESIQuant and presents a detailed step by step tutorial, how to use the software with MS datasets obtained from ICPL duplex, triplex and quadruplex experiments. Requiring MS raw data in .mzXML file format and Mascot search results in .dat format as input, ICPL_ESIQuant reliably quantifies ICPL labeled proteins and provides additional information about all detected, sequenced and identified features in the sample. The software supports both the shotgun and the directed proteomics strategy, enabling the user to apply mass inclusion lists for identifying peptides not fragmented in the first MS cycle. The software together with a test dataset is freely available under http://sourceforge.net/projects/icplquant/. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Stable isotope labeling methods in protein profiling

Mass spectrometry (MS) analysis of peptides and proteins has evolved dramatically over the last 20 years. Improvement of MS instrumentation, computational data analysis, and the availability of complete sequence databases for many species have made large-scale proteomics analyses possible. The measurement of global protein abundance by quantitative mass spectrometry has the potential to increase both speed and impact of biological and clinical research. However, to be able to detect and identify potential biomarkers, reproducible and accurate quantification is essential. The following chapter describes how to perform quantitative protein profiling using stable isotope labeling methods. Throughout, there is a focus on guidance in selection of an appropriate labeling strategy. With that in mind, we have included a section on acquisition and understanding of the liquid chromatography-mass spectrometry (LC-MS) data format. Further, we describe the different stable isotope labeling methods and their pros and cons. We start by giving an overview of the overall quantitative proteomics workflow in which extracting relevant biological information from the acquired data is the ultimate goal.

Isotope Coded Protein Labeling analysis of plasma specimens from acute severe dengue fever patients

Background

Dengue fever is the most important arthropod born viral disease of public health significance. Although most patients suffer only from flu-like symptoms, a small group of patient experiences more severe forms of the disease. To contribute to a better understanding of its pathogenesis this study aims to identify proteins differentially expressed in a pool of five viremic plasma from severe dengue patients relative to a pool of five non-severe dengue patients.

Results

The use of Isotope Coded Protein Labeling (ICPL^TM) to analyze plasma depleted of twenty high-abundance proteins allowed for the identification of 51 differentially expressed proteins, which were characterized by mass spectrometry. Using quantitative ELISA, three of these proteins (Leucine-rich glycoprotein 1, Vitamin D binding-protein and Ferritin) were confirmed as having an increased expression in a panel of severe dengue plasma. The proteins identified as overexpressed by ICPL^TM in severe dengue plasma involve in clear up action after cell injury, tissue coherence and immune defense.

Conclusion

This ICPL^TM study evaluating differences between acute severe dengue plasmas and acute non-severe dengue plasmas suggests that the three proteins identified are overexpressed early in the course of the disease. Their possible use as biomarkers for the prognostic of disease severity is discussed.

Quantitative proteomic Isotope-Coded Protein Label (ICPL) analysis reveals alteration of several functional processes in the glioblastoma

Glioblastoma (GB), the most frequent primary tumor of the central nervous system, remains one of the most lethal human cancers despite intensive researches. Current paradigm in the study of GB has been focused on inter-patient variability and on trying to isolate new classification elements or prognostic factors. Here, using ICPL, a technique for protein relative quantification by mass spectrometry, we investigated protein expression between the four regions of GB on clinically relevant biopsies from 5 patients. We identified 584 non-redundant proteins and 31 proteins were found to be up-regulated in the tumor region compared to the peri-tumoral brain tissue, among which, 24 proteins belong to an interaction network linked to 4 biological processes. The core of this network is mainly constituted of interactions between beta-actin (ACTB) with heat shock proteins (HSP90AA1, HSPA8) and 14-3-3 proteins (YWHAZ, YWHAG, YWHAB). A cluster of three isoforms of the sodium pump α-subunit (ATP1A1, ATP1A2, ATP1A3) was also identified outside this network. The differential expression observed for ACTB and 14-3-3γ was further validated by western blot and/or immunohistochemistry. Our study confirms the identity of previously proposed molecular targets, highlights several functional processes altered in GB such as energy metabolism and synaptic transmission and could thus provide added value to new therapeutic trails.

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.

Recent advances in clinical proteomics using mass spectrometry

The ultimate objective of clinical proteomics is the successful discovery, validation and translation of biomarkers, together with new therapeutic targets into medical practices. New highly developed technologies in proteomics and their use in understanding tumor biology have significant clinical potential in the diagnosis, prognosis and treatment of disease. Areas such as MS, new labeling technologies and advancements in bioinformatics systems are now used to successfully detect disease-associated biomarkers together with therapeutic targets in complex biological specimens, including biofluids, cell lysates and tissue biopsies. Recent improvements in sample preparation (specifically focused on fractionation and enrichment) are enabling the analysis of low-abundance proteins together with many types of post-translational modifications. Targeted proteomic diagnostics will play a significant role in the development of personalized molecular medicine, a process that will be vital in modernizing healthcare structures.

Novel post-digest isotope coded protein labeling method for phospho- and glycoproteome analysis

In the field of proteomics there is an apparent lack of reliable methodology for quantification of posttranslational modifications. Present study offers a novel post-digest ICPL quantification strategy directed towards characterization of phosphorylated and glycosylated proteins. The value of the method is demonstrated based on the comparison of two prostate related metastatic cell lines originating from two distinct metastasis sites (PC3 and LNCaP). The method consists of protein digestion, ICPL labeling, mixing of the samples, PTM enrichment and MS-analysis. Phosphorylated peptides were isolated using TiO(2), whereas the enrichment of glycosylated peptides was performed using hydrazide based chemistry. Isolated PTM peptides were analyzed along with non enriched sample using 2D-(SCX-RP)-Nano-HPLC-MS/MS instrumentation. Taken together the novel ICPL labeling method offered a significant improvement of the number of identified (∼600 individual proteins) and quantified proteins (>95%) in comparison to the classical ICPL method. The results were validated using alternative protein quantification strategies as well as label-free MS quantification method. On the biological level, the comparison of PC3 and LNCaP cells has shown specific modulation of proteins implicated in the fundamental process related to metastasis dissemination. Finally, a preliminary study involving clinically relevant autopsy cases reiterated the potential biological value of the discovered proteins.

Mass spectrometry-based proteomics and its potential use in haematological research

In the last decade proteomics has made great progress reaching throughput and comprehensiveness comparable to genomics technologies. Mass spectrometry plays a key role in proteomics and has become an indispensable method for molecular and cellular biology because many cellular changes in response to internal or external stimuli can only be detected at the proteome level. Furthermore, different from genomics which depends on the availability of DNA or RNA, proteomics is not restricted to cellular samples, but also allows the analysis of biological fluids like serum, plasma or urine. This article provides an overview of the recent developments in proteomics techniques useful for haematological research.