分割
计算机科学
人工智能
原位杂交
转录组
空间分析
荧光原位杂交
计算生物学
模式识别(心理学)
免疫染色
原位
计算机视觉
图像分割
生物
信使核糖核酸
基因
基因表达
遗传学
遥感
化学
地质学
有机化学
免疫学
免疫组织化学
染色体
作者
Viktor Petukhov,Rosalind J. Xu,Ruslan A. Soldatov,Paolo Cadinu,Konstantin Khodosevich,Jeffrey R. Moffitt,Peter V. Kharchenko
标识
DOI:10.1038/s41587-021-01044-w
摘要
Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.
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