Development of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types.
Development of the Theta Comparative Cell Scoring Method to Quantify Diverse Phenotypic Responses Between Distinct Cell Types.
复制标题
开发 Theta 比较细胞评分方法来量化不同细胞类型之间的不同表型反应。
DOI：
10.1089/adt.2016.730
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发表时间：
2016-09
影响因子：
1.8
通讯作者：
Carragher NO
中科院分区：
文献类型：
--
作者：
Warchal SJ;Dawson JC;Carragher NO
在本文中，我们开发了新的数据可视化工具和Theta比较细胞评分（TCCS）方法，该方法支持通过多参数高含量分析测量不同细胞表型的高通量体外药物基因组学研究。TCCS方法提供了不同细胞类型之间不同化合物诱导的表型反应的单变量描述符，可用于与遗传学，表观遗传学和蛋白质组学数据集的关联，以支持生物标志物的鉴定并进一步阐明药物的作用机制。将这些方法应用于高含量的化合物分析，包括表征疾病已知分子亚型的表征良好的细胞，支持个性化医疗保健策略的发展，而无需事先了解药物靶点。我们提出的原理证明数据量化不同的表型反应之间的八个乳腺癌细胞代表四种疾病亚类。TCCS方法的应用与下一代测序、诱导多能干细胞技术、基因编辑和高含量表型筛选的新进展一起，将很好地推动在更广泛的疾病类型和治疗类别中识别预测性生物标志物和个性化医疗方法。
In this article, we have developed novel data visualization tools and a Theta comparative cell scoring (TCCS) method, which supports high-throughput in vitro pharmacogenomic studies across diverse cellular phenotypes measured by multiparametric high-content analysis. The TCCS method provides a univariate descriptor of divergent compound-induced phenotypic responses between distinct cell types, which can be used for correlation with genetic, epigenetic, and proteomic datasets to support the identification of biomarkers and further elucidate drug mechanism-of-action. Application of these methods to compound profiling across high-content assays incorporating well-characterized cells representing known molecular subtypes of disease supports the development of personalized healthcare strategies without prior knowledge of a drug target. We present proof-of-principle data quantifying distinct phenotypic response between eight breast cancer cells representing four disease subclasses. Application of the TCCS method together with new advances in next-generation sequencing, induced pluripotent stem cell technology, gene editing, and high-content phenotypic screening are well placed to advance the identification of predictive biomarkers and personalized medicine approaches across a broader range of disease types and therapeutic classes.
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影响因子：
64.8
作者：
Nik-Zainal S;Davies H;Staaf J;Ramakrishna M;Glodzik D;Zou X;Martincorena I;Alexandrov LB;Martin S;Wedge DC;Van Loo P;Ju YS;Smid M;Brinkman AB;Morganella S;Aure MR;Lingjærde OC;Langerød A;Ringnér M;Ahn SM;Boyault S;Brock JE;Broeks A;Butler A;Desmedt C;Dirix L;Dronov S;Fatima A;Foekens JA;Gerstung M;Hooijer GK;Jang SJ;Jones DR;Kim HY;King TA;Krishnamurthy S;Lee HJ;Lee JY;Li Y;McLaren S;Menzies A;Mustonen V;O'Meara S;Pauporté I;Pivot X;Purdie CA;Raine K;Ramakrishnan K;Rodríguez-González FG;Romieu G;Sieuwerts AM;Simpson PT;Shepherd R;Stebbings L;Stefansson OA;Teague J;Tommasi S;Treilleux I;Van den Eynden GG;Vermeulen P;Vincent-Salomon A;Yates L;Caldas C;van't Veer L;Tutt A;Knappskog S;Tan BK;Jonkers J;Borg Å;Ueno NT;Sotiriou C;Viari A;Futreal PA;Campbell PJ;Span PN;Van Laere S;Lakhani SR;Eyfjord JE;Thompson AM;Birney E;Stunnenberg HG;van de Vijver MJ;Martens JW;Børresen-Dale AL;Richardson AL;Kong G;Thomas G;Stratton MR
通讯作者：
Stratton MR
影响因子：
7.8
作者：
Lee, Jiyoun;Bogyo, Matthew
通讯作者：
Bogyo, Matthew
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
--
作者：
Reisen, Felix;Zhang, Xian;Selzer, Paul
通讯作者：
Selzer, Paul
影响因子：
7.3
作者：
Fliri, AF;Loging, WT;Volkmann, RA
通讯作者：
Volkmann, RA