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Cell segmentation-free inference of cell types from in situ transcriptomics data
- Park, J (author)
- Choi, W (author)
- Tiesmeyer, S (author)
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- Long, B (author)
- Garren, E (author)
- Nguyen, TN (author)
- Tasic, B (author)
- Codeluppi, S (author)
- Graf, T (author)
- Schlesner, M (author)
- Stegle, O (author)
- Eils, R (author)
- Ishaque, N (author)
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- (creator_code:org_t)
- 2021-06-10
- 2021
- English.
- In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1, s. 3545-
- Journal article (peer-reviewed)
Abstract
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- Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating 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. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
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- Park, J
- Choi, W
- Tiesmeyer, S
- Long, B
- Borm, LE
- Garren, E
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- Nguyen, TN
- Tasic, B
- Codeluppi, S
- Graf, T
- Schlesner, M
- Stegle, O
- Eils, R
- Ishaque, N
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