{"title":"利用扰动细胞和组织图谱建立因果细胞和组织生物学基础模型","authors":"Jennifer E. Rood, Anna Hupalowska, Aviv Regev","doi":"10.1016/j.cell.2024.07.035","DOIUrl":null,"url":null,"abstract":"<p>Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not—and sometimes cannot—be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.</p>","PeriodicalId":9656,"journal":{"name":"Cell","volume":null,"pages":null},"PeriodicalIF":45.5000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas\",\"authors\":\"Jennifer E. Rood, Anna Hupalowska, Aviv Regev\",\"doi\":\"10.1016/j.cell.2024.07.035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not—and sometimes cannot—be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.</p>\",\"PeriodicalId\":9656,\"journal\":{\"name\":\"Cell\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":45.5000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cell.2024.07.035\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.cell.2024.07.035","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not—and sometimes cannot—be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
期刊介绍:
Cells is an international, peer-reviewed, open access journal that focuses on cell biology, molecular biology, and biophysics. It is affiliated with several societies, including the Spanish Society for Biochemistry and Molecular Biology (SEBBM), Nordic Autophagy Society (NAS), Spanish Society of Hematology and Hemotherapy (SEHH), and Society for Regenerative Medicine (Russian Federation) (RPO).
The journal publishes research findings of significant importance in various areas of experimental biology, such as cell biology, molecular biology, neuroscience, immunology, virology, microbiology, cancer, human genetics, systems biology, signaling, and disease mechanisms and therapeutics. The primary criterion for considering papers is whether the results contribute to significant conceptual advances or raise thought-provoking questions and hypotheses related to interesting and important biological inquiries.
In addition to primary research articles presented in four formats, Cells also features review and opinion articles in its "leading edge" section, discussing recent research advancements and topics of interest to its wide readership.