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Updated: Sep 6, 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

A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.

Jiawen Chen¹, Weifang Liu¹, Tianyou Luo¹

¹Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Briefings in Bioinformatics

|June 26, 2022

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) technologies lack single-cell resolution, necessitating cell-type deconvolution. This review compares ST deconvolution methods, finding RCTD and stereoscope most accurate for analyzing gene expression in intact tissues.

Keywords:

cell-type deconvolution deep learning probabilistic modeling single-cell spatial transcriptomics

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Last Updated: Sep 6, 2025

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Published on: July 6, 2022

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) provides gene expression data with spatial information from intact tissues.
Current ST technologies do not achieve single-cell resolution, resulting in spot-level data representing multiple cells.
Accurate cell-type composition inference is crucial for downstream analysis of ST data.

Purpose of the Study:

To review and compare state-of-the-art spatial transcriptomics deconvolution methods.
To assess the performance of deconvolution tools using both simulated and real ST data.
To identify the most robust and accurate methods for cell-type decomposition in ST data.

Main Methods:

Constructed ST spots from single-cell ST data to evaluate 10 deconvolution methods.
Assessed method performance using ideal and non-ideal reference datasets.
Validated performance on spot- and bead-level ST data against matched single-cell ST data.

Main Results:

Compared 10 spatial transcriptomics deconvolution methods across various tissues and platforms.
Evaluated performance using simulated ST spots and real spot- and bead-level ST data.
Identified RCTD and stereoscope as the top-performing methods for cell-type inference.

Conclusions:

RCTD and stereoscope demonstrate superior accuracy and robustness in spatial transcriptomics deconvolution.
These methods are recommended for reliable cell-type composition analysis in ST data.
Accurate deconvolution is essential for leveraging the full potential of spatial transcriptomics.