A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses

Summary

This protocol presents a reproducible workflow for analyzing spatial transcriptomics data, guiding users from public data acquisition and Seurat-based quality control through integration, spatial feature detection, cell-type deconvolution, region-of-interest annotation, and cell–cell communication analysis, with practical checkpoints that support transparent execution.

Abstract

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Introduction

Spatial transcriptomics (ST) is a transformative family of technologies that measures genome-wide gene expression while retaining the spatial coordinates of messenger RNA (mRNA) molecules within tissue sections. ST methods include sequencing-based approaches that use position-barcoded arrays and in situ imaging approaches that map transcriptional signals within intact tissue microenvironments^1,2. By preserving spatial context, ST enables analysis of tissue architecture, cellular neighborhood organization, cell-cell communication, and microenvironment-associated biological processes that cannot be full....

Protocol

All biological datasets analyzed in this protocol are publicly available and used strictly for demonstration. Specific data accessions and source repositories are provided in the relevant steps. The original datasets were generated by the original investigators in compliance with institutional ethical guidelines applicable to each source study. See the Table of Materials to verify all required software and R-package versions.

Hardware requirements: The computational memory required for this workflow scales with the number of samples and spots analyzed. For a typical spatial transcriptomics dataset (e.g., approximately 3,000....

Results

Workflow implementation and data integration illustrate major tissue features

The computational workflow was applied to mouse colon spatial transcriptomics data to illustrate the expected outputs across the analytical stages. As depicted in the workflow schematic (Figure 1), the pipeline began with data acquisition and quality control, where spatial feature plots delineated tissue boundaries (Figure 2A,B

Discussion

This protocol provides a comprehensive computational workflow for spatial transcriptomics data analysis that balances analytical depth with practical accessibility. The step-by-step approach guides researchers through the complete analytical pipeline, from initial data acquisition to advanced spatial analyses, while emphasizing critical decision points and potential pitfalls.

Several steps in the protocol warrant particular attention due to their impact on downstream results. The quality contr.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ggplot2	Posit Software, PBC	v4.0.0(CRAN)	Advanced data visualization
Giotto	Dries Lab	v4.2.2 (GitHub)	Spatial network and cell-cell communication analysis
patchwork	Thomas Lin Pedersen	v1.3.2 (CRAN)	Plot composition and arrangement
R software	R Foundation for Statistical Computing	v4.4.3	Core execution environment (macOS aarch64)
scater	Davis McCarthy et al.	v1.34.1 (Bioconductor)	Single-cell quality control and visualization
scran	Aaron Lun et al.	v1.34.0 (Bioconductor)	Single-cell variance modeling and marker detection
Select Spatial Spots (Custom Python Tool)	LeafLight	v1.0.0 (GitHub)	Interactive spatial region-of-interest (ROI) selection (https://github.com/LeafLight/SelectSpatialSpots)
Seurat	Satija Lab	v5.3.0 (CRAN)	Spatial data preprocessing, integration, and clustering
SeuratObject	Satija Lab	v5.2.0 (CRAN)	Data structures for single-cell and spatial data
SingleCellExperiment	Bioconductor Core Team	v1.28.1 (Bioconductor)	Standardized data container for scRNA-seq
SPOTlight	Marc Elosua-Bayes et al.	v1.10.0 (Bioconductor)	Reference-guided spatial deconvolution
Stdeconvolve	Jean Fan Lab	v1.3.2 (Bioconductor)	Unsupervised latent topic modeling
tidyverse	Posit Software, PBC	v2.0.0 (CRAN)	Core data manipulation and formatting suite