美国斯坦福大学Sylvia K. Plevritis团队开发出新算法CELESTA，可识别多路复用原位图像中的细胞类型。该项研究成果于2022年6月2日在线发表在《自然—方法学》杂志上。

研究人员开发了一种无监督的机器学习算法CELESTA，该算法利用细胞的标记物表达谱和必要时的空间信息，单独识别每个细胞的类型。研究人员在结直肠癌和头颈部鳞状细胞癌（HNSCC）的多重免疫荧光图像上展示了CELESTA的性能。利用CELESTA识别的细胞类型，研究人员确定了与HNSCC的淋巴结转移相关的组织结构，并在一个独立的队列中验证了这个发现。

通过将空间分析与同一标本的近端切片的单细胞RNA测序数据相结合，研究人员确定了与淋巴结转移相关的细胞-细胞互动，从而证明了CELESTA促进识别临床相关互动的能力。

据了解，多重原位成像的进展正在揭示空间生物学的重要见解。然而，细胞类型识别仍然是成像分析中的一个主要挑战，大多数现有的方法涉及大量的人工评估和对数千个细胞的主观决定。

附：英文原文

Title: Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Author: Zhang, Weiruo, Li, Irene, Reticker-Flynn, Nathan E., Good, Zinaida, Chang, Serena, Samusik, Nikolay, Saumyaa, Saumyaa, Li, Yuanyuan, Zhou, Xin, Liang, Rachel, Kong, Christina S., Le, Quynh-Thu, Gentles, Andrew J., Sunwoo, John B., Nolan, Garry P., Engleman, Edgar G., Plevritis, Sylvia K.

Issue&Volume: 2022-06-02

Abstract: Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

DOI: 10.1038/s41592-022-01498-z

Source: https://www.nature.com/articles/s41592-022-01498-z