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<p>Repeated implantation failure (RIF) presents a significant challenge&#xa0;to <i>in vitro</i> fertilization (IVF), and abnormalities in the endometrium often play a crucial role in RIF. To enhance and deepen our understanding of tissue context in the endometrium underlying RIF, we applied spatial transcriptomics (ST) sequencing using the 10x Visium platform to 8 endometrial tissues from 4 normal individuals (CTR) and 4 RIF patients during the mid-luteal phase in this study. We achieved 10131 high-quality spots, with a median detected gene number of 3156. Seven distinct cellular niches (Niche 1–7) with specific characteristics were identified in our data. Deconvolution of ST data was performed by integrating with a public single-cell RNA (scRNA) dataset, and it was found that unciliated Epithelia were the dominant components in our data. To the best of our knowledge, this is the first spatial transcriptomics atlas of endometrial tissue in normal and RIF conditions, and the spatial data can provide a valuable source for further studies investigating the RIF mechanism.</p>

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A spatial transcriptomics dataset of the endometrium from repeated implantation failure patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Yan Zhang,
  • Junlin Pan,
  • Zhitao Liu,
  • Meimei Zheng,
  • Ruijuan Liu,
  • Lingshan Lei,
  • Haiping Liu,
  • Peng Wang

摘要

Repeated implantation failure (RIF) presents a significant challenge to in vitro fertilization (IVF), and abnormalities in the endometrium often play a crucial role in RIF. To enhance and deepen our understanding of tissue context in the endometrium underlying RIF, we applied spatial transcriptomics (ST) sequencing using the 10x Visium platform to 8 endometrial tissues from 4 normal individuals (CTR) and 4 RIF patients during the mid-luteal phase in this study. We achieved 10131 high-quality spots, with a median detected gene number of 3156. Seven distinct cellular niches (Niche 1–7) with specific characteristics were identified in our data. Deconvolution of ST data was performed by integrating with a public single-cell RNA (scRNA) dataset, and it was found that unciliated Epithelia were the dominant components in our data. To the best of our knowledge, this is the first spatial transcriptomics atlas of endometrial tissue in normal and RIF conditions, and the spatial data can provide a valuable source for further studies investigating the RIF mechanism.