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Mapping T cell dynamics to molecular profiles through behavior-guided transcriptomics

delete2025-02-10
delete0
PRE
AI
A
Amber K. L. Wezenaar
U
Utsav Pandey
F
Farid Keramati
M
M Hernandez-Roca
P
Péter Brázda
M
Mario Barrera Román
A
Astrid Cleven
F
Froso Karaiskaki
T
Tineke Aarts‐Riemens
S
Sam de Blank
P
Patricia Hernández-López
S
Sabine Heijhuurs
A
Anna Alemany
J
Jürgen Kuball
Z
Zsolt Sebestyén
J
Johanna F. Dekkers
H
Hendrik G. Stunnenberg
M
María Alieva *
DOI：10.1038/s41596-024-01126-4delete
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摘要

摘要

En 中文
细胞免疫疗法在癌症治疗中的兴起，促使免疫肿瘤共培养技术被用于模拟T细胞与癌细胞之间的相互作用，以评估其抗肿瘤反应。此前，我们开发了BEHAV3D，一个患者来源的肿瘤类器官（PDO）与工程化T细胞共培养的三维活体成像平台，该平台通过分析T细胞的动态行为，深入了解其在肿瘤靶向过程中的行为特征。然而，仅靠活体成像无法确定这些行为背后的分子驱动因素。相反，单细胞RNA测序（scRNA-seq）虽然允许研究人员分析单个细胞的转录谱，但缺乏空间-时间分辨率。在此，我们提出BEHAV3D协议的扩展——行为引导转录组学（BGT），用于整合T细胞活体成像数据与单细胞转录组学，从而实现对与动态T细胞行为相关的基因程序的解析。BGT利用经BEHAV3D处理的活体成像数据，指导基于细胞与PDO相互作用水平的细胞分离实验，随后进行荧光激活细胞分选（FACS）和scRNA-seq。随后，该技术通过整合这些实验的计算机模拟，在scRNA-seq数据中推断T细胞行为，揭示高功能性和无效T细胞的新型生物标志物，这些标志物可被利用以增强治疗效果。该协议专为具备基础细胞培养、成像及编程技能的用户设计，可轻松适配于不同的共培养体系，并在一个月内完成。
Keyword:
GENE-EXPRESSION
ORGANOIDS
CULTURE
ATLAS
LINKS

期刊

Nature Protocols 封面图
Nature Protocols
IF:
16
论文数:
4.0K
被引数:
5.6W

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