Spatial Transcriptomics · 10x Visium & CytAssist

Keep the map in your transcriptome.

Dissociated single-cell RNA-seq answers "which cells are present" but throws away the tissue architecture that defines biology — immune niches, tumour margins, developmental gradients. Spatial transcriptome sequencing measures gene expression across intact tissue sections, keeping every expression measurement anchored to its physical location.

Plan Your Project → Learn the Basics
Whole transcriptome
profiling
FFPE & fresh-frozen
compatible
CytAssist
for standard slides
Full spatial analysis
pipeline
About the Service

Spatial Transcriptome Sequencing at Nucleome Informatics

Spatial transcriptome sequencing captures gene expression across an intact tissue section while recording the two-dimensional position of every measurement. Nucleome Informatics offers the 10x Genomics Visium platform family:

  • Spatially-resolved gene expression matrix with tissue coordinates and image registration
  • H&E image (FFPE/CytAssist workflows) aligned to expression data
  • Clustering and spatial domain identification (Seurat/Scanpy; Squidpy/Giotto)
  • Cell-type deconvolution of spots using a single-cell reference (RCTD, cell2location, or Tangram)
  • Spatially-aware differential expression and ligand–receptor interaction analysis (COMMOT/MISTy)
  • Publication-ready figures: spatial feature plots, domain maps, interaction networks

Why spatial matters

  • Architecture is biology: tumour margins, immune niches, and epithelial–stromal interfaces are spatial phenomena invisible to dissociated methods.
  • FFPE archives unlock: CytAssist brings spatial transcriptomics to retrospective cohorts with clinical annotation and follow-up data.
  • Morphology + expression: H&E and expression data are co-registered, letting pathologist annotation guide molecular analysis.
Learning Path

From first principles to expert-level analysis

New here? Start with Foundations. Planning a study or reviewing results? Jump to Practitioner or Expert. Each level builds on the one before it, so you can read this page as a short course or dip in where you need to.

Start here if you are new to this service — six ideas that explain what it is and why it matters.

LEVEL 1

What is spatial transcriptomics?

A method that measures gene expression across a tissue section while keeping the physical position of every measurement — a molecular map layered on the tissue image.

LEVEL 1

How is it different from single-cell RNA-seq?

scRNA-seq dissociates the tissue first, losing location. Spatial methods keep the tissue intact. Spot-based methods measure groups of cells per spot; imaging-based methods measure defined gene panels per cell.

LEVEL 1

What tissue types work?

Fresh-frozen tissue works with standard Visium; FFPE tissue (the most common pathology archive format) works with CytAssist. Both deliver whole-transcriptome data.

LEVEL 1

What is a "spot"?

The unit of capture — a barcoded area on the slide that collects mRNA from the cells above it. Each spot may contain one to several cells; deconvolution estimates the cell-type mix per spot.

LEVEL 1

What does the output look like?

A gene-by-spot expression matrix with coordinates, plus the tissue image. Analysis reveals clusters of similar expression ("spatial domains") that often correspond to anatomical structures.

LEVEL 1

What samples do I send?

Fixed or frozen tissue blocks or sections. Section quality (RNA integrity, morphology) is the main determinant of success — we provide guidance and QC before sequencing.

For scientists planning a project or reading a report: how each step works and how it can mislead.

LEVEL 2

Tissue preparation

Fresh-frozen sections are placed on capture slides and fixed/permeabilised on-slide. FFPE sections are H&E-stained, imaged, and processed through CytAssist, which transfers released mRNA to the capture slide beneath.

LEVEL 2

Library construction and sequencing

Captured mRNA is reverse-transcribed with spatial barcodes, library-constructed, and sequenced on Illumina instruments (NovaSeq 6000/X). Depth requirements depend on tissue complexity and analysis goals.

LEVEL 2

Alignment and counting

Space Ranger aligns reads, assigns UMIs to spots, and registers the tissue image. Outputs include the filtered/unfiltered matrices and image alignments.

LEVEL 2

Clustering and spatial domains

Standard scRNA-style clustering (Seurat/Scanpy) identifies transcriptional states; spatial statistics (Squidpy/Giotto) detect spatially coherent domains and neighbourhood relationships.

LEVEL 2

Deconvolution

Spot-level mixtures are resolved with reference-based methods (RCTD, cell2location) or reference-free approaches where a good single-cell atlas is unavailable — with honest caveats about deconvolution accuracy.

LEVEL 2

Ligand–receptor and neighbourhood analysis

COMMOT/MISTy-style analyses infer cell–cell communication from spatial co-localisation of ligand and receptor expression — the layer that makes spatial data mechanistically interpretable.

For study design: the decisions that determine whether the data answers your question.

LEVEL 3

Resolution vs coverage trade-offs

Spot-level Visium maximises transcriptome breadth; high-resolution configurations trade cost and throughput for finer spatial detail. Define the biological question first, then choose.

LEVEL 3

Reference atlas quality

Deconvolution is only as good as the single-cell reference. Where none exists, consider generating a matched scRNA-seq reference from adjacent tissue — we can run both.

LEVEL 3

Batch and slide effects

Tissue sections processed on different slides carry batch effects. Randomise conditions across slides, include replicate sections, and plan for integration analysis (Harmony/Seurat integration adapted for spatial data).

LEVEL 3

FFPE RNA quality

FFPE blocks vary in RNA quality. We QC sections before committing to full runs and report degradation metrics honestly.

LEVEL 3

Longitudinal and multi-section designs

Serial sections enable 3D reconstruction and multi-omics layering (spatial transcriptomics + matched scRNA-seq + bulk omics on the same specimen).

LEVEL 3

Reporting standards

We document capture chemistry, slide lot, alignment parameters, and analysis versions — following emerging spatial-omics reporting norms (e.g. STorms-style checklists).

Spot
the barcoded capture area on a Visium slide.
CytAssist
10x instrument transferring mRNA from an FFPE section to a capture slide.
Spatial domain
a contiguous tissue region with coherent expression identity.
Deconvolution
estimating per-spot cell-type composition from expression.
UMI
unique molecular identifier; enables molecule counting.
H&E
haematoxylin and eosin; the standard pathology tissue stain.
The Science

How it works, in two figures

The Visium platform and CytAssist workflows are described in 10x Genomics technical notes; spatial domain detection, deconvolution, and interaction-inference methods are established in the primary literature (RCTD, cell2location, Tangram, COMMOT, MISTy). See references below.

Figure 1 Dissociation vs spatial. schematic
Dissociated single-cell RNA-seq Spatial transcriptomics Tissue dissociated before capture Spatial architecture lost Cell identities without locations Expression measured in intact tissue Spatial domains and niches preserved H&E morphology co-registered
Figure 1 | Dissociation vs spatial. (Illustrative schematic, not sample data.)
Figure 2 Workflow. schematic
Tissue section FFPE: H&E + CytAssist transfer Visium capture slide NovaSeq sequencing Space Ranger processing Domains, deconvolution & interactions
Figure 2 | Workflow. (Illustrative schematic, not sample data.)
Technology Highlights

Built for the questions you are asking

The platforms, pipelines and standards behind this service.

10x Visium capture slides

Thousands of barcoded capture spots (~55 µm) arrayed across the tissue section, each measuring the whole transcriptome above it.

CytAssist for FFPE

Transfers mRNA from a standard glass-slide FFPE section to the capture slide — compatible with routine pathology archives and standard H&E staining.

Whole-transcriptome assays

No pre-selected gene panel: the full transcriptome is measured, enabling unbiased discovery and post-hoc focus on any gene.

Image–expression co-registration

H&E morphology is aligned to the expression matrix, supporting pathologist-guided region analysis.

Squidpy / Giotto spatial analysis

Spatial statistics (Moran's I, neighbourhood enrichment), spatial domain detection, and tissue-graph analyses beyond standard clustering.

Deconvolution and interaction mapping

RCTD/cell2location/Tangram deconvolve spot-level data with single-cell references; COMMOT/MISTy infer spatial ligand–receptor communication. ---

Scope Note

What this service does and does not cover

Scope note. Spot-based spatial transcriptomics measures expression at spot resolution (multiple cells per spot in standard Visium); single-cell spatial resolution requires high-resolution configurations or imaging-based platforms with targeted gene panels. Deconvolution estimates are model-dependent and reported with caveats. This service covers RNA; spatial protein and multi-omic modalities are discussed case by case.---
Why Nucleome

Service highlights

What you can expect from our team in Hyderabad.

FFPE-compatible

CytAssist workflows open pathology archives to spatial transcriptomics.

Whole transcriptome

No gene-panel bias — discover first, focus later.

Full analysis stack

Seurat, Scanpy, Squidpy, Giotto, RCTD, cell2location, COMMOT — analysis included, not optional.

Morphology co-registered

H&E images aligned to expression for pathologist-guided analysis.

India-based, end-to-end

From tissue QC to publication figures in one Hyderabad laboratory.

Consultative design

Platform and resolution chosen for your question, with honest trade-off discussion. ---

Questions

Frequently asked, in plain language

From the basics to the finer points of design and interpretation.

basicWhat is the difference between Visium and imaging-based spatial methods?
Visium measures the whole transcriptome at spot resolution; imaging methods (e.g. Xenium) measure targeted gene panels at single-cell/sub-cellular resolution. Choose by whether you need breadth (Visium) or per-cell precision on known genes (imaging).
basicCan I use FFPE tissue?
Yes — the CytAssist workflow is designed for standard FFPE sections, including archived pathology blocks.
basicHow much tissue do I need?
Typically one to several sections per sample, plus QC sections. We provide tissue submission guidance at intake.
intermediateHow is the data analysed?
Space Ranger for processing; Seurat/Scanpy for clustering; Squidpy/Giotto for spatial statistics; RCTD/cell2location for deconvolution; COMMOT/MISTy for interaction analysis. All included in the service.
intermediateCan you deconvolve spots into cell types?
Yes, given a suitable single-cell reference. If none exists, we can generate a matched scRNA-seq reference from adjacent tissue.
intermediateWhat sequencing depth is required?
It depends on tissue complexity and analysis goals; we scope depth during project design based on published Visium benchmarks.
advancedCan I do 3D reconstruction from serial sections?
Yes — serial sections with registration support 3D domain reconstruction; plan section spacing and registration strategy at design stage.
advancedHow do I handle batch effects across slides?
Randomise conditions across slides, include replicates, and use spatial-aware integration methods. We advise on design and run the integration.
advancedCan spatial data be integrated with scRNA-seq and bulk omics?
Yes — matched-specimen integration (spatial + single-cell + bulk) is one of the strongest study designs; we support the full integration analysis.
advancedWhat are the limitations of deconvolution?
Deconvolution is reference-dependent and model-based; estimates carry uncertainty. We report confidence and avoid over-interpretation of spot-level cell-type calls.
References

Key literature & resources

Selected sources behind the methods and reference values on this page. Figures labelled "schematic" or "illustrative" are generated for this page, not client data.

  1. Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):66–71.
  2. 10x Genomics. Visium and CytAssist technical notes and user guides. 10x Genomics documentation, 2022–2025.
  3. Cable DM, et al. Robust decomposition of cell type mixtures in spatial transcriptomics (RCTD). Nature Biotechnology. 2022;40(4):517–526.
  4. Kleshchevnikov V, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology. 2022;40(5):661–671.
  5. Biancalani T, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nature Methods. 2021;18(11):1462–1470.
  6. Longo SK, et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics (MISTy). Nature Reviews Genetics. 2021;22(10):627–644.
  7. Cang Z, et al. Screening cell–cell communication in spatial transcriptomics via collective optimal transport (COMMOT). Nature Methods. 2023;20(2):218–228.
  8. Marconato L, et al. Spatially resolved transcriptomics: challenges and perspectives. Nature Reviews Genetics. 2025 (spatial data analysis benchmarks, Squidpy: Palla G, et al. Nature Methods. 2022;19(2):171–178).