Nirmal Ajit J, Yapp Clarence, Santagata Sandro, Sorger Peter K
Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA.
Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
bioRxiv. 2023 Nov 17:2023.11.15.567196. doi: 10.1101/2023.11.15.567196.
Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.
高度多重组织成像和原位空间分析旨在从包含形态各异且紧密排列细胞的标本中提取单细胞数据。由于难以准确划分细胞之间的边界（分割）并生成每个细胞的染色强度，这一过程颇具挑战性。现有方法使用门控将每个细胞的强度数据转换为正分数和负分数；这是流式细胞术中的常用方法，但在成像中存在问题。相比之下，人类专家利用形态、邻域和强度信息在拥挤环境中识别细胞。在此，我们描述了一种计算方法（细胞识别器或CSPOT），该方法使用监督机器学习结合经典分割来进行自动细胞类型识别。CSPOT对通常困扰组织成像的伪影具有鲁棒性，并且可以取代传统的门控。CSPOT的端到端Python实现可以集成到基于云的图像处理管道中，以大幅提高单细胞空间数据的速度、准确性和可重复性。
