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Spatial Transcriptomics

Mapping gene expression in situ to decode tissue architecture and cell–cell interactions.

mysimulator teamUpdated June 2026≈ 3 min read▶ Open the simulation

Technology Landscape

Capture-based (10x Visium, Slide-seq V2): spot-level resolution

Imaging-based (MERFISH, seqFISH, CosMx): subcellular resolution

In situ sequencing and hybrid methods

Sample Prep and QC

Preserve RNA integrity, optimize permeabilization, and include spatial controls. Record tissue thickness, fixation, and probe panels.

Computational Workflow

Preprocessing: background correction, alignment, and molecule counting

Normalization and batch correction

Spot deconvolution using scRNA-seq references

Spatial domain detection and differential expression

Ligand–receptor interaction analysis

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Integration with scRNA-seq

Use anchors or joint embedding; map cell types to spatial coordinates; validate with histology and marker panels.

Applications

Tumor microenvironment profiling

Developmental gradients and lineage tracing

Neuroscience: laminar organization

Inflammation and fibrosis mapping

Examples

Example 1: Visium Brain Slice

Run Space Ranger; map spots to histology.

Deconvolute with scRNA references; identify layers.

Validate with layer-specific markers.

Example 2: MERFISH Tumor Panel

Design a 500-gene panel including immune markers.

Segment cells; quantify transcripts; compute neighborhood statistics.

Infer interaction hotspots and therapeutic targets.

Frequently asked questions

How to choose between capture and imaging methods?

Balance resolution, throughput, target gene count, and instrument access.

How many cells per spot should I expect?

Typically 1–10 depending on platform and tissue; motivates deconvolution.

How to validate spatial domains?

Use histological markers, in situ hybridization, and known anatomy.

How to handle autofluorescence?

Apply spectral unmixing, select dyes accordingly, and use tissue clearing when compatible.

Which metrics indicate good data quality?

UMIs per spot/cell, genes detected, alignment rate, and spatial smoothness.

How to correct for tissue folding?

Register sections with control points; discard warped regions when necessary.

How to perform cell–cell interaction analysis?

Integrate ligand–receptor databases with proximity-based statistics.

How to combine multiple sections?

Register into a common coordinate space; account for batch effects.

What are common pitfalls?

Under-permeabilization, overexposure, incomplete probe hybridization, and poor registration.

How to report results?

Provide processing pipeline, parameters, probe lists, and QC summaries.

Try it live

Everything above runs in your browser — open Michaelis-Menten Kinetics and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Michaelis-Menten Kinetics simulation