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Newsletter of the Human Proteome Organization

Current and past HUPOSTs are posted here for your review. Stories, highlights, news, and announcements are gladly accepted for inclusion in the HUPOST. Please submit your information to the HUPO Office at office@hupo.org.

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  • 29 Sep 2020 3:36 PM | Anonymous member (Administrator)

    By Michelle Hill, QIMR Berghofer Medical Research Institute, Brisbane, Australia, and Stephen Pennington, University College Dublin, Dublin, Ireland.

    Developing new therapies for clinical use is a long and costly process. Optimizing dose and schedule in early clinical trials and selecting the right patients for the therapy are two critical aspects for the drug development process. Read how Dr Henrik Neubert (Pfizer), Dr Amanda Paulovich (Fred Hutchinson Cancer Research Center) and Dr Carl Barrett (Astra Zeneca) have used targeted proteomics to facilitate drug development.

    In the words of Dr Carl Barrett (Astra Zeneca), “90% of drugs fail for 2 reasons, bad chemistry or bad biology”. Protein technologies have been central in drug development, as most drug targets and their downstream effectors are proteins. In recent years proteins (often antibodies) are also being used as new therapeutics. Recently, proteomics technologies have matured to the stage where they are now sufficiently robust and reproducibly that they being developed into robust high throughput assays. Proteomics can be key to helping to identify bad chemistry (of the drug), and to illuminate bad biology by facilitating pharmacodynamic (PD) and proof of mechanism (POM) studies.

    Selecting therapeutic targets with the right properties

    With PhD and postdoctoral training in quantitative protein mass spectrometry, peptide synthesis and MALDI surface chemistry, it was natural for Dr Hendrik Neubert to bring his proteomics knowledge to drug development when he joined Pfizer in 2004. Hendrik now leads a translational biomeasures and protein biomarkers group that develops proteomics assays based on mass spectrometry to measure synthesis rates and concentrations of therapeutic targets as well as their engagement with biotherapeutics. This proteomic data when combined with critical experimental data enables mechanistic modeling to support key drug development program decisions such as the feasibility of modulating the activity of a particular protein target or establishing drug dosing regimens. Hendrik and his team have been particularly influential in the area of immunoaffinity mass spectrometry and worked to robustly position this technology in early clinical trials of Pfizer’s drug candidates. His team was the “terminator” for Pfizer’s osteopontin neutralizing antibody program due to undesirable target properties.

    Osteopontin seemed to be a perfect drug target and it was being pursued by several biopharmaceutical companies. It is a circulating protein and pre-clinical studies implicated it in many immune-related diseases, liver fibrosis and cancer. In a cynomolgus monkey model, a neutralizing antibody to osteopontin was effective in ameliorating arthritis. The literature describes a successful phase I trial demonstrating safety and tolerability in men, however, no improvement in disease was observed in a subsequent Phase IIA trial on rheumatoid arthritis.

    The Pfizer team was interested in developing a neutralizing antibody against osteopontin for a different indication but before proceeding, Neubert’s team was tasked to investigate the potential PK/PD risks associated with the osteopontin target. The team realized that the half-life of osteopontin in human blood had never been determined before. This is an important parameter for a neutralizing antibody, because if osteopontin protein is quickly eliminated from the body, then the amount and frequency of antibody dosing required for efficacy may not be feasible therapeutically.

    Protein half-lives were previously measured by injecting radioactively or fluorescently labelled recombinant protein into preclinical models. Unfortunately, this method is really inadequate for a reliable physiologically relevant half-life measurement as it only measures the rate of elimination, the protein being measured is not endogenous and is typically administered above endogenous concentrations. Most importantly the approach cannot be applied to humans!

    With their proteomics expertise, Neubert’s team used longitudinal serum samples from a previously conducted stable-isotope labelled leucine (13C-Leu) pulse-chase study in humans and enriched osteopontin using an anti-osteopontin antibody prior to tryptic digestion. Liquid chromatography-tandem mass spectrometry of a proteotypic peptide with and without 13C-Leu incorporation was used to determine the half-life of osteopontin in human blood from healthy individuals. Surprisingly, with a half-life of around just 20 minutes osteopontin has one of the shortest half-lives Neubert’s team has ever observed for a human protein. Considering the rapid synthesis and clearance from human blood it was evident that the dosing schedule for a neutralizing antibody that would be required was simply not feasible. Hence, Pfizer stopped the discovery project very early before significant further investments were made. Of course, pharma teams all want their therapeutic programs to be successful, but if a drug development program does fail it is ideal it fails early to avoid costly clinical studies so resources can be spent on programs that are more likely to succeed.

    Elucidating tumor biology and drug mechanisms of action

    Quantifying proteins and protein networks for pharmacodynamic and proof of mechanism studies is critical for translating novel therapies, to confirm the biological mechanisms underlying new compounds and to inform drug dose and scheduling in clinical trials. Immunohistochemistry (IHC) is generally the preferred technology for these studies because it is sensitive, provides biomarker spatial distribution and is semi-quantitative. For clinical implementation, IHC is a well-understood analytical modality. However, IHC is critically dependent on the absolute specificity of individual antibodies, and establishing this specificity is costly in terms of time and resource. As a result, only a handful of fully validated IHC protocols can be developed for each drug project, where the choice of which IHC assays to develop is largely done on the basis of “best educated guess” arising from orthogonal preclinical methods such as Western blotting.

    Adding to the challenge of measuring specific proteins by IHC, it is recognized that proteins act as interconnected networks, and the effects of cancer driver mutations for example spread throughout the networks. Ideally we would have assays to quantify panels of multiple proteins in early phase clinical trials to assess the activity of pathways/networks that determine treatment responses, and this developmental effort is not practical using IHC

    More specific and quantitative “NextGen” proteomic techniques are now starting to be used, exemplified by the stimulating interdisciplinary collaboration between Dr. Carl Barrett (AstraZeneca) and Dr. Amanda Paulovich (Fred Hutchinson Cancer Research Center). Paulovich is a geneticist and oncologist who has run a translational proteomic laboratory at Fred Hutch for the past 17 years. Barrett has a PhD in biophysical chemistry and is VP Translational Sciences Onc iMed at AstraZeneca.

    A recent collaborative project between their teams identified phospho-RAD50 as a novel pharmacodynamic biomarker for inhibitors of DNA damage checkpoint signaling kinases ATM and ATR, which are being tested in clinical trials in a variety of cancers. While pharmacodynamic biomarkers were available, the assays and biomarkers were not ideal.

    Paulovich’s lab developed a multiplexed immuno-multiple reaction monitoring mass spectrometry assay to measure proteins and phosphoproteins in the signaling cascade downstream of the DNA damage checkpoint. The team used this targeted mass spectrometry-based assay panel to identify Ser635-phosphorylated RAD50 as a novel pharmacodynamic biomarker of ATR and ATM kinase inhibitor pharmacology. The pRad50 biomarker was further validated by Barret’s team using two preclinical xenograft models and using archived human tumor material. Together this supported clinical utilization of pRAD50 as a biomarker to probe clinical pharmacokinetic/pharmacodynamic relationships, thereby informing recommended Phase 2 dose/schedule.

    Towards broader clinical testing

    The productive interdisciplinary academia-pharma collaboration between Barrett and Paulovich has propelled immuno-MRM assays from research to established Clinical Laboratory Improvement Amendments (CLIA)/clinical grade assays, suitable for clinical trials to facilitate drug development. Paulovich’s team has developed >1,400 targeted mass spectrometry-based assays, which her laboratory runs in its recently-established CLIA environment. CLIA establishes the quality laboratory framework for human diagnostic testing, so Paulovich is taking targeted proteomics assays a step closer to clinical use.

    Barrett and Paulovich are actively involved with the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC), a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. A major goal of CPTAC is to translate proteomic technologies into clinical use. To facilitate distribution and uptake of proteomic technologies by the community, CPTAC has developed public resources, such as the CPTAC Assay Portal and the CPTAC Antibody Portal. One of the CPTAC antibodies is incorporated into a clinical thyroglobulin mass spectrometry test, which is used for thyroid cancer patients with autoantibodies that interfere with widely used immunoassays.

    The specificity, sensitivity and robustness of targeted proteomics assays make them highly attractive for clinical trials where thousands of samples require analysis using validated methods. Indeed, Neubert’s team are now using targeted proteomics assays not only to assess the effect of biotherapeutics but increasingly also to examine transgene protein expression in gene therapy studies, both preclinically and clinically.

    Interested to know more? Register for HUPO Connect 2020 to hear exciting scientific presentations from both Hendrik Neubert and Amanda Paulovich as well as other global leaders.

  • 21 Sep 2020 7:30 AM | Anonymous member (Administrator)

    Geoffrey G. Hesketh1 & Anne-Claude Gingras1,2
    1Lunenfeld-Tanenbaum Research Institute, Sinai Health System
    2Department of Molecular Genetics, University of Toronto
    Toronto, Ontario, Canada

    Cells can be viewed as functioning in four dimensions: three-dimensional space (structural organization) and one-dimensional time (dynamics). To understand cell function, we must therefore understand how their constituent macromolecules, including proteins, lipids, carbohydrates and nucleic acids, exist in space as well as in time. Here, we discuss the use of proteomics methods to probe such questions.

    The era of molecular cell biology (i.e., studying the mechanisms by which molecules orchestrate cell function) was ushered in during the 1950s with the development of two key techniques – cellular electron microscopy and cell fractionation by differential centrifugation. By combining these powerful approaches with existing classical biochemistry methods, critical insight into how cells are organized into distinct membrane-bound organelles (e.g., ER, Golgi, mitochondria, lysosomes) and molecular structures and machines (e.g., chromatin, ribosomes) was gained. Importantly, this led to the realization that these different compartments carry out distinct cellular functions, largely due to the differential partitioning of specific macromolecules to distinct compartments.

    In the late 1990s and early 2000s, genome sequencing and accelerated development in mass spectrometry technologies for peptide sequencing set the stage for the new field of proteomics, which was ideally suited to addressing molecular cell biology questions. Rather than analyzing differential cell fractions by low throughput biochemistry methods, the ability to assign proteins en masse to distinct fractions (and therefore distinct cell compartments) became possible. The speed, sensitivity, and resolution of mass spectrometers dramatically improved over the years, and when combined with modern proteomics technologies (including multiplexed quantitation by isotope tagging strategies and improvements in data analysis), spatial proteomics was able to develop into a mature field1.

    An inherent limitation to spatial proteomics methods that rely on cell lysis and differential fractionation is that spatial information is captured after both lysis and fractionation have occurred. Lysis necessarily involves some form of membrane disruption (often by mechanical means and the use of detergents) and the fractionation (the stage at which ‘spatial’ information is captured) is carried out under non-native conditions. While the integrity of certain cellular compartments may be maintained during such procedures, this is not universally true for all compartments. Furthermore, it is becoming increasingly appreciated that many cell functions are driven by extensive contacts between distinct organelles2, and these associations are poorly captured by lysis and fractionation approaches.

    Methods that bypass the requirement for isolating intact structures prior to identification by mass spectrometry can overcome many of these limitations. The first practical application of proximity-dependent biotinylation followed by mass spectrometry (BioID) was described in 20123; reviewed in4). BioID employs a bacterial biotin ligase (BirA) in which a single point mutation (R118G) yields an abortive enzyme (BirA*) that creates a ‘cloud’ of reactive biotinyl-AMP. This allows biotinylation of proteins on accessible lysine residues in the vicinity of the BirA*-tagged bait (estimated to be within a ~5 nM radius in one study5). Since the introduction of BioID, the proximity-dependent biotinylation enzyme toolbox has grown to include peroxidase-based enzymes (e.g., APEX2) that can label tyrosines6, biotin ligases from other species7,8 and more catalytically efficient versions of the BirA enzymes generated through directed evolution (such as TurboID and miniTurbo)9. A key feature of proximity-dependent biotinylation approaches is that spatial information is captured in living cells prior to lysis, through covalent labeling of the proximal proteins. In this case, organelles and protein interactions do not need to be maintained during lysis and purification, and as such a view of the ‘neighborhood’ of a protein of interest is obtained inside living cells.

    By definition, proximity-dependent biotinylation provides an assessment of the distance relationship between a bait and a prey (three-dimensional space). Importantly, labelling can be carried out in a specific window of time, and the use of enzymes with labelling kinetics on the order of minutes (e.g., APEX2, miniTurbo and TurboID) allows for the design of experiments with a temporal perspective. With careful experimental design, spatio-temporal relationships may therefore be decoded through a variety of data analysis approaches.

    We and others have begun employing proximity-dependent biotinylation approaches to map organelles and other structures in space and time. When analyzed with tools for cell localization ontologies (such as GO cellular component), proximal preys help reveal the localization of the bait to specific compartments. However, the quantitative recovery of individual preys by two baits with superficial localization to the same structure is not necessarily identical, but rather reflects the respective organization of the baits and preys within the structure10. Preys that directly bind to a bait, or are in close proximity to it within a protein complex, tend to be more strongly labeled than more distant preys within the same structure. While there is rarely sufficient information in a single bait BioID experiments to untangle these complex relationships (also see4,11 for discussions), analyzing large datasets in aggregate has enabled reconstruction of the organization of several structures and organelles – including the centrosome-cilium12, stress granules and P-bodies10, and more recently the mitochondria13. By expanding the analysis to include baits localizing across multiple distinct compartments throughout the cell we have begun to create a global ‘proximity-map’ of a cell, localizing over 4000 proteins to distinct cellular locations14 (see humancellmap.org). Expansion of these studies to explore dynamic changes in subcellular organization will constitute a stimulating challenge for experimental design, execution and data analysis.

    In recent work, our group has also explored the use of BioID baits as ‘organelle sensors’ to evaluate changes in organelle proteomes following pharmacological or genetic perturbations. BioID labelling profiles are highly reproducible across experiments, and therefore performing BioID experiments with a given ‘sensor’ under different conditions (e.g., knocking-out a gene of interest, drug treatment, differential cell growth conditions) can illuminate dynamic changes that occur on specific organelle surfaces. Leveraging this approach, we used the lysosomal R-SNARE proteins VAMP7 and VAMP8 as ‘sensors’ to identify proteins localizing to the cytosolic face of late endocytic membranes (i.e., late endosomes, lysosomes, and the product of their fusion, endolysosomes) (see Figure 1). This strategy revealed novel relationships between lysosome membrane trafficking complexes and proteins involved in nutrient signalling through the large kinase complex mTORC1 (mechanistic Target Of Rapamycin, complex 1). Our follow-up studies demonstrated an unanticipated interplay between two key mTORC1 activation pathways – namely, activation by exogenous amino acids and by lysosome-derived amino acids. Importantly, the latter pathway is implicated in the growth of Ras-driven cancer cells, which can use lysosome-derived amino acids (acquired through macropinocytosis of exogenous protein) to fuel their growth. It is likely that the concept of BioID ‘organelle sensors’ will find wider application in cell biology over the coming years.

    In summary, proximity-dependent biotinylation approaches offer complementary views to fractionation approaches in spatio-temporal proteomic studies. Their expanded use will allow increasingly complex cell biological questions to be answered directly in living cells.

    Legend


    Figure 1 – The use of BioID ‘organelle sensors’ to map the surface proteomes of late endocytic organelles.

    Bio


    Dr. Geoffrey Hesketh is a postdoctoral fellow in Dr. Anne-Claude Gingras’ lab at the Lunenfeld-Tanenbaum Research Institute in Toronto, where he uses proteomic methods to explore how lysosomes control cell growth. He was previously a postdoctoral fellow in Dr. Paul Luzio’s lab at the Cambridge Institute for Medical Research at the University of Cambridge in the UK, where he developed his interest in lysosome biology by studying mechanisms of late endosome-lysosome fusion and recycling. Prior to this, he obtained his PhD in Dr. Jennifer Van Eyk’s lab at The Johns Hopkins University School of Medicine.

    References

    1. Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 1 (2019). doi:10.1038/s41580-018-0094-y

    2. Prinz, W. A., Toulmay, A. & Balla, T. The functional universe of membrane contact sites. Nat. Rev. Mol. Cell Biol. 21, 7–24 (2020).

    3. Roux, K. J., Kim, D. I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–10 (2012).

    4. Samavarchi-Tehrani, P., Samson, R. & Gingras, A.-C. Proximity Dependent Biotinylation: Key Enzymes and Adaptation to Proteomics Approaches. Mol. Cell. Proteomics 19, 757–773 (2020).

    5. Kim, D. I. et al. Probing nuclear pore complex architecture with proximity-dependent biotinylation. Proc. Natl. Acad. Sci. U. S. A. 111, E2453-61 (2014).

    6. Lam, S. S. et al. Directed evolution of APEX2 for electron microscopy and proximity labeling. Nat. Methods 12, 51–54 (2015).

    7. Kim, D. I. et al. An improved smaller biotin ligase for BioID proximity labeling. Mol. Biol. Cell 27, 1188–96 (2016).

    8. Ramanathan, M. et al. RNA-protein interaction detection in living cells. Nat. Methods 15, 207–212 (2018).

    9. Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    10. Youn, J.-Y. et al. High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. Mol. Cell 517–532 (2018). doi:10.1016/j.molcel.2017.12.020

    11. Gingras, A.-C., Abe, K. T. & Raught, B. Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles. Curr. Opin. Chem. Biol. 48, 44–54 (2019).

    12. Gupta, G. D. et al. A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface. Cell 163, 1483–1499 (2015).

    13. Antonicka, H. et al. A High-Density Human Mitochondrial Proximity Interaction Network. Cell Metab. 32, 479-497.e9 (2020).

    14. Go, C. et al. A proximity biotinylation map of a human cell. (2019). doi:10.1101/796391

  • 26 Aug 2020 4:09 PM | Anonymous member (Administrator)

    By Svetlana Murzina, Karelian Research Centre of the Russian Academy of Sciences, Petrozavodsk, Russia, and Michelle Hill, QIMR Berghofer Medical Research Institute, Brisbane, Australia

    Take a journey with us across three Russian lakes and learn how Dr Polina Drozdova, Dr Ekaterina Borvinskaya and PhD student Albina Kochneva are using proteomics to understand the wonderful aquatic ecosystem. Their research starts with field-trips for sample collection, from colorful crustaceans to the not-so-colorful fish tapeworms.

    Proteomics research is increasing the understanding of fundamental issues in ecology and parasitology, and also has important applications in pharmacology and agriculture, to identify new targets for antihelmintic drugs including those against tapeworms of fish.

    Location 1: Irkutsk, Baikal Lake, Siberia

    Lake Baikal is a fascinating place for anyone, and especially for those interested in molecular ecology. The deepest and oldest lake on Earth is home to 300+ species of small crustaceans (gammarid amphipods). They occupy various niches, differ in size and diet, and, most interestingly in color, which varies from transparent and milky white to bright orange, red, blue, and even dark violet.

    These bright colors triggered Dr Polina Drozdova to move across half the country to pursue Baikal research - after her first summer trip to Baikal in 2012.

    Polina recently obtained support from the Russian Science Foundation to study coloration and vision of endemic Baikal amphipods.

    “As a starting point, we chose a very abundant species Eulimnogammarus cyaneus that looks like a result of a natural experiment. The majority of individuals have blue bodies, exactly as the species epithet suggests, but sometimes orange individuals can be found. Importantly, if protein integrity is in any way compromised in samples of the blue animals, the color changes into orange. This change reminds the mechanism well-known for crayfish and lobsters, which turn bright red when cooked due to the degradation of carotenoid-binding proteins called crustacyanins. This similarity motivated us to search for possible proteomic differences between animals of different colors. Indeed, we found two proteins, levels of which were much higher in blue animals that in the orange ones” said Polina. 

    The proteomics results were astounding. Instead of crustacyanins, Drozdova’s team found the proteins share similar domains to insect pheromone/odorant-binding proteins that recognize a wide range of hydrophobic molecules.

    “Even though carotenoids have not been included in this range, it was logical to suggest that the amphipod proteins (let us call them crustacyanin analogs) bind carotenoids. Indeed, further experiments supported this hypothesis” – said Polina.

    These fascinating results were recent published, and Polina’s team plan to dig deeper into the molecular mechanism underlying carotenoid binding by these proteins and explore the diversity of these proteins in species with different body colors.

    Location 2: Petrozavodsk, Onego Lake, Karelia

    More than 4,000 km away, two young biochemists at the laboratory of environmental biochemistry IB KarRC RAS in Karelia are using proteomics to tackle a practical problem important for aquaculture and fishery sciences.

    Dr Ekaterina Borvinskaya explains “Helminths of the order Bothriocephalidea are parasites of marine and freshwater teleost fish common throughout the world.”

    “Like many other researchers, we work with non-model organisms for which there is no transcriptome, genomic, or proteomic data. Thus, we first decided to assemble the de novo transcriptome and annotate it for the tapeworm T. nodulosus, a common parasite of Holarctic freshwater fish. In our recent publication in Marine Genomics, we presented a functional annotation of transcripts and predicted the parasite proteome. Amazingly, in cestodes, about two-thirds of proteins is known to differ significantly in structure and, therefore, functions from proteins of other living organisms. Analysis of the T. nodulosus transcriptome revealed about a quarter of proteins with a completely unique structure even in comparison with other studied flatworm species. Such incredible biochemical diversity represents a huge parasite taxon that is very hard to get used to! However, the findings gave us an idea of a separate universe of cestodes with various unknown biologically active compounds in it”- said Ekaterina.

    PhD student Albina Kochneva first joined the laboratory for her Bachelor Thesis, and has been researching parasitic worms of the genus Triaenophorus ever since. After successfully completing her Masters thesis using proteomics to study T. nodulosus and antioxidant protection enzymes of its intermediate host perch (Perca fluviatilis), Albina is now pursuing PhD in parasite proteomics.

    “I became interested and did not stop wonder to how these amazing organisms adapt to life inside another organism.” - said Albina.

    She studied two species of the Cestoda class which live in the same ecosystems and may infect the same definitive host (the same one fish), Triaenophorus nodulosus and Triaenophorus crassus. But, through the evolution, these tapeworms were “spatially” dispersed: T. nodulosus larvae are able to infect in the liver parenchyma of a very wide range of second intermediate hosts (different fish species) while T. crassus larvae almost exclusively inhabit the muscles of fish of Salmonidae family.

    To understand the mechanisms of adaption, Albina compared the protein profiles of T. nodulosus and T. crassus at different life stages, and in different segments of the parasite body.

    “To confirm the biochemical heterogeneity of different parts of the worm's body, we applied 2D-DIGE electrophoresis and LC/ESI-MS/MS together with analysts from St. Petersburg State University (St. Petersburg, Russia). We found that there is a quantitative and qualitative variability of some proteins in different parts of the parasite's body, which distinguishes and maintains their morphological and physiological characteristics.” – said Albina.

    These results were presented at the 11th International Conference of Bioinformatics of Genome Regulation and Structure \ Systems Biology in 2018.

    Besides these finding, it was revealed the huge amount of the secreted protein with unknown function in the head of the plerocercoid larva of T. nodulosus. This protein was completely absent in T. crassus. It was assumed that the protein might be responsible for the attachment and co-exist of T. nodulosus with its various host via the liver parenchyma. In contrast, T. crassus is unable to locate in the liver and performs specialization to its host.

    Furthermore, proteomics analyses of larval stage T. nodulosus collected from the liver of different species of fish (perch Perca fluviatilis L., ruffe Gymnocephalus cernuus L. and burbot Lota lota L) revealed that the expression of some proteins at the same development stage depends on the environment (host-specific). These results support the Red Queen Hypothesis by Valen (1973) on the co-evolution of parasites and their hosts.

    “For several hundred million years, the threat of infection by cestodes (tapeworms) has been a factor in the evolution of vertebrates and, definitely, to some extent, affect the formation of this taxon. This never-ending “attack and defense” processes are realized with contrivances at the molecular level resulting in inevitable reactions and inventions on both sides” – said Ekaterina


    Location 3: Freshwater lakes, White Sea Basin, Kola Peninsula

    The team recently turned their attention to the highly complex lifecycle of the helminth Schistocephalus solidus (Cestoda), which journeys from host to host via the trophic net, inhabiting two categories of environment: the first order is “inside the host” or internal environment, and the second order – “the host external environment”.

    “The parasite transfers from its intermediate hosts - poikilothermic animals: zooplankton, a representatives of cyclopoid copepods, to fish, the three-spined stickleback (Gasterosteus aculeatus), and finally to the homeothermic animals, usually fish-eating birds.” – explains Ekaterina.

    “Indeed, for the first time, the temperature-induced re-organization of proteins and lipids of S. solidus during the transition from the fish host to warm-blooded host will be carried out. The studies have already been done at the transcriptome level but we are interested in direct registration of biomolecules of the parasite and its host, especially at the surface of the parasite body, which are regions of active metabolic exchange or communication with the host. It should be noted, that such biological task can be satisfied only by analysis of proteome and lipidome together. These molecules maintain the interactions of organisms with the environment whether in a parasitic, symbiotic, or trophic activity. The proteomic analysis will be performed with the participation of specialists from the “Human Proteome” Core Facility of the Institute of Biomedical Chemistry (IBMC, Moscow, Russia)” - said Ekaterina.

    But, the research starts not in the laboratory but in the field. For this, Albina was fishing the three-spined stickleback in the freshwater lakes of the White Sea Basin in June. She needs to develop good fishing skills because three-spined stickleback adults swim fast and are hard to catch! Albina also maintains an aquaria for three-spined stickleback in the laboratory.

    Apart from the essential outdoor activities, conferences and practical schools offered by HUPO, RHUPO and EuPA have also been essential aspects of Albina’s scientific life. “All these activities help me to meet friends and colleagues in the field, to tell about my results and getting valuable feedback, to follow the announcements about the recent achievements in mass-spectrometry and proteomic approaches. It is interesting to think how to apply it for my research” - tells Albina.


    Three-spined stickleback in the aquaria. Photo by Anastasia Prokhorova.

  • 31 Jul 2020 1:52 PM | Anonymous member (Administrator)

    By Michelle Hill, QIMR Berghofer Medical Research Institute, Brisbane Australia

    Swapping shovels and brushes for mass spectrometers and proteomics knowledge, Prof Paul Haynes and Dr Caroline Solazzo are using ancient proteomics to reveal new understanding of human cultural heritage.

    With a background in chemistry and a research program on food and environmental proteomics, Prof Paul Haynes journeyed into ancient proteomics with his Macquarie University (Australia) colleague Egyptologist Dr Jana Jones as well as Dr Raffaella Bianucci (University of Turin, Italy).

    Archaeological material is highly valuable, and Bianucci had the first challenge to obtain permission to use even a small sample from precious Egyptian mummies for proteomics analysis. Having obtained permission to collect loose skin samples from 4,200-year old mummies originating from ancient Middle Egypt, the researchers were blown away by the proteomics results. In addition to the expected collagen proteins, which are known in the field as “Survivor proteins”, proteomics provided rare molecular insight into the medical histories of two of the mummies – the proteins identified suggest the individuals might have died from cancer and lung infection, respectively. This study was published in Philosophical Transactions of the Royal Society A.

    At the Smithsonian Museum Conservation Institute, Dr Caroline Solazzo has been using proteomics to identify animal tissues in historical and archaeological textiles, to better understand the techniques of production of these cultural heritage artefacts. 

    Solazzo used proteomics to confirm the use of dog fiber in Coast Salish blankets. The Coast Salish peoples are indigenous to the Pacific Northwest coastal areas of northern Washington and southern British Columbia, notable for large, finely woven blankets. The rich Salish oral history alludes to the use of dog hair was used as a weaving fiber, but the importance of dog fiber was questioned. Solazzo used proteomics to identify dog fiber in 19 textile samples, thus proving the oral tradition. Further, based on her finding that dog fibers were always weaved with other fibers such as mountain goat hair, more detailed information can be provided in museums.

    An even more challenging question was the differentiation of sheep and goat wool in textiles used to wrap burial objects, due to their similarity, and degradation state of the buried ancient samples. Solazzo used peptide mass fingerprinting to analyse the wool wrapping from a 10th century Viking-age grave in Britain, and from 2,000 year old Mongolian bronze burial objects.

    “At the time the keratin sequences were not well known for these two species, and although the two species are very close, I was able to determine one key peptide to differentiate them. Some of these archaeological fibers were challenging because they had been mineralized so microscopy was inconclusive.” says Solazzo.

    Both Solazzo and Haynes highlight non-invasive sampling of ancient, precious materials as a challenge. To this end, recent development of innovative sampling techniques, such as strip-taping, should greatly facilitate ancient proteomics. While sample degradation and modern contamination are additional technical considerations, Haynes is intrigued by recent work on the use of deamidation levels as a molecular clock to estimate the age of proteins. On this point, Solazzo observed lower deamination levels in better preserved textiles, and suggested in her Journal of Archeological Science article, that the corroding copper salts from the wrapped bronze objects acted as a biocide to help preserve the textiles.

    Now hooked on mummies, Haynes and several PhD students are part of an interdisciplinary team on The Mummy Project at The University of Sydney’s Chau Chak Wing Museum. Meanwhile, Solazzo has been developing proteomics tools to identify baleen, tortoiseshell and horn, materials that are difficult to recognize and are poorly preserved in archaeological contexts, making them challenging for DNA sequencing technologies, but are of cultural significance, often being used in buttons, jewellery and furniture.


  • 29 Jun 2020 4:43 PM | Anonymous member (Administrator)

    Michelle M. Hill, QIMR Berghofer Medical Research Institute, Brisbane, Australia and Ellen D. McPhail, Mayo Clinic, Rochester, MN, USA

    Joe's Story

    Following a 32-year career in plant pathology R&D for agricultural and biosecurity applications, Joe Kochman, PhD, is well-versed in DNA-based diagnostic technologies. But he never imagined that at 68-years of age, he would be the topic of a difficult diagnostic investigation which required innovations in proteomics diagnostics.

    While undergoing prostate cancer treatment in 2015, amyloid deposits were detected in Joe’s prostate gland biopsy. The chance finding led to further investigation and a diagnosis of early systemic amyloidosis with heart involvement. Joe was very fortunate that he was referred to Dr Peter Mollee, a haematologist at the forefront of systemic amyloidosis diagnosis techniques in Australia.

    “He was found to have a monoclonal lambda immunoglobulin free light chain in the blood which can be the causative protein underling AL amyloidosis.” Peter explains. “The alternative possibility was that the amyloid deposits were of transthyretin type (ATTR), and the circulating monoclonal immunoglobulin lambda free light chain was unrelated to the amyloidosis but due to condition called MGUS.”

    Cardiac ATTR has a favourable survival rate compared to AL amyloidosis, with a median survival of 75 versus 11 months. As the treatments are completely different, it was important to determine the precise amyloid protein in the deposit.

    The current standard diagnostic method of immunohistochemistry was conducted, but was inconclusive. Aware of this common problem and unmet diagnostic need, Peter had worked with my team to set up laser-capture microdissection-coupled tandem mass spectrometry for amyloidosis typing method at the Princess Alexandra Hospital (PAH) Amyloidosis Centre in Brisbane, Australia.

    Using a section of Joe’s prostate biopsy tissue, the proteomics method identified immunoglobulin lambda light chain, as well as signature amyloid associated proteins (ApoE, SAP, ApoA4, vitronectin and clusterin). “Thus, a confident diagnosis of lambda light chain amyloidosis was made and appropriate treatment was able to be commenced.” says Peter.

    Martin Middleditch, Lead Mass Spectrometry Technologist at the University of Auckland, New Zealand, is no stranger to the life-changing impact of proteomics, having typed ~140 systemic amyloidosis cases.

    “We have seen many occasions where the current clinical assumption about the case based on other techniques has been overturned by our results, significantly changing the therapeutic direction.” says Martin.

    Global collaboration to bring the benefits of proteomics diagnostics to all patients

    Systemic amyloidosis is a rare disorder estimated to affect 5 to 13 people per million person-years. Due to the low prevalence, first-line clinicians frequently lack amyloidosis-specific experience, and getting the correct diagnosis is often a long process. However, it is crucial as an incorrect diagnosis can lead to potentially devastating outcomes.

    There are 36 known types of amyloid, and establishing the correct amyloid type is critically important as prognosis and treatment vary widely depending on the amyloid type. As illustrated in Joe’s case, antibody-based methods can be inconclusive, that’s where proteomics has come to the rescue.

    Since developing the laser-capture microdissection-coupled tandem mass spectrometry amyloidosis typing test for clinical use in 2008, the Mayo Clinic (Rochester, MN, USA) has typed over 20,000 amyloid specimens, and been prolific in reporting the applications and benefits of the proteomics test. As FFPE tissue blocks can be safely delivered by post, the Mayo Clinic offers proteomic service for domestic and international patients, when referred by clinicians.

    Mass spectrometry-based amyloid typing is rapidly becoming the new gold standard in light of its outstanding sensitivity and specificity. Globally, the number of centres offering systemic amyloidosis typing by mass spectrometry is still limited, although interest in establishing testing centers is increasing. There are two entry barriers for new centers, namely, the cost of high-end instruments (laser capture microdissection, mass spectrometer) and the high level of laboratory and bioinformatics expertise.


    So far, all of the established centres have had successful local clinician-proteomics scientist collaboration, and mostly established their protocols independently. A harmonized international effort in education, cross-training and standardization will further broaden the availability and use of this diagnostic test to benefit more patients.

    For example, abdominal fat pad aspirate is another clinically useful sample type for amyloidosis diagnosis and typing, but can present different technical challenges compared to FFPE. Cross-training with centers with greater expertise for fat aspirates, such as the University Hospital San Matteo (Pavia, Italy) or the Mayo Clinic, would facilitate the adoption and availability of global centers offering proteomics typing for amyloidosis.

    Other key areas that will benefit from international collaboration and standardization include data analysis, interpretation and reporting.

    Joe Kochman was lucky to have the chance biopsy finding, and referral to the only amyloidosis clinic in Australia to perform the proteomics test. He describes the events as “life changing”, and has been a patient advocate of the PAH Amyloidosis Centre since 2019.

    Now an international effort in dissemination, standardization and harmonization will allow more patients globally to benefit from precision proteomics diagnosis.

  • 28 May 2020 5:53 PM | Anonymous member (Administrator)

    Michelle Hill, QIMR Berghofer Medical Research Institute, and The University of Queensland, Australia

    Popularized by crime scene investigation dramas, forensic DNA technology is widely known to the public. The potential for using human DNA fingerprinting in forensic science was first reported by Professor Alec Jeffreys in 1985. By matching incriminating genetic material to DNA fingerprints from suspects (or a database), it’s possible to surmise “who” may or may not have been present at the crime scene. The same DNA fingerprinting technology has since been commercialised for ancestry tracing and parentage confirmation.

    While DNA profiling has helped to solve the question of “who” in crime investigations, it has no or limited power to enlighten on the “what” or “how” of a crime. But never fear, the new super power protein profiling is here!!

    The scientific term for protein profiling is “proteomics”, which is used to describe large scale analysis of known or unknown proteins. For unknown mixtures of proteins, such as that encountered in forensic investigations, the technique of mass spectrometry (shortened to MS) is used along with DNA sequence databases to compute the protein identities. The recent advances in MS and gene sequencing technologies have given birth to a new super power - forensic proteomics.

    In Italy, the super power of forensic proteomics has been pioneered by Dr Gianluca Picariello (Institute Food Sciences at National Research Council of Italy) upon input of the forensic toxicologist Dr Maria Pieri (Legal Medicine Section of University of Naples). Normally, Gianluca’s research laboratory applies proteomics technique in the investigation of food composition and evolution, so he is used to analysing partly digested food of varying stage of decay. These skills became super powers to help Maria determine the truth of statements from persons of interest in 2 different criminal cases.

    In the first case on the death of a mental health patient at a clinic, there was a discrepant account on whether the victim had eaten breakfast. The usual forensic methods of visual investigation of the gastric content at autopsy could not answer this question, even when examined under the microscope. Gianluca’s forensic proteomics super power provide unequivocal evidence that the victim had eaten recently milk and baked wheat food, consistently with a typical Italian breakfast. The inconsistency between evidence and declaration of the attending sanitary staff, prompted Legal Authority to undertake further investigations to ascertain facts and possible responsibilities.

    Secondly, in rape case, the investigators had to determine whether traces of biological material was vomit. DNA profiling was not helpful because the dispute was whether the victim gave consent, or had vomited and fainted, and therefore could not have consented. The problem is, the car had been washed since the incident, leaving scant traces of biological material which could not be analysed by standard forensic methods. Once again, Gianluca’s super power easily provided the answers, identifying human proteins from saliva, stomach and intestine, in addition to food proteins. “Data were clearly indicative of vomit, thereby supporting consistence between victim's report and facts.”, said Gianluca.

    The forensic proteomics super power was fuelled by Gianluca’s research, which has established marker proteins for different food types. Furthermore, his previous work with Professor Francesco Addeo of University of Naples on the dynamics of food degradation provided the knowledge to reconstruct the meal composition from the puzzle of the fragments of partially digested food. While these two cases demonstrate the power of ad hoc food proteomics in forensic science, the full super power of forensic proteomics can only be unleashed after establishing validated methods and reference standards. This is exactly what Dr Eric Merkley and colleagues Drs. Kristin Jarman and Karen Wahl at Pacific Northwest National Laboratory (PNNL), Washington, USA, has been doing, on the other side of the globe.

    PNNL was tasked by US Department of Homeland Security to develop guidelines for the National Bioforensics Analysis Center to use in the analysis of ricin, a deadly biological toxin that had been used in several murders.

    As Eric explains, “Ricin is toxic because it enzymatically degrades ribosomes and shuts down protein synthesis. The proteomics approach complements and confirms results from existing biochemical assays, which have some limitations.”

    To meet the specific and stringent requirements for admissible scientific evidence in the U.S. Federal court system, the PNNL team had to establish rigorous statistical and scientific methods for forensic proteomics. To this end, the efforts of Human Proteome Organisation in standardising mass spectrometry data reporting was appreciated by the PNNL team, who consulted the Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 2.1, along with standards documents from the World Anti-Doping Organization and the Organization for the Prevention of Chemical Weapons.

    PNNL’s effort in standardising proteomics analysis of ricin has allowed this super power to be routinely used in criminal case work to provide support previous methods. Clearly, the super power of forensic proteomics is beginning to emerge in different parts of globe!

    To unleash the super power of proteomics, international standardization efforts are critical to establish proteomics as a rigorous scientific method that can progress beyond “research-only” to “application-ready” technology. This has been one of the objectives of the Human Proteome Organisation (HUPO), to cultivate more proteomics super powers.

    Interested to know more? Read Gianluca and Maria’s detective work in their scientific articles published in Journal of Proteome Research and Journal of Proteomics. A new book edited by Dr Eric Merkley “Applications in Forensic Proteomics: Protein Identification and Profiling” published by The American Chemical Society provides an overview of proteomics in human body fluids, bone and microbial samples, as well as identification of toxins and considerations of accreditation and defensibility of proteomics evidence.

  • 28 May 2020 4:42 PM | Anonymous member (Administrator)

    Mohamed Elzerk and Kathryn S. Lilley, Cambridge Centre for Proteomics, Department of Biochemistry, The Milner Therapeutics Institute, University of Cambridge, Cambridge, UK

    The interior of eukaryotic cells is characterised by a high degree of structural and functional partitioning into distinct microenvironments dedicated to diverse and specific roles. Trafficking between subcellular niches allows proteins to drive biological processes such as maintaining homeostasis and regulating stress response. Aberrant trafficking is known to be the root of many diseases 1,2. Methods such as microscopy or affinity tagging are essential to determine the location of individual proteins or protein repertoires of purified organelles 3,4. However, a thorough understanding of functional dynamics of the proteome, requires a high throughput ability to define the spatial context of the entire proteome of the cell across different cell types, conditions and time points. Many of the current spatial proteomics techniques have been inspired by the protein correlation profiling principle exploited by cell biologists in the 1950 and 1960 to uncover new organelle 5–12. Membrane bound organelles and protein complexes co-fractionate upon centrifugation purely on the basis of their physical properties such as size, shape and density. Proteins with similar distributions to those exhibited by organelle marker proteins are assigned a single or multiple locations. Since its inception, the Cambridge Centre for Proteomics has contributed extensively to the establishment of spatial proteomics as a field primarily through the development of a technique known as Localization of Organelle Proteins by Isotope Tagging (LOPIT).

    The LOPIT approach combines organelle separation based on their characteristic buoyant densities or sedimentation rate by ultracentrifugation, with quantitative proteomics employing multiplexed by in vitro stable isotope labelling, highly sensitive mass spectrometers and multivariate statistical analysis (figure 1). Dunkley et al published the first draft of LOPIT In 2004, providing localisation annotations in Arabidopsis thaliana using Isotope-coded affinity tag (ICAT)5. A partial least squares-discriminant analysis (PLS-DA) algorithm enabled novel localisation of a number of proteins to ER, Golgi, and mitochondrial/plastid. Subsequently, the LOPIT methodology has evolved with the development of the field of mass spectrometry-based proteomics with the emergence of the multiplexing capacity of isotope labelling and the higher resolution of Orbitrap mass spectrometers. Furthermore, data analysis and visualisation tools have been tailored towards the output of LOPIT analysis. The pRoloc and pRolocGUI R packages cover a broad range of computational methods from unsupervised, supervised and semi-supervised machine learning, novelty detection and cluster separation assessment to, more recently, transfer learning and Bayesian modelling 13,14. Over the years, the applications of LOPIT extended to multiple biological systems, most recently the first protein atlas of Toxoplasma gondii 15–19.

    In 2016, Christoforou and colleagues exquisitely portrayed the protein map of mouse stem cells in a single experiment15. This improved version of LOPIT was rebranded as hyperplexed LOPIT (hyperLOPIT) thanks to higher multiplexing capabilities of amine-reactive tandem mass tags (TMT) 10-plex, MS/MS acquisition using synchronous precursor selection to improve quantitative accuracy and application of support vector machines (SVM) for data analysis. Almost half of the mouse stem cell proteome were annotated to multiple subcellular locations which was later also supported by the human cell atlas project3. Moreover, hyperLOPIT enabled subcellular localisation of some protein isoforms, protein complexes and signalling pathways. A year ago, a comparable system-wide resolution was also obtained differential ultracentrifugation based LOPIT, or LOPIT-DC, in which the spatial proteome of human osteosarcoma U-2 OS cell line was fully characterised using less time, material and resources18.

    Figure 1: A schematic overview of HyperLOPIT (left) and LOPIT-DC (right) workflows

    Membrane trafficking is an exemplar field which could benefit greatly from LOPIT. In particular, retrograde trafficking from endosomes to the trans-Golgi network (TGN) involves multiple partially redundant pathways that generate distinct pools of vesicles which are difficult to purify using other antibodies based techniques. Recently, Shin et al applied LOPIT-DC to characterise the endosome-to-Golgi vesicles that are selectively captured by golgin tethers at the TGN19. These golgins were ectopically redirected to mitochondria in order to determine the content of the specific endosome-to-Golgi vesicles they capture. Bayesian non-parametric testing was employed to identify protein movements towards the mitochondria. A profile shift of 45 transmembrane proteins and 51 peripheral membrane proteins of the endosomal network were detected including known cargo and trafficking machinery of the clathrin/AP-1, retromer-dependent and -independent transport pathways.These findings opens the exciting prospect of using LOPIT to interrogate dynamic spatial protein movements.

    Spatial proteomics is still an emerging technology. The continual development of multiplexed mass spectrometry analysis promises enhanced subcellular resolution and motivating dynamic protein localisation studies while alleviating the technical variability 20. Furthermore, LOPIT is a modular technique which facilitates the use of complementary techniques such as RNA sequencing and metabolic profiling. The combination of LOPIT with transcriptomic and metabolic profiling in a spatial multi-omics map has the capacity to drastically reshape our understanding of cell biology. In conclusion, LOPIT has been substantially developed to be a user-friendly approach with the availability of detailed online experimental protocols and an open-source bioinformatics suite 14,21,22. We encourage our readers to consider applying workflows such as LOPIT to their experiments to harness the power of spatial proteomics.

    References

    1. Wang, E. T. et al. Dysregulation of mRNA Localization and Translation in Genetic Disease. J. Neurosci. Off. J. Soc. Neurosci. 36, 11418–11426 (2016).

    2. Bridges, R. J. & Bradbury, N. A. Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator and Drugs: Insights from Cellular Trafficking. in Targeting Trafficking in Drug Development (eds. Ulloa-Aguirre, A. & Tao, Y.-X.) 385–425 (Springer International Publishing, 2018). doi:10.1007/164_2018_103.

    3. Thul, P. J. et al. A subcellular map of the human proteome. Science 356, (2017).

    4. Go, C. D. et al. A proximity biotinylation map of a human cell. bioRxiv 796391 (2019) doi:10.1101/796391.

    5. Dunkley, T. P. J., Watson, R., Griffin, J. L., Dupree, P. & Lilley, K. S. Localization of organelle proteins by isotope tagging (LOPIT). Mol. Cell. Proteomics MCP 3, 1128–1134 (2004).

    6. Foster, L. J. et al. A mammalian organelle map by protein correlation profiling. Cell 125, 187–199 (2006).

    7. Jean Beltran, P. M., Mathias, R. A. & Cristea, I. M. A Portrait of the Human Organelle Proteome In Space and Time during Cytomegalovirus Infection. Cell Syst. 3, 361-373.e6 (2016).

    8. De Duve, C. Tissue fractionation. Past and present. J. Cell Biol. 50, 20d–55d (1971).

    9. Itzhak, D. N. et al. A Mass Spectrometry-Based Approach for Mapping Protein Subcellular Localization Reveals the Spatial Proteome of Mouse Primary Neurons. Cell Rep. 20, 2706–2718 (2017).

    10. Krahmer, N. et al. Organellar Proteomics and Phospho-Proteomics Reveal Subcellular Reorganization in Diet-Induced Hepatic Steatosis. Dev. Cell 47, 205-221.e7 (2018).

    11. Orre, L. M. et al. SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization. Mol. Cell 73, 166-182.e7 (2019).

    12. Jadot, M. et al. Accounting for Protein Subcellular Localization: A Compartmental Map of the Rat Liver Proteome. Mol. Cell. Proteomics MCP 16, 194–212 (2017).

    13. Gatto, L., Breckels, L. M., Wieczorek, S., Burger, T. & Lilley, K. S. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics 30, 1322–1324 (2014).

    14. Crook, O. M., Breckels, L. M., Lilley, K. S., Kirk, P. D. W. & Gatto, L. A Bioconductor workflow for the Bayesian analysis of spatial proteomics. F1000Research 8, 446 (2019).

    15. Christoforou, A. et al. A draft map of the mouse pluripotent stem cell spatial proteome. Nat. Commun. 7, 1–12 (2016).

    16. Nightingale, D. J., Geladaki, A., Breckels, L. M., Oliver, S. G. & Lilley, K. S. The subcellular organisation of Saccharomyces cerevisiae. Curr. Opin. Chem. Biol. 48, 86–95 (2019).

    17. Barylyuk, K. et al. A subcellular atlas of Toxoplasma reveals the functional context of the proteome. bioRxiv 2020.04.23.057125 (2020) doi:10.1101/2020.04.23.057125.

    18. Geladaki, A. et al. Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun. 10, 1–15 (2019).

    19. Shin, J. J. H. et al. Determining the content of vesicles captured by golgin tethers using LOPIT-DC. bioRxiv 841965 (2019) doi:10.1101/841965.

    20. Thompson, A. et al. TMTpro: Design, Synthesis, and Initial Evaluation of a Proline-Based Isobaric 16-Plex Tandem Mass Tag Reagent Set. Anal. Chem. 91, 15941–15950 (2019).

    21. Mulvey, C. M. et al. Using hyperLOPIT to perform high-resolution mapping of the spatial proteome. Nat. Protoc. 12, 1110–1135 (2017).

    22. Breckels, L. M., Mulvey, C. M., Lilley, K. S. & Gatto, L. A Bioconductor workflow for processing and analysing spatial proteomics data. F1000Research 5, 2926 (2018).

  • 28 Apr 2020 10:56 AM | Anonymous member (Administrator)

    Sudhir Srivastava, National Insititues of Health, National Cancer Institute, USA

    The National Cancer Institute has recently announced the funding opportunity for Single Cell Proteomics in Interrogating the premalignant and early malignant lesions. The purpose of this Notice of Special Interest (NOSI) is to: (1) encourage investigators to apply single-cell proteomics for interrogation of premalignant and early malignant lesions; (2) develop new multiparametric biomarkers for cancer screening, early detection and risk assessment; and, (3) establish a biomarkers workflow for a wide coverage of disease variability and individuals at the population level.

    Recent advances in single-cell genomic and transcriptomic technologies enabled a better understanding of the cellular content of tumorigenic lesions, including early detection of clonal evolution, detection of the emergence of drug-resistant cells and improved phenotypic characterization of the lesion’s cellular heterogeneity. Single-cell mass spectroscopy-based proteomics and antibody-based targeted proteomics are emerging as powerful complementary approaches for the characterization of individual cell types within a lesion, their intercellular networks, and their dynamic physiological state. In addition, these approaches can be used to identify, map and visualize aberrant subcellular structures, intracellular networks, molecular interactomes, and proteoforms some with cancer-associated posttranslational modifications that cannot be predicted by genomic/transcriptomic analysis. The detected molecular, structural and functional aberrations are potential cancer markers and targets for prevention and therapy.

    For further details on how to apply, click here. Any questions regarding this NOSI can be addressed to:

    Sudhir Srivastava, Ph.D., MPH
    srivasts@mail.nih.gov

    Jacob Kagan, Ph.D.
    kaganj@mail.nih.gov

  • 30 Mar 2020 2:25 PM | Anonymous member (Administrator)

    Sanjeeva Srivastava, IIT Bombay, India

    Prof. Sanjeeva Srivastava, convener of this event, delivered a lecture on “Precision Medicine” and seeded the concept of latest revolutions in DNA and Protein based OMICS technologies to the budding scientists. Further, several renowned scientists gave mentorship to the student & glimpse of “magic of science”.

    From all over India over 850 teams participated in such innovative competition and top 10 teams displayed their innovative science & technology projects. This education and outreach initiative was highly appreciated by the community.


  • 30 Mar 2020 2:11 PM | Anonymous member (Administrator)

    Sanjeeva Srivastava, IIT Bombay, India

    Omics Big Data handling and making sense out of the data for its usage in precision medicine has become a hot topic recently with technical advancements and researchers better understanding of big data analysis and management. IIT Bombay, India and NTU, UK in collaboration organized this event to provide training and spread knowledge of big data handling and analysis with the help of Artificial Neural Networking and Machine learning using basic softwares available to us.

    This event was a success due to eminent scientists and researchers from India and abroad sharing their knowledge and expertise’ about the field. Additionally, clinicians’ contributed to the issues being faced currently in various cancers like ovarian, breast, cervical, brain and others, along with infectious diseases like tuberculosis and malaria. This provided researchers’ and scientists’ idea to work on various collaborative projects on infectious diseases and cancers.

    This advanced workshop was conducted for limited number of young faculty and researchers to be able to understand advances in the field of big data research and how it could be used translation into the clinics.

    Hands-on sessions for proteomics sample preparation, Mass spectrometry based label-free, labeled (TMT/iTRAQ) and targeted proteomics sessions were intensive but very useful. Participants also got training for metabolomics, genome sequencing basics and data interpretation.

    Additionally, an Indo-UK round table brainstorming session was conducted to bring great minds of the fields (researchers, academia, industry, clinicians and policy makers) together to conceive collaborative project(s) and initiatives to make a common database for big data handling, which could be shared with the community. This will help us taking steps towards converting the idea of personalized medicine to reality.

    This event was supported from Department of Science and Technology, Government of India and UKIERI, British Council UK.

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