【佳學(xué)基因檢測(cè)】基因檢測(cè)自動(dòng)識(shí)別人類細(xì)胞陣列上的亞細(xì)胞表型

基因突變?cè)趺粗委熋貨Q

課題調(diào)研基因檢測(cè)機(jī)構(gòu)自我培訓(xùn)教材中的基因檢測(cè)技術(shù)優(yōu)勢(shì)專題中，通過(guò)《腫瘤突變基因檢測(cè)與個(gè)性化治療方案的制定》知道《Genome Res》在. 2004 Jun;14(6):1130-6.發(fā)表了一篇題目為《自動(dòng)識(shí)別人類細(xì)胞陣列上的亞細(xì)胞表型》腫瘤靶向藥物治療基因檢測(cè)臨床研究文章。該研究由Christian Conrad , Holger Erfle, Patrick Warnat, Nathalie Daigle, Thomas Lörch, Jan Ellenberg, Rainer Pepperkok, Roland Eils等完成。這表明基因解碼技術(shù)與細(xì)胞治療中的分型技術(shù)走向結(jié)合，開(kāi)啟了細(xì)胞學(xué)技術(shù)與基因檢測(cè)技術(shù)的跨學(xué)科結(jié)合，從而增加基因檢測(cè)的可信度和全面完整性。

腫瘤靶向藥物大數(shù)據(jù)臨床研究?jī)?nèi)容關(guān)鍵詞：

細(xì)胞芯片,高通量,高內(nèi)涵,細(xì)胞陣列

腫瘤靶向治療基因檢測(cè)臨床應(yīng)用結(jié)果

細(xì)胞形態(tài)的光學(xué)顯微鏡分析提供了細(xì)胞功能和蛋白質(zhì)定位的高內(nèi)涵數(shù)據(jù)。對(duì)培養(yǎng)細(xì)胞的細(xì)胞陣列和微孔轉(zhuǎn)染分析使細(xì)胞表型分析可用于高通量實(shí)驗(yàn)。蛋白質(zhì)組中每種蛋白質(zhì)的定位和單個(gè)基因的 RNAi 敲低對(duì)細(xì)胞形態(tài)的影響都可以通過(guò)手動(dòng)檢查顯微圖像來(lái)測(cè)定。然而，使用功能基因組學(xué)的形態(tài)讀數(shù)需要快速和自動(dòng)識(shí)別復(fù)雜的細(xì)胞表型。在這里，我們提出了一個(gè)全自動(dòng)平臺(tái)，用于結(jié)合人類活細(xì)胞陣列、篩選顯微鏡和基于機(jī)器學(xué)習(xí)的分類方法進(jìn)行高通量細(xì)胞表型篩選。該平臺(tái)的效率通過(guò)對(duì)由 GFP 標(biāo)記的蛋白質(zhì)標(biāo)記的 11 種亞細(xì)胞模式進(jìn)行分類來(lái)證明。我們的分類方法幾乎可以適用于任何基于細(xì)胞形態(tài)的顯微分析，從而開(kāi)啟了廣泛的應(yīng)用，包括人類細(xì)胞中的大規(guī)模 RNAi 篩選。

腫瘤發(fā)生與反復(fù)轉(zhuǎn)移國(guó)際數(shù)據(jù)庫(kù)描述：

Light microscopic analysis of cell morphology provides a high-content readout of cell function and protein localization. Cell arrays and microwell transfection assays on cultured cells have made cell phenotype analysis accessible to high-throughput experiments. Both the localization of each protein in the proteome and the effect of RNAi knock-down of individual genes on cell morphology can be assayed by manual inspection of microscopic images. However, the use of morphological readouts for functional genomics requires fast and automatic identification of complex cellular phenotypes. Here, we present a fully automated platform for high-throughput cell phenotype screening combining human live cell arrays, screening microscopy, and machine-learning-based classification methods. Efficiency of this platform is demonstrated by classification of eleven subcellular patterns marked by GFP-tagged proteins. Our classification method can be adapted to virtually any microscopic assay based on cell morphology, opening a wide range of applications including large-scale RNAi screening in human cells.