Autologous cell replacement: a noninvasive AI approach to clinical release testing.
Autologous cell replacement: a noninvasive AI approach to clinical release testing.
复制标题
自体细胞替代：一种用于临床放行测试的非侵入性人工智能方法。
DOI：
10.1172/jci133821
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Stone,EdwinM
中科院分区：
文献类型：
--
作者：
Tucker,BuddA;Mullins,RobertF;Stone,EdwinM
人类诱导多能干细胞（iPSCs）的出现为避免与使用从人类胚胎中分离的细胞相关的伦理问题提供了一种手段。目前，使用iPSCs生成感光细胞、视网膜色素上皮细胞（RPE）以及最近的脉络膜内皮细胞的实验室数量呈指数级增长。然而，为了使自体细胞替代有效，制造策略需要改变。许多手工完成的任务将需要简化和自动化。在这一期的jci中，Schaub及其同事将定量明场显微镜和人工智能（深度神经网络和传统机器学习）结合起来，无创地监测ipsc衍生的移植物成熟，预测供体细胞身份，并在移植前评估移植物功能。这种方法使作者能够先发制人地识别和切除异常移植物。值得注意的是，该方法具有(a)可转移性，(b)成本和时间有效性，(c)高通量，以及(d)对初级产品验证有用。
The advent of human induced pluripotent stem cells (iPSCs) provided a means for avoiding ethical concerns associated with the use of cells isolated from human embryos. The number of labs now using iPSCs to generate photoreceptor, retinal pigmented epithelial (RPE), and—more recently—choroidal endothelial cells has grown exponentially. However, for autologous cell replacement to be effective, manufacturing strategies will need to change. Many tasks carried out by hand will need simplifying and automating. In this issue of theJCI, Schaub and colleagues combined quantitative bright-field microscopy and artificial intelligence (deep neural networks and traditional machine learning) to noninvasively monitor iPSC-derived graft maturation, predict donor cell identity, and evaluate graft function prior to transplantation. This approach allowed the authors to preemptively identify and remove abnormal grafts. Notably, the method is (a) transferable, (b) cost and time effective, (c) high throughput, and (d) useful for primary product validation.