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Updated: Aug 27, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Computational solutions for spatial transcriptomics.

Iivari Kleino¹, Paulina Frolovaitė¹, Tomi Suomi¹

¹Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland.

Computational and Structural Biotechnology Journal

|September 23, 2022

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) offers insights into gene expression within tissues. This review guides researchers in selecting appropriate ST platforms and computational tools for analyzing complex biological systems.

Keywords:

AOI, area of illumination BICCN, Brain Initiative Cell Census Network BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses Baysor, Bayesian Segmentation of Spatial Transcriptomics Data BinSpect, Binary Spatial Extraction CCC, cell–cell communication CCI, cell–cell interactions CNV, copy-number variation Computational biology DSP, digital spatial profiling DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing FA, factor analysis FFPE, formalin-fixed, paraffin-embedded FISH, fluorescence in situ hybridization FISSEQ, fluorescence in situ sequencing of RNA FOV, Field of view GRNs, gene regulation networks GSEA, gene set enrichment analysis GSVA, gene set variation analysis HDST, high definition spatial transcriptomics HMRF, hidden Markov random field ICG, interaction changed genes ISH, in situ hybridization ISS, in situ sequencing JSTA, Joint cell segmentation and cell type annotation KNN, k-nearest neighbor LCM, Laser Capture Microdissection LCM-seq, laser capture microdissection coupled with RNA sequencing LOH, loss of heterozygosity analysis MC, Molecular Cartography MERFISH, multiplexed error-robust FISH NMF (NNMF), Non-negative matrix factorization PCA, Principal Component Analysis PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing PL-lig, padlock ligation QC, quality control RNAseq, RNA sequencing ROI, region of interest SCENIC, Single-Cell rEgulatory Network Inference and Clustering SME, Spatial Morphological gene Expression normalization SPATA, SPAtial Transcriptomic Analysis ST Pipeline, Spatial Transcriptomics Pipeline ST, Spatial transcriptomics STARmap, spatially-resolved transcript amplicon readout mapping Single-cell analysis Spatial data analysis frameworks Spatial deconvolution Spatial transcriptomics TIVA, Transcriptome in Vivo Analysis TMA, tissue microarray TME, tumor micro environment UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction UMI, unique molecular identifier ZipSeq, zipcoded sequencing.scRNA-seq, single-cell RNA sequencing scvi-tools, single-cell variational inference tools seqFISH, sequential fluorescence in situ hybridization sequ-smFISH, sequential single-molecule fluorescent in situ hybridization smFISH, single molecule FISH t-SNE, t-distributed stochastic neighbor embedding

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Integrating gene expression with spatial organization is crucial for understanding biological systems.
Spatial transcriptomics (ST) platforms aim to provide this integrated data but face limitations in resolution, throughput, and transcriptome coverage.
Existing computational tools for single-cell RNA sequencing (scRNA-seq) are foundational but require adaptation for ST data.

Purpose of the Study:

To review current ST platforms and computational solutions for spatial transcriptomics research.
To guide researchers in selecting appropriate ST methods and analysis tools.
To summarize the strengths, limitations, and compatibility of available ST technologies and frameworks.

Main Methods:

Review of current spatial transcriptomics platforms and their technical specifications.
Analysis of computational approaches for spatial transcriptomics data, including modifications of scRNA-seq tools and deep learning methods.
Summary of data types, analysis steps, and available tools within ST analysis frameworks.

Main Results:

Current ST platforms have varying spatial resolutions, capture efficiencies, and throughput, impacting study design.
Computational ST analysis leverages scRNA-seq methodologies, with advancements incorporating spatial information and deep learning.
ST data analysis can reveal significant biological insights into spatial gene expression patterns, cell signaling, and tissue organization.

Conclusions:

Careful consideration of ST platform strengths/limitations and computational tool compatibility is essential for successful spatial transcriptomics studies.
The development of advanced computational solutions is key to extracting comprehensive biological information from spatially resolved transcriptomes.
This review provides a framework for researchers to navigate the evolving landscape of spatial transcriptomics technologies and analyses.