美国基因泰克公司Aviv Regev，Jason A. Vander Heiden，Josh Kaminker和Graham Heimberg共同合作，近期取得重要工作进展。他们研究开发了用于大规模搜索相似人类细胞的细胞图谱基础模型。相关研究成果2024年11月20日在线发表于《自然》杂志上。

据介绍，迄今为止，单细胞RNA-seq （scRNA-seq）已经在器官、疾病、发育和扰动中分析了数亿个人类细胞。挖掘这些不断增长的图谱可以揭示细胞疾病的关联，发现意外组织环境中的细胞状态，并将体内生物学与体外模型联系起来。这些需要一种通用的全身细胞相似性测量方法和一种有效的搜索方法。

研究人员开发了SCimilarity，这是一个度量学习框架，用于学习统一和可解释的表征，该表征能够快速查询来自不同研究的数千万个细胞特征，这些细胞特征在转录上与输入的细胞特征或状态相似。研究人员使用SCimilarity查询了2340万个细胞图谱，其中包含412个scRNA-seq研究，用于间质性肺病的巨噬细胞和成纤维细胞谱，并揭示了其他纤维化疾病和组织的相似细胞谱。巨噬细胞查询的体外得分最高的是3D水凝胶系统，研究人员通过实验证明了它再现了这种细胞状态。

总之，SCimilarity是单细胞图谱的基础模型，使研究人员能够查询人体内相似的细胞状态，为从人类细胞图谱中生成生物学见解提供了强大的工具。

附：英文原文

Title: A cell atlas foundation model for scalable search of similar human cells

Author: Heimberg, Graham, Kuo, Tony, DePianto, Daryle J., Salem, Omar, Heigl, Tobias, Diamant, Nathaniel, Scalia, Gabriele, Biancalani, Tommaso, Turley, Shannon J., Rock, Jason R., Corrada Bravo, Hctor, Kaminker, Josh, Vander Heiden, Jason A., Regev, Aviv

Issue&Volume: 2024-11-20

Abstract: Single-cell RNA-seq (scRNA-seq) has profiled hundreds of millions of human cells across organs, diseases, development, and perturbations to date. Mining these growing atlases could reveal cell-disease associations, discover cell states in unexpected tissue contexts, and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here, we develop SCimilarity, a metric learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4 million cell atlas of 412 scRNA-seq studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body, providing a powerful tool for generating biological insights from the Human Cell Atlas.

DOI: 10.1038/s41586-024-08411-y

Source: https://www.nature.com/articles/s41586-024-08411-y