小柯机器人

美国哥伦比亚大学闵玮团队在研究中取得进展。他们提出了一种新的高含量光谱分析方法分析方法：光谱分析单细胞药物反应。2024年2月19日，国际知名学术期刊《自然—方法学》发表了这一成果。

他们提出了一种新的高内涵光谱分析方法，名为VIBRANT，它集成了中红外振动成像、多重振动探针和优化的数据分析流程，用于测量单细胞药物反应。设计了三种红外活性振动探针，用于测量人类癌细胞中的不同基本代谢活动。收集了超过20,000个单细胞药物反应，对应23种药物处理。得到的光谱特征对药物干扰下的表型变化非常敏感。

利用这一特性，他们建立了一个机器学习分类器，可以准确预测药物的作用机制，而且在最小化批次效应的同时实现了单细胞水平的预测。进一步设计了一种算法来发现具有新作用机制的药物候选物，并评估药物组合。总的来说，VIBRANT 在多个表型筛选领域展现了巨大的潜力。

据介绍，高内涵细胞分析已被证明在对化学干扰的单细胞表型分析中具有非常高的价值的。然而，仍然需要具有提高吞吐量、信息内容和可负担性的方法。

附：英文原文

Title: VIBRANT: spectral profiling for single-cell drug responses

Author: Liu, Xinwen, Shi, Lixue, Zhao, Zhilun, Shu, Jian, Min, Wei

Issue&Volume: 2024-02-19

Abstract: High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

DOI: 10.1038/s41592-024-02185-x

Source: https://www.nature.com/articles/s41592-024-02185-x

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex