論文 - 詳細
| RRC ID | 86298 |
|---|---|
| 著者 | Chu SL, Abe K, Yokota H, Cho D, Hayashi Y, Tsai MD. |
| タイトル | Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images. |
| ジャーナル | J Microsc |
| Abstract |
Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images. We also propose a U-net structure to extract extraembryonic regions on a micropattern, and an algorithm to compares intensities of the fluorescence images staining respective germ-layer cells and extract their regions. The proposed method thus can quantify the pluripotency of a hiPSC line with spatial patterning including cell numbers, areas and distributions of germ-layer and extraembryonic cells on a micropattern, and reveal the formation process of hiPSCs and germ layers in the early differentiation stage by segmenting live-cell bright-field images. In our assay, the cell-number accuracy achieved 86% and 85%, and the cell region accuracy 89% and 81% for segmenting Hoechst and bright-field micropattern images, respectively. Applications to micropattern images of multiple hiPSC lines, micropattern sizes, groups of markers, living and fixed cells show the proposed method can be expected to be a useful protocol and tool to quantify pluripotency of a new hiPSC line before providing it to the scientific community. |
| 巻・号 | 296(1) |
| ページ | 79-93 |
| 公開日 | 2024-10-1 |
| DOI | 10.1111/jmi.13346 |
| PMID | 38994744 |
| MeSH | Cell Differentiation* Deep Learning* Germ Layers / cytology Humans Image Processing, Computer-Assisted / methods Induced Pluripotent Stem Cells* / cytology |
| IF | 1.575 |
| オルトメトリクス指標 |
オルトメトリクス指標項目
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| 最多言及媒体 | X(Twitter) |
| 各媒体での言及数の合計 | 4 |
| 過去6か月間でのオルトメトリクス指標の変動値 | 0.0 |
| リソース情報 | |
| ヒト・動物細胞 | 201B7(HPS0063) 1383D6(HPS1006) |