Segmentation-free inference of cell-types from in situ transcriptomics data [article]

Jeongbin Park, Wonyl Choi, Sebastian Tiesmeyer, Brian Long, Lars E. Borm, Emma Garren, Thuc Nghi Nguyen, Simone Codeluppi, Matthias Schlesner, Bosiljka Tasic, Roland Eils, Naveed Ishaque
2019 bioRxiv   pre-print
Multiplexed fluorescence in situ hybridization techniques have enabled cell class or type identification by mRNA quantification in situ. However, inaccurate cell segmentation can result in incomplete cell-type and tissue characterization. Here, we present a robust segmentation-free computational framework, applicable to a variety of in situ transcriptomics platforms, called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM). SSAM assumes that spatial
more » ... ution of mRNAs relates to organization of higher complexity structures (e.g. cells or tissue layers) and performs de novo cell-type and tissue domain identification. Optionally, SSAM can also integrate prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. SSAM outperforms segmentation-based results, demonstrating that segmentation of cells is not required for inferring cell-type signatures, cell-type organization or tissue domains.
doi:10.1101/800748 fatcat:h3hquog3gjgefdrn7ycfwa543u