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Updated: Jun 23, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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SpotSweeper: spatially-aware quality control for spatial transcriptomics.

Michael Totty1, Stephanie C Hicks1,2,3,4, Boyi Guo1

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

Biorxiv : the Preprint Server for Biology
|June 19, 2024
PubMed
Summary
This summary is machine-generated.

SpotSweeper provides new quality control (QC) methods for spatially-resolved transcriptomics (SRT) data. This spatially-aware approach identifies low-quality spots and tissue artifacts, improving downstream analysis of spatial domains.

Keywords:
data-drivenquality controlsoftwarespatially-awarespatially-resolved transcriptomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Quality control (QC) is essential for accurate RNA sequencing data, especially in spatially-resolved transcriptomics (SRT).
  • Current QC methods adapted from single-nucleus RNA sequencing (snRNA-seq) are unsuitable for SRT due to spatial considerations.
  • Histological tissue artifacts unique to SRT lack dedicated identification methods.

Purpose of the Study:

  • Introduce SpotSweeper, a novel spatially-aware QC tool for SRT data.
  • Develop methods to identify local outliers and regional artifacts specific to SRT.
  • Enhance the reliability and accuracy of SRT data analysis.

Main Methods:

  • SpotSweeper evaluates individual spot quality against its local neighborhood to mitigate biological heterogeneity bias.
  • Utilizes multiscale analysis to detect regional artifacts within SRT data.
  • Applies spatially-aware QC metrics to identify and flag problematic spots and regions.

Main Results:

  • Identified a consistent set of low-quality Visium barcodes/spots in publicly available SRT datasets.
  • Demonstrated SpotSweeper's accuracy in detecting two distinct types of regional artifacts.
  • Showcased improvements in downstream clustering and marker gene detection for spatial domains after applying SpotSweeper.

Conclusions:

  • SpotSweeper offers a robust solution for SRT quality control, addressing limitations of existing methods.
  • The tool effectively identifies both localized and widespread artifacts, crucial for accurate spatial biology interpretation.
  • Implementing SpotSweeper leads to more reliable downstream analyses and discoveries in spatially-resolved transcriptomics.