Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
Cell Rep Methods. 2023 Sep 25;3(9):100570. doi: 10.1016/j.crmeth.2023.100570. Epub 2023 Aug 31.
Reprogramming somatic cells into pluripotent stem cells (iPSCs) enables the study of systems in vitro. To increase the throughput of reprogramming, we present induction of pluripotency from pooled cells (iPPC)-an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs pooled sequencing of single-nucleotide polymorphisms (SNPs), we accurately estimated individual donor proportions of the pooled iPSCs. With iPPC, we concurrently reprogrammed over one hundred donor lymphoblastoid cell lines (LCLs) into iPSCs and found strong correlations of individual donors' reprogramming ability across multiple experiments. Individual donors' reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and the expression of certain immunoglobulin precursor genes may impact reprogramming ability. The pooled iPSCs were also able to differentiate into cerebral organoids. Our procedure enables a multiplex framework of using pooled libraries of donor iPSCs for downstream research and investigation of in vitro phenotypes.
将体细胞重编程为多能干细胞（iPSCs）使体外系统的研究成为可能。为了提高重编程的通量，我们提出了从混合细胞（iPPC）中诱导多能性的方法——这是一种高效、可扩展和可靠的重编程程序。我们使用了一种解卷积算法，该算法采用了单核苷酸多态性（SNP）的混合测序，能够准确估计混合 iPSC 中每个供体的比例。使用 iPPC，我们同时将一百多个供体淋巴母细胞系（LCL）重编程为 iPSC，并在多个实验中发现了单个供体的重编程能力具有很强的相关性。单个供体的重编程能力在同一天的重复实验和多个实验运行中保持一致，某些免疫球蛋白前体基因的表达可能会影响重编程能力。混合 iPSC 也能够分化为脑类器官。我们的程序为使用供体 iPSC 的混合文库进行下游研究和体外表型研究提供了一个多重框架。
