Structured Illumination Microscopy Improves Spot Detection Performance in Spatial Transcriptomics.

Cells

Epigenetics and Neurobiology Unit, European Molecular Biology Laboratory, 00015 Rome, Italy.

Published: May 2023

AI Article Synopsis

  • Spatial biology is an emerging field that studies gene expression at the single-cell level while maintaining tissue structure.
  • Imaging techniques like spatial transcriptomics use microscopy to visualize multiple genes, but traditional methods are limited by light diffraction.
  • This study shows that structured illumination microscopy (SIM) enhances the detection of gene transcripts, particularly in dense clusters, indicating SIM could significantly improve data quality in spatial transcriptomics.

Article Abstract

Spatial biology is a rapidly growing research field that focuses on the transcriptomic or proteomic profiling of single cells within tissues with preserved spatial information. Imaging-based spatial transcriptomics uses epifluorescence microscopy, which has shown remarkable results for the identification of multiple targets in situ. Nonetheless, the number of genes that can be reliably visualized is limited by the diffraction of light. Here, we investigate the effect of structured illumination (SIM), a super-resolution microscopy approach, on the performance of single-gene transcript detection in spatial transcriptomics experiments. We performed direct mRNA-targeted hybridization in situ sequencing for multiple genes in mouse coronal brain tissue sections. We evaluated spot detection performance in widefield and confocal images versus those with SIM in combination with 20×, 25× and 60× objectives. In general, SIM increases the detection efficiency of gene transcript spots compared to widefield and confocal modes. For each case, the specific fold increase in localizations is dependent on gene transcript density and the numerical aperture of the objective used, which has been shown to play an important role, especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to improve spot detection and overall data quality in spatial transcriptomics.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177490PMC
http://dx.doi.org/10.3390/cells12091310DOI Listing

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