Bio Science & Engineering Laboratories, Fujifilm Corporation, Madison, WI 53711.
Imaging Technology Center, Fujifilm Corporation, Ashigarakami-gun, Kanagawa 258-0023, Japan.
Mol Biol Cell. 2023 May 1;34(5):ar45. doi: 10.1091/mbc.E22-06-0215. Epub 2023 Mar 22.
Purification is essential before differentiating human induced pluripotent stem cells (hiPSCs) into cells that fully express particular differentiation marker genes. High-quality iPSC clones are typically purified through gene expression profiling or visual inspection of the cell morphology; however, the relationship between the two methods remains unclear. We investigated the relationship between gene expression levels and morphology by analyzing live-cell, phase-contrast images and mRNA profiles collected during the purification process. We employed these data and an unsupervised image feature extraction method to build a model that predicts gene expression levels from morphology. As a benchmark, it was confirmed that the method can predict the gene expression levels from tissue images for cancer genes, performing as well as state-of-the-art methods. We then applied the method to iPSCs and identified two genes that are well predicted from cell morphology. Although strong batch (or possibly donor) effects resulting from the reprogramming process preclude the ability to use the same model to predict across batches, prediction within a reprogramming batch is sufficiently robust to provide a practical approach for estimating expression levels of a few genes and monitoring the purification process.
在将人类诱导多能干细胞（hiPSCs）分化为完全表达特定分化标记基因的细胞之前，必须进行纯化。通常通过基因表达谱分析或细胞形态的目视检查来纯化高质量的 iPSC 克隆；然而，这两种方法之间的关系尚不清楚。我们通过分析在纯化过程中收集的活细胞、相差图像和 mRNA 谱，研究了基因表达水平和形态之间的关系。我们利用这些数据和无监督的图像特征提取方法，构建了一个可以从形态学预测基因表达水平的模型。作为基准，我们确认该方法可以从癌症基因的组织图像中预测基因表达水平，其性能与最先进的方法相当。然后，我们将该方法应用于 iPSCs，并从细胞形态学中鉴定出两个可以很好预测的基因。尽管由于重编程过程中的强烈批次（或可能是供体）效应，无法使用相同的模型在批次之间进行预测，但在重编程批次内的预测足够稳健，可以提供一种实用的方法来估计少数基因的表达水平并监测纯化过程。
