23rd Human Proteome Organization World Congress

Professor

π-HuB: the proteomic navigator of the human body

HUPO initiatives and committees (posters only)

Fuchu He,*, Ruedi Aebersold*, et al., &amp; The π-HuB Consortium*Correspondence should be addressed to F.H. and R.A.(hefc@bmi.ac.cn;aebersold@imsb.biol.ethz.ch)The human body contains trillions of cells, classified in specific cell types, diverse morphologies and functions. Additionally, cells of the same type can assume different states within an individuals" body during their lifetime e.g. by reversibly or irreversibly adjusting to changing conditions. Understanding the complexities of the proteome in the context of a human organism and its many potential states is an inevitable requirement to understand human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here, we introduce an international big-science endeavor called π-HuB (Proteomic Navigator of the Human Body). The π-HuB project is a 30-year, large-scale, international high technology and life science research program supported by a substantial multibillion China Renminbi (RMB) investment. The π-HuB project has three central goals: 1) to build proteomic maps of the human body based on anatomy-based proteomic spaces at single-cell or cell-type resolution; 2) to develop the Meta Homo Sapiens model for tracing lineages of proteomic states in lifetime at the population scale; 3) to build a computation-driven, iteratively designed "navigator" for the human body and its states, with the ultimate goals to increase understanding of human biology, to facilitate disease risk assessment and to devise early disease diagnosis, optimal therapeutic interventions and intelligent healthcare. To achieve these goals, the π-HuB project is structured into six pillars: i) human biospecimen isolation and processing, ii) measurement technology deployment and innovation, iii) computational technology deployment and innovation, iv) establishment of a collaborative Big-Science infrastructure, v) sharing of open resources, and vi) establishment of international research teams. During the initiation and development phase (2024-2033), the π-HuB project will assemble an international consortium focused on achieving three major outcomes, specifically, 1) establish principles of cell type-based tissue organization, 2) establish proteomics-driven health guidelines, and 3) generalize proteomics-driven precision medicine. These initial goals will provide the π-HuB project with significant momentum and establish modes of operation and governance of the consortium. Moving forward, the π-HuB project will continue to catalyze global collaboration to integrate results from of a worldwide community of multidisciplinary scientists into a comprehensive, widely accessible resource. We anticipate that the π-HuB project will make major contributions to the understanding of biological systems, biomedical research and medical care for decades to come.

Poster presentation

Canadian artificial intelligence and mass spectrometry consortium (CAN-AIMS)

Disease spans diverse demographics and negatively impacts human health, affecting each individual in a specific manner. The ability to diagnose, monitor, and treat individuals in a strategic and personalized manner is limited due to lack of disease knowledge and discrepancies in accessibility of healthcare. To overcome these limitations, an increased understanding of the causes, regulatory mechanisms, and treatment options for diseases are needed. Importantly, the identification of proteins, metabolites, and pathways responsible for disease presents a critical starting point. However, the integration of datasets across biological networks and platforms is challenging to ensure comprehensive and robust analyses. Herein, we introduce the Canadian Artificial Intelligence and Mass Spectrometry Consortium (CAN-AIMS), which brings together researchers from across Canada with diverse expertise in human disease, proteomics, computation, and bioethics. The goals of CAN-AIMS are three-fold, to: i) explore innovative research strategies from discovery to translation, ii) develop a hands-on training platform for the next generation of scientists, and iii) build capacity in leading-edge instrumentation and computational recourses for Canada. Together, CAN-AIMS provides the first cohesive group of researchers working collectively to define and mitigate disease within Canada using a combination of mass spectrometry-driven technologies and computational platforms for integration of disease knowledge to improve diagnostics and treatments.

Prof. Dr.

EUPA Initiatives

The current mission of the EuPA Initiatives Committee is:support existing European proteomics initiatives by providing funding and infrastructure as well as promoting their scientific and educational activities in a fair and transparent wayencourage the creation of new initiatives that value scientific excellence, equality, inclusion and diversityfoster links between initiatives and stimulate interdisciplinary exchanges to favor scientific innovationVisit our website https://eupa.org/eupa-initiatives/ to discover the 5 Initiatives currently supported by EuPA:European Bioinformatics Community for Mass Spectrometry (EuBIC-MS)Food and Nutrition InitiativeInitiative for Model Organism Proteomics (iMOP)Standardization InitiativeMetaproteomics InitiativesThe EuPA Initiatives Committee is composed of researchers from diverse countries in Europe - you probably already know one of us!Visit our poster during the session dedicated to Initiatives to meet us in person, to discuss what the initiatives do, what EuPA can do for you and your colleagues and maybe to explore how to create your own initiative.

Initiative for Model Organism Proteomics (iMOP)

The initiative for Model Organism Proteomics (iMOP), active within HUPO and EuPa, aims to advance knowledge of proteins important for human health and disease, and more generally for the environment, as part of the &quot;One-Health&quot; concept. This goal is achieved by promoting the emergence of new model organisms suitable for i) the study of biological mechanisms important to human health and disease, ii) a better understanding of pathogenicity, pathogen reservoirs, and the emergence of resistance, and iii) deciphering the toxic effects of pollutants and exposomes on biological systems and in this context to define sensitive sentinels of our environment, or to be relevant to the One-Health concept. Furthermore, iMOP aims to characterize non-referenced proteins across various models by describing their structural features, determining their activity and function, and establishing interspecies comparisons. The application of these efforts range from Evolutionary Biology to Medicine, as well as Environmental Proteomics and Ecotoxicology. The main innovative approaches include comparative proteomics, proteogenomics, and (eco)toxicoproteomics. By fostering collaborations between biologists and multi-omics specialists and promoting multidisciplinary approaches, new scientific questions have already been tackled, including, e.g., how age and social environment both shape the molecular phenotype of animals, the deciphering of the molecular effects of inorganic compounds on various species, the identification of the initiating molecular events when model cell lines are exposed to toxic substances identifying a circadian clock in cyanobacteria, a key biological mechanism in new prokaryotic models and showing that proteogenomics can identify and characterize the structure of new coding sequences to better define their function. Overall, iMOP aims to bring together expertise to enhance our understanding of proteome at the structural and functional level to answer fundamental biological questions using the most relevant biological models. Researchers working or interested in those questions are invited to join us for discussion at this poster session and through the iMOP initiative homepages at any time (https://hupo.org/Initiative-for-Model-Organisms-Proteomics-(iMOP) or https://eupa.org/eupa-initiatives/imop/).

The Metaproteomics Initiative: an international community by and for metaproteomics researchers

The Metaproteomics Initiative promotes dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. We aim to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize and accelerate experimental and bioinformatic methodologies in this field. Currently, we host over 300 members from 85 labs in 22 countries, and are proudly supported by both the European Proteomics Association (EuPA, https://eupa.org/eupa-initiatives/metaproteomics/) and the Human Proteome Organization (HUPO, www.hupo.org/metaproteomics). More information about our vision can be found in Van Den Bossche, T. et al., Microbiome 9, 243 (2021) (doi: 10.1186/s40168-021-01176-w).Our efforts are structured around three core pillars: communication &amp; collaboration, education, and standardization. Through the first pillar, communication &amp; collaboration, we share advancements, connect researchers, and organize community-driven benchmark studies, the so-called CAMPI studies. Two, currently ongoing projects, are CAMPI-2 and CAMPI-3. CAMPI-2 aims to establish methods for sample handling to preserve metaprotein function prior to microbiota analysis, while CAMPI-3 evaluates critical decision points in the bioinformatics workflow. Through the second pillar, education, we provide the microbiome community with technical insights and easy access to expert knowledge. At this moment, we&quot;re drafting a comprehensive guide, "Metaproteomics for Beginners", to introduce and explain metaproteomics to researchers. Through the third pillar, standardization, our efforts are focused on developing and implementing robust (meta)data standards to ensure the FAIRness (Findable, Accessible, Interoperable, Reusable) of data in the field. Therefore, we are currently working on SDRF-metaproteomics metadata standards, which will integrate attributes from the MIxS metadata standard for metagenomics environments in the SDRF-Proteomics metadata standard.We invite microbiome and proteomics researchers to join our growing community of over 300 members, to stay up-to-date on the latest developments in metaproteomics. For more information (and to become a member), please visit our website (www.metaproteomics.org). You can also contact us via e-mail (info@metaproteomics.org) or follow us on X (@MetaP_Init).

Sperm is still a relevant source for detecting missing proteins in the context of the chromosome-centric human proteome project

The Chromosome-centric Human Proteome Project (C-HPP) aims at identifying the proteins as gene products encoded by the human genome, characterizing their isoforms and functions. The existence of products has now been confirmed for 93.1% of the genes at the protein level. The remaining mostly correspond to proteins of low abundance or difficult to access. Over the past decade, we have significantly contributed to the identification of over 300 missing proteins in the human sperm. Thanks to technical progresses in mass spectrometry and to the development of efficient sample preparation, we have been able to push further our MS analyses of the human spermatozoa, with the aim to identify new missing proteins. Interestingly, we were able to characterize additional missing proteins using the TimsTOF Ultra coupled to a NanoElute but also using the TimsTOF Pro coupled to a NanoElute based on seven different sample preparations with PreOmics kits. Here, we present the characterization of 9 new missing proteins, and can also propose 12 one-hit wonders proteins, thus contributing again to the efforts of the C-HPP.Keywords: Human Proteome Project, Missing Proteins, human Sperm, Bruker, PreOmics

Defining a functional evidence metric for the human proteome project

As the HPP draws near to completion of its first goal of achieving confident detections of all entries in the human proteome parts list, the HPP has embarked on a more audacious Grand Challenge Project of determining at least one molecular function of each protein. There will, of course, always be more to learn about every protein, but the HPP sets as its goal that we achieve a solid understanding of at least one function of each protein at the molecular level, ideally via at least two orthogonal methods. In order to track the progress in this goal, a metric must be developed since there is currently no suitable metric with which to measure the distance from our goal.The PE score, developed by Swiss-Prot decades ago, has served the HPP well in measuring its progress in its first goal, detection of each protein and information about where it is found. We have therefore developed an analogous FE ("function evidence") score. As PE1 is the ultimate goal for detection, FE1 is the ultimate goal for each protein for function, denoting a good understanding of its molecular function, which, as noted above, is not to imply that nothing more can be learned about an FE1 protein. FE5 represents the lowest category, meaning that essentially nothing is known.In practice, to be a useful metric the FE score must be computable from a repository of protein function information, and UniProtKB is currently the most complete repository of protein functions. All functional information in UniProtKB can be downloaded as an XML file, parsed, and used for calculation. The FE working group has defined a set of heuristics for computing the FE score that are precisely based on information available in UniProtKB. This information includes a free-text functional description and associated publications, Gene Ontology terms, and EC numbers. Furthermore, all annotations have an "ECO code", which is a term from the Evidence and Confidence Ontology describing how each piece of information is known. The best ECO codes describe annotations that are manual assertions based on direct experimental evidence. These stem from Swiss-Prot curators reading papers in the literature that describe experiments that provide functional evidence. At the lowest level are automatic assertions based on computational results not individually validated by curators.Here we present a draft of the FE score, how it is computed, and the progress of the Human Proteome Project in pursuit of achieving a solid understanding of at least one function of each protein in the human proteome at the molecular level.

Young proteomics investigators club: Empowering tomorrow's proteomics talents

YPIC (Young Proteomics Investigators Club) is an inclusive group of early career researchers in proteomics from all over Europe. YPIC was founded at the 10th annual EuPA conference in Istanbul in 2016. The team aims at representing the extra-scientific aspects of being a young researcher and favoring the networking of young European scientists in proteomics. All in all, our aim is to connect and support early career researchers on their path through and beyond academia! Throughout the year, YPIC organizes career, mentoring, &quot;Meet-the-Expert&quot;, and proteomics intro sessions. This includes the educational day at the yearly EuPA conference and our own YPIC annual proteomics gathering, where the winner of the Student Proteomics Fund is announced. The team also organizes non-scientific seasonal webinars as well as proteomics challenges.

Second community evaluation of glycoproteomic informatics solutions

The field of glycoproteomics has experienced considerable growth within the last decade. Analytical improvements in instrumentation, fragmentation, and sample preparation have allowed for biological discoveries in how glycosylation contributes to protein function and cellular health. However, one critical bottleneck in glycoproteomics is data analysis via search algorithms due to the vast heterogeneity in glycosylation. In the first Human Glycoproteomics Initiative study, researchers were sent the same data file and were asked to report glycopeptides found. Unfortunately, vast differences were reported between the groups. Additionally, since then, many new programs and developments have emerged that warrant consideration. Thus, here we propose the second HGI study, wherein developer teams will be sent data files comprising N-glycopeptides, O-glycopeptides, and/or a mixture of both. The results will allow us to recommend suitable settings, parameters, and programs for various sample types. Ultimately, we believe this work will prove useful to the broad glycoproteomics community.

Proteomics Multi-laboratory Experiment 13 (PME13). EuPA Standardization Initiative

This study has been done by all the authors listed below, who share the authorship. The abstract has been submitted by the coordinator of the EuPA Standardization Initiative.Single cell analysis at the proteome level has become feasible as new technologies for sample preparation and mass spectrometry analysis ensure efficient protein extraction and processing, as well as analytical sensitivity in the attomolar range. However, the implementation of high sensitivity proteomics (HSP) protocols remains a challenge. In this context, the EuPA standardization initiative launched the Proteomics Multicentric Experiment (PME13) with the main objectives of benchmarking HSP analysis protocols, providing tools to facilitate HSP implementation and assessing laboratory performance. The reference sample used in the study consisted of a serial dilution of E. coli protein extract from 100 to 1 ng, provided to participants in intact and trypsin digested formats. A pilot analysis was conducted to optimise the analytical pipeline and to minimize sources of uncertainty. After analysis, DDA and DIA datasets were generated and submitted for centralized processing along with metadata specific to each participant. Data were collected via NextCloud, processed using MaxQuant v2.0.2.0 and integrated, visualized and analyzed using R. Searches were mainly performed using default parameters, agreement between runs within the same concentration level and sample, and 1% FDR at peptide and protein level. Thirty-six laboratories from eight different countries requested PME13 samples, almost half of which (17 out of 36) had not participated in previous PMEs. We received DDA results from 28 out of 36 laboratories (78%) and DIA results from 16 out of 23 laboratories (70%). Overall, our data indicate that the reference sample provides a reliable basis for benchmarking, establishing protocols and assessing performance. The influence of analytical variables and the correlation with the physico-chemical properties of the analyzed peptides are presented and discussed.

EuBIC-MS

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) is a EuPA initiative that was started in November 2015. Since then, it brings together people from the bioinformatics community who work on mass spectrometry (MS) data to provide an open, collaborative and constructive environment. It is carried enthusiastically by the bioinformatics and MS community and welcomes everyone who is willing to contribute.EuBIC-MS hosts an annual conference focused on computational MS, uniting leading bioinformatics researchers, early-career scientists, and industry professionals from omics fields such as proteomics and metabolomics. This event alternates each year between a Winter School, featuring keynote lectures and hands-on workshops, and a Developers Meeting, centered on hackathons. In addition to the main conference, EuBIC-MS offers workshops at various MS and proteomics conferences and actively contributes to bioinformatics hubs at these events. Along with organizing and being present at events, EuBIC-MS also has several community driven projects. Our poster will describe the main activities of EuBIC including the upcoming Developers Meeting in the beginning of 2025, give an overview of the active community projects, and provide information about how to become a member and contribute to EuBIC-MS.

Prof.

Critical Assessment of Metaproteome Investigations (CAMPI): a Series of International-Lab Comparisons of Experimental and Bioinformatic Workflows

Metaproteomics is a powerful approach for characterizing the functional metabolic activities of complex microbiome systems. For example, clinical metaproteomics provides details about how the human microbiome affects/responds to clinical conditions, whereas environmental metaproteomics provides information on how the microbiome affects/responds to the ecosystem. Metaproteomics is still under rapid development, with a few standardized methods and approaches even among the experienced practitioners.To better understand and enhance the overall power of metaproteomics, the Metaproteomics Initiative [1] has launched a series of Critical Assessment of Metaproteome Investigations, termed "CAMPI." The first campaign, CAMPI-1, compared experimental and bioinformatic workflows across international laboratories, demonstrating a strong correlation in microbial functionals [2]. Building on this, two new CAMPI projects are currently in progress. CAMPI-2, involving 11 labs, focuses on evaluating a variety of sample preparation protocols for fecal or soil microbiomes to enable sample shipment at ambient temperatures. CAMPI-3, meanwhile, is evaluating bioinformatic tools for protein assignments and inferring taxonomic assignments using metaproteomic raw data from a synthetic community (with a known representation) and a complex fecal sample. Over 20 labs participated, revealing a wide variety of strategies and accuracies in the analysis. These findings are being compiled into a manuscript that will provide critical insights and propose best practices. Future CAMPI projects are being prioritized to address key challenges in metaproteomics, driving the field forward to become an essential tool for tackling global challenges in microbiome research.References[1] Van Den Bossche, T., Arntzen, M.Ø., Becher, D. et al. The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes. Microbiome 9, 243 (2021). https://doi.org/10.1186/s40168-021-01176-w[2] Van Den Bossche, T., Kunath, B.J., Schallert, K. et al. Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows. Nat Commun 12, 7305 (2021). https://doi.org/10.1038/s41467-021-27542-8

HUPO ECR Committee: Empowering the next generation proteomics researcher

The mission of the HUPO Early Career Researcher (ECR) Committee is to create a vibrant network for early-career proteomics researchers, enabling them to thrive, share knowledge, and develop the skills necessary to lead in the ever-evolving field of proteomics. The committee aims to:Promote integration and networking opportunities for ECRs within the HUPO community and with members at various career stages. Provide scientific visibility and research opportunities for ECRs. Offer career development activities that enhance the competitiveness of ECRs in the job market. Improve equity, diversity, and inclusion (EDI) within proteomics by integrating EDI principles into all ECR activities. Create inclusive spaces where diverse voices are heard, fostering collaboration and mutual support. Disseminate HUPO&quot;s activities, mission, and vision among ECRs globally.To achieve these goals, the HUPO ECR Committee organizes various activities throughout the year, with a strong focus on the HUPO World Congress. These activities include:ECR Mentoring Sessions: Offering invaluable insights into career development, these sessions feature lively discussions and interactive polls, allowing participants to address critical career-related challenges openly.Proteomics Highlight of the Year: This manuscript competition showcases groundbreaking research by postdocs and early-career principal investigators, with finalists presenting their work in a dedicated plenary session. The competition emphasizes the significant contributions of young scientists to the field of proteomics.3-Minute Thesis Competition: In a dynamic, fast-paced format, selected PhD students present their research in just three minutes to a lay audience, highlighting the importance of clear and effective science communication.Poster Competition: Graduate students and postdocs present their research in a competitive setting, with evaluators recognizing excellence in innovation, impact, and presentation.Networking Evening: This relaxed event allows early-career researchers and senior scientists to connect, share ideas, and explore potential collaborations.Beyond the annual HUPO Congress, the committee also organizes regular online panel discussions throughout the year on various topics relevant to early-career researchers, such as scientific communication, grant writing, and starting a new lab. Additionally, we have ECR representatives on all HUPO committees to advocate for the needs of early-career researchers within the proteomics community.The HUPO ECR Committee plays a pivotal role in the professional development of young proteomics researchers. Through our activities, we are building a supportive community that empowers ECRs to excel in their scientific endeavors and contribute meaningfully to the global proteomics community. As we look to expand our offerings with new opportunities for skill development, networking, and career advancement, we encourage all ECRs to join our efforts, stay engaged with our activities, and take advantage of the resources and support we offer.Stay tuned for more exciting events and opportunities and don&quot;t miss any HUPO ECR event at HUPO2024!

HUPO BD-HPP Single Cell Proteomics Initiative "A multicenter study primer"

HUPO Human Brain Proteome Project

Harness AI for Breakthroughs in Proteomics: Join the AI Proteomics Initiative

HUMAN PLASMA PROTEOME PROJECT: The circulating proteome - technological developments, current challenges and future trends

HUPO Cardiovascular Initiative: Current Advances and Future Perspectives

Associate professor

Education and Training Committee: Learning together

Expanding proteomics horizons through metadata

Multiomics Approaches

Large-scale data analysis in omics is becoming increasingly prevalent, driven by advancements in technical and data analysis methods. These advancements produce vast datasets with significant potential for AI applications and multi-omics pipelines, uncovering novel biological knowledge. One essential factor in these endeavours is the presence of comprehensive metadata, ensuring interpretability. This is however notably lacking within proteomics. The Sample and Data Relationship Format (SDRF) for Proteomics, introduced in 2020, maps data files to sample characteristics using ontology terms. Despite its potential, its complexity and the absence of easy-to-use annotation tools have limited its use. To address this issue, we have developed lesSDRF, a user-friendly, web-accessible application facilitating the SDRF annotation process.lesSDRF is designed for ease of use, requiring no installation and available online via Streamlit. It includes all necessary ontologies for SDRF-Proteomics, eliminating the need to navigate the Ontology Lookup Service and ensuring adherence to standardized terminology. The application guides users through five steps: species selection, metadata file upload, labeling information, and input of required and additional columns using ontology terms. This structured approach ensures SDRF-Proteomics compliance and user convenience. Importantly, lesSDRF relies heavily on community input, contributing to the development of new SDRF-Proteomics standards for emerging subfields within proteomics, including single-cell proteomics, metaproteomics, and metabolomics. The application is available at https://lessdrf.streamlit.app/Despite only being published in Nature Communications in October 2023, lesSDRF has already demonstrated a clear impact, with numerous datasets featuring lesSDRF-generated metadata annotation submitted to PRIDE in May 2024. Current developments include integration into the PRIDE submission pipeline, creation of a desktop application, and incorporation into automated proteomics reanalysis pipelines like quant-ms, open-ms, and wombat-p. To minimize user effort, search engine developers such as FragPipe, PeptideShaker, ionbot, and MSAmanda are integrating the export of a Technical SDRF file into their tools. This feature provides a partially filled SDRF-Proteomics file with technical details in the desired format. Ongoing discussions aim to expand this effort, while MSStats and MSqRob support SDRF-Proteomics compatibility for experimental design files.This metadata initiative extends beyond proteomics. Recognizing the potential of multi-omics analysis, there is a need for a comprehensive metadata standard across various fields. Collaborating with the Research Data Alliance, we are working to establish a multi-omics metadata standard that includes proteomics, genomics, metabolomics, phenomics, and beyond.

ProteoBench: a community-curated platform for comparison of proteomics data analysis workflows

Data Integration: With Bioinformatics to Biological Knowledge

Mass spectrometry (MS)-based proteomics is a crucial method for analyzing complex biological mixtures. With the abundance of dedicated data analysis pipelines, continuously updated and developed, the community would benefit from a platform to compare their performance in an objective and unbiased manner.Here we propose ProteoBench (proteobench.cubimed.rub.de/), a comprehensive and open web platform that allows for easy and controlled comparison of proteomics tools developed or used by the participants to other state-of-the-art pipelines. ProteoBench originated as a community project by the European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) and its design and development are open to all interested researchers. Its goal is to provide a centralized resource, which is easy to use for non-coders.ProteoBench will comprise several benchmarking modules dedicated to the comparison of specific analysis tools developed for identification, quantification (DDA or DIA), and statistical analysis. Users shall be able to provide their analysis results for comparison, with the option of making these results publicly available.Currently, we have five modules in preparation, of which two are in active development and one is already available as a prototype. For this module you need to download a set of six publicly-available runs containing 3 species with known quantities. Any user can analyse these data with any workflow, and then upload analysis outputs back to ProteoBench, which then evaluates sensitivity and quantification accuracy (mean difference between expected and measured ratio) for precursor ions. It allows the quick visualisation of the corresponding results in the cloud of already-computed analysis outputs.The submitted data will continuously grow and should remain up-to-date with the latest developments. It will create a frame of reference for evaluating the performance of new and/or custom tools when publishing results and should increase transparency and reproducibility between data analysis pipelines developed in the field. It will allow end-users to select a well-performing workflow to meet their needs; developers to discern the strengths and limitations of their workflow, thereby directing further development; publishers and reviewers to easily position workflows in the context of existing state-of-the-art workflows.

Poster presentations of HUPO & EuPA initiatives

Termin

Programm