Cell segmentation-free inference of cell types from in situ transcriptomics data.
Nat Commun
; 12(1): 3545, 2021 06 10.
Article
em En
| MEDLINE
| ID: mdl-34112806
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Encéfalo
/
Hibridização in Situ Fluorescente
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Biologia Computacional
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Perfilação da Expressão Gênica
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Imageamento Tridimensional
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Análise de Célula Única
Limite:
Animals
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Alemanha