Monday, 16 July 2018

Top-down Mass Spectrometry based proteomics

Mass Spectrometry (MS)-based proteomics is a powerful tool for systems biology since it provides a systematic, global, unbiased, and quantitative assessment of proteins, including interactions, modifications, location, and function. 

Post-translational modifications (PTMs) modulate protein activity, stability, localization, and function, playing essential roles in many critical cell signaling events in both healthy and disease states. Dysregulation of a number of PTMs such as protein acetylation, glycosylation, hydroxylation, and phosphorylation, has been implicated in a spectrum of human diseases. The conventional peptide-based bottom-up shotgun proteomics approach is widely used but has intrinsic limitations for mapping protein modifications due to the dramatically increased complexity in examining an already complicated proteome as each protein is digested into many peptide components as well as loss of specific information concerning the protein since only a small and variable fraction of the digested peptides are recovered.

In contrast, the protein-based top-down MS-based proteomics approach is arguably the most powerful technique for analysis of protein modifications. In the top-down approach, intact proteins are analyzed, which greatly simplifies sample preparation and reduces the mixture complexity as no proteolytic digestion is required. Subsequently, specific proteins of interests can be “gas-phase” purified and modification sites can be mapped by tandem MS (MS/MS) strategies. The top-down MS provides comprehensive sequence information for the whole protein by detecting all types of PTMs (e.g. phosphorylation, proteolysis, acetylation) and sequence variants (e.g. mutations, polymorphisms, alternatively spliced isoforms) simultaneously in one spectrum (a "bird's eye view”) without a priori knowledge. We have made significant advances in top-down MS for analysis of large intact proteins purified from complex biological samples including cell and tissue lysate as well as body fluids. We have shown that top-down MS has unique advantages for unraveling the molecular complexity, quantifying modified protein forms, deep sequencing of intact proteins, mapping modification sites with full sequence coverage, discovering unexpected modifications, identifying and quantifying positional isomers and determining the order of multiple modifications. Moreover, we have shown that a tandem mass spectrometry technique, electron capture dissociation (ECD), is especially useful for mapping labile PTMs such as phosphorylation which is well-preserved during the ECD fragmentation process. Notably, we have been able to isotopically resolve large proteins (>115 kDa) with very high mass accuracy (1-3 ppm) and extended ECD to characterize very large phosphoproteins (>140 kDa)
Nevertheless, the top-down MS approach still faces significant challenges in terms of protein solubility, separation, and the detection of low abundance and large proteins, as well as under-developed data analysis tools. Consequently, new technological developments are urgently needed to advance the field of top-down proteomics. We have been establishing an integrated top-down disease proteomics platform to globally examine intact proteins extracted from tissues for the identification and quantification of proteins and possible PTMs present in vivo. Specifically, we are developing novel approaches to address the current challenges in top-down MS-based proteomics.

A. To address the protein solubility challenge, we are developing new degradable surfactants that can effectively solubilize proteins and are compatible with top-down MS. we have recently developed an MS-compatible slowly degradable Surfactant (MasDeS) that can effectively solubilize proteins.24 Furthermore, we demonstrated that the solubility of membrane protein was significantly improved with the addition of this new surfactant. We are also developing different types of degradable surfactants and evaluating their performance for top-down proteomics.

B. To address the proteome complexity challenge, we are developing new chromatography materials and novel multi-dimensional liquid chromatography (MDLC) strategies to separate intact proteins. To address the proteome complexity challenge, we are developing new chromatography materials and novel strategies for multi-dimensional liquid chromatography (MDLC) to separate intact proteins. We have demonstrated the use of ultrahigh-pressure size exclusion chromatography (UHP-SEC)and hydrophobic interaction chromatography (HIC)for top-down proteomics. Moreover, we have developed a novel 3DLC strategy by coupling HIC with ion exchange chromatography (IEC) and reverse phase chromatography (RPC) for intact protein separation. We demonstrated that this 3D (IEC-HIC-RPC) approach greatly outperformed the conventional 2D IEC-RPC approach. We are now developing novel chromatography materials for intact protein separation.

C. To address the proteome dynamic range, we have been developing novel nanomaterials that can bind low abundance proteins with PTMs (e.g. phosphorylation) with high specificity in collaboration with a nanotechnologist, Prof. Song Jin (U. of Wisconsin). The current focus is to develop multivalent nanoparticle (NP) reagents for capturing phosphoproteins globally out of the human proteome for top-down MS analysis of intact phosphoproteins.

D. To address the challenge in under-developed software, we are developing user-friendly and versatile software interface for comprehensive analysis of high-resolution top-down MS-based proteomics data. Previously, we have developed a MASH Suite, a versatile and user-friendly software interface for processing, interpreting, visualizing and presenting high-resolution MS data. Recently, we have developed MASH Suite Pro, a comprehensive, user-friendly and freely available program tailored for top-down high-resolution mass spectrometry (MS)-based proteomics (Manuscript submitted). MASH Suite Pro significantly simplifies and speeds up the processing and analysis of top-down proteomics data by combining tools for protein identification, quantitation, characterization, and validation into a customizable and user-friendly interface.
We envision that by taking this multi-pronged approach to overcome the challenges facing top-down proteomics, we will significantly advance the burgeoning top-down proteomics field, which recently gained momentum through the creation of the Consortium for Top-down Proteomics

Friday, 13 July 2018

Systems proteomics of liver mitochondria function

Expanded proteomic analysis of metabolism

Combined analysis of large data sets characterizing genes, transcripts, and proteins can elucidate biological functions and disease processes. Williams et al. report an exceptionally detailed characterization of mitochondrial function in a genetic reference panel of recombinant inbred mice. They measured the metabolic function of nearly 400 mice under various environmental conditions and collected detailed quantitative information from livers of the animals on over 25,000 transcripts. These data were integrated with quantitation of over 2500 proteins and nearly 1000 metabolites. Such analysis showed a frequent lack of correlation of transcript and protein abundance, enabled the identification of genomic variants of mitochondrial enzymes that caused inborn errors in metabolism, and revealed two genes that appear to function in cholesterol metabolism.

Structured Abstract


Over the past two decades, continuous improvements in “omics” technologies have driven an ever-greater capacity to define the relationships between genetics, molecular pathways, and overall phenotypes. Despite this progress, the majority of genetic factors influencing complex traits remain unknown. This is exemplified by mitochondrial supercomplex assembly, a critical component of the electron transport chain, which remains poorly characterized. Recent advances in mass spectrometry have expanded the scope and reliability of proteomics and metabolomics measurements. These tools are now capable of identifying thousands of factors driving diverse molecular pathways, their mechanisms, and consequent phenotypes and thus substantially contribute toward the understanding of complex systems.


Genome-wide association studies (GWAS) have revealed many causal loci associated with specific phenotypes, yet the identification of such genetic variants has been generally insufficient to elucidate the molecular mechanisms linking these genetic variants with specific phenotypes. A multitude of control mechanisms differentially affect the cellular concentrations of different classes of biomolecules. Therefore, the identification of the causal mechanisms underlying complex trait variation requires quantitative and comprehensive measurements of multiple layers of data—principally of transcripts, proteins, and metabolites and the integration of the resulting data. Recent technological developments now support such multiple layers of measurements with a high degree of reproducibility across diverse sample or patient cohorts. In this study, we applied a multilayered approach to analyze metabolic phenotypes associated with mitochondrial metabolism.


We profiled metabolic fitness in 386 individuals from 80 cohorts of the BXD mouse genetic reference population across two environmental states. Specifically, this extensive phenotyping program included the analysis of metabolism, mitochondrial function, and cardiovascular function. To understand the variation in these phenotypes, we quantified multiple, detailed layers of systems-scale measurements in the livers of the entire population: the transcriptome (25,136 transcripts), proteome (2622 proteins), and metabolome (981 metabolites). Together with full genomic coverage of the BXDs, these layers provide a comprehensive view on overall variances induced by genetics and environment regarding metabolic activity and mitochondrial function in the BXDs. Among the 2600 transcript-protein pairs identified, 85% of observed quantitative trait loci uniquely influenced either the transcript or protein level. The transomic integration of molecular data established multiple causal links between genotype and phenotype that could not be characterized by any individual data set. Examples include the link between D2HGDH protein and the metabolite D-2-hydroxyglutarate, the BCKDHA protein mapping to the gene Bckdhb, the identification of two isoforms of ECI2, and mapping mitochondrial supercomplex assembly to the protein COX7A2L. These respectively measured variants in these mitochondrial proteins were in turn associated with varied complex metabolic phenotypes, such as heart rate, cholesterol synthesis, and branched-chain amino acid metabolism. Of note, our transomics approach clarified the contested role of COX7A2L in mitochondrial supercomplex formation and identified and validated Echdc1 and Mmab as involved in the cholesterol pathway.


Overall, these findings indicate that data generated by next-generation proteomics and metabolomics techniques have reached a quality and scope to complement transcriptomics, genomics, and phenomics for transomic analyses of complex traits. Using mitochondria as a case in point, we show that the integrated analysis of these systems provides more insights into the emergence of the observed phenotypes than any layer can by itself, highlighting the complementarity of a multilayered approach. The increasing implementation of these omics technologies as complements, rather than as replacements, will together move us forward in the integrative analysis of complex traits.

Wednesday, 11 July 2018

Clinical Chemistry


BACKGROUND: There is an urgent need for blood-based molecular tests to assist in the detection and diagnosis of cancers at an early stage, when curative interventions are still possible, and to predict and monitor response to treatment and disease recurrence. The rich content of proteins in the blood that are impacted by tumor development and host factors provides an ideal opportunity to develop noninvasive diagnostics for cancer.

CONTENT: Mass spectrometry instrumentation has advanced sufficiently to allow the discovery of protein alterations directly in plasma across no less than 7 orders of magnitude of protein abundance. Moreover, the use of proteomics to harness the immune response in the form of seropositivity to tumor antigens has the potential to complement circulating protein biomarker panels for cancer detection. The depth of analysis currently possible in a discovery setting allows the detection of potential markers at concentrations of less than 1 μg/L. Such low concentrations may exceed the limits of detection of ELISAs and thus require the development of clinical assays with exquisite analytical sensitivity. Clearly, the availability for discovery and validation of biospecimens that are highly relevant to the intended clinical application and have been collected, processed, and stored with the use of standard operating procedures is of crucial importance to the successful application of proteomics to the development of blood-based tests for cancer.

SUMMARY: The realization of the potential of proteomics to yield blood biomarkers will benefit from a collaborative approach and a substantial investment in resources.

For disease investigation, the profiling of blood constituents, notably serum and plasma, using protein characterization technologies holds long-standing interest because of the easy accessibility of this circulating fluid and its rich content of proteins that inform scientists about the health status of an individual. The available methodologies to analyze proteins have evolved dramatically over the past few decades. The initial method consisted of 1-dimensional protein separations, which was followed by the use of 2-dimensional polyacrylamide gel electrophoresis coupled with Edman sequencing. The advent of mass spectrometry, coupled with the sequencing of human and other genomes, has had a dramatic impact on the field of proteomics. The capabilities of current proteomics technologies in terms of coverage of the proteome and depth of analysis that can be achieved in a quantitative manner are truly astounding compared with just a decade ago. Recent advances include substantial increases in speed, analytical sensitivity, and dynamic range, and the availability of multiple fragmentation techniques. Equally important is orthogonal sample fractionation before mass spectrometry. Yet there remains a perception that proteomics technologies are inadequate to address the protein complexities inherent in cells, tissues, and biological fluids. Here we outline strategies for the application of proteomics to the development of blood-based cancer markers.


Protein Identification

Proteomics is the study of the complete set of proteins that is expressed at a given time in a cell, tissue, organ or organism, i.e. the proteome. Proteins are the machinery of the cell, responsible for performing the functions essential for cells to operate, survive, grow and divide. Proteins are also e.g. responsible for relaying signals or messages within and between cells that drive biological and metabolic processes. In addition, proteins are the main constituent of the physical structures of cells and organism.
There is a general interest to proteomics in life sciences since as compared to e.g. genomics/functional genomics, proteomics is able to give a much more complete understanding of the studied biological system. Due to events like alternative splicing and post-translational modifications (like phosphorylation, glycosylation, ubiquitinylation, myristyolation, palmityolation etc.), the number of different proteins expressed in an organism is by far greater than the number of its functional genes. The expression level of functional proteins is regulated at many post-transcriptional levels, like the action of inhibitory RNA species, protein localization and degradation, in addition, post-translational modifications, like phosphorylations, can have the role of switching an inactive protein to active. Due to this reasons, simple mRNA transcript measurements by functional genetics may give a biased reading of the actual protein activity.

In most cases, changes at protein level determine the differences between sickness and health and can be used to monitor the progression of a disease. Almost all drugs on the market and being developed act by trying to alter a protein (mal)function. Therefore, typically one of the first steps in the drug discovery project is the identification and validation of the protein target(s) or markers associated with a disease (one type of biomarkers).
Our expertise in proteomics

We offer our customers a wide spectrum of proteomics expertise and services. Our services are based on nanoLC-MS/MS and other instrumentation platforms, coupled with bioinformatics and computational systems biology. The samples can be fluids, like serum, plasma, urine, cell lysates or solid tissue samples as well as processed samples like SDS gel plugs.

Monday, 9 July 2018

Microbial Proteomics

Microbes are by far the most abundant organisms living on our planet, outnumbering the human population by more than 1020 times. Even our own body is populated by 10 times more microbial than human cells. In the light of such numbers it becomes apparent, that we will always have to deal with all microbial life forms that surround us, and that we have to share living space and resources. Therefore, a thorough understanding of microbial physiologies, infection and defense systems, metabolic processes or survival strategies are of great interest in order to actively defeat or cultivate microorganism.

Our group works on microorganisms, on the one hand as model organisms and for technology development, on the other hand because of their clinical relevance. We currently focus on Saccharomyces cerevisiae, Mycobacterium tuberculosis, Streptococcus pyogenes, as well as various viruses. In recent years, for most of these microbes we have generated comprehensive, high quality assay repositories for the sensitive detection and relative as well as absolute quantification of virtually all annotated proteins using targeted mass spectrometric techniques like SRM or SWATH-MS.
In the context of M. tuberculosis we use these unique resources to improve genome annotation by proteogenomic analysis, as well as to study protein-level responses to different stress conditions, such as starvation and oxygen depletion, which causes a transition into a clinically relevant dormant state. Further, we are interested in elucidating host-pathogen interactions upon infection of human cells, such as altered protein expression in M. tuberculosis, as well as changes in the human MHC peptidome.

In the case of S. pyogenes our aim is to identify and understand virulence factors by integrating genomic, proteomic, and phenotypic data of clinically isolated S. pyogenes strains. Specifically, we aim to understand how genomic variation may lead to differences in proteome expression and ultimately differences in phenotype (virulence).

Our research interests in the smallest eukaryotic model organism S. cerevisiae are multifaceted. One focus is on the better understanding of metabolic regulation and adaptation upon changing nutritional conditions based on protein abundance changes as well as phosphoproteomic analyses. Further, we are interested in the phosphorylation-based signaling networks, including for example the TOR pathway and the pheromone pathway. Also the regulation of gene expression by transcriptional regulatory elements we investigate using targeted mass spectrometry. Finally, we use yeast as a model organism to study how genetic loci are influencing the levels of related proteins by quantitative trait loci analysis (QTLs).

New rendering from 3D Protein Imaging. Reproduction of the current molecule of the month made with the new version of the Protien Imager that will be soon available at #proteinimaging #molecularbiology #molecularmodeling #molecularneuroscience #molecularart #3dproteinimaging #rendering #reproduction #3d

Friday, 6 July 2018

Proteomics Cells

Researchers can count on improved proteomics method: 

Every cell in the body contains thousands of different protein molecules and they can change this composition whenever they are induced to perform a particular task or convert into a different cell type. Understanding how cells function depends on proteomics, the ability to measure all of the changes in a cell's protein components.
In a recent paper published in the journal Analytical Chemistry, Martin Wühr and colleagues in Princeton University's Department of Molecular Biology described an improved method to accurately count the proteins present in a cell under different circumstances.

The basic tool for counting proteins is a machine called a mass spectrometer. Cell samples can be run through this type of instrument one at a time, but this is laborious and it can be difficult to detect any changes between different samples. An alternative approach is to label all of the proteins in a particular sample with a unique "isobaric" tag. Multiple samples--up to 11--can then be mixed together and run through the mass spectrometer at the same time, with the isobaric tag functioning as an identifying barcode that tells the researcher which sample the protein originally came from. This speeds things up and makes it easier to quantify any changes in the protein composition of different samples.
"However, with the simplest version of isobaric tagging, known as TMT-MS2, there are major difficulties in distinguishing real signals from background noise," Wühr explains. "That makes the readouts unreliable and only semi-quantitative."
A more complex version of isobaric tagging, called TMT-MS3, can improve this signal-to-noise problem, but it is slower and less sensitive. Moreover, it relies on a much more expensive type of mass spectrometer beyond the reach of most researchers.
While he was a postdoc at Harvard University, Wühr developed a different approach to isobaric tagging that solved the signal-to-noise problem while remaining compatible with cheaper, widely available mass spectrometers. But the technique--known as TMTc--was not without its own problems, particularly a lack of precision that made it hard to obtain consistent results.
In their recent Analytical Chemistry paper, Wühr and two of his graduate students, Matthew Sonnett and Eyan Yeung, described an improved version of TMTc that they named TMTc+. By changing how the cell samples are prepared and altering the computer algorithm that extracts data from the mass spectrometer, Wühr and colleagues were able to address many of the limitations associated with the various methods of isobaric tagging.
"The TMTc+ method is in a kind of sweet spot compared to the other methods," Wühr says. "It provides superb measurement accuracy and precision, it's at least as sensitive as any other method, and it's compatible with around ten times more mass spectrometers than TMT-MS3."
Naturally, Wühr says, there is still room for improvement. TMTc+ only allows a maximum of 5 samples to be run at the same time, and the detection of proteins in these samples is relatively inefficient. Both of these problems can be solved by developing new types of isobaric tags. "We have to explore the chemical space of these tags and find ones that work really well," Wühr says. "To this end, we have started a collaboration with the Carell group, organic chemistry experts at the LMU Munich, and already published a proof of principle paper. Eventually, these efforts should lead to an approach that will allow researchers to count every protein in a cell as it changes its form and function."

Host/Pathogen Proteomics

Host/Pathogen Proteomics: Mycoplasma pneumoniae

The bacterium Mycoplasma pneumoniae colonizes host pulmonary epithelium and is the most common cause of human community-acquired pneumonia. It successfully avoids detection by the host immune system, as the microbe alters its own cell membrane to mimic its host in order to establish chronic respiratory infections. Progression to autoimmune disease is not unknown.

Initial infection by M. pneumoniae stimulates production and release of proteins by the host cell in response to binding to the infectious agent and its internalization. Li et al. (2014) used a label-free shotgun quantitative proteomics approach to investigate the host secretome.1By characterizing the biologically active proteins released in this initial phase, the researchers hope to elucidate the pathways involved and the roles of these secretory proteins in disease pathogenesis.

Li and colleagues used the human alveolar carcinoma cell line A549 to establish initial proteomic events following infection by M. pneumoniae. They chose airway epithelial cells because these are the most common primary cells colonized by the microorganism upon infection.

The researchers cultured A549 cells either with or without the infectious agent before harvesting the conditioned media. The scientists analyzed the tryptic digests by nano liquid chromatography–tandem mass spectrometry (LC-MS/MS) using an LTQ Velos ion trap mass spectrometer (Thermo Scientific). The authors searched the data obtained against the IPI human protein database v3.60 to identify proteins secreted by the cell cultures.

Initial LC-MS/MS quantification using DeCyder software showed that 113 out of the 256 proteins identified showed at least 1.5-fold differential expression between the control (uninfected) and the infected cell cultures. Of these, 65 were elevated in abundance and 48 were reduced in the infected cells. Nine proteins were found only in the uninfected control cells, whereas 10 were exclusive to the cells post-infection. Interleukin-33 (IL-33) was one of these proteins, and the researchers confirmed increased levels following infection by utilizing an enzyme-linked immunosorbent assay (ELISA).

The researchers confirmed the proteomics results using Western blotting and real-time polymerase chain reaction (PCR) for selected proteins. Immunoblotting identified the same proteins in cell lysates and conditioned media, showing results consistent with the LC-MS/MS findings for proteins ADAM9, SERPINE1, IL-33, IGFBP4, Gal-1, and MIF, which were more abundant in conditioned media from infected cells.

Out of the 256 identified, over 59% (n = 152) of the proteins were classified as either classical secretory (n = 83) or non-classical secretory (n = 69). A total of 190 proteins were associated with exosomes. Further analysis using GO classification showed that the majority were nuclear-associated. The researchers used DAVID 6.7 for functional annotation clustering analysis and found that most proteins were associated with vesicles or were from the extracellular region or matrix.

Using the KEGG database for pathway analysis, the researchers found that those associated with metabolism, infection and proliferation were over-represented in the proteins identified post-infection. Li and co-workers further analyzed their data to discover more about the functional processes targeted by the post-infection secretome, using the BiNGO tool and STRING algorithm to examine differentially expressed proteins. The STRING analysis highlighted clusters involving stress and immune response pathways, among others. In summary, pathway analyses gave the researchers indications of where M. pneumoniae could induce protein secretion that alters the host cell function.

Finally, Li and co-authors repeated their investigation and found similar proteins in broncheoaveolar lavage samples and plasma from patients with confirmed M. pneumoniae infection. When they measured IL-33 in these two samples, they found elevated levels compared with a control group of patients who presented with respiratory foreign body. Statistical testing showed that IL-33 could indeed be used as a diagnostic marker of infection.

Overall, Li et al. are confident that, by characterizing the post-infection secretome, they have uncovered new regulatory pathways in the host response to M. pneumoniae that can be explored further to elucidate disease pathogenesis and treatment options.


Monday, 2 July 2018

Proteomics- Methodology for Studying RNA/Protein interactions

Protein microarray is one of the technology and it is a critical tool in biochemistry and molecular biology. The analytical microarray is the most powerful multiplexed detection technology. The functional microarray is one of the important tools for the high-throughput and large-scale system for biological studies. Some application of functional microarray is the detection of protein binding properties as protein-protein interaction, protein -DNA interaction, protein-RNA interaction and antigen-antibody interaction. There are numerous cellular processes in which protein kinases are involved. Recording and analyzing of immune responses.

An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics


Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. We used single-cell transcriptomics and mass spectrometry to quantify changes in cellular activity states of 30 cell types and the tissue proteome from lungs of young and old mice. Aging led to increased transcriptional noise, indicating deregulated epigenetic control. We observed highly distinct effects of aging on cell type level, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts as a novel hallmark of lung aging. Proteomic profiling revealed extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and Collagen XIV. Computational integration of the aging proteome and single cell transcriptomes predicted the cellular source of regulated proteins and created a first unbiased reference of the aging lung. The lung aging atlas can be accessed via an interactive user-friendly web tool at

Thursday, 28 June 2018

Proteomics -Targeted proteomics

Biognosys’ label-free targeted proteomic workflows allow absolute quantification of up to 150 pre-defined proteins that can be analyzed in a high-throughput mode in thousands of samples. In contrast to HRM where all peptides and fragment ions are recorded, targeted proteomics limits the number of peptides that will be monitored and only focuses on those peptides during acquisition to achieve the highest sensitivity and throughput for hundreds or thousands of samples. Targeted proteomics allows absolute quantification of the proteins of interest by including stable isotope standards in the analysis.
Targeted proteomics currently relies on two main approaches: Multiple and Parallel Reaction Monitoring (MRM and PRM). MRM is a well-established method for targeted proteomics and is primarily performed on triple quadrupole mass spectrometers, while its novel variant PRM was introduced recently and is performed on the latest generation of high-resolution mass spectrometric instruments.

Targeted proteomics is based on three essential parts:

Assay development and set-up: This is the first step in targeted proteomics workflows and resembles spectral library generation in HRM. For the proteins of interest a collection of assays (also called transition or exclusion lists) is built and optimized. An assay describes the characteristic features of peptide spectra such as fragment ion intensities and retention time (iRT). These assays are later used in data acquisition.
Data acquisition: Data acquisition is performed on triple quadrupole (MRM) or high-resolution mass spectrometric instruments (PRM). In both approaches, an upfront quadrupole is used to isolate the targeted precursor ions from the assay list followed by a collision cell where product ions (transitions) are generated. While MRM only measures pre-selected transitions with the third quadrupole, PRM detects all product ions in a high-resolution mass analyzer increasing the number of quantifiable proteins in one run.
Data analysis is historically a limitation in high-throughput analysis of multiplexed MRM and PRM runs while a significant manual input is required with available tools. Biognosys overcomes these challenges with its proprietary software SpectroDive that features smartest peak picking algorithms and enables extremely fast analysis of large datasets.
Biognosys’ targeted proteomics platform is most suitable for verification or validation studies when many samples have to be analyzed for a pre-determined set of proteins of interest.

Reference :

Wednesday, 27 June 2018

Quantitative Proteomics in Precision Medicine

Traditionally, medicine has taken a “one-size-fits-all” approach to disease prevention and treatment strategies. However, as time has passed, and lessons have been learned, changes have begun to treat the individual rather than the average human. For many years, the need for administration of transfused blood from a donor with a compatible blood group to the recipient has been known but more recently greater attention has turned to look at other aspects.

We are all individuals, not only at the genetic level but in the environment in which we exist and lifestyles we lead, all of which are important factors in determining the diseases we may suffer and how effective the treatments are.  Hail the dawn of precision medicine, a system that takes into account all of these factors in tailoring predictive, preventive and treatment strategies to the individual.

A key part of this process has been understanding all the systems that contribute to a patient’s response; genetic, epigenetic, transcriptional, posttranslational modifications, metabolic, environmental, and proteomics.

Completion of the human genome and subsequent genome sequencing projects were a major step towards understanding differences between individuals. However, this is only one piece of the puzzle. Genetics cannot predict the diversity of protein expression patterns, modifications or how proteins may interact following translation. Therefore, studying the proteome of individuals and incorporating this information with genetic data has been key to advancing the field.

Protein expression is dynamic, correlates with disease timing, severity, and response to therapy. The proteome of a given tissue will not be the same during infection as it was prior to infection or during recovery and is therefore very informative for tailoring individual diagnostics and therapy. 

Mass spectrometry (MS) based quantitative proteomics has therefore emerged as an important tool in the precision medicine armoury, enabling known and unknown biomolecules to be identified in a single analysis. The sensitivity of the technology also enables discrimination between isoforms of the same molecule. Publication of the draft human proteome and subsequent publications of specific tissue proteomes are helping to build data that will enable comparative analysis. Comparison of patient samples with this database, or ultimately with the patient’s own archived healthy samples, would allow scientists and clinicians to associate changes in the proteomes with particular disease states and monitor responses to therapies. However, despite the promising results from this area, very few validated biomarkers or assays are available to doctors or patients currently, likely due to a multitude of factors.

Identifying Low Concentration Targets in Protein-Rich Samples

The identification of low concentration proteins within a complex sample, such as urine or blood has been one such hurdle. The presence of high molecular weight or high abundance proteins, such as albumin or immunoglobulins, in these samples, can overwhelm scarce proteins. Depletion or enrichment of these specimens by antibody purification or chromatographic separation has often been required, complicating and protracting workflows and increasing the opportunities for the introduction of sample bias or processing errors. Advances in technology however, such as developments in SWATH-MS which has a simplified workflow and has been successfully applied to a wide variety of biological samples1, will hopefully see improvements in this area in the near future.

Data Processing – Proteome Analysis, Interpretation and Metadata Integration

The complexity of the data generated has also been a stumbling block in introducing the technique to mainstream medicine. Unlike a PCR-based test or genetic screening, proteome analysis does not provide a simple yes/no answer but rather requires interpretation. Developers have now produced software, such as Simcyp and Gastroplus, that will enable protein quantification results to be merged with genetic information and physiological data, such as organ volume or body fat composition, to give a more integrated analysis. When it comes to assessing the pharmacokinetic and pharmacodynamic aspects of drugs on patients however, even these integrated software packages fail to fully incorporate the information relating to interindividual variability. A lack of well-characterized tissue repositories has also been highlighted as a gap that requires filling to enable satisfactory comparators for “unknown” samples.

Proteomic Biomarker Reliability 

Biomarkers have already been identified for a number of cancers, intestinal bowel disease and cardiac events as well as for rapid diagnosis of infectious diseases, including E. coli and Salmonella. However, the failure of identified biomarkers to apply over large populations is problematic and further work to verify and validate reliable biomarkers are clearly needed.

The implementation of proteomics as a clinical tool shows promise for the future of precision medicine, however there are clearly still challenges to be overcome in translating basic research into medicine. Most conditions incorporate multiple systems and cell types and so the identification and validation of biomarkers across many tissue types will be key to success.

Monday, 25 June 2018

Rapid Proteomic Analysis

Rapid Proteomic Analysis for Solid Tumors RevealsLSD1 as a Drug Target in an end‐stage Cancer Patient

Recent advances in mass spectrometry (MS)‐based technologies are now set to transform translational cancer proteomics from an idea to a practice. Here, we present a robust proteomic workflow for the analysis of clinically relevant human cancer tissues that allows quantitation of thousands of tumor proteins in several hours of measuring time and a total turnaround of a few days. We applied it to a chemorefractory metastatic case of the extremely rare urachal carcinoma. Quantitative comparison of lung metastases and surrounding tissue revealed several significantly upregulated proteins, among them lysine‐specific histone demethylase 1 (LSD1/KDM1A). LSD1 is an epigenetic regulator and the target of active development efforts in oncology. Thus, clinical cancer proteomics can rapidly and efficiently identify actionable therapeutic options. While currently described for a single case study, we envision that it can be applied broadly to other patients in a similar condition.