Targeted Spatial Multi-Omics Profiling Service
When candidate genes, variants, proteins, or host–microbe targets are already defined, broad discovery data may not provide the focused spatial evidence needed for validation. Our targeted spatial multi-omics profiling service measures approved targets directly in intact specimens and connects molecular signals to cells, tissue regions, and morphology.
What the service is designed to resolve:
- Where selected RNA, DNA-variant, and protein signals occur in tissue
- Which cells or microenvironments carry the target combination
- How target relationships change across groups, regions, or serial sections
- Whether discovery candidates retain their biological context during validation
Focused Spatial Evidence for Defined Biological Targets
This service is intended for hypothesis-driven studies in which the biological candidates are known but their tissue context remains unresolved. It can support configurable RNA, DNA or variant, protein, and host–microbe measurements, subject to target and specimen feasibility. The resulting data can connect target abundance or presence with cell identity, neighborhood composition, histology, and region-specific phenotypes.
Targeted RNA
Localize selected coding and noncoding transcripts, splice features, or expression signatures when compatible.
DNA / Variants
Evaluate approved loci or allele-specific signals within their tissue and cellular context.
Protein
Add selected protein markers to support cell phenotyping and spatial interpretation.
Host–Microbe
Map selected microbial targets alongside host features where assay design supports both.
Targeted profiling is not positioned as a replacement for unbiased discovery. Projects that still need candidate generation may begin with 10x Visium HD Spatial Transcriptomics or Stereo-seq Spatial Transcriptomics, followed by targeted spatial validation.
Configure the Readout Around the Primary Evidence Needed
Figure 2. Configurable readouts for focused spatial validation.
Start With the Decision, Not the Platform
We first define the biological claim the study must test, the target class, the required spatial scale, and the comparison groups. Panel size, target sequence, specimen preservation, morphology, and co-detection requirements are then reviewed together.
- Single-section mapping: determine whether targets co-localize with a defined cell type or tissue niche.
- Serial-section mapping: compare target patterns across matched levels or build a section-aligned three-dimensional view.
- Cross-modal validation: relate selected RNA signals to protein phenotypes or approved variant signals.
- Focused host–microbe profiling: test whether selected microbial signatures associate with local host responses.
Not every target type can be combined in every specimen. Final scope is established after feasibility review, and a pilot may be recommended for technically difficult panels.
Targeted Spatial Multi-Omics Service Workflow
- Research Question and Target Design
Define the claim, target classes, spatial scale, groups, controls, and validation criteria.
- Specimen Review and QC
Assess preservation, morphology, target integrity, dimensions, sectioning history, and metadata.
- Custom Target or Panel Configuration
Review annotations, sequence specificity, expected abundance, multiplex compatibility, and controls.
- In Situ Spatial Profiling
Apply the approved detection design while retaining tissue coordinates and morphology.
- Cell or Region Segmentation and Quantification
Assign signals to cells or defined regions and generate target-by-location measurements.
- Integrated Analysis and Delivery
Compare groups, evaluate co-localization, interpret tissue niches, and document outputs.
Figure 3. From target definition to cell- and region-level spatial interpretation.
Sample Types and Project Information
Fresh-frozen or OCT-embedded tissue, FFPE tissue, fresh tissue, cultured cells, and archived specimens can be reviewed. The quantities below are recommended starting amounts for project planning, not universal acceptance thresholds. Final requirements depend on target class, tissue area, preservation, multiplex level, and detection route.
| Sample Type | Recommended Starting Submission | Section / QC Reference |
|---|---|---|
| FFPE tissue | 1 block or 5–10 unstained sections, plus 1 adjacent H&E section | 4–5 µm sections are commonly used; fixation history, block age, morphology, and target integrity are reviewed |
| Fresh-frozen / OCT tissue | 1 intact block; reserve at least 3 serial sections for assay, morphology, and backup | 8–10 µm is a common planning range; RNA quality and freezing artifacts are assessed |
| Fresh tissue | 1 specimen per biological replicate in the approved preservation medium | Processing and sectioning route must be confirmed before collection |
| Cultured cells | At least 2 prepared cell-bearing slides or an agreed cell pellet per condition | Cell density, attachment, morphology, and target abundance are evaluated in a pilot |
| Study design | At least 3 biological replicates per group are recommended for group comparisons | Controls, region of interest, primary contrast, and batch allocation must be defined |
| Target list | 1 ranked table containing gene or locus ID, transcript/variant, modality, sequence information, and rationale | Positive and negative controls and expected abundance should be included where available |
Detection, Imaging, and Data Analysis
| Item | Recommended Configuration |
|---|---|
| Detection platform | High-resolution fluorescence imaging, whole-slide fluorescence scanning, confocal imaging, or cyclic in situ sequencing according to the approved panel |
| Optical resolution | Objective and z-stack settings are selected from the required cell, subcellular, or three-dimensional endpoint |
| Sequencing | Not required for imaging-only panels; sequencing-assisted designs receive a project-specific instrument and read configuration |
| Primary outputs | Target-positive molecule counts, cell or region labels, x–y coordinates, segmentation masks, co-localization statistics, and group comparisons |
| Quality control | Signal-to-background review, decoding rate, segmentation quality, control-target behavior, and replicate concordance |
Spatial Quantification and Integrated Analysis
Analysis is matched to the approved readout and research question. Signal detection is reviewed before cell or region assignment, preventing weak technical performance from being mistaken for biological absence.
Core Analysis
- Image and signal quality review
- Cell or region segmentation
- Target-by-location quantification
- Spatial distribution and co-localization
- Group and region comparisons
- Annotated tissue maps
Optional Integration
- RNA–protein relationship analysis
- Variant-to-phenotype spatial mapping
- Host–microbe neighborhood analysis
- Serial-section alignment and three-dimensional reconstruction
- Integration with discovery or single-cell datasets
- Custom hypothesis tests
Figure 4. Readout-specific analysis branches connect signal QC and spatial quantification with RNA–protein, variant–phenotype, host–microbe, or serial-section interpretation.
Broader computational support is available through our Spatial Transcriptomics Data Analysis Service.
Deliverables
Figure 5. Representative maps and integrated comparisons; final outputs depend on project scope.
- QC summary and assay-performance documentation
- Processed target-by-cell or target-by-region matrices
- Segmentation masks and annotated spatial coordinates
- High-resolution target and morphology maps
- Group, region, and co-localization results
- Methods, parameters, figures, tables, and analysis report
Research Questions This Service Can Help You Address
Biomarker Validation
Discovery studies often produce a short list of candidate biomarkers without showing which cells or tissue compartments generate the signal. Targeted spatial profiling localizes those markers in intact tissue, helping you confirm whether an apparent association reflects the intended biology rather than a change in tissue composition.
Tumor Microenvironment
For studies of treatment response or immune escape, the service maps selected tumor, immune, stromal, and protein markers across local cellular neighborhoods. The resulting co-occurrence and boundary-specific evidence helps you identify niches associated with response, resistance, or immune exclusion. See our Tumor Microenvironment Spatial Multi-Omics Solutions.
Neuroscience and Development
Neuroscience and developmental studies may need to determine whether a candidate pathway acts within a specific layer, anatomical region, or developmental stage. Targeted measurements place pathway markers within tissue architecture, helping you compare spatially restricted cell states and define where the proposed mechanism is active.
Host–Microbe Biology
To investigate whether selected microbes influence nearby host cells, compatible dual-target designs relate microbial localization to local immune or epithelial programs. This spatial evidence helps distinguish proximity-associated host responses from changes observed only at the whole-sample level.
Drug and Perturbation Studies
Drug and perturbation projects can use targeted spatial readouts to test whether a proposed mechanism changes in the intended cell population. Mapping the same targets across exposed and control groups also shows whether the effect reaches resistant or protected niches, supporting mechanism evaluation and follow-up study design.
Serial-Section Reconstruction
Processes that extend through tissue depth cannot always be interpreted from a single section. Aligning targeted measurements across serial sections reveals whether target-rich niches persist, connect, or change through the specimen, helping you separate isolated two-dimensional patterns from spatially continuous structures.
Case Study: Spatial Mapping of Cancer Clones
Source: Spatial genomics maps the structure, nature and evolution of cancer clones
Figure 6. Illustrative reconstruction of how targeted variants, clone territories, and matched phenotypes can be interpreted together; not reproduced from the source paper.
Background
Genomic sequencing can identify tumor subclones, but dissociated or bulk measurements do not show where those clones grow or how they relate to histology and the local microenvironment. The study tested whether clone-defining variants could be mapped quantitatively across intact tumor sections and interpreted with spatial phenotypes.
Methods
Lomakin and colleagues analyzed 8 fresh-frozen tissue blocks from 2 multifocal primary breast cancers. Whole-genome sequencing first defined subclonal mutations. Base-specific in situ sequencing probes were then designed for mutant and wild-type alleles, and the clone maps were integrated with targeted spatial transcriptomics, immunohistochemistry, histology, and laser-capture microdissection sequencing.
- 51 alleles, including 25 single-base substitutions with matched wild-type alleles and an amplified FGFR1 locus, were used to report branches of the tumor phylogeny.
- A spatial model combined decoded signals, local cell density, variable probe efficiency, and bulk variant allele fractions to infer continuous clone-composition maps.
- Clone territories were aligned with 91-gene oncology and 62-gene immune targeted transcript panels, H&E morphology, and protein staining.
- Technical reproducibility was assessed across adjacent sections, and spatial variant estimates were compared with microdissection-based whole-genome sequencing.
Results
The workflow mapped 2–4 subclones per primary breast cancer across whole-tumor sections. Approximately 97% of detected in situ signals were converted to feasible barcodes. Replicate spatial variant measurements showed Pearson correlations of 0.76–0.93. After spatial modeling, variant allele fractions correlated with microdissection sequencing at approximately 0.90. The maps showed that some clones crossed ductal carcinoma in situ and invasive boundaries, while others occupied distinct histological territories. PTEN-mutant clone regions also showed increased Ki-67 staining, and 12 of 91 measured transcripts differed between two invasive subclones.
Conclusion
The study showed that targeted in situ variant detection, spatial modeling, and matched RNA or protein measurements can distinguish clone territories and connect genetic evolution with tissue phenotype. In a comparable service project, mutant-versus-wild-type maps, target-by-cell or target-by-region matrices, co-localization results, and clone-associated neighborhood comparisons can test whether a candidate event is confined to one compartment, precedes a histological transition, or associates with a specific cellular niche.
When Targeted Spatial Profiling Is the Right Choice
Targeted spatial profiling is most useful after candidate discovery, when the study needs focused evidence showing where selected molecules occur and how they relate to defined cells, regions, or tissue boundaries.
Best for
- Predefined RNA, variant, protein, or microbial targets
- Spatial validation in intact tissue
- Focused group, region, or neighborhood comparisons
Consider another approach when
If targets remain uncertain or whole-transcriptome discovery is the primary goal, begin with 10x Visium HD Spatial Transcriptomics or Stereo-seq Spatial Transcriptomics before designing a focused panel.
Frequently Asked Questions
References
- Lomakin A, Svedlund J, Strell C, et al. Spatial genomics maps the structure, nature and evolution of cancer clones. Nature. 2022;611:594–602.
- He S, Bhatt R, Brown C, et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nature Biotechnology. 2022;40:1794–1806.
- Chen KH, Boettiger AN, Moffitt JR, et al. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348:aaa6090.
- Eng CL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature. 2019;568:235–239.