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在单个样本中本地生成和高效评估大量药物组合。

Local generation and efficient evaluation of numerous drug combinations in a single sample.

机构信息

Department of Medicine, Brigham and Women's Hospital, Boston, United States.

Harvard Medical School, Boston, United States.

出版信息

Elife. 2023 Apr 11;12:e85439. doi: 10.7554/eLife.85439.

Abstract

We develop a method that allows one to test a large number of drug combinations in a single-cell culture sample. We rely on the randomness of drug uptake in individual cells as a tool to create and encode drug treatment regimens. A single sample containing thousands of cells is treated with a combination of fluorescently barcoded drugs. We create independent transient drug gradients across the cell culture sample to produce heterogeneous drug combinations. After the incubation period, the ensuing phenotype and corresponding drug barcodes for each cell are recorded. We use these data for statistical prediction of the treatment response to the drugs in a macroscopic population of cells. To further application of this technology, we developed a fluorescent barcoding method that does not require any chemical drug(s) modifications. We also developed segmentation-free image analysis capable of handling large optical fields containing thousands of cells in the sample, even in confluent growth condition. The technology necessary to execute our method is readily available in most biological laboratories, does not require robotic or microfluidic devices, and dramatically reduces resource needs and resulting costs of the traditional high-throughput studies.

摘要

我们开发了一种方法,可让您在单细胞培养样本中测试大量药物组合。我们依靠单个细胞中药物摄取的随机性来创建和编码药物治疗方案。单个样本中含有数千个细胞,用荧光标记的药物组合处理。我们在细胞培养样本中创建独立的瞬时药物梯度,以产生异质药物组合。孵育期后,记录每个细胞的后续表型和相应的药物条形码。我们使用这些数据对细胞群体的药物治疗反应进行统计预测。为了进一步应用这项技术,我们开发了一种荧光标记方法,不需要任何化学药物修饰。我们还开发了无分割图像分析,能够处理包含样本中数千个细胞的大光学场,即使在细胞融合生长条件下也能处理。执行我们的方法所需的技术在大多数生物实验室中都很容易获得,不需要机器人或微流控设备,并且大大减少了传统高通量研究的资源需求和成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8191/10171870/6ef5d509782b/elife-85439-box1-fig1.jpg

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