Data Management of Sensitive Human Proteomics Data: Current Practices, Recommendations, and Perspectives for the Future

π-HuB: the proteomic navigator of the human body

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding 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 a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.

Astronaut omics and the impact of space on the human body at scale

Future multi-year crewed planetary missions will motivate advances in aerospace nutrition and telehealth. On Earth, the Human Cell Atlas project aims to spatially map all cell types in the human body. Here, we propose that a parallel Human Cell Space Atlas could serve as an openly available, global resource for space life science research. As humanity becomes increasingly spacefaring, high-resolution omics on orbit could permit an advent of precision spaceflight healthcare. Alongside the scientific potential, we consider the complex ethical, cultural, and legal challenges intrinsic to the human space omics discipline, and how philosophical frameworks may benefit from international perspectives.

The Importance, Challenges, and Possible Solutions for Sharing Proteomics Data While Safeguarding Individuals' Privacy

Proteomics data sharing has profound benefits at the individual level as well as at the community level. While data sharing has increased over the years, mostly due to journal and funding agency requirements, the reluctance of researchers with regard to data sharing is evident as many shares only the bare minimum dataset required to publish an article. In many cases, proper metadata is missing, essentially making the dataset useless. This behavior can be explained by a lack of incentives, insufficient awareness, or a lack of clarity surrounding ethical issues. Through adequate training at research institutes, researchers can realize the benefits associated with data sharing and can accelerate the norm of data sharing for the field of proteomics, as has been the standard in genomics for decades. In this article, we have put together various repository options available for proteomics data. We have also added pros and cons of those repositories to facilitate researchers in selecting the repository most suitable for their data submission. It is also important to note that a few types of proteomics data have the potential to re-identify an individual in certain scenarios. In such cases, extra caution should be taken to remove any personal identifiers before sharing on public repositories. Data sets that will be useless without personal identifiers need to be shared in a controlled access repository so that only authorized researchers can access the data and personal identifiers are kept safe.

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.

Foresight in clinical proteomics: current status, ethical considerations, and future perspectives

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Cold exposure-induced alterations in the brain peptidome and gut microbiome are linked to energy homeostasis in mice

Energy homeostasis of mammals during cold exposure involves complicated neural regulation and is affected by gut microbiota. However, the regulatory mechanism remains unclear partially due to a lack of comprehensive knowledge of the signaling molecules involved. Herein, we performed region-resolvable quantitative profiling of the brain peptidome using cold-exposed mouse models and interrogated the interaction between gut microbes and brain peptides in response to cold. Region-specific alterations in the brain peptidome were observed during chronic cold exposure and were correlated with gut microbiome composition. Several proSAAS-derived peptides exhibited a positive correlation with Lactobacillus. The hypothalamus-pituitary axis exhibited a sensitive response to cold exposure. We obtained a candidate pool of bioactive peptides that potentially participate in the regulation of cold-induced energy homeostasis. Intervention with cold-adapted microbiota in mice decreased the abundance of hypothalamic neurokinin B and subsequently contributed to shifting the fuel source for energy consumption from lipids to glucose. Collectively, this study demonstrated that gut microbes modulate brain peptides contributing to energy metabolism, providing a data resource for understanding the regulatory mechanism of energy homeostasis upon cold exposure.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Effect of different drying techniques on rose (Rosa rugosa cv. Plena) proteome based on label-free quantitative proteomics

To explore the molecular mechanisms of different processing technologies on rose tea (Rosa rugosa cv. Plena), we investigated the rose tea proteome (fresh rose tea [CS], vacuum freeze-drying rose tea [FD], and vacuum microwave rose tea [VD]) using label-free quantification proteomics (LFQ). A total of 2187 proteins were identified, with 1864, 1905, and 1660 proteins identified in CS, FD, and VD, respectively. Of those, 1500 proteins were quantified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and enrichment analysis of differential expression proteins (DEPs) in VD vs. CS, FD vs. CS, and FD vs. VD showed that these pathways were associated with energy metabolism, the metabolic breakdown of energy substances and protein biosynthesis, such as oxidative phosphorylation, citrate cycle, carbon metabolism pathways, and ribosome and protein processing in endoplasmic reticulum. FD could ensure the synthesis of protein translation and energy metabolism, thereby maintaining the high quality of rose tea.

The ProteomeXchange consortium at 10 years: 2023 update

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

Identifying individuals using proteomics: are we there yet?

Multi-omics approaches including proteomics analyses are becoming an integral component of precision medicine. As clinical proteomics studies gain momentum and their sensitivity increases, research on identifying individuals based on their proteomics data is here examined for risks and ethics-related issues. A great deal of work has already been done on this topic for DNA/RNA sequencing data, but it has yet to be widely studied in other omics fields. The current state-of-the-art for the identification of individuals based solely on proteomics data is explained. Protein sequence variation analysis approaches are covered in more detail, including the available analysis workflows and their limitations. We also outline some previous forensic and omics proteomics studies that are relevant for the identification of individuals. Following that, we discuss the risks of patient reidentification using other proteomics data types such as protein expression abundance and post-translational modification (PTM) profiles. In light of the potential identification of individuals through proteomics data, possible legal and ethical implications are becoming increasingly important in the field.

Acute, Chronic, and Treated Aortic Diseases Present Distinguishable Serum Proteome Fingerprints with Protein Profiles That Correlate with Disease Severity

Aortic diseases are a rare but potentially life-threatening condition. We present a serum proteomic study for a spectrum of aortic diseases including thoracic aortic aneurysms (n = 11), chronic dissections (n = 9), acute aortic dissections (n = 11), and surgically treated dissections (n = 19) as well as healthy controls (n = 10) and patients of coronary heart disease (n = 10) to represent non-aortic cardiovascular disease. In total, we identified and quantified 425 proteins across all 70 samples. The different aortic diseases represented distinguishable proteome profiles. We identified protein clusters that positively or negatively correlate with disease severity, including increase of cytosolic tissue leakage proteins and decrease of components of the coagulation and complement system. Further, we identified a serum proteome fingerprint of acute aortic dissections, consisting, among others, of enriched inflammatory markers such as C-reactive protein and members of the S100 protein family. The study underlines the applicability of serum proteomics for the investigation of aortic diseases and highlights the possibility to establish disease-specific prognostic markers.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

iProX in 2021: connecting proteomics data sharing with big data

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

Proteomics of Cryptococcus neoformans: From the Lab to the Clinic

Fungal pathogens cause an array of diseases by targeting both immunocompromised and immunocompetent hosts. Fungi overcome our current arsenal of antifungals through the emergence and evolution of resistance. In particular, the human fungal pathogen, Cryptococcus neoformans is found ubiquitously within the environment and causes severe disease in immunocompromised individuals around the globe with limited treatment options available. To uncover fundamental knowledge about this fungal pathogen, as well as investigate new detection and treatment strategies, mass spectrometry-based proteomics provides a plethora of tools and applications, as well as bioinformatics platforms. In this review, we highlight proteomics approaches within the laboratory to investigate changes in the cellular proteome, secretome, and extracellular vesicles. We also explore regulation by post-translational modifications and the impact of protein-protein interactions. Further, we present the development and comprehensive assessment of murine models of cryptococcal infection, which provide valuable tools to define the dynamic relationship between the host and pathogen during disease. Finally, we explore recent quantitative proteomics studies that begin to extrapolate the findings from the bench to the clinic for improved methods of fungal detection and monitoring. Such studies support a framework for personalized medical approaches to eradicate diseases caused by C. neoformans.

Proteome biology of primary colorectal carcinoma and corresponding liver metastases

Colorectal adenocarcinomas (CRC) are one of the most commonly diagnosed tumors worldwide. Colorectal adenocarcinomas primarily metastasize into the liver and (less often) into the peritoneum. Patients suffering from CRC-liver metastasis (CRC-LM) typically present with a dismal overall survival compared to non-metastasized CRC patients. The metastasis process and metastasis-promoting factors in patients with CRC are under intensive debate. However, CRC studies investigating the proteome biology are lacking. Formalin-fixed paraffin-embedded (FFPE) tissue specimens provide a valuable resource for comprehensive proteomic studies of a broad variety of clinical malignancies. The presented pilot study compares the proteome of primary CRC and patient-matched CRC-LM. The applied protocol allows a reproducible and straightforward identification and quantification of over 2,600 proteins within the dissected tumorous tissue. Subsequent unsupervised clustering reveals distinct proteome biologies of the primary CRC and the corresponding CRC-LM. Statistical analysis yields multiple differentially abundant proteins in either primary CRC or their corresponding liver metastases. A more detailed analysis of dysregulated biological processes suggests an active immune response in the liver metastases, including several proteins of the complement system. Proteins with structural roles, e.g. cytoskeleton organization or cell junction assembly appear to be less prominent in liver metastases as compared to primary CRC. Immunohistochemistry corroborates proteomic high expression levels of metabolic proteins in CRC-LM. We further assessed how the in vitro inhibition of two in CRC-LM enriched metabolic proteins affected cell proliferation and chemosensitivity. The presented proteomic investigation in a small clinical cohort promotes a more comprehensive understanding of the distinct proteome biology of primary CRC and their corresponding liver metastases.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

The growing need for controlled data access models in clinical proteomics and metabolomics

More and more clinical studies include potentially sensitive human proteomics or metabolomics datasets, but bioinformatics resources for managing the access to these data are not yet available. This commentary discusses current best practices and future perspectives for the responsible handling of clinical proteomics and metabolomics data.

Peptidomics and proteogenomics: background, challenges and future needs

INTRODUCTION

With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development.

AREAS COVERED

Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date.

EXPERT OPINION

This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.

Individual COVID-19 disease trajectories revealed by plasma proteomics

Since the start of 2020, the world has been upended by the pandemic caused by the severe acute respiratory coronavirus type 2 (SARS‐CoV‐2), the causative agent of coronavirus disease 2019 (COVID‐19). It has not only led to a tragic loss of life and terrible economic costs but has also been met with an unprecedented response of the scientific and medical communities. In an effort to better understand this viral infection, scientists around the world generated the largest surge in research in documented history for any topic (Lever & Altman, 2021). A part of this work has included the need to better understand the impact of the virus on human proteins—the key machinery of the cell—and human physiology. In their recent study, Geyer and colleagues (Geyer et al, 2021) analyzed a total of 720 proteomes from longitudinal serum samples of 31 hospitalized COVID‐19 patients and control individuals with COVID‐19‐like symptoms but not infected with SARS‐CoV‐2, providing a comprehensive characterization of the plasma proteome changes along the time course of infection.