Image based phenotypic profiling of single-cell responses to perturbations

基于图像的单细胞对扰动反应的表型分析

• 批准号: 8120533
• 负责人: LANI F WU
• 金额: $ 32.76万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8120533
• 关键词: Antibodies; Antineoplastic Agents; Biological Markers; Cell Count; Cell Cycle; Cells; Complex; Detection; Diagnostic; Discrimination; Disease; Dose; Drug Combinations; Drug Delivery Systems; Drug resistance; Environment; Genetic; Heterogeneity; Hormones; Image; Immunofluorescence Microscopy; Individual; Label; Measures; Metabolic; Methods; Monitor; Nature; Performance; Pharmaceutical Preparations; Pharmacology; Phenotype; Physiological; Population; Population Heterogeneity; Research Personnel; Series; Source; Therapeutic; analytical method; base; fluorophore; improved; insight; programs; research study; response

DESCRIPTION (provided by applicant): Increasingly, disease states and responses to therapeutics are seen to be comprised of a heterogeneous mix of cellular states and responses. Insight into the nature of diverse cellular responses to perturbations has immediate applications to pharmacology and disease treatment. Identifying physiologically or clinically important subpopulations, such as cancer drug-resistant or hormone-insensitive cells, is. an important step towards identifying diagnostic biomarkers and developing targeted therapies. Measuring high-dimensional physiological phenotypes of large numbers of individual cells in diverse conditions will enable the characterization of heterogeneous cellular responses to perturbations, and the identification of discrete subpopulations of distinct phenotypic states. Our long term objects are therefore: 1) enabling the detection and comparison of single-cell responses to broad ranges of perturbations; 2) elucidating physiological mechanisms of cellular perturbations; and 3) identifying physiologically important subpopulations. We propose to use immunofluorescence microscopy to monitor complex cellular responses to systematic drug treatments. For the proposed aims, drug treatments are ideal choices for perturbations as they are fast- acting, titratable, and reliable methods for eliciting diverse cellular responses. The specific aims are to: 1. Increase the discriminative capacity of single-cell phenotypic readouts. Single-cell phenotypes, measured independently from small numbers of fluorescent markers, may not distinguish complex cellular states. We will expand readout capacity by increasing the number of markers and quality of readouts per cell, and by correlating readouts of different markers from different cells. 2. Classify perturbation effects using single-cell phenotypic readouts. Classifying the effects of perturbations requires the extraction of informative features from single-cell phenotypic readouts. We will apply new methods for feature selection and drug profiling to the problems of identifying multiphasic and off-target drug effects, and predicting the response to combinations of drug treatments. 3. Represent population heterogeneity as subpopulations of distinct phenotypic states. We will characterize drugs in terms of their effects on these subpopulations of cells.

描述（由申请人提供）：越来越多的疾病状态和对治疗剂的反应被认为由细胞状态和反应的异质组成。对各种细胞对扰动的多种细胞反应性质的洞察力在药理学和疾病治疗中立即应用。鉴定生理或临床上重要的亚群，例如抗癌药物或对激素不敏感的细胞。迈向识别诊断生物标志物并开发靶向疗法的重要一步。在不同条件下测量大量单个细胞的高维生理表型将使异质细胞对扰动的反应表征，并鉴定出不同表型状态的离散亚群。因此，我们的长期对象是：1）启用对扰动范围的单细胞响应的检测和比较； 2）阐明细胞扰动的生理机制； 3）识别生理上重要的亚群。我们建议使用免疫荧光显微镜监测对系统药物治疗的复杂细胞反应。对于拟议的目的，药物治疗是扰动的理想选择，因为它们是快速作用，可滴定和可靠的方法来引起各种细胞反应的方法。具体目的是：1。增加单细胞表型读数的判别能力。独立于少量荧光标记的单细胞表型可能无法区分复杂的细胞状态。我们将通过增加每个单元格的标记数量和读数质量，以及通过不同单元格不同标记的读数来扩大读数能力。 2。使用单细胞表型读数对扰动效应进行分类。对扰动的效果进行分类，需要从单细胞表型读数中提取信息性特征。我们将应用新方法进行特征选择和药物分析，以识别多相和脱靶药物效应的问题，并预测对药物治疗组合的反应。 3。表示种群异质性作为不同表型状态的亚群。我们将以药物对这些细胞亚群的影响来表征。

项目成果

Cellular heterogeneity: do differences make a difference?

• DOI: 10.1016/j.cell.2010.04.033
• 发表时间: 2010-05-14
• 期刊: Cell
• 影响因子: 64.5
• 作者: Altschuler SJ;Wu LF
• 通讯作者: Wu LF

PhenoRipper: software for rapidly profiling microscopy images.

• DOI: 10.1038/nmeth.2097
• 发表时间: 2012-06-28
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Rajaram, Satwik;Pavie, Benjamin;Wu, Lani F.;Altschuler, Steven J.
• 通讯作者: Altschuler, Steven J.

Targeting Insulin-Degrading Enzyme to Treat Type 2 Diabetes Mellitus.

• DOI: 10.1016/j.tem.2015.11.003
• 发表时间: 2016-01
• 期刊: Trends in endocrinology and metabolism: TEM
• 作者: Tang WJ

Only two ways to achieve perfection.

只有两种方法可以达到完美。

• DOI: 10.1016/j.cell.2009.08.010
• 发表时间: 2009
• 期刊: Cell
• 影响因子: 64.5
• 作者: Artyukhin,AlexanderB;Wu,LaniF;Altschuler,StevenJ
• 通讯作者: Altschuler,StevenJ

An approach for extensibly profiling the molecular states of cellular subpopulations.

• DOI: 10.1038/nmeth.1375
• 发表时间: 2009-10
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Loo, Lit-Hsin;Lin, Hai-Jui;Steininger, Robert J., III;Wang, Yanqin;Wu, Lani F.;Altschuler, Steven J.
• 通讯作者: Altschuler, Steven J.