Sheppard, S. J. | bioRxiv Science

bioRxiv2025

Curved Axially Scanned Light-Sheet Microscopy

Light-sheet fluorescence microscopy enables high-throughput multidimensional imaging. However, attempts to further improve throughput by incorporating commercially available or custom high space-bandwidth product objectives have been limited by objective field curvature, which violates the co-planar overlap required for light-sheet imaging and ultimately reduces the usable field of view. Inspired by machine-vision strategies that curve the image plane to match the field curvature, we introduce curved axially scanned light-sheet microscopy, which adapts the light-sheet excitation to the detection objectives field curvature via synchronized control of the remote refocus scan and motorized mirror. Using our technique, we increase the usable field of view along the light-sheet propagation axis from [~]2.5 mm to [~]6.3 mm for a commercial high-SBP detection objective with significant field curvature, while maintaining sub-cellular resolution.

biophysics↗

bioRxiv2025

GPU-accelerated, self-optimizing processing for 3D multiplexed iterative RNA-FISH experiments

Imaging-based spatial transcriptomic approaches rely on iterative labeling and imaging of carefully prepared samples, followed by solving a computational inverse problem to determine the location and identity of the targeted RNA. Because these approaches require high-resolution optics, the Nyquist-Shannon determined voxel size is small relative to typical tissue sample footprints. A common solution to speed up both experiments and computation is to increase the distance in between focal planes, trading off local information content to sample a larger imaging area in a reasonable time. In this work we introduce a GPU-accelerated computational framework, merfish3d-analysis, designed to speed up the computational processing of barcoded, in situ imaging-based spatial transcriptomics. Using this framework, we quantify the information lost due to axial sampling changes in simulated imaging-based spatial transcriptomic experiments, robustly reprocess publicly available multiplexed error-robust fluorescence in situ hybridization (MERFISH) datasets, and analyze new MERFISH experiments performed on a post-mortem human olfactory bulb sample. To improve the quality of experimental data in the post-mortem human sample, we designed a multi-step autofluorescence quenching protocol specific for in situ imaging-based spatial transcriptomic strategies. Taken together, we hope that the sample preparation protocols and single workstation, GPU-accelerated processing will further democratize imaging-based spatial transcriptomic experiments.

bioinformatics↗