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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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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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Related Experiment Video

Updated: Mar 28, 2026

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

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STiLE: Automated Tissue Microarray Dearraying for Spatial Transcriptomics.

Harsh Sinha1, Arun Das2,3, Yu-Chiao Chiu3,4,5

  • 1Intelligent Systems Program, University of Pittsburgh, PA, USA.

Biorxiv : the Preprint Server for Biology
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

We developed STiLE, an automated tool for tissue microarray (TMA) dearraying using only cell coordinates. This method overcomes manual bottlenecks and image-related artifacts in spatial transcriptomics analysis.

Keywords:
clusteringdearrayingspatial transcriptomicstissue microarray

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Spatial Transcriptomics

Background:

  • Tissue microarrays (TMAs) are crucial for high-throughput spatial transcriptomics.
  • Current dearraying methods rely on histological images, which are incompatible with coordinate-based spatial transcriptomics outputs.
  • Manual dearraying is a significant bottleneck in spatial transcriptomics workflows.

Purpose of the Study:

  • To introduce STiLE, a novel computational tool for automated tissue microarray dearraying.
  • To enable dearraying using only cell centroid coordinates, bypassing the need for image data.
  • To provide a robust and efficient solution for spatial transcriptomics data processing.

Main Methods:

  • STiLE employs connectivity-based component detection and density-based clustering (HDBSCAN).
  • The algorithm incorporates component-guided cluster merging and optional grid-based peak detection.
  • It operates solely on cell centroid coordinates, eliminating image dependency.

Main Results:

  • STiLE achieved an Adjusted Rand Index (ARI) > 0.99 on eleven public TMA samples.
  • Benchmarking on 396 synthetic datasets demonstrated robust performance with a mean ARI of 0.992.
  • The tool is robust to artifacts like variable staining and uneven illumination.

Conclusions:

  • STiLE automates the dearraying process for tissue microarrays in spatial transcriptomics.
  • Its coordinate-based approach enhances robustness and efficiency, overcoming limitations of image-based methods.
  • The platform-agnostic tool supports various spatial transcriptomics platforms and includes an interactive interface.