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Related Experiment Video

Updated: Jun 5, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

SpatialArtifacts: a computational framework for tissue artifact detection in spatial transcriptomics data.

Jiali Harriet He1, Jacqueline R Thompson2, Michael Totty2

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.

Biorxiv : the Preprint Server for Biology
|June 4, 2026
PubMed
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This summary is machine-generated.

SpatialArtifacts is a new framework to identify and classify technical artifacts in spatial transcriptomics data. It uses outlier detection and morphology operations to improve data quality for better biological insights.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics data often contain technical artifacts like dry patches and uneven coverage.
  • These artifacts, particularly at tissue borders, lead to low unique molecular identifier (UMI) counts and can be difficult to detect with current methods.

Purpose of the Study:

  • To introduce SpatialArtifacts, a novel computational framework for identifying and classifying spatial artifacts in transcriptomics data.
  • To provide a robust method for distinguishing true biological signals from technical noise in spatial datasets.

Main Methods:

  • Employs median absolute deviation (MAD)-based outlier detection combined with mathematical morphology operations.
  • Utilizes focal operations (fill, outline, star-pattern connectivity) to identify low-quality spots while preserving biological domains.

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Published on: October 31, 2025

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Last Updated: Jun 5, 2026

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Published on: July 6, 2022

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

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Published on: October 31, 2025

  • Implements a hierarchical classification system to differentiate artifact types (edge/interior, large/small).

    • Main Results:

    • Successfully identified and classified spatially contiguous tissue artifacts across diverse human tissues (hippocampus, prefrontal cortex, colorectal cancer).
    • Demonstrated effectiveness on 10x Genomics Visium and VisiumHD platforms.
    • The method effectively distinguishes artifacts from genuine biological regions.

    Conclusions:

    • SpatialArtifacts offers a reliable solution for addressing technical artifacts in spatial transcriptomics.
    • The framework enables improved data quality, facilitating more accurate downstream analysis and interpretation.
    • The SpatialArtifacts package is publicly available for use in the research community.