体内
生物
计算生物学
细胞生物学
计算机科学
遗传学
作者
Lauren E. Milling,Samuel C. Markson,Qin Tjokrosurjo,Nicole M. Derosia,Ian Streeter,Grant H. Hickok,Ashlyn M. Lemmen,Thao H. Nguyen,P. T. Prathima,William Fithian,Marc A. Schwartz,Nir Hacohen,John G. Doench,Martin W. LaFleur,Arlene H. Sharpe
摘要
In vivo T cell screens are a powerful tool for elucidating complex mechanisms of immunity, yet there is a lack of consensus on the screen design parameters required for robust in vivo screens: gene library size, cell transfer quantity, and number of mice. Here, we describe the Framework for In vivo T cell Screens (FITS) to provide experimental and analytical guidelines to determine optimal parameters for diverse in vivo contexts. As a proof-of-concept, we used FITS to optimize the parameters for a CD8+ T cell screen in the B16-OVA tumor model. We also included unique molecular identifiers (UMIs) in our screens to (1) improve statistical power and (2) track T cell clonal dynamics for distinct gene knockouts (KOs) across multiple tissues. These findings provide an experimental and analytical framework for performing in vivo screens in immune cells and illustrate a case study for in vivo T cell screens with UMIs.
科研通智能强力驱动
Strongly Powered by AbleSci AI