BioVenic supports veterinary spatial transcriptomics studies from pathology-led tissue selection and assay planning through region-of-interest strategy, spatial data analysis, and integrated reporting. Our research-use workflows help preserve tissue context while resolving localized molecular programs in tumors, inflammatory lesions, infection foci, and other heterogeneous animal tissues.
Pathology-guided spatial omics
Bulk RNA sequencing averages signals across a specimen and can obscure small but biologically meaningful regions. In veterinary tumors, inflamed organs, or infected tissues, a localized immune niche, invasive margin, necrotic zone, or pathogen-associated focus may drive the research question even when it represents only a small portion of the section.
BioVenic helps research teams plan animal tissue spatial omics around the pathology rather than treating histology and transcriptomics as separate outputs. We align tissue selection, morphology review, region annotation, assay feasibility, quality controls, spatial statistics, and interpretation so that molecular patterns can be evaluated in their original anatomical context.
Select an integrated program or a focused support module according to tissue availability, platform readiness, and the intended pathology question.
Review species, tissue type, preservation format, lesion distribution, RNA quality expectations, section thickness, replicate structure, and available normal or disease controls. A pilot can be prioritized when tissue is rare, mineralized, necrotic, or otherwise technically demanding.
Define regions of interest with reference to tissue architecture, lesion borders, stromal compartments, inflammatory aggregates, infection foci, and other histologic features. Annotation criteria are documented before comparative analysis to reduce retrospective selection bias.
Match whole-transcriptome or targeted spatial approaches to the biological question, tissue format, required resolution, analyte quality, and sample throughput. The plan includes controls, batch structure, capture strategy, sequencing depth assumptions, and predefined quality checkpoints.
Process expression matrices with spatial coordinates and image-derived annotations. Depending on the study, analysis may include quality filtering, normalization, dimensionality reduction, spatial domain detection, differential expression, pathway analysis, cell-type deconvolution, and neighborhood assessment.
Compare spatial molecular findings with H&E morphology, pathology annotations, experimental groups, and available immunohistochemistry or other omics results. This integration distinguishes a statistically detectable feature from a biologically coherent tissue pattern.
Rank candidate genes, pathways, cell neighborhoods, and regions for follow-up. Recommendations can connect discovery results to targeted RNA assays, veterinary IHC, tissue metabolomics, or broader animal omics studies without implying diagnostic validation or clinical utility.
Spatial results depend on upstream choices. BioVenic defines decision criteria before samples enter the analytical path, helping teams allocate limited tissue and select comparisons that are interpretable.
| Decision Area | Key Inputs | Planning Value |
|---|---|---|
| Tissue inclusion | Morphology, necrosis, lesion fraction, preservation, RNA quality | Reduces failure risk and uninformative capture areas |
| Region definition | Pathology question, anatomical landmarks, comparator regions | Links molecular contrasts to reproducible tissue features |
| Replication | Animal-level variation, section-level variation, cohort constraints | Separates biological patterns from local technical effects |
| Resolution and panel | Cellular scale, transcript breadth, target hypotheses, budget | Balances discovery breadth with spatial precision |
| Validation path | Marker specificity, reagent availability, remaining tissue | Creates a practical route from pattern to confirmation |
A pathology-led sequence keeps tissue context, analytical criteria, and downstream validation connected from project definition to reporting.
Set species, pathology question, tissue groups, contrasts, and research-use endpoints.
Review morphology, preservation, RNA quality, sectioning constraints, and capture suitability.
Define platform, controls, replicates, capture areas, sequencing plan, and QC thresholds.
Generate aligned image, coordinate, expression, and quality-control data layers.
Resolve domains, markers, pathways, cell composition, and tissue-neighborhood relationships.
Summarize evidence, limitations, prioritized candidates, and recommended confirmation studies.
Deliverables are configured to the selected assay and analytical modules, with clear links between tissue regions and molecular outputs.
Sample manifest, study design summary, pathology annotation plan, processing record, quality metrics, filtering rationale, and deviations that affect interpretation.
Tissue-aligned expression visualizations, spatial domains, prioritized marker patterns, group or ROI contrasts, and pathway-level summaries with appropriate statistical context.
When supported by data and reference resources, estimated cell composition, co-localization patterns, ligand–receptor hypotheses, or spatial neighborhood features.
Methods, figures, interpretation boundaries, candidate ranking, reusable result tables, and a practical recommendation for orthogonal follow-up or broader cohort testing.
Share species, tissue format, cohort structure, pathology question, and available section images for an initial feasibility discussion.
The figure shows H&E-stained mouse nasal sections paired with spatially selected bone, epithelial, and lymphoid regions, followed by region-specific gene-expression summaries. Morphology-defined capture areas recovered expected tissue markers and lower-abundance immune transcripts, illustrating how spatial transcriptomics can retain anatomical context that would be lost when a heterogeneous specimen is analyzed only as bulk RNA.
The study also highlights practical considerations directly relevant to veterinary pathology workflows: decalcification time, RNA preservation, section quality, morphology-based region selection, and orthogonal interpretation. BioVenic applies the same planning logic to animal tissues by qualifying specimens, defining regions and controls before analysis, integrating spatial outputs with pathology annotations, and prioritizing markers for IHC, ISH, qPCR, or additional omics confirmation.
Integrated planning helps convert complex animal tissue maps into defensible research decisions.
Tissue architecture guides sampling, contrasts, interpretation, and validation priorities.
Reference genomes, annotations, reagents, and tissue constraints are reviewed early.
Select focused modules without forcing an oversized analytical package.
Results connect spatial evidence to realistic orthogonal confirmation steps.
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