Neuro Targeted Panel In Situ Spatial Multiomics (RNA + Protein + Morphology)
This Spatial Omics service delivers in situ spatial multiomics by combining RNA–protein co-detection with morphological profiling (Cell Painting) in one specimen. You receive localization-aware, cell-resolved evidence to connect pathway activity to phenotype with fewer follow-up assays.
What we help you solve
- Results that do not align when RNA, protein, and imaging are run separately
- Missed localization patterns that explain function and mechanism
- Longer validation cycles caused by fragmented workflows
Why CD Genomics
- A targeted, multi-modal workflow keeps RNA, protein, and morphology features aligned for clearer interpretation.
- Localization-aware outputs add context beyond "up/down," strengthening mechanism hypotheses.
- A neuro-focused targeted panel accelerates project start, with expansion options when specific pathways matter.
Why In Situ Spatial Multiomics for Neuro Projects
Spatial omics preserves where molecular signals appear, not just how much is present. In this service, RNA, protein markers, and morphology features are captured in the same specimen, so neuro-relevant phenotypes can be interpreted with localization context.
Neurobiology often depends on pattern and location. A pathway marker can be meaningful only if it localizes to the right compartment or co-occurs with a specific morphology change. When assays are split across platforms, those relationships can be lost or misassigned.
This targeted panel approach is designed to reduce interpretation drift. By keeping modalities aligned, you can compare conditions with higher confidence and spend less time reconciling conflicting readouts.
Common questions this service helps answer
- Which molecular programs explain a neurite-like morphology shift or stress phenotype?
- Do protein-state markers support the same conclusion as RNA markers?
- Are signals diffuse, punctate, or compartment-enriched in the cell model?
What You Can Measure: Targeted RNA + Protein + Cell Painting Morphology
You will measure targeted RNA markers, protein targets, and Cell Painting morphology features, with localization-aware outputs that retain where signals appear within the specimen.
Targeted RNA markers (neuro-focused)
RNA targets are selected to support hypothesis-driven neuro studies. This keeps the dataset interpretable and reduces time spent filtering non-informative signals. It is well-suited for comparing conditions, timepoints, or perturbations in cell models.
Protein targets (including pathway-state markers where applicable)
Protein markers provide functional confirmation when RNA changes are ambiguous. Where pathway-state targets are included, you can evaluate signaling activation alongside phenotype features, improving confidence in mechanism interpretation.
Morphological profiling (Cell Painting)
Cell Painting-style morphology features quantify shape and organelle patterns. These features help stratify phenotype groups and connect observable changes to molecular programs, without building a separate imaging-only workflow.
Localization-aware outputs
Localization adds context to interpretation. It helps distinguish patterns such as compartment enrichment versus diffuse signal, which can explain functional differences even when total abundance looks similar.
Neuro Targeted Panel Strategy
We use a neuro-focused targeted panel to keep results interpretable and comparable across conditions. Below is a public summary of coverage and representative examples.
Public target coverage summary (what the panel is designed to capture)
This panel is designed to support neuro-relevant phenotype interpretation, including:
- Neuronal identity and maturation programs
- Synaptic function and connectivity-related pathways
- Neuroinflammation and glial-response signaling
- Stress response, survival, and injury-like states
- Core signaling nodes linked to morphology change
Representative examples (illustrative only)
The examples below indicate the types of targets included. They are not exhaustive.
- RNA examples: identity markers • synapse-associated markers • inflammation programs • stress-response genes
- Protein examples: pathway-state markers • lineage markers • immune-response markers • structural markers
Full Neuro Panel RNA & Protein target list is available upon request—please to receive the complete table.
Target availability may vary by configuration; the final target set is confirmed at project kickoff.
Workflow
We run an end-to-end in situ spatial omics workflow that keeps RNA, protein, morphology, and localization signals aligned in the same specimen, then deliver analysis-ready outputs with QC documentation.
- Study design & panel plan
Define conditions, endpoints, and marker strategy to match your neuro project goals.
- Sample intake & readiness checks
Confirm specimen preparation supports morphology preservation, clean imaging, and reliable localization patterns.
- In situ run + imaging capture
Generate RNA/protein signals in context and capture morphology and spatial patterns in the same pass.
- Segmentation & quantification
Convert images into structured datasets: RNA and protein feature tables, morphology features, and spatial coordinates/localization summaries.
- QC review & delivery package
Provide final matrices and spatial exports with a concise QC summary for reproducible comparisons.
Deliverables: What You Receive
You receive aligned RNA and protein feature tables, Cell Painting morphology features, and localization-aware spatial exports from the same specimen, plus a concise QC summary.
Core deliverables
- RNA feature matrix (cell-level): Target-level measurements organized in an analysis-ready table for condition comparisons.
- Protein feature matrix (cell-level): Marker-level measurements matched to the same cell identifiers as the RNA table.
- Morphology feature table (Cell Painting): Quantitative descriptors of shape and organelle patterns to support phenotype stratification.
Spatial context exports
- Cell coordinates + localization summaries: Files that connect each cell's features to location and signal distribution patterns.
- Representative visual overlays: Image exports to review signal quality and spatial patterning at a glance.
Quality documentation
- QC summary report: Key run and signal metrics to assess reliability before deep analysis.
Data Analysis: From Phenotype Groups to Pathway Insights
We help you translate spatial omics outputs into interpretable findings by linking phenotype patterns to RNA and protein markers, with localization context considered during interpretation.
What the analysis is designed to answer
- Which phenotype groups are present?
Use morphology features to stratify cells into interpretable phenotype clusters.
- Which markers explain those phenotypes?
Identify RNA/protein patterns associated with each phenotype group and condition.
- How do conditions differ?
Quantify shifts across treatments or timepoints using consistent identifiers and QC-aware filters.
- Do localization patterns support the mechanism?
Highlight compartment-enriched or punctate patterns that clarify functional differences.
Optional interpretation support
If you want a faster path to conclusions, we can provide a brief interpretation session focused on: key phenotype groups, priority markers, and recommended follow-up experiments.
Sample Types & Preparation
This service is designed for cell-based specimens where morphology and localization patterns can be preserved during in situ processing. Consistent preparation improves segmentation and reduces background artifacts.
Commonly supported formats
- Adherent cell models (imaging-compatible surfaces)
- Suspension cell models prepared for imaging workflows
- Co-culture systems (marker-guided interpretation)
Preparation essentials
- Keep morphology stable before fixation
- Use appropriate cell density for segmentation
- Standardize timing across conditions
- Minimize background with clean handling
Method Selection: Is This the Right Spatial Omics Approach?
Choose this service when you need targeted RNA and protein markers interpreted alongside morphology features, with localization-aware context preserved. Consider other approaches when your priority is broad discovery, tissue architecture, or imaging-only throughput.
Best fit for this service
- You need a targeted panel that stays interpretable across conditions.
- Your conclusions depend on phenotype-to-pathway mapping.
- Localization patterns are part of the biology or mechanism.
When to consider alternatives
- Discovery-first transcript coverage is the main goal.
- You need tissue section architecture and regional context.
- You want high-content imaging only without molecular layers.
Quick comparison (neutral)
| Approach | Best used for | Key limitation |
|---|---|---|
| This in situ spatial multiomics service | Targeted mechanism and phenotype mapping in cell models | Targeted panels prioritise interpretability over breadth |
| Sequencing-forward transcript surveys | Broad expression discovery | Limited morphology and localization alignment without extra assays |
| Tissue-section spatial omics | Regional biology in sections | Not optimised for cell-model phenotyping workflows |
| Imaging-only high-content profiling | Rapid phenotype screening | Lacks direct RNA/protein evidence |
Related services: Spatial Transcriptomics Services • Spatial Proteomics Services
FAQs
References
- Bray, Mark-Anthony, et al. "Cell Painting, a High-Content Image-Based Assay for Morphological Profiling Using Multiplexed Fluorescent Dyes." Nature Protocols, vol. 11, 2016, pp. 1757–1774.
- Eng, Chee-Huat Linus, et al. "Transcriptome-Scale Super-Resolved Imaging in Tissues by RNA seqFISH+." Nature, vol. 568, no. 7751, 2019, pp. 235–239.
- Chen, Kok Hao, et al. "RNA Imaging. Spatially Resolved, Highly Multiplexed RNA Profiling in Single Cells." Science, vol. 348, no. 6233, 2015, article aaa6090.
- Liu, Yang, et al. "High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue." Cell, vol. 183, no. 6, 2020, pp. 1665–1681.e18.
- Giesen, C., et al. "Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry." Nature Methods, vol. 11, 2014, pp. 417–422.
