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

Kruithoff, R.; Spendlove, M. D.; Sheppard, S. J.; Schweiger, M. C.; Pessoa, P.; Abbasi, M.; Presse, S.; Bartelle, B. B.; Shepherd, D. P.

doi:10.1101/2025.10.10.681751

bioRxiv · 10.1101/2025.10.10.681751

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

Kruithoff, R.·Spendlove, M. D.·Sheppard, S. J.·Schweiger, M. C.·Pessoa, P.·Abbasi, M.·Presse, S.·Bartelle, B. B.·Shepherd, D. P.

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Abstract

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.

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Kruithoff, R., Spendlove, M. D., Sheppard, S. J., Schweiger, M. C., Pessoa, P., Abbasi, M., Presse, S., Bartelle, B. B., Shepherd, D. P.. 2025-10-13. GPU-accelerated, self-optimizing processing for 3D multiplexed iterative RNA-FISH experiments. https://doi.org/10.1101/2025.10.10.681751

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