Identification of rare cell populations in autofluorescence lifetime image data.
Identification of rare cell populations in autofluorescence lifetime image data.
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自发荧光寿命图像数据中稀有细胞群的识别。
DOI：
10.1002/cyto.a.24534
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发表时间：
2022-06
期刊：
影响因子：
--
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中科院分区：
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--
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肿瘤块内的耐药细胞和抗炎免疫细胞导致肿瘤侵袭、侵袭和更差的患者结局。这些细胞可以是肿瘤总细胞群的一小部分（<10%）。由于其数量少，稀有细胞的鉴定对于传统的测定是具有挑战性的。自体荧光图像的单细胞分析提供活细胞测定以量化细胞异质性。代谢辅酶还原型烟酰胺腺嘌呤二核苷酸和氧化型黄素腺嘌呤二核苷酸的荧光强度和寿命允许定量细胞代谢，并提供具有不同代谢表型的细胞分类的特征。在这项研究中，高斯分布建模和机器学习分类算法被用于识别罕见的细胞内的模拟自体荧光寿命图像数据的一个大的肿瘤组成的肿瘤细胞和T细胞。随机森林机器学习算法实现了从20，000个细胞的异质肿瘤的模拟光学代谢成像数据识别细胞类型的95%的总体准确度，该异质肿瘤由70%的药物响应性乳腺癌细胞、5%的耐药乳腺癌细胞、20%的静止T细胞和5%的活化T细胞组成。高分辨率成像方法与单细胞定量分析相结合，可以识别和定量异质培养物中的稀有细胞群
Drug‐resistant cells and anti‐inflammatory immune cells within tumor masses contribute to tumor aggression, invasion, and worse patient outcomes. These cells can be a small proportion (<10%) of the total cell population of the tumor. Due to their small quantity, the identification of rare cells is challenging with traditional assays. Single cell analysis of autofluorescence images provides a live‐cell assay to quantify cellular heterogeneity. Fluorescence intensities and lifetimes of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and oxidized flavin adenine dinucleotide allow quantification of cellular metabolism and provide features for classification of cells with different metabolic phenotypes. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within simulated autofluorescence lifetime image data of a large tumor comprised of tumor cells and T cells. A Random Forest machine learning algorithm achieved an overall accuracy of 95% for the identification of cell type from the simulated optical metabolic imaging data of a heterogeneous tumor of 20,000 cells consisting of 70% drug responsive breast cancer cells, 5% drug resistant breast cancer cells, 20% quiescent T cells and 5% activated T cells. High resolution imaging methods combined with single‐cell quantitative analyses allows identification and quantification of rare populations of cells within heterogeneous cultures
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影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
4.6
作者：
Walsh, Alex J.;Cook, Rebecca S.;Skala, Melissa C.
通讯作者：
Skala, Melissa C.
影响因子：
64.5
作者：
Chang CH;Curtis JD;Maggi LB Jr;Faubert B;Villarino AV;O'Sullivan D;Huang SC;van der Windt GJ;Blagih J;Qiu J;Weber JD;Pearce EJ;Jones RG;Pearce EL
通讯作者：
Pearce EL
DOI：
10.1073/pnas.1711872115
发表时间：
2018-03-20
影响因子：
11.1
作者：
Pavillon, Nicolas;Hobro, Alison J.;Smith, Nicholas I.
通讯作者：
Smith, Nicholas I.
影响因子：
4.6
作者：
Sharick JT;Favreau PF;Gillette AA;Sdao SM;Merrins MJ;Skala MC
通讯作者：
Skala MC