Structured Illumination Microscopy Improves Spot Detection for Spatial Transcriptomics

Session

Poster Session

Authors

Alejandro Linares (2), Carlo Brighi (1), Sergio Espinola (2), Francesco Bacchi (1), Alvaro Crevenna (2)

Affiliations

1. Crest Optics
2. EMBL Rome

Keywords

Structured-illuminaton microscopy, spatial transcriptomics, super-resolution

Abstract text

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 microscopy (SIM), a super-resolution microscopy approach, on the performance of single-gene transcript detection in imaging-based 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 performed 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. In particular, it is the NA of the objective that plays a key role in identifying individual transcripts, especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to substantially improve spot detection and overall data quality in imaging-based spatial transcriptomics.

References

Linares, A.; Brighi, C.; Espinola, S.; Bacchi, F.; Crevenna, Á.H. Structured Illumination Microscopy Improves Spot Detection Performance in Spatial Transcriptomics. Cells 2023, 12, 1310.https://doi.org/10.3390/cells12091310