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DNA Microarrays02:34

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Artifacts in spatial transcriptomics data: their detection, importance, prevalence, and prevention.

Erich Kummerfeld1, Leland Williams1, Yinzhao Wang1

  • 1Institute for Health Informatics, University of Minnesota, 8-101 Phillips-Wangensteen Building, 516 Delaware St. SE, Minneapolis, MN 55455, United States.

Briefings in Bioinformatics
|August 1, 2025
PubMed
Summary
This summary is machine-generated.

Data artifacts can skew spatial transcriptomics results. We developed Border, Location, and edge Artifact DEtection (BLADE), an automated method to identify and remove common artifacts, ensuring more reliable transcriptomics data.

Keywords:
Visiumcellular senescencemodelingquality controlspatial transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics technologies enable gene expression analysis within tissue context.
  • Data artifacts, such as border effects and batch malfunctions, can compromise the accuracy of spatial transcriptomics findings.
  • Existing quality control methods may not adequately address these specific artifact types across different platforms.

Purpose of the Study:

  • To develop and validate an automated, cross-platform method for detecting and removing data artifacts in spatial transcriptomics.
  • To address common artifacts including border effects, tissue edge effects, and location batch malfunctions.
  • To improve the reliability and reproducibility of spatial transcriptomics data analysis.

Main Methods:

  • Development of Border, Location, and edge Artifact DEtection (BLADE), a suite of automated statistical methods.
  • Application of BLADE to diverse spatial transcriptomics platforms, including Visium and CosMx.
  • Evaluation of BLADE using a library of 37 10x Visium samples from human and mouse liver and adipose tissues.

Main Results:

  • BLADE successfully detected and enabled the removal of border effects, tissue edge effects, and location batch malfunctions.
  • Artifacts were found to be prevalent and significantly impactful in the evaluated spatial transcriptomics samples.
  • The study demonstrated the critical need for robust artifact detection in spatial transcriptomics quality control.

Conclusions:

  • BLADE provides a novel, automated solution for identifying and mitigating common artifacts in spatial transcriptomics data.
  • The developed software is cross-platform compatible and publicly available, facilitating broader adoption in the research community.
  • Implementing artifact detection methods like BLADE is essential for ensuring the integrity of spatial transcriptomics findings.