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Updated: Aug 26, 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

AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics.

Jesper B Lund¹, Eric L Lindberg², Henrike Maatz^2,3

¹Digital Health & Machine Learning Research Group, Hasso Plattner Institut for Digital Engineering, Potsdam, Germany.

NAR Genomics and Bioinformatics

|October 13, 2022

Summary

This summary is machine-generated.

Researchers developed AntiSplodge, a new pipeline for spatial transcriptomics (ST) deconvolution. This tool accurately identifies cell types within tissues using synthetic data, outperforming existing methods.

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Last Updated: Aug 26, 2025

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) enables in situ analysis of cellular interactions within tissues.
Current ST measurements yield mixed mRNA profiles, necessitating cell type deconvolution.
Existing deconvolution tools are often complex and difficult to integrate.

Purpose of the Study:

To develop an effective and user-friendly pipeline for deconvoluting spatial transcriptomics data.
To improve the accuracy of cell type identification in complex tissue samples.
To provide a lightweight and efficient alternative to current deconvolution methods.

Main Methods:

Developed AntiSplodge, a feed-forward neural network-based pipeline.
Utilized synthetic ST profiles generated from single-cell (SC) RNA datasets.
Applied the pipeline to human heart and mouse brain ST data.

Main Results:

Successfully deconvoluted human heart ST profiles, verifying accuracy across time points.
Demonstrated accurate deconvolution of mouse brain ST data, with spot patterns aligning to tissue structures, particularly the hippocampus.
AntiSplodge exhibited superior performance compared to state-of-the-art deconvolution tools.

Conclusions:

AntiSplodge offers a simple, fast, and intuitive solution for spatial transcriptomics deconvolution.
The pipeline effectively identifies cell types in ST data by leveraging synthetic profiles.
AntiSplodge represents a significant advancement in analyzing complex tissue microenvironments.