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Results for “transcriptomic platform”

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PubMed2006

Monitoring the health to disease continuum with global metabolic profiling and systems biology.

Global metabolic profiling, which includes both metabolomics and metabonomics studies, is the latest 'omics' research platform that is being applied to understand the health and disease continuum. Metabolic profiling analyses have been demonstrated for the investigation of inborn errors of metabolism, organ transplant rejection, drug toxicity, disease diagnosis and prognosis, drug efficacy and nutritional status. Combining information generated from a metabolic profiling platform with that obtained based on genetics, transcriptomics and proteomics research paradigms will pave the way for a better understanding of the mechanisms of disease and toxicity. Metabolomics and nutrition will lay the groundwork for the application of personalized medicine in the 21st century.

Animals↗

PubMed2026

Proteomics as a theranostic compass in BCR::ABL1-negative myeloproliferative neoplasms: Integrating biomarker discovery with therapeutic stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal haematopoietic stem cell disorders with marked heterogeneity in clinical phenotype, disease trajectory, and therapeutic response. Genomic stratification by driver and cooperating mutations only partially accounts for this variability, leaving gaps in predicting thrombotic risk, fibrotic progression, leukaemic transformation, and treatment benefit. Proteomics bridges this gap by providing function-proximal readouts of protein abundance, post-translational modifications, pathway activity, and intercellular signalling that genomics and transcriptomics cannot capture, positioning it as a theranostic platform in which the same molecular readouts simultaneously inform diagnostic stratification and therapeutic decision-making. We propose a five-stage translational framework spanning from discovery-scale mass spectrometry and affinity-based plasma profiling to targeted validation, multicentre standardisation, and machine learning-integrated clinical panels. Proteomic evidence is synthesised across the following four disease axes: clonal fitness in haematopoietic stem and progenitor cells; bone marrow microenvironmental remodelling and fibrosis; chronic inflammation and thrombosis; and leukaemic transformation. We further describe how phosphoproteomics reveals resistance mechanisms to JAK inhibitors, including AXL-MAPK bypass and PP2A-autophagy-mediated tolerance, and how protein-level biomarkers (BCL2-BCL-XL, RAS-ERK, CAMK2G, and ROCK1/2) can guide individualised therapeutic selection. Affinity-based platforms (Olink PEA and SomaScan) and spatially resolved technologies (CODEX and single-cell proteomics) complement discovery proteomics. At present, however, this evidence base is constrained by small and heterogeneous cohorts, limited cross-platform reproducibility, and a scarcity of independent external validation for candidate protein panels. Realising this vision will require multicentre standardisation, analytically validated panel assays, and prospective clinical studies that translate molecular findings into decision-grade tools for patients with MPNs.

Humans↗

PubMed2006

Identification of candidate maternal-effect genes through comparison of multiple microarray data sets.

Transcriptional profiling by microarray hybridization has become a standard method to analyze global gene expression and has resulted in the availability of enormous amounts of experimental data. Given the number of different microarray platforms currently in use, it is critical to determine how reproducible results are from one platform to another. Additional variability may also arise from tissue collection and protocol differences among laboratories. In an effort to identify genes whose maternal mRNA pools are critical during preimplantation development, we compared published results of three independent studies of the mouse preimplantation embryo transcriptome, each performed in a different laboratory using different microarray platforms. We searched the combined data set for genes whose expression patterns were consistent among the three experiments. Querying for presence or absence at single developmental windows indicates that between 52% and 60% of genes are in agreement among the three experiments. Searching for expression patterns across three developmental windows (oocyte + 1-cell, 2- through 8-cell, and blastocyst stage) revealed approximately 33% agreement among the three experiments, although the majority of these genes were either always present or always absent. Using this approach, we identified 51 genes with a predicted expression pattern of maternal RNA only (not present during 2-cell through 8-cell or at the blastocyst stage). RT-PCR validation indicates 37 (72%) of these candidates have the microarray-predicted expression pattern and represent candidate maternal-effect genes. Based on our analysis, we conclude that data mining microarray experiments in this way greatly enhances candidate gene expression pattern accuracy.

Animals↗

PubMed2006

Establishment of the epithelial-specific transcriptome of normal and malignant human breast cells based on MPSS and array expression data.

INTRODUCTION: Diverse microarray and sequencing technologies have been widely used to characterise the molecular changes in malignant epithelial cells in breast cancers. Such gene expression studies to identify markers and targets in tumour cells are, however, compromised by the cellular heterogeneity of solid breast tumours and by the lack of appropriate counterparts representing normal breast epithelial cells. METHODS: Malignant neoplastic epithelial cells from primary breast cancers and luminal and myoepithelial cells isolated from normal human breast tissue were isolated by immunomagnetic separation methods. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using massively parallel signature sequencing (MPSS) and four different genome wide microarray platforms. Functional related transcripts of the differential tumour epithelial transcriptome were used for gene set enrichment analysis to identify enrichment of luminal and myoepithelial type genes. Clinical pathological validation of a small number of genes was performed on tissue microarrays. RESULTS: MPSS identified 6,553 differentially expressed genes between the pool of normal luminal cells and that of primary tumours substantially enriched for epithelial cells, of which 98% were represented and 60% were confirmed by microarray profiling. Significant expression level changes between these two samples detected only by microarray technology were shown by 4,149 transcripts, resulting in a combined differential tumour epithelial transcriptome of 8,051 genes. Microarray gene signatures identified a comprehensive list of 907 and 955 transcripts whose expression differed between luminal epithelial cells and myoepithelial cells, respectively. Functional annotation and gene set enrichment analysis highlighted a group of genes related to skeletal development that were associated with the myoepithelial/basal cells and upregulated in the tumour sample. One of the most highly overexpressed genes in this category, that encoding periostin, was analysed immunohistochemically on breast cancer tissue microarrays and its expression in neoplastic cells correlated with poor outcome in a cohort of poor prognosis estrogen receptor-positive tumours. CONCLUSION: Using highly enriched cell populations in combination with multiplatform gene expression profiling studies, a comprehensive analysis of molecular changes between the normal and malignant breast tissue was established. This study provides a basis for the identification of novel and potentially important targets for diagnosis, prognosis and therapy in breast cancer.

Biomarkers, Tumor↗

PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗

PubMed2026

Molecular biomarker profiling in noninfectious uveitis: a chronological review of discovery.

PURPOSE OR REVIEW: Noninfectious uveitis (NIU) encompasses a heterogeneous group of immune-mediated intraocular inflammatory diseases whose complexity has driven systematic molecular biomarker discovery. This review presents NIU molecular biomarkers organized by biological category; autoantigens, human leukocyte antigens (HLA) and genetic markers, cellular immune subsets, cytokines, chemokines, and multiomics platforms including proteomics, microbiome metagenomics, metabolomics, and single-cell transcriptomics with each category presented in strict chronological order of landmark discovery. RECENT FINDINGS: We present a review organized along two nested timelines. Categories are presented in the order they historically emerged in the field, and within each category, landmark discoveries appear in chronological sequence. This allows the reader to trace how each biomarker category evolved: from foundational autoantigen identification in experimental uveitis models, through the genomic revolution of HLA association studies, into cellular immunophenotyping, cytokine profiling of aqueous humor, chemokine mapping of intraocular trafficking, and finally the emerging omics platforms that may potentially anchor precision medicine in NIU. Each biomarker is paired in line with its linked targeted therapeutic. SUMMARY: Biomarker research has transformed the understanding of NIU from a clinically defined syndrome into a group of molecularly distinct immune disorders. Advances spanning autoantigens, genetics, immune-cell profiling, cytokines, chemokines, and multiomics have revealed novel pathogenic mechanisms and therapeutic targets. Integration of these biomarkers with targeted therapies may accelerate the transition toward precision medicine in uveitis care.

cytokines↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMed2003

Comparing the use of Affymetrix to spotted oligonucleotide microarrays using two retinal pigment epithelium cell lines.

PURPOSE: The present study was designed to compare the results obtained from two different microarray platforms: spotted cDNAs using a two-color system (Clontech, Atlas Glass Human 3.8) and the Affymetrix platform. We evaluated the internal consistency within each of the platforms, and compared the results across the two platforms. METHODS: RNA was isolated from two retinal pigment epithelial (RPE) cell lines, D407 cells and ARPE19 cells. Each microarray system requires a specific RNA isolation and target preparation procedure. To compare the results between the two platforms, the intensity values for each platform were standardized and scaled. This allowed for a direct comparison of the data between two very different microarray platforms. Real-time RT-PCR was used as an independent conformation of expression levels for selected transcripts. The protein levels for some of these genes were determined using a quantitative immunoblot method. RESULTS: First, we compared the transcriptome of the D407 cell line to itself. Within each of the platforms there was a high degree of consistency. However, when the data from the Atlas Glass Human 3.8 microarray platform was compared to that of the Affymetrix platform there was a dramatic lack of agreement. The second step was to compare the mRNA profile of the ARPE19 cell line to the D407 cell line. Again there was good agreement within each platform. When the results of the Atlas Glass Human 3.8 platform were compared to the Affymetrix platform, there was a surprising lack of agreement between the two data sets. Real-time RT-PCR was used as independent means of defining RNA levels in the two cell lines. In general, the real-time RT-PCR results were in better agreement with the Affymetrix platform (85%) than the Atlas Glass platform (33%). In addition, we also examined the levels of 11 proteins in these two cell lines using a quantitative immunoblot method. The results from this protein analysis had a higher degree of concordance with the results from Affymetrix platform. CONCLUSIONS: In both the Atlas Glass Human 3.8 system and the Affymetrix platform, there is a high degree of internal consistency. However, comparisons between the two platforms show a lack of agreement. In general, the real-time RT-PCR confirmed the results on the Affymetrix system more often than those from Atlas Glass arrays. However, in both cases, conformation by an independent method proves to be of considerable value.

Actinin↗

PubMed2005

Validation of RNA arbitrarily primed PCR probes hybridized to glass cDNA microarrays: application to the analysis of limited samples.

BACKGROUND: The applicability of microarray-based transcriptome massive analysis is often limited by the need for large amounts of high-quality RNA. RNA arbitrarily primed PCR (RAP-PCR) is an unbiased fingerprinting PCR technique that reduces both the amount of initial material needed and the complexity of the transcriptome. The aim of this study was to evaluate the feasibility of using hybridization of RAP-PCR products as transcriptome representations to analyze differential gene expression in a microarray platform. METHODS: RAP-PCR products obtained from samples with limited availability of biological material, such as experimental metastases, were hybridized to conventional cDNA microarrays. We performed replicates of self-self hybridizations of RAP-PCR products and mathematical modeling to assess reproducibility and sources of variation. RESULTS: Gene/slide interaction (47.3%) and the PCR reaction (33.8%) accounted for the majority of the variability. From these observations, we designed a protocol using two pools of three independent RAP-PCR reactions coming from two independent reverse transcription reactions hybridized in duplicate and evaluated them in the analyses of paired xenograft-metastases samples. Using this approach, we found that HER2 and MMP7 may be down-regulated during distal dissemination of colorectal tumors. CONCLUSION: RAP-PCR glass array hybridization can be used for transcriptome analysis of small samples.

Acetyltransferases↗

PubMed2026

Full-length single-cell spatial transcriptomics reveals spatial and cell-type-specific transcript isoforms in the primate brain.

The primate brain exhibits complex RNA alternative splicing heterogeneity crucial for functional complexity, yet systematic spatial isoform characterization has been lacking. We developed Fullscope-seq, a full-length single-molecule large field-of-view spatial transcriptomics sequencing method at single-cell resolution, based on programmed concatenation cDNA for multiple long-read sequencing platforms. Applying Fullscope-seq to the macaque brain, we uncovered thousands of genes exhibiting differential transcript usage (DTU) across cortical layers, cell types and brain regions. Fullscope-seq resolved hundreds of major isoform switches across distinct brain regions and identified DTUs between superficial and deep cortical layers. Cortical layer-specific DTUs showed cell-composition dependence, whereas regional DTUs were regulated according to both cellular composition and spatial contexts. These isoform variations showed substantial enrichment for neuropsychiatric disorder-associated genes and were conserved across platforms and species. Our study establishes a scalable framework for spatial isoform analysis and provides a resource for understanding transcriptomic diversity in complex tissues.

Animals↗

PubMed2026

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.

Humans↗

PubMed2005

Platform influence on DNA microarray data in postmortem brain research.

In addition to the substantial biological diversity among humans, our limited ability to reliably measure expression changes of small magnitude significantly reduces our capacity to obtain convergent sets of transcriptome data in postmortem brain. In particular, differences in the structure and sensitivity/reproducibility of microarray platforms, and in the variety of tools used to analyze microarray data, strongly influence experimental outcome. In order to better understand the sensitivity, dynamic range, and reproducibility of three common DNA microarray platforms, we compared two human postmortem samples on cDNA microarrays with dual-fluorescence, oligonucleotide GeneChips (Affymetrix), and single-color gel matrix deposited CodeLink oligonucleotide arrays. All three microarray platforms reported a good dynamic range and high correlation in replicate experiments, but they failed to consistently identify the same genes as differentially expressed between the same samples. Given their reproducibility and proven accuracy, different microarray platforms appear to be measuring different things by nature of their design and function. This needs to be taken into account when comparing data across studies.

Brain↗

PubMed2006

Comparative profiling of the sense and antisense transcriptome of maize lines.

BACKGROUND: There are thousands of maize lines with distinctive normal as well as mutant phenotypes. To determine the validity of comparisons among mutants in different lines, we first address the question of how similar the transcriptomes are in three standard lines at four developmental stages. RESULTS: Four tissues (leaves, 1 mm anthers, 1.5 mm anthers, pollen) from one hybrid and one inbred maize line were hybridized with the W23 inbred on Agilent oligonucleotide microarrays with 21,000 elements. Tissue-specific gene expression patterns were documented, with leaves having the most tissue-specific transcripts. Haploid pollen expresses about half as many genes as the other samples. High overlap of gene expression was found between leaves and anthers. Anther and pollen transcript expression showed high conservation among the three lines while leaves had more divergence. Antisense transcripts represented about 6 to 14 percent of total transcriptome by tissue type but were similar across lines. Gene Ontology (GO) annotations were assigned and tabulated. Enrichment in GO terms related to cell-cycle functions was found for the identified antisense transcripts. Microarray results were validated via quantitative real-time PCR and by hybridization to a second oligonucleotide microarray platform. CONCLUSION: Despite high polymorphisms and structural differences among maize inbred lines, the transcriptomes of the three lines displayed remarkable similarities, especially in both reproductive samples (anther and pollen). We also identified potential stage markers for maize anther development. A large number of antisense transcripts were detected and implicated in important biological functions given the enrichment of particular GO classes.

Expressed Sequence Tags↗

PubMed2025

Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platform's utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, .eu, .org.au, .fr).

Journal Article↗

PubMed2025

Charting Postnatal Heart Development Using In Vivo Single-Cell Functional Genomics.

The transition at birth, marked by increased circulatory demands and rapid growth, necessitates extensive remodeling of the heart's structure, function, and metabolism. This transformation requires precise spatial and temporal coordination among diverse cardiac cell types; central to this process is cardiomyocyte maturation, yet the regulatory mechanisms driving these changes remain poorly understood. Here, we present a temporal and spatial atlas of postnatal hearts by integrating single-nucleus transcriptomics with image-based spatial transcriptomics, which uncovers the dynamic regulatory networks of cardiomyocyte maturation. To functionally interrogate candidate regulators in vivo , we developed Probe-based Indel-detectable Perturb-seq (PIP-seq), a high-throughput platform that uses probe-based chemistry to directly capture sgRNA expression, perturbation status, and transcriptomic profiles at single-nucleus resolution. Applying PIP-seq to postnatal cardiac development identified 21 novel regulators of cardiomyocyte maturation, highlighting critical nodal points in this process. Our study establishes a high-resolution framework for dissecting postnatal heart development, underscoring the integrative and highly ordered roles of microenvironment and intercellular communication in cardiomyocyte maturation. Importantly, PIP-seq enables systematic, high-throughput exploration of gene function and networks underlying complex biological processes in their native in vivo context.

Journal Article↗

PubMed2025

Single-cell RNA sequencing defines developmental progression and reproductive transitions of Pneumocystis carinii.

UNLABELLED: Pneumocystis species are host-obligate fungal pathogens that cause severe pneumonia in immunocompromised individuals. Despite their clinical importance, their life cycle remains poorly understood, in part because Pneumocystis depends on the host environment for most nutrients and requires sexual reproduction for survival, which occurs exclusively in vivo. This study presents the first single-cell RNA sequencing (scRNA-seq) atlas of Pneumocystis carinii, generated from isolated organisms recovered from the bronchoalveolar lavage fluid of infected rats to map the life cycle of P. carinii. Transcriptomes from 87,716 cells were analyzed using the 10× Genomics platform, revealing 13 transcriptionally distinct clusters representing key developmental stages, including biosynthetically active trophic forms, mating-competent intermediates, and asci undergoing sporulation. These states were characterized by expression of MAPK signaling components, β-glucan-modifying enzymes, and spore-associated genes, respectively. The scRNA-seq data support previous evidence that these host-obligate fungi undergo sexual reproduction and provide new insights into the gene expression patterns associated with different life cycle phases. Biomarkers associated with ascus formation identified by scRNA-seq were validated by RT-qPCR, showing decreased expression levels in ascus-depleted populations treated with anidulafungin, a drug that halts ascus formation. More broadly, this approach provides a strategy for studying the full life cycles of fungal pathogens that cannot be continuously cultured. IMPORTANCE: Pneumocystis species (spp.) are clinically significant fungal pathogens that cannot be sustainably cultured in vitro due to their host-obligate nature. This longstanding limitation has impeded progress in understanding their life cycle and identifying therapeutic vulnerabilities. Here, we apply scRNA-seq to P. carinii isolated directly from infected rat lungs, generating the first transcriptional map of its developmental progression. Our results define discrete gene expression states associated with trophic growth, mating activation, and ascus formation and provide transcriptional evidence for a structured life cycle, clarifying key developmental transitions and identifying potential regulatory targets for therapeutic intervention. Importantly, this study demonstrates that scRNA-seq can resolve the developmental biology of host-restricted fungal pathogens that cannot be cultured in vitro. This approach offers a generalizable framework for investigating other unculturable or obligate microbial pathogens directly within their native host environments, where traditional experimental tools are limited.

Pneumocystis carinii↗

PubMed2026

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

The increasing availability of genomic and transcriptomic sequencing has uncovered diverse genomic alterations and distinct gene expression profiles driving hematologic diseases, yet a data integration and sharing platform dedicated to hematology remains lacking. We developed the American Society of Hematology (ASH) HematOmics Program (ASHOP; ashop.hematology.org), a resource for exploring somatic alterations and gene fusions, transcriptomic results, and clinical data from 5960 patients spanning B-cell precursor and T-cell acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndromes, and chronic lymphocytic leukemia. Users can explore genomic alteration landscapes and comutation patterns via lollipop and matrix plots and analyze significantly altered genes in user-defined subcohorts. Transcriptomes can be explored through interactive uniform manifold approximation and projections, clustering, differential expression, and pathway enrichment. Genomic, transcriptomic features, and clinical outcomes can be correlated in a user-driven manner or combined to precisely define study cohorts. We illustrate the following 4 use cases of ASHOP: (1) stratification of DUX4-rearranged B-cell leukemias into Early/Multipotent and Committed subgroups with distinct outcomes, (2) characterization of HOXA/HOXB expression patterns in acute myeloid leukemias, (3) correlating mutational burden with mismatch repair deficiency and mutational signatures, and (4) investigation of TP53 alteration landscape. ASHOP is an open-access resource to inform genomic and transcriptomic data interpretation for hematologic malignancies and will expand to support additional diseases and data modalities from the ASH community.

Humans↗

PubMed2005

[Research strategies towards a holistic characterization of rheumatoid arthritis--a systems biology approach].

Genome-wide screening methods used in functional genomics (genome, transcriptome, proteome and metabolom analysis) have increasingly been conducted in integrative research platforms to enable a comprehensive holistic characterization of multifactorial polygenic diseases. First results of this research strategy demonstrate that extended data sets are compiled whose quality is ensured by the application of standard operating procedures (SOPs) and the integration of specific laboratory information management systems (LIMS). Experimental data derived from this technology and methodology platform are obtained by applying standardized sampling procedures followed by comprehensive experimental validation and bioinformatic comparisons with the world knowledge publicly available. This research strategy should finally lead to a holistic understanding of the pathogenesis presented in rheumatoid arthritis by identifying disease-associated regulatory networks (pathways) and assigning them to cell populations involved in the disease mechanisms. In addition, it has to be investigated to what extent genetic as well as epigenetic factors direct disease initiation and progression in potential conjunction with environmental impacts (infections, smoking, etc.).

Animals↗