NextGen Omics, Spatial & Data US 2026

Profiling the composition and states of immune and tumor cells patients at baseline and upon treatment. Evaluate the effects of therapeutic molecules on immune cell state evolution. Identify new treatment combinations

How to handle data at the terabyte & petabyte scale.
Making large-scale integration feasible and
reliable.
Storage, processing power, cloud adoption,
and reproducibility of analyses, as well as the role of AI and machine learning.

Defining and quantifying “harmonisation” in multi-omics data. Value of integrated multi-omics datasets. Moving and streamlining complicated omics data whilst ensuring real-world utility

How does AI bridge the gap between data and biological insight? Ensuring maintenance of interpretability &
reproducibility when using AI models. Challenges with data standardization and privacy. What are the future possibilities of AI technology in R&D research?

The problem: Gene therapies hold great promise for treating serious diseases, but they are often very expensive and can cause side effects at high doses, making them difficult for patients to access. The approach: Using artificial intelligence, we developed a new method to design improved gene therapy delivery vehicles that are both more effective at reaching target tissues and easier to manufacture—something traditional methods have struggled to achieve simultaneously. The result: The AI-driven approach identified a new delivery vehicle that is 10x better at reaching the target tissue (the back of the eye) and 10x easier to produce, potentially paving the way for more affordable and effective gene therapies for patients.

65% of the ~ 75,000 Gencode human genes do not encode proteins and have been largely ignored in drug development. We computed the intersection of single-nucleotide polymorphims (SNPs) from Genome-Wide Association Studies (GWAS) with exons of long non-coding RNA genes. We identified a lncRNA that controls fasting glucose levels and body weight, and targeted it in-vivo with an RNA drug that can replace insulin for diabetics and GLP1R agonists for obesity.

Computational teams running Nextflow RNA seq pipelines, molecular dynamics simulations with GROMACS and OpenMM, and AI models such as AlphaFold and Boltz face growing challenges around scale, cost control, and operational complexity on the cloud. This session shows how an AI powered, serverless HPC approach on AWS enables organizations to achieve over 100× faster time to insight and 80–85% lower cloud costs while eliminating cluster management and manual execution strategy selection. We will cover how large scale genomics, multi omics, structural biology, and protein modeling workloads can be optimized for sustained performance, predictable costs, and faster experimentation.

Discover how the Concerto system from Codetta Bio is redefining multi-omics by enabling simultaneous, high-sensitivity quantification of proteins and nucleic acids in a single, harmonized workflow. Built on microbead-based target capture and a powerful combination of digital PCR, real-time PCR, and immunoPCR, Concerto leverages proprietary noise-canceling chemistry, advanced optics, and precision thermal control to generate up to six million parallel reactions per run—delivering sub-pg/mL [WH1.1] sensitivity and up to eight logs of dynamic range without serial dilutions. Designed for ease of use, the platform integrates encoded bead sets and automated chip loading to streamline multiplex biomarker detection from liquid biopsies. By consolidating workflows traditionally spread across multiple instruments, Concerto reduces complexity, lowers operational costs, and unlocks deeper, more actionable multi-omic insights for precision medicine research and diagnostics.

The explosion of spatial transcriptomics, single-cell, and multi-omic datasets is outpacing researchers’ ability to access, integrate, and reason across them. In this session, the Broad Institute and Manifold demonstrate what becomes possible when those bottlenecks are removed — from interactive spatial data exploration to AI powered cross-modal analysis at scale. Sami Farhi (Broad Institute Spatial Technology Platform) will walk through the STP’s spatial transcriptomics pipeline and Celldega, a cloud native, open-source visualization tool purpose built for large spatial datasets. He’ll show how Celldega running on Manifold enables external collaborators to explore and interact with spatial data directly — no infrastructure setup, no data transfers. The session then shifts to the broader challenge of working across modalities. Gervaise Henry (Manifold) will demonstrate how the platform brings together diverse single-cell, bulk, and reference datasets — including TCGA and CCLE — into a unified environment for cross-modal discovery. He’ll show how Manifold’s AI Agents let scientists move from raw data to governed, reproducible analysis through natural language, making multimodal exploration accessible to computational and bench researchers alike.

Integrating spatial transcriptomics with complementary modalities such as mass spectrometry imaging
Translational and disease-focused applications, including bronchopulmonary dysplasia and other inflammatory lung conditions, demonstrating how spatial biology informs therapeutic development

Spatial transcriptomics is evolving from exploratory research into a tool for clinical-trial decisions, stratification, PD/MoA readouts, and response/resistance, when paired with fit-for-purpose design and operational rigor. Integrating it with digital pathology links morphology to molecular programs, enabling scalable QC, consistent annotations, and interpretable biomarkers across sites and cohorts. Agentic AI workflows streamline the pipeline by auto-discovering relevant public datasets, generating structured study/donor/sample metadata sheets, and boosting speed, reproducibility, and auditability.

Over the past decade, breakthroughs in spatial molecular biology technologies as well as deep digitization and automation of laboratory processes have transformed traditional pathology laboratories. Robust multimodal platforms like COMET or Xenium enable the implementation of higher throughput assays with the generation of extensive molecular data sets from tissue sections in the biopharmaceutical research setting
Such implementation requires proficient infrastructures with quality control checkpoints along the various workflows, from bench to multimodal digital analytical platforms. Herein we will review our multimodal spatial-omic pipelines and provide a few examples of what worked and what did not work.

Data normalization and data quality check. Analysis results. Lessons learned from data acquisition and suggestions on depositing high quality/usable data into public databases.

Genetic but also epigenetic abnormalities drive caner evolution and reflect identities of normal cells of cancer origins and their “states of cell programming” that influence tumor development. Understanding these regulatory layers could prove critical for developing more precise and durable strategies for marking cancer risk and developing strategies for prevention, early interception and durable therapies. I will stress that how the above science may be exploited for these goals.

Same-slide spatial multiomics of paired bladder biopsies identified layered drivers of Nectin-4 ADC resistance.
FGFR3/SMAD/TGF-β activation promoted M2 polarization and IL-12 insufficiency, establishing immunosuppressive niches that spatially excluded activated T cells implying multi-node blockade may be required.

Protein glycosylation controls diverse biological functions of multiple cell types within the tumor microenvironment. However, glycans have been notoriously challenging to study using conventional approaches. The first-in-kind Glysite™ Explorer™ in situ Proximity Ligation Assay (isPLA) Glycan Detection Kit overcomes this hurdle by providing an innovative platform for spatial visualization of protein glycosylation of several tissues and cell types. It uses an immunostaining workflow and image analysis pipeline compatible with standard microscopy to deliver same-day results. This next-generation technology is poised to broadly impact the life sciences by accelerating glycobiological knowledge and therapeutic discovery as well as biomarker-based disease detection and prognosis

How spatial omics datasets are built, integrated, and translated into biologically and clinically meaningful insights
Applications across disease research and human biology

Comprehensive multi-omics profiling across systemic sclerosis subtypes reveals distinct molecular signatures and disease phenotypes, providing deeper insight into disease heterogeneity. Precision medicine strategies: ongoing efforts and challenges in integrating multi-omics insights to enable data-driven patient stratification and therapeutic development.

Decoding genotype-to-phenotype relationships
Charting developmental pathways
Combining spatial transcriptomics with other technologies (e.g., CRISPR)

Integrating temporally separated spatial proteomics studies using LC-MS. Can we gain insights from comparing data acquired years apart?
Strategies and insights for optimizing sample shipping and handling across multiple sites, enabling broader adoption of spatial proteomics techniques by cross-functional teams

The role of proteomics and metabolomics in human atlases & disease models
Using proteomic & metabolomic data to find disease mechanisms

The Orion spatial proteomics platform reproducibly delivers the biomarker depth at a capacity demanded by clinical trials for precision therapies. Discover how this groundbreaking technology redefines how spatial biology is used for clinical trials.

WTX RNA+68-plex protein+CRISPR detection in FFPE 3D spheroids. Unbiased discovery on different aspects of CAF-tumor interplay: ECM composition, ligand-receptor interaction, spatially variable genes. Cost-effective approach allows profiling hundreds of spheroids (tens of thousands of cells) in one single run.

The newest areas of application for spatial technologies
Infectious disease
Rare disease
Where can spatial go next?

Addressing data integration, scalability, and interpretability, highlighting what must be solved to translate complex spatial datasets into actionable insights for infectious disease, immunology, and clinical research

Spatial transcriptomics to map cellular organization and tissue architecture
Identification of stromal–immune niches that shape tissue homeostasis and disease pathology
Translational implications for immune regulation, target discovery, and biomarker development

Dr. Obaid will present an end-to-end Imaging Mass Cytometry™ (IMC™) workflow and highlight how IMC enables high-parameter, spatially resolved profiling of immune architecture in autoimmune disease. She will discuss emerging post-transcriptomics IMC workflows to strengthen multimodal biological interpretation, and show how single-cell spatial insights can accelerate advanced translational immunology research and support therapeutic discovery and development.

We present a 3D spatial multi-omics platform combining STARmap and RIBOmap assays with the Pyxa instrument to map gene expression and protein localization across sub-cellular and multicellular scales in intact thick tissue.

How these tools enable high-resolution
mapping of tissues and cellular states.
Technological advances, current capabilities, and limitations that shape how spatial data can be generated and interpreted.

This study examined the spatial organization of fibroblasts in rheumatoid arthritis (RA) using histology and spatial transcriptomics. We integrated data from Xenium and Visium, or from Xenium and LC/MS proteomics to examine fibroblast pathobiology in RA
In RA synovium, we found distinct laminar histological configuration of up to nine fibroblast subtypes compared to the random organization observed in healthy synovium. This suggests that fibroblast subtypes expand, transition, and support organized cellular niches with distinct pathophysiological roles in RA

While spatial technologies are rapidly advancing, their use in clinical applications remains in its early stages and differs from workflows and expectations established in preclinical model systems characterizations
We will present a pilot workflow using a FFPE human colon tissue dataset analyzed with spatial transcriptomics via the 10x Genomics Xenium platform, followed by post Xenium multiplexed imaging with Akoya. We will share key lessons learned, along with practical laboratory considerations and sample selection criteria, to help ensure robust data quality in multimodal spatial assays

The Power of the Pre-Competitive Space – Examples from the USA and Europe
Pre-competitive collaboration is where trust is built before competition begins — enabling shared standards, aligned evidence, and faster translation of innovation into clinical practice. This session shows how cross-sector communities in the U.S. and Europe are using this space to accelerate safe, scalable AI and integrated diagnostics.

The Pathology Innovation Collaborative Community (PIcc) is a multi-stakeholder forum uniting clinicians, regulators, industry, and policy leaders to advance shared foundations for innovation in pathology and diagnostics. PIcc will present its current project on validation terminology, addressing how consistent language for analytical, clinical, and real-world validation can enable clearer regulatory pathways and more trustworthy deployment of AI in medicine.

The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) represents laboratory medicine across Europe and leads strategic efforts to shape the future of the profession. EFLM will introduce the Integrative Diagnostics Workforce Task Force, outlining its scope and vision for a new generation of professionals equipped to work across disciplines, data streams, and technologies — ensuring that integrated diagnostics is not only technically possible, but institutionally sustainable.

Together, these perspectives illustrate how the pre-competitive space becomes a catalyst for alignment: turning fragmented innovation into shared progress across the Atlantic.

High-plex spatial proteomics, in tandem with complementary single-cell and transcriptomic
technologies, reveal critical insights in tissue mapping, characterization of the tumor
microenvironment, and establishing clinical relevance in CAR T research and development. The MACSima® System enables a flexible and gentle approach to developing spatial panels with antibodies and RNA probes for a wide breadth of applications while incorporating a computational workflow that empowers researchers. Next-level dimensionality employs a 3D light sheet-directed spatial biology workflow to dive deep into the sections that matter

Ultra high-plex proteomic profiling of the tumour microenvironment - from 50 to 1200 proteins in tissue. Single cell and sub-cellular transcriptomic profiling of the tumour microenvironment. Applications of spatial-omics in clinical cohort studies

Strategies for prioritising, sustaining, or
reconfiguring research in resource limited
settings
Initiatives to fund young researchers
Cultivating start-ups

Esophageal adenocarcinoma (EA) usually develops following Barrett esophagus as a consequence of long-term GERD. Its incidence has been rising significantly in the Western world, faster than most other cancer subtypes. As an emerging global health problem with few specific treatment options, it requires strong research focus and effort.

We have utilized modern single cell and spatial transcriptomics technologies from 10X Genomics to characterize transcriptomic profiles of tumor and stromal cell populations in EA and their spatial relationships. We applied Xenium 5,000 human gene spatial transcriptomics on tumor and stroma samples derived from 30 patients, (matched single cell and spatial transcriptomics), . We identified multiple tumor cell subpopulations which displayed spatially distinct distribution. Notably, a minor tumor cell cluster was present in all samples with high expression of proliferation markers. This tumor subpopulation was tightly interacting with other tumor clusters and possibly contributing to tumor propagation. In-depth characterization of all tumor subpopulations will help pinpoint their respective contributions to tumor development and tumor-stroma interactions.

Immunotherapy failure driven by antigen loss, suppressive signaling, and hostile tumor microenvironments can be elucidated through spatial biology, enabling high-resolution mapping of resistance mechanisms to support optimized response assessment.

G4X Spatial Platform enables high-resolution mapping of cellular architecture and immune niches within intact tumor tissues. AI-driven modeling integrates spatial features to predict tumor classification, immunotherapy response, and clinical outcomes. Scalable, interpretable frameworks bridge spatial biology with translational precision oncology.

We introduce spatiAlytica, a graphic user interface powered by multi-agents designed for discovery from spatial datasets. It functions an interactive AI co-scientist for biologists and clinicians to identify, explain, and correlate gene- or cellular-level spatial features with ease by asking questions in natural language. Multi-agent orchestration and memory advance beyond static explorers to enable extensible, literature-informed spatial discovery, compressing hypothesis validation cycles from weeks to hours.

Utilizes hybrid-DIA mass spectrometry, an intelligent acquisition strategy that integrates Data-driven DIA (Data-Independent Acquisition) and hypothesis-driven PRM (Parallel Reaction Monitoring); enables comprehensive proteome profiling and accurate quantification of complement compounds in biofluids; significantly enhances assay throughput, improving efficiency; and conserves valuable sample material, making the method resource efficient.

The profound and rapid changes of spaceflight present a unique opportunity to understand human adaptation at the molecular level. TRISH established the EXPAND program to collect and store physiological, functional, and environmental data and bio-samples from spaceflight missions. The database’s multifaceted nature will enable analyses between multi-omic, physiological, and functional measurements in humans and allow cross-disciplinary questions to be asked.

We present a unified spatial pathology framework that integrates molecular imaging and histology to link tissue architecture with outcome, mechanism, and diagnosis. Applications in colorectal cancer, triple-negative breast cancer, and kidney disease demonstrate predictive, interpretable, and clinically deployable tissue biomarkers.

Evaluating technologies across therapeutic
areas
Choosing the right spatial strategy for your
biological question
How spatially located targets influence R&D
decisions; moving from maps of cellular
neighborhoods to actionable insights

Spatially resolved protein-protein interactomics go beyond protein location with its functional single cell post-translational-omics analysis.
Spatial metabolomics are barcoded with cell types using super-resolution imaging and generative deep learning.
Spatial biomarkers are discovered using a foundational model and visual query answering system in large-scale multimodal spatial omics datasets.

CD34+ progenitor lymphatic endothelial cells are the primary KSHV target cells, with their clonal expansion driving Kaposi’s sarcoma growth.
KSHV infection reprograms broad cell types into hybrid identities, and drives tumor-specific niche and vascular remodeling, endothelial plasticity, and immune modulation.
KSHV-reprogrammed macrophages drive inflammation, angiogenesis, and immune modulation.
Spatially resolved molecular and cellular signatures predict Kaposi’s sarcoma progression, offering novel therapeutic strategies targeting the tumor microenvironment.

Spatial single-cell insights into early specification of cortical layers and regional boundaries in the developing human brain.
Emerging workflow enhancements and spatial multi-omic applications enabling deeper analysis of tissue architecture, organoid models, and cellular interactions.

Map up to eight molecular and drug classes on a single platform
3D quad‑omic and tri‑omic datasets from Alzheimer’s tissue experiments
Highlight correlative multi‑omic analysis that uncovers deeper biological insights

Building investable omics companies

Investor appetite - what is fundable and why?

What investors are looking for? What are the non-negotiables?

Building partnerships that work

The role of AI as an enabler

Success stories & lessons learnt

Leveraging population scale genomic data
Value of longitudinal health studies
Cross-continent collaboration

Beyond scRNA-seq: co-sequence RNA and targeted DNA in the same single-cell with Atrandi’s Semi-Permeable Capsules (SPCs).
Confirm CRISPR edits, link genotype to transcriptional state, and characterize mutations at scale.
Shown on >100k primary PBMCs

To systematically dissect the molecular determinants of drug sensitivity and resistance, we employed a high-throughput single-cell multi-omic profiling framework using a PRISM pool, a barcoded co-culture platform of ~400 human cancer cell lines spanning diverse lineages and genetic backgrounds. We treated the PRISM pool with two RAS inhibitors to capture early drug response dynamics. Leveraging SBX as our sequencing engine, we employed a modified 10x Genomics Flex protocol enabling simultaneous capture of the whole transcriptome, target proteome (~320-plex MultiPro Human Discovery Panel), and
PRISM identity via expressed DNA barcodes. In this presentation, I will share run performance and how we are leveraging this multiomic information to distill putative mechanisms of drug tolerance.

We generated a genome-wide perturb-seq dataset of 100M+ single cells using CRISPRi, CRISPRa, and siRNA perturbations across diverse human cell types (iPSCs and cancer lines), profiled with the Illumina emulsion-based single-cell platform (PIP-seq)

How to best use multi-omics data to gain new insights
Rethinking drug discovery for cellular complexity
How to prioritize which cell populations, pathways, or transitions matter biologically and clinically

Single cell technology comparison – what to use and when.
Innovative techniques to enhance resolution and accuracy.
Uncovering & identifying novel and rare cell types.

Validation isn't just about binding; it’s about performance in context. We explore why application-specific validation is the backbone of single-cell multi-omics, using our Hallmarks of Validation to prevent false protein signals.

The integration of single-cell omics data into translational drug development and clinical applications offers unparalleled insights into cellular heterogeneity and dynamic biological processes. However, the longitudinal nature of clinical studies introduces layers of variability—across timepoints, individuals, and data modalities—that challenge conventional analytical pipelines. In this talk, I will present PALMO (Platform for Analyzing Longitudinal Multi-Omics), a comprehensive and modular framework designed to address these challenges. By enabling high-resolution, time-aware insights into molecular dynamics, this platfom provides a powerful tool for biomarker discovery, patient stratification, and therapeutic response monitoring in clinical and translational research, paving the way for improved therapeutic strategies and personalized medicine.

Single cell analytics is enabling unprecedent dissection of disease.
Meaningfully impacting drug discovery requires different approaches that academic researchers.
Use cases of how single cell analytics can advance novel target identification, understand mechanism of action and delivery, and patient heterogeneity.

Translating high-resolution tissue data into
clinical decision-making and biomarker
discovery
Future directions in therapeutic targeting

Leveraging multi‑omics to deconvolute tumor heterogeneity.
Rethinking drug discovery with AI‑driven single‑cell analytics.
Prioritizing actionable gene targets/molecular pathways to guide precision medicine.

Modern oncology drug discovery measures outcomes with extraordinary precision, yet remains largely blind to the real-time, organelle-level events that initiate them. This talk we introduce Quanutm biomicroscopy platform (Quantum Nuova), which uses fluorescent nanodiamond NV-center sensors and T₁ relaxometry to quantify free-radical dynamics in live cells with organelle-level targeting. Instead of inferring mechanism from endpoints, the platform is built to capture (i) the first measurable subcellular trigger, (ii) the kinetic profile of the response, and (iii) causal dependency between initiating events and downstream phenotypes.
We focus on a cancer proof point centered on the clinically relevant imipridone dordaviprone (ONC201/Modeyso), which received FDA accelerated approval in 2025 for H3 K27M–mutant diffuse midline glioma, and on the broader class of mitochondrial ClpP activators. Using mitochondria-proximal quantum sensing, we show how early redox shifts can be detected within the first hour of treatment—well before transcriptomic and multi-omics consequences dominate—creating an upstream “fail-fast” decision gate in lead optimization. We further illustrate how comparing wild-type versus CLPP-knockout models can function as a rapid causality filter to distinguish on-mechanism activity from nonspecific stress, enabling more confident compound prioritization and more predictive go/no-go decisions.

Your sample prep method can alter gene expression, distort cell type composition, and even generate artifacts mistaken for biology. We examine case studies illustrating how preparation choices shape downstream single-cell sequencing outcomes.

Cell type annotation and Cross‑species scRNA‑seq integration as a foundation for translatability
Highlighting how probabilistic latent variable models enable harmonized integration of scRNA‑seq data across preclinical species and experimental systems, supporting both broad cell atlas alignment and targeted cell type resolution using species‑aware resources, while preserving biologically meaningful variation critical for translational inference.
 
Mechanistic bridging of in vitro perturbations to in vivo biology
Advances in perturbation modeling that learn shared response manifolds across species and contexts, enabling prediction of in vivo‑like transcriptional states from in vitro systems and improving confidence in translational extrapolation of drug and genetic perturbations.
 
Downstream inference optimized for batch structure and intercellular biology
Outlining batch‑aware differential expression strategies that favor covariate modeling over naïve batch‑corrected expression for sparse scRNA‑seq data, and demonstrate how these choices impact higher‑order analyses such as cell–cell communication inference when comparing signaling conservation across species and experimental modalities

Visualization and analysis of single-cell and spatial transcriptomics data.
Scalable and linked visualizations for large spatial datasets.
Novel neighborhood-level analysis

Cell type annotation in emerging model organisms is limited by sparse references, inconsistent gene panels, and cross-species variability in single-cell data. We evaluated existing single-cell foundation models that learn unified cellular representations across species, enabling more accurate annotation in under-studied organisms. The models were tested across diverse contexts, including whole-atlas and tissue-specific references, and transfers between both closely and distantly related species.

Beyond clinical trials: How real-world evidence reveals treatment patterns, patient heterogeneity, and gaps not captured in controlled studies
Spatial context matters: Using spatial transcriptomics to link tumor heterogeneity to resistance phenotypes in tissue

• Predicting treatment response
• Challenges to integrating multi-omics into
• precision medicine

This presentation introduces NGS-based analytical approaches for rAAV product characterization, including short-read methods for variant calling and process contaminant quantification, and long-read sequencing for evaluating transgene integrity and complex genome rearrangements. Challenges in data interpretation and emerging industry efforts to standardize descriptions of unintended genome species are discussed.

Sensitive detection of measurable residual disease (MRD) and therapy-resistant clones is critical in myeloid malignancies, yet standard bulk assays often miss rare subpopulations or misclassify benign clonal hematopoiesis. Single-cell multiomic profiling overcomes these limitations by simultaneously capturing genotype and phenotype. This approach offers superior sensitivity over conventional methods, accurately distinguishing leukemic from preleukemic cells while mapping co-mutant subclones and clonal hierarchies. These high-resolution insights improve relapse prediction, define biomarkers, and characterize resistance mechanisms. Consequently, single-cell multiomics establishes a unified framework that strengthens both clinical monitoring and precision therapeutic development.

Companion diagnostic (CDx) development for T cell receptor therapies (TCR-Ts)
Challenges and solutions to enable precise patient selection for TCR-Ts in advanced solid tumor indications

Efficient methods development requires rapid exploration of reaction conditions. Uniform amplification protocols have proved to be a long standing bottleneck, constraining our ability to evaluate individually optimal amplification conditions across samples. This presentation will share examples of how the icon96 system, with its per-well thermal control and AutoNorm adaptive normalization, enables rapid, high-throughput methods development without the constraints of fixed-cycle PCR.

Designing large synthetic barcoded libraries to screen cis-regulatory elements (MPRA).
Use of NGS screens to assess CRE activity and infer regulatory grammar in the Gene Therapy context.
CLARA - a DeepLearning model - can predict and generalize regulatory elements activity.

B cells undergoing physiologically programmed or aberrant genomic alterations provide an opportunity to study the causes and consequences of genome mutagenesis. Using three DNA alteration processes--(i) VDJ recombination, (ii) class switch recombination (CSR), and (iii) somatic hypermutation (SHM)—driven by DNA mutator proteins RAG1/2 recombinase (i) or Activation Induced Deaminase (AID) (ii, iii), B cells somatically mutate their antibody-encoding genes to incorporate mutations in the Ig gene loci IgH and IgL. In practice, VDJ recombination, SHM, and CSR improve the ability of the B cells to tailor antibodies to negate viral and bacterial antigens but the very processes involved in creating the diversity can go rogue and cause genomic alterations, culminating in lymphomas. Here, we discuss identification and characterization of lymphoma causing mutations identified from a patients.

The evolution of sequencing technologies and describe opportunities and challenges for implementing long read sequencing into clinical diagnostics
Describe a comprehensive clinical workflow for variant identification using long read sequencing for inherited disorders
Strategies to incorporate epigenetics to improve clinical diagnostics for inherited disorders

FinnGen is a large-scale public–private partnership combining genomic data with longitudinal health registry data from over 500,000 participants in Finland.

The Nordic health register system enables lifelong follow-up of healthcare events since the 1960s, providing unique opportunities to study disease onset, progression, and outcomes.

The genetic characteristics of the Finnish population, together with close collaboration between academia, biobanks, hospitals, and 15 international pharmaceutical companies, enable novel insights into disease mechanisms and translational research.

How to evaluate trade-offs between coverage, read length, throughput, cost, and sensitivity, dependent on goals
When is it worth investing in deeper, more
precise approaches versus high-throughput,
population-level methods?
How to combine multiple platforms

Omics techniques to guide rare disease
diagnosis.
Moving from diagnostics to clinical trials.
Where are the bottlenecks?

Direct RNA sequencing (DRS) provides high accuracy of transcript quantification and full-length isoform detection, surpassing cDNA-based long-read sequencing.
DRS uniquely enables identification of RNA modifications, such as m6A, offering new insights into epitranscriptomic regulation.
DRS has been applied to Alzheimer’s Disease models to uncover transcriptomic and epitranscriptomic changes, providing novel insights into disease.

Integrative Insight: Harnessing genomics, transcriptomics, proteomics, and beyond to unravel complex disease mechanisms.
Personalized Care: Translating multi-omics data into tailored diagnostics, prognostics, and therapeutic strategies.
Clinical Impact: Bridging research and real-world application to improve patient outcomes through precision medicine