Molugu Kaivalya, Battistini Giovanni A, Heaster Tiffany M, Rouw Jacob, Guzman Emmanuel C, Skala Melissa C, Saha Krishanu
Biophysics Graduate Program, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA.
Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, USA; Madison, Wisconsin, USA.
GEN Biotechnol. 2022 Apr 1;1(2):176-191. doi: 10.1089/genbio.2022.0001. Epub 2022 Apr 20.
The process of reprogramming patient samples to human-induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient, leading to a heterogeneous population of cells. In this study, we track the reprogramming status of patient-derived erythroid progenitor cells (EPCs) at the single-cell level during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. EPCs isolated from human peripheral blood of three donors were used for our proof-of-principle study. We found distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) and flavin adenine dinucleotide during reprogramming. Random forest models classified iPSCs with ∼95% accuracy, which enabled the successful isolation of iPSC lines from reprogramming cultures. Reprogramming trajectories resolved at the single-cell level indicated significant reprogramming heterogeneity along different branches of cell states. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique, real-time, and nondestructive method to assess the quality of iPSCs in a biomanufacturing process, which could have downstream impacts in regenerative medicine, cell/gene therapy, and disease modeling.
将患者样本重编程为诱导多能干细胞（iPSC）的过程是随机、异步且低效的，会导致细胞群体的异质性。在本研究中，我们在重编程过程中通过对细胞代谢和核形态进行无标记活细胞成像，在单细胞水平上追踪患者来源的红系祖细胞（EPC）的重编程状态，以识别高质量的iPSC。从三名供体的人类外周血中分离出的EPC用于我们的原理验证研究。我们发现在重编程过程中烟酰胺腺嘌呤二核苷酸（磷酸）还原形式和黄素腺嘌呤二核苷酸的自发荧光寿命存在不同模式。随机森林模型对iPSC的分类准确率约为95%，这使得能够从重编程培养物中成功分离出iPSC系。在单细胞水平解析的重编程轨迹表明，沿着不同细胞状态分支存在显著的重编程异质性。这种微图案化、自发荧光成像和机器学习的组合提供了一种独特、实时且无损的方法，可在生物制造过程中评估iPSC的质量，这可能会对再生医学、细胞/基因治疗和疾病建模产生下游影响。
