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• Shedding light over COVID-19 susceptibility and severity

  “One of the most remarkable features of SARS-CoV-2 infection is the variation in consequence ranging from asymptomatic to life-threatening” state the authors of the majestic Nature paper released in accelerated pre-view on July 2021. This variation in consequences represent not only an issue of burning medical relevance, but also a scientific and intellectual challenge for scientists around the globe. Researchers are known to be easily triggered by a challenge, and they recently demonstrated to be even better at double their efforts to focus on an urgent societal need. In this work led and coordinated by the COVID-19 Host Genetics Initiative (COVID-19 HGI), researchers gathered data from 49,562 cases and two million controls from 46 studies performed worldwide. The majority of the data has been deposited at the EGA, where 29 datasets have been collected under the study number EGAS00001005304. Data gathered from diverse sources conferred a huge robustness to the results. Genetic data from different ancestry origin were included in the analyses, even though data from patients with European origin was highly predominant (approx. 75%). Separated analysis of the different ancestries gave rise to relevant differences in the results, highlighting how important it is to take this aspect in consideration for future studies. Data from the individual studies was compiled into three categories according to COVID-19 severity for a meta-analysis: (a) critically ill cases (b) moderate or severe cases (c) all cases. They were paired with non-COVID-19-reported controls. All three analysis resulted in a total of 13 genetic loci associated with COVID-19 severity. Out of those, 9 loci associated specifically with critical illness, while 6 (a few overlapped) associated with moderated illness. Some genomic position matched previous findings. Interestingly, working with such an enormous amount of data allowed the depth to discriminate association with disease susceptibility vs severity, an issue of large medical relevance. Several loci mapped into biological relevant genes, like TYK2, PPP1R15A and FOXP4, previously implicated in immune response and/or lung functions. Researchers participating in the COVID-19 Host Genetics Initiative have worked in an unprecedented time-tight collaboration, prioritizing data sharing and collective results to tackle the pandemic affecting the world. The scientific community won’t miss any of the lessons to learn from this, and the EGA is ready to keep working to enable safe data sharing and fruitful collaborations.

  Blog covid-19-susceptibility-and-severity

• GCAT | Genomes for life

  The GCAT project is a prospective cohort study that was designed to recruit the general population of Catalonia, the north-east region of Spain. This study is a long-term genomic, environmental and lifestyle cohort project that aims to evaluate and track multiple pathologies as well as biologically related traits. By the end of 2017, the GCAT Study had recruited a total of 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants are followed biannually for/during, at least, 20 years after recruitment. Therefore, the GCAT Study offers a unique opportunity to integrate diverse data to allow the identification of novel relations among different biomarkers and conditions. Results may lead to the development of new genetic, genomic, epigenomic and proteomic diagnoses and screening tests, as well as new public health recommendations. The only currently available population-based prospective cohort study in Spain to the date, including more than 4899 genotyped participants and 800 whole genome sequences is now stored in the EGA archive under accession id EGAS00001003018.

  Blog gcat-genomes-for-life

• Providing access to COVID-19 data: one year later

  response efforts On 11 March 2020 the World Health Organization declared the novel coronavirus outbreak a global pandemic. Four months later, the European Genome-phenome Archive (EGA) released its first COVID-19 dataset. This dataset – single cell RNA and VDJ sequencing of B cells from 60 COVID-19 patients – showed that neutralizing antibodies could be identified by high-throughput sequencing in response to SARS-CoV-2 infection. That was one year ago. Today, the EGA provides access to fifteen COVID-19 datasets from researchers across seven countries in Asia, Europe, and North America. These studies represent almost 17,000 individuals and have resulted in at least sixteen publications and preprints. Researchers deposit controlled access COVID-19 data at EGA The global research community has come together rapidly to investigate the SARS-CoV-2 coronavirus and better understand the related disease, COVID-19. These research efforts generate valuable genetic and phenotypic data from patients and research participants that can be shared with approved researchers. The EGA enables sharing of this research data by providing a service for archiving and controlled distribution of sensitive data. Over the past year, the EGA has worked with researchers to archive and distribute COVID-19 data from high-throughput sequencing experiments, genotyping studies, and phenotypic information. These datasets investigate the immune system, blood, and cells and tissues of the lung, which are relevant for studying a contagious respiratory illness caused by a viral infection. *Study Spotlight. In January 2021, Ancestry.com demonstrated the utility of deep phenotyping based on self-reported outcomes from a large population of mild and asymptomatic COVID-19 cases. They identified genetic associations with eight COVID-19 phenotypes and showed distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. The supporting data is available at the EGA to approved researchers and includes both genotype and phenotype data for 15,000 individuals. EGA collaborates with global COVID-19 community Since the coronavirus outbreak, the EGA has collaborated with other life science resources to support discovery and access to COVID-19 datasets. COVID-19 Host Genetics Initiative. With the NHGRI’s Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) platform, the EGA enables sharing of individual-level genetic and phenotypic data from the COVID-19 Host Genetics Initiative (HGI). This initiative aims to generate, share, and analyze data from COVID-19 host genetics research projects to better understand the genetic determinants of COVID-19 susceptibility, severity, and outcomes. In response to COVID-19, the EGA actively supports COVID-19 data submissions and integration of data access and flow into the COVID-19-HGI analysis platform. *Study Spotlight. The COVID-19 HGI has combined individual-level data for 13,868 COVID-19 positive patients (N=7,167 hospitalized) from 17 cohorts in nine countries. The data were used to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications, and laboratory values. The genotype and phenotype data for 10 of these cohorts is available at the EGA to approved researchers under accession EGAS00001005304. Fig 1: EGA COVID-19 studies are searchable in the European COVID-19 Data Portal alongside other COVID-19 and SARS-CoV-2 public datasets. European COVID-19 Data Portal. EGA-archived COVID-19 data are discoverable via the European COVID-19 Data Portal (Fig. 1), which brings together public and controlled access data to accelerate coronavirus research for the international community. By indexing all COVID-19-related data in one place, researchers can more easily discover relevant datasets of interest, thus increasing the “FAIRness” (Findability, Accessibility, Interoperability, Reusability) of this valuable data. Fig 2: SARS-CoV-2 viral sequences are imported from ENA and analysed in Galaxy to detect variants. Results are accessible to researchers through the COVID-19 Viral Beacon. COVID-19 Viral Beacon. The COVID-19 Viral Beacon tool was developed in collaboration with the European Nucleotide Archive and Galaxy to enable near real-time browsing of SARS-CoV-2 variability at genomic, amino acid, and motif levels (Fig. 2). The COVID-19 Viral Beacon allows researchers to (i) perform detailed searches about genomic variants, (ii) filter queries and find unique cases, (iii) filter data based on strain-specific variants, and (iv) explore associated metadata. It uses the Global Alliance for Genomics and Health (GA4GH) Beacon standard including new Beacon v2 features. With this tool, researchers can study intra-host mutations on genomic regions of interest, or trace any variant frequency over time using raw read data. More than 200,000 SARS-CoV-2 analysed genomic data files are now available to researchers for further exploration. Ongoing COVID-19 efforts at EGA Addressing the COVID-19 pandemic is a global effort. Federated resources are necessary to support transnational deposition, access, and analysis of sensitive COVID-19 host genetics and other related data. At the same time, many countries now have emerging personalized medicine programmes which are generating data from national or regional healthcare initiatives. These data are subject to more stringent information governance than research data and often must comply with national data protection legislation. To address this need, the Federated EGA was established to serve as the primary global resource for discovery and access of sensitive human omics and associated data consented for secondary use. The Federated EGA will comprise a network of national human data repositories and will implement community standards and common interfaces. Launching Federated EGA promises to accelerate not only global research efforts to understand, diagnose, and treat COVID-19, but also to foster data reuse, enable reproducibility, and accelerate biomedical and disease research to ultimately improve human health.

  Blog providing-access-to-covid-19

• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

  Blog the-last-addition-to-the-list-of-clonal-evolution-studies-in-the-ega

• The Federated EGA network

  In the last 10 years, most human omics data has been generated in the context of research consortia while recently there has been an emergence of large cohorts of human data generated by healthcare initiatives. Many countries in Europe now have nascent personalised medicine programmes. Thus, human genomics is shifting from being predominantly research-driven to being funded through healthcare systems. Genetic data generated in a healthcare context is subject to more stringent information governance than research data and nonetheless all data must adhere to national data protections laws. In this context, EGA identified the need for the development of a federated network to enable secure sharing of data whilst enabling genetic data to remain within the jurisdiction in which it was generated. The Federated EGA is designed to support national data management requirements for genomic and clinical data collected from citizens as part of healthcare or biomedical research projects. It includes a secure authorised access mechanism to support research use of these data across Europe and worldwide. We have engaged with over 14 ELIXIR countries to develop the federation model. The EGA federated configuration is composed of Central EGA, Federated EGA nodes and Community EGA nodes: Central EGA offers international submissions and helpdesk support, currently EGA co-managed by EMBL-EBI and CRG, Federated EGA nodes offer EGA services to researchers within their national jurisdiction, Community EGA nodes are individual institutions or initiatives with human genetic data intended to be shared with the research community. Key services available in the Federated EGA structure are: Central EGA Federated EGA node Community EGA nodes Data submission Offers international submissions service Offers submission service in a particular jurisdiction Does not offer an external submissions service Helpdesk support Provides external international helpdesk Provides helpdesk support for submitters in its jurisdiction and for approved users of data managed at its facilities Provides helpdesk support only for approved users of data managed at its facilities Data distribution Manages worldwide distribution for data hosted at Central EGA Manages worldwide distribution for data hosted at Federated EGA node Distribution for data hosted at Community EGA node EMBL-EBI and CRG have prepared a series of documents describing the overall governance and coordination framework. These describe the Federated EGA structure, the rights and responsibilities of the different parties and governing committees, and node operation guidelines. The federation governance proposal is under review by the first countries invited to join as Federated nodes, with further interested countries likely to join in the coming years. The Central EGA team has been working closely with many ELIXIR partners, including ELIXIR Finland, Luxembourg, Germany, Norway, Spain and Sweden.

  Blog the-federated-ega-network

• Data upcycling, powered by EGA

  In 2012 a collaborative effort headed by the teams of Carlos Caldas in Cambridge, UK and Samuel Aparicio, Canada, produced a pivotal study aimed at defining the mutational landscape of breast cancer. They gathered a collection of over 2000 breast cancer samples (METABRIC) associated with long-term clinical follow-up. They delineated an array of inherited and somatically acquired genetic variants and associated them to gene expression. This led to the identification of putative cancer-driving events and improved stratification of breast cancer patients. The study elicited huge attention and was tackled as herculean effort by fellow researchers. Cristina Curtis and her colleagues deposited the data in the EGA archive (EGAS00000000083). What they probably could not foresee is that their data would be reused more than 100 times, becoming one of the most downloaded datasets at EGA. In 2015, Frederik S. Varn and colleagues accessed the above-mentioned data from the New Hampshire, USA, to correlate it to transcriptional programs of hematopoietic cells infiltrated in breast tumours and found that those can influence patients' prognosis. Their findings, published in Nature Communications, improved the understanding of the interplay between immune system and cancer. Again, in 2017 the dataset was downloaded by an Italian/Polish collaboration and used to corroborate their mechanistic study that demonstrate how a long non-coding RNA (lncRNA) is aberrantly localized into the chromatin, where it modulates oncogenic isoforms expression, thus contributing to breast cancer. To date, the METABRIC dataset has been reused to contribute to 147 publications. Its impact on breast cancer research is incalculable, and it even spread beyond that, with recent contributions to the understanding of pan-cancer mechanisms and features. Some of the publications are translating into improvements in diagnosis and treatment of different types of breast cancer. This is just an example of how data upcycling can amplify the potential of any dataset, well past the scope of its creation, the imagination of its owners and any geographical border. At EGA, we are proud to empower such fruitful worldwide cycles of knowledge, providing a platform that enables safe sharing of sensitive genetic data.

  Blog data-upcycling-powered-by-ega

• Youth-GEMs, the project using data to define the mental health trajectories of young people

  Mental health trajectories are shaped by genetic, cognitive, social, and environmental factors that are particularly influential during human development. How exactly the interplay between these factors is impacting mental health, however, is yet unknown. The project “Gene Environment interactions in Mental health trajectories of Youth (Youth-GEMs)”, funded by the European Commission under the Horizon Europe programme, aims to help reducing mental suffering among European youth. How do Youth-GEMs contribute to bettering mental health in young people? Youth-GEMs aims to identify specific neurological, (epi)genetic and environmental drivers on mental health trajectories of young people. To do so, the project is working on a public knowledge base on functional genomics of the developing human brain, between birth and 24 years old. Reliable predictive models for trajectories of onset and course of mental illness in young people of the general population in Europe will be developed. Additionally, a secure and FAIR data infrastructure in mental health is being produced. Engaging and empowering young people Youth-GEMs is also working on the engagement of young people in the scientific field of Psychiatry. For that, the project is developing initiatives such as Science Café, intended to create a learning community where ideas and stories are shared. The 10th Science Café will take place today, with the participation of Lauren Fromont from the EGA. How do the EGA contribute to Youth-GEMs? The contributions require careful data management and harmonisation: we are dealing with an extraordinary amount of data types, sources, and time points (since the studies are longitudinal). To analyse them together and to determine which factors and interactions are truly key to account for mental health trajectories, the variables need to be transformed in a common “language”, i.e., harmonised under a common data model. Further, to guarantee FAIRness of data storage and sharing for future projects that will further advance our knowledge in the mental health field, we are ensuring that data are processed securely but can also be discovered easily. Find out more information about the project in the Youth-GEMs website.

  Blog youth-gems-mental-health

• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• Reduced platelet formation associated with serine metabolic dysregulation in integrin αIIbβ3-deficient megakaryocytes

  Glanzmann Thrombasthenia (GT) is characterized by absent platelet aggregation in response to all agonists except ristocetin and is caused by recessive inactivating variants in ITGA2B or ITGB3. While GT patients typically are described as having normal platelet counts, autosomal dominant activating variants in ITGA2B or ITGB3 cause macrothrombocytopenia. Interestingly, in our cohort of 16 GT patients, eight consistently exhibited platelet counts at the lower end of the normal range. We studied the role of integrin αIIbβ3 in platelet formation using megakaryocytes (MKs) derived from genetically modified immortalized megakaryocyte cell lines (imMKCLs), focusing on two modifications of ITGB3: ITGB3-/- (inactivating) and ITGB3WT/D673_E713del (activating). In static differentiation cultures, ITGB3-/- and ITGB3WT/D673_E713del imMKCLs exhibited normal MK differentiation but reduced proplatelet formation. Platelet production was also impaired in a 3D silk-based bone marrow system and in shaking cultures, confirming a quantitative role for ITGB3 in platelet production, independent of the type of variant. While TRAP-activated in vitro-generated platelets lacking αIIbβ3 failed to bind the activation-dependent PAC-1 antibody, ITGB3WT/D673_E713del platelets bound PAC-1 prior to activation mimicking the patient’s phenotype. Transcriptome profiling and metabolomic analyses ofintegrin αIIbβ3 deficient MKs revealed impaired serine metabolism and downregulation of SLC3A2 (CD98hc), an amino acid transporter chaperon known to interact with the β3 subunit. Flow cytometry confirmed decreased CD98hc in mutant MKs, while re-expression of wild-type ITGB3 in ITGB3-/- MKs restored αIIbβ3 and CD98hc expression, normalized proplatelet formation, and enhanced serine uptake. These results uncover a previously unrecognized role of integrin αIIbβ3 in coupling serine metabolism to platelet biogenesis.

  Study EGAS50000001711

• RNA sequencing of genetically modifed immortalized megakaryocytes

  The dataset contains bulk RNAseq samples from 7 ITGB3 mutants (3 with ITGB3 knock-out and 4 with an ITGB3 splice variant) compared to 4 wildtypes. The ITGB3 mutants and WT_1 and WT_2 are from the same batch; WT_3 and WT_4 are from another sequencing batch. The library was performed using the TruSeq Stranded Total RNA with Ribo-Zero H/M/R_Gold kit and the read length is 100 bp.

  Dataset EGAD50000002462

• VLAIO DenMark Project Data Access Committee

  The VLAIO DenMark Project Data Access Committee is responsible for reviewing and approving controlled-access requests for human genetic, immunological, omics, and associated phenotype data generated within the VLAIO O&O project HBC.2022.0988, titled “Use of multimodel omics technologies to study Dengue host response markers”. The project is a collaboration between Janssen Pharmaceutica NV, Universiteit Gent, and the Institute of Tropical Medicine Antwerp, covering dengue host-response data generated from human challenge, natural infection, and related biomarker studies. The DAC evaluates whether proposed data use is consistent with participant consent, applicable ethics approvals, data protection requirements, institutional policies, and the data access policy linked to the relevant EGA dataset. Access decisions should be made by the project representatives from Janssen, ITG, and UGent, including Ole Lagatie, Kevin Ariën, and Filip Van Nieuwerburgh. EGA will distribute controlled-access data only upon approval granted by this DAC.

  Dac EGAC50000001078

• Germline variants in patients with five cutaneous melanoma

  Skin melanoma arises from a complex interplay between UV radiation exposure and inherited genetic susceptibility. A subset of invidiuals experience multiple independent tumours, suggestingh a stronger underlying genetic component. In this study, we investigate the germline contribution to melanoma development in individuals diagnosed with five or more primary melanomas.

  Study EGAS50000001361

• RASA2 deletion rescues immune synapse dysfunction, enhancing CAR T cell efficacy against DMGs

  Methods: We compared the efficacy of B7-H3 CAR T-cells between SJ-DIPGX7c (DMG) and U87-MG (adult glioblastoma) patient-derived cell lines and showed impaired efficacy both in vitro and in vivo. We performed live-cell imaging and single-cell RNA sequencing to investigate deficiencies in immune synapse (IS) formation between CAR T-cells and DMGs. Lastly, we genetically deleted RASA2, a negative regulator of T cell activation, and evaluated the resulting impact on IS formation and quality, as well as in vitro and in vivo functionality. Sample preparation and sequencing For single cell RNA sequencing, two sets of co-culture arrays were prepared: (1) 2.5×106 Ctrl T cells were co-cultured with 2.5×106 tumor cells (SJ-DIPGX7c, SJ-DIPGX9c, SJ-DIPGX37c, D425, ST1, and U87-MG) and (2) 1×106 CAR T-cells were co-cultured with 4×106 tumor cells (SJ-DIPGX7c, SJ-DIPGX9c, SJ-DIPGX37c, D425, ST1, and U87-MG). Both co-culture arrays were incubated at 37°C 5% CO2 for 16 hours. Co-cultures were enzymatically digested with Accutase (Thermofisher; A1110501) for 10 min at room temperature, and cells were filtered by using Miltenyi dead cell removal kit (Miltenyi; 130-090-101). Highly viable cells were recovered and prepared using the Fixation Kit (PARSE Bioscience) according to manufacturer ‘s protocol. After fixation, the cell count was determined by using a Neubauer hemocytometer. A total of 1.14×105 cells from 12 coculture conditions were used, resulting in approximately 9.5×104 cells per condition. These cells were subjected to the Single Cell Whole Transcriptomic kit v2 (PARSE Bioscience) for library construction. Libraries were subsequently sequenced using the Illumina NovaSeq 6000 platform, generating paired-end 200 bp reads.

  Dataset EGAD50000002785

• Single-cell RNA sequencing of liver metastasis-derived colorectal cancer organoids profiling cellular heterogeneity and lineage dynamics across time

  The objective of this analysis was to identify, at single-cell resolution, trasncriptionally distinct subpopulations in untreated metastatic colorectal cancer organoids with diverse genetic backgrounds and to investigate their temporal dynamics.

  Study EGAS50000001927

• Population histories of the Indigenous Adivasi and Sinhalese from Sri Lanka using whole genomes

  Sri Lanka has played a key role in the peopling of South Asia, with archaeological evidence for human presence on the island dating back to ⁓40,000 years ago. Present-day Indigenous peoples of the island, the Adivasi, are proposed to have descended from early inhabitants of the region, while urban populations like the Sinhalese, the major ethnic group on the island, migrated from India in historical times. Using whole genomes from 19 Adivasi individuals belonging to two clans and from 35 Sinhalese, we find that the Adivasi and Sinhalese share high genetic similarities with each other and with other Sri Lankan and Indian populations, especially those with greater genetic affinity to Ancestral South Indians (ASI). Admixture modeling of the Sri Lankan groups reveals that despite shared ancestral components, the Adivasi retain higher genetic contributions from ancient hunter-gatherers compared with the Sinhalese. Additionally, in contrast to the Sinhalese, the Adivasi have maintained low effective population size and undergone strong founder events, which is consistent with their hunter-gatherer lifestyle, historic relocations, and habitat fragmentation. While the two Adivasi clans are genetically more similar to each other than to any other populations, we observe differing demographic histories, with the Interior Adivasi experiencing a stronger bottleneck than the Coastal Adivasi since their split. This whole-genome-based study addresses gaps in our understanding of the demographic and migratory history of two key Sri Lankan groups and, consequently, of broader South Asia by illuminating complex population structure that has been shaped by both demographic and socio-cultural factors.

  Study EGAS50000001928

• Plasma CTCF ChIP-seq Reveals Cancer-Associated cfDNA Binding Profiles

  Cell-free DNA (cfDNA) in plasma predominantly exists as nucleoprotein complexes. Although circulating CTCF-bound cfDNA (cfCTCF-DNA) has previously been reported, the occupancy of individual CTCF binding sites in plasma has not been systematically characterised. This study describes the isolation of endogenous plasma cfCTCF-DNA by chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) in samples obtained from cancer patients and healthy individuals. A two-step enrichment strategy was employed, combining physical removal of background cfDNA by immunoprecipitation with bioinformatic filtering to remove residual non-CTCF-derived sequences. Sequencing reads were aligned to the UCSC hg38 reference genome using BWA-MEM v0.7.17 and processed to generate CRAM files containing alignments restricted to the primary chromosomes (chr1–chr22, chrX, chrY and chrM). CTCF-associated cfDNA fragments were classified according to their similarity to a consensus CTCF binding motif, enabling discrimination between low-affinity and high-affinity CTCF binding sites. Comparative analyses identified quantitative and qualitative differences in plasma cfCTCF-DNA profiles between healthy individuals and patients with cancer. The deposited dataset comprises plasma cfCTCF-DNA ChIP-seq data from four clinical cohorts and includes both CTCF-enriched and total cfDNA samples where applicable. These data support the investigation of CTCF occupancy patterns as a novel class of liquid biopsy biomarkers for cancer detection and monitoring.

  Study EGAS50000001937

• Urine cfDNA workflow for epigenomic profiling.

  Urine cell-free DNA (cfDNA) holds great promise as a non-invasive biomarker for diagnosis, prognosis and treatment monitoring of diseases. However, its clinical utility is challenged by high nuclease activity and rapid degradation, necessitating an optimized urine cfDNA workflow. We evaluated a workflow for urine cfDNA methylation and fragmentation profiling, comparing PAXgene Urine Technology, with Streck Urine Preserve and unstabilized urine, using enzymatic methyl sequencing of cfDNA from healthy individuals, across multiple urine storage time points (immediate, 6 h, and 72 h). Urine stabilization significantly improved cfDNA yield and preserved cfDNA fragment sizes, while unstabilized samples exhibited marked degradation and reduced sequencing quality. Methylation analysis revealed high concordance between stabilized samples, with subtle divergence after 72 h. In contrast, unstabilized samples showed widespread methylation changes. Biological patterns were retained in stabilized urine cfDNA. Our study underscores the importance of well-designed (pre)analytical urine workflows to allow urine cfDNA (epi)genomic analyses.

  Study EGAS50000001535

• Circulating cell-free DNA methylation profiles as noninvasive multiple sclerosis biomarkers: A proof-of-concept study

  In chronic neurodegenerative diseases such as multiple sclerosis (MS), there is a critical unmet need for cost-effective non-invasive blood biomarkers to concurrently classify disease subtypes, monitor disability severity, and predict long-term progression. In this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples collected from a well-characterized real-world prospective clinic cohort with longitudinal disability outcome measurements. We assessed the clinical utility of cfDNA methylation profile for differentiating MS patients from non-MS controls, classifying MS subtypes, estimating disability severity, and predicting disease trajectories.

  Study EGAS50000001277

• INTEGRATIVE MOLECULAR ANALYSIS OF SKIN TUMORS FROM CYLD CUTANEOUS SYNDROME PATIENTS

  CYLD cutaneous syndrome (CCS) is a rare autosomal dominant disorder characterized by germline CYLD mutations and by multiple benign skin tumors dependent on NF-kB pathway. We assembled a large cohort of CCS rare skin tumors that was profiled with whole exome or genome sequencing, RNA sequencing and methylation arrays to better understand genetic mechanisms of CCS tumorogenesis.

  Dataset EGAD50000000362

• Multi-omic signatures in multiple myeloma

  Multiple myeloma (MM) is a genetically and clinically heterogeneous plasma-cell malignancy. Quadruplet therapy, combining anti-CD38 antibodies, immunomodulatory drugs, proteasome inhibitors, and dexamethasone, is now the standard induction treatment for newly diagnosed multiple myeloma (NDMM). To gain a comprehensive view of molecular changes in MM and their association with treatment response in this new standard care, we performed a multi-omic study of 423 NDMM from the MIDAS trial.

  Study EGAS50000001573

• Epigenetic and Transcriptomic profiling of hiPSC- and UCB-HSC derived CD4+CD8+ T progenitors

  Generation of T cell subsets from human induced pluripotent stem cells (hiPSCs) in defined in vitro systems could provide a valuable model to study the mechanisms of human T cell development in the thymus and serve as a scalable source for therapeutic cell products. We generated a unique DNA methylation (DNAmeth) and RNA-seq dataset of in vitro generated hiPSC-derived and umbilical cord blood (UCB-) derived T cell progenitor subsets from a feeder-free differentiaion system and integrated them with data from primary human thymocyte populations.

  Study EGAS50000001929

• Genotyping data from low-coverage whole genome sequencing (lc-WGS) for 90 Asian individuals

  A uniquely comprehensive genetic profile of 90 "healthy" Asian individuals who are representative of the diverse multi-ethnic population of Singapore, with 65 females and 25 males, aged 21 to 61 years, from the major ethnic groups (63 Chinese, 9 Malay, 18 Indian). This is a subset of the 92 participants described in PMID 41387625 (DOI: 10.1038/s43856-025-01298-4), who agreed to share their genetic data for future research beyond the original study. Replicates for one female (Coriell Cat# GM18592, RRID:CVCL_P195) and one male (Coriell Cat# GM18609, RRID:CVCL_P207) control cell line of type B-Lymphocyte, ethnicity Han Chinese from the NHGRI Sample Repository for Human Genetic Research were also included in each of the four flowcell lanes. This dataset consists of individual-level genetic data from low-coverage whole genome sequencing (lc-WGS, read length 2X150bp). Data from 4 commercially available genotyping arrays for the same samples is also available on EGA (EGAD00010002834).

  Dataset EGAD50000002806

• Genotyping data from four arrays for 90 Asian individuals (Illumina GDA, Illumina GSA, Illumina OncoArray, and ThermoFisher Axiom)

  A uniquely comprehensive genetic profile of 90 "healthy" Asian individuals who are representative of the diverse multi-ethnic population of Singapore, with 65 females and 25 males, aged 21 to 61 years, from the major ethnic groups (63 Chinese, 9 Malay, 18 Indian). This is a subset of the 92 participants described in PMID 41387625 (DOI: 10.1038/s43856-025-01298-4), who agreed to share their genetic data for future research beyond the original study. Replicates for one female (Coriell Cat# GM18592, RRID:CVCL_P195) and one male (Coriell Cat# GM18609, RRID:CVCL_P207) control cell line of type B-Lymphocyte, ethnicity Han Chinese from the NHGRI Sample Repository for Human Genetic Research were also included. This dataset consists of individual-level genetic data from 4 genotyping arrays -- the Illumina Infinium Global Diversity Array (GDA) v1.0, the Illumina Infinium Global Screening Array (GSA) v3.0, the Illumina Infinium OncoArray-500K v1.0, and a custom ThermoFisher Scientific's Applied Biosystems Axiom array. Data from low-coverage whole genome sequencing (lc-WGS read length 2X150bp) for the same samples is also available on EGA (EGAD50000002806).

  Dataset EGAD00010002834

• Large-scale single-cell phylogenetic mapping of clonal evolution in the human aging esophagus

  SMART-PTA single-cell whole-genome and full transcriptome sequencing form normal esophagus samples

  Dac EGAC50000000968

• Telomere variant sequences encode a heritable genetic blueprint for allele-specific telomere length

  Long read sequencing of 195 blood samples for allele-specific telomere detection

  Dataset EGAD50000002360