Custom-fit FFPE transcriptomics for oncology target identification
Whole-transcriptome, single-cell, focused panel, or spatial — we run the FFPE transcriptomic strategy that matches your tumor biology, indication, and decision points.
Explicyte is equipped across the major FFPE-compatible transcriptomic platforms, helping you source the right oncology cohorts and turn the dataset into ranked, defensible target candidates.
Recent transcriptomic papers
Our target identification workflow
From samples to ranked targets, in three steps
FFPE cohort & QC
Cohort design that drives target signal
The cohort you build determines the targets you find. We design stratified FFPE cohorts — responder vs. non-responder, treatment-naive vs. treated, indication subtypes — with platform-ready QC, RNAscope, and pathology review built in.
Key outputs
- Stratification framework aligned with your target nomination logic
- Inclusion/exclusion criteria with clinical metadata mapping
- Pathology review and platform-ready FFPE prep (RNAscope-based RNA QC)
Transcriptomic data generation
The right platform & panel for the question
We help you choose between whole-transcriptome, focused panel, single-cell, and spatial approaches — balancing gene coverage, spatial resolution, throughput, and budget — then execute the strategy across our 10x-certified and bulk transcriptomic platforms.
Key outputs
- Recommended platform, panel, and multiplexing strategy
- Raw and processed gene expression matrices
- QC report with sequencing and dataset metrics
Dataset analysis
Target nomination and ranking
Our in-house data scientists turn high-dimensional transcriptomic data into defensible target candidates — ranked on prevalence, expression specificity, druggability signals, and safety markers, with the evidence trail behind each call.
Key outputs
- Cleaned, normalized, and annotated dataset
- Integration with clinical metadata and relevant public datasets
- Ranked targets with evidence trail (prevalence, specificity, druggability, safety)
10x genomics-certified service provider for FFPE transcriptomics
Certified across Xenium, Visium HD, and Chromium X
We are platform agnostic. We recommend the FFPE transcriptomic strategy that best fits your biological question.
Explore our 10x Genomics platforms
Which FFPE transcriptomic strategy fits your target discovery project?
| Technology | Use case for target ID | Trade-off | Best fit when |
|---|---|---|---|
| Chromium X | Single-cell transcriptomic profiling across large numbers of samples | Limited spatial context | Transcriptome-wide discovery, cohort screening, and target ranking at scale |
| Visium HD | Spatial transcriptomics with broad discovery power across tissue | ~2µm bins approximate single-cell resolution but don't deliver true single-cell precision | You need tissue architecture and broad exploratory power in the same experiment |
| Xenium | High-plex spatial profiling at single-cell resolution | Panel-based (up to 5,000 genes) rather than unbiased whole-transcriptome | Spatial localization, cell-type specificity, and microenvironment context are central |
| GeoMx DSP | Region-of-interest spatial profiling in selected tissue compartments | Less cellular resolution than single-cell spatial methods | The question is compartment-level (tumor vs. stroma) or pathology-defined regions |
| Bulk RNA-seq | Highest-throughput unbiased transcriptomic profiling at the lowest cost per sample | No spatial or cellular resolution — population-averaged signal only | You need broad signal across many samples and single-cell/spatial detail isn't required for nomination |
Strategies to optimize FFPE-based target identification studies
Rationalizing target discovery: case studies
Paul Marteau, PharmD (study director), Imane Nafia, PhD (CSO), Loïc Cerf, MSc (COO), Alban Bessede, PhD (founder, CEO), Jean-Philippe Guégan, PhD (CTO)
contact our team
Discuss your target identification project and request a quote
Whether you start from FFPE blocks, a specific indication, or an existing shortlist of candidate targets, we can help design the right transcriptomic strategy and define the next validation steps.
Answers about precision oncology discovery services
Frequently asked questions
What makes Explicyte different for FFPE-based target identification?
Explicyte is 10x Genomics Certified across Xenium, Visium HD, and Chromium X — and equipped to run GeoMx and bulk transcriptomic approaches as well. This breadth lets us recommend the platform that fits the question, not the platform we have to push. Our in-house data science team applies the same prioritization framework across platforms, so projects deliver ranked target candidates with a defensible evidence trail. Reflected in publications including Cell Reports Medicine, Annals of Oncology, and JITC.
What is FFPE transcriptomics-based target identification?
FFPE transcriptomics-based target identification uses gene expression data from archival tumor tissues to discover, nominate, and prioritize therapeutic targets and biomarkers for downstream validation. It helps translate human tumor biology into ranked candidate targets supported by molecular evidence.
When is transcriptomics the right starting point for target identification?
Transcriptomics is a strong starting point when you want to explore human tumor biology in an unbiased or semi-targeted way, compare subtypes or clinical strata, or identify candidates before moving into tissue-level validation. It is particularly useful when the goal is nomination and prioritization rather than immediate confirmation of a preselected target.
How do you choose between Xenium, Visium HD, Chromium, and GeoMx?
The right platform depends on the balance you need between transcriptome breadth, spatial context, resolution, throughput, and budget. In practice, we recommend the technology based on the biological question and study design rather than the platform alone. Some projects require broad discovery across many samples, while others benefit more from spatially resolved analysis of tumor and microenvironment context.
How do you choose between whole-transcriptome and focused panels?
Whole-transcriptome approaches are best for broad, unbiased discovery and biological exploration. Focused panels are more efficient for hypothesis-driven questions, faster iteration, and tighter budget control. The right choice depends on how exploratory the project needs to be and how much transcriptomic breadth is required to support target nomination.
Can you combine spatial and non-spatial approaches in one target identification strategy?
Yes. Many projects benefit from combining broad discovery on one platform with spatial follow-up on another. For example, a cohort-scale transcriptomic screen can be used to nominate candidates, followed by spatial profiling to understand cell-type specificity, tissue architecture, or tumor microenvironment context before moving into validation.
What makes a target rank highly in your prioritization workflow?
A strong target typically combines several favorable signals rather than one metric alone. Depending on the project, we may prioritize candidates based on prevalence, specificity, biological relevance, association with clinical or pathological variables, druggability, and safety-related considerations. The weighting of these criteria is adapted to the indication and program objectives.
Can transcriptomics alone validate a target?
Not usually. Transcriptomics is highly valuable for discovery and prioritization, but full validation often requires orthogonal confirmation at the tissue or protein level. Once promising candidates are identified, the next step is often target expression profiling to assess prevalence, localization, heterogeneity, and normal tissue distribution.
When should a target identification study be followed by target expression profiling?
Target expression profiling is the natural next step when you need to confirm whether nominated targets are truly expressed in the relevant cells and tissue compartments, and whether that expression pattern supports downstream development. It is especially important when prevalence, localization, heterogeneity, or normal tissue distribution will influence go or no-go decisions.