无需分割细胞核，融合形态与转录组信息分析视网膜发育
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images
- 用自监督学习从荧光核染色图提取形态特征
- 在不依赖分割情况下提升基因表达聚类效果
- 适合研究视网膜发育中细胞空间异质性
本研究提出SEFI（SEgmentation-Free Integration），一种将细胞核形态特征与空间转录组数据融合的新方法。传统细胞分割在空间转录组分析中面临挑战，尤其在组织结构复杂或细胞密集区域难以建立通用方案。SEFI通过自监督学习从荧光核染色图像中提取形态特征，无需分割即可增强基因表达数据的聚类性能。该方法在使用多重单分子荧光原位杂交（smFISH）获取的发育视网膜空间基因表达图谱上得到验证。SEFI已公开于 https://github.com/eduardchelebian/sefi。
原文摘要 · Abstract (English)
This study introduces SEFI (SEgmentation-Free Integration), a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data. Cell segmentation poses a significant challenge in the analysis of spatial transcriptomics data, as tissue-specific structural complexities and densely packed cells in certain regions make it difficult to develop a universal approach. SEFI addresses this by utilizing self-supervised learning to extract morphological features from fluorescent nuclear staining images, enhancing the clustering of gene expression data without requiring segmentation. We demonstrate SEFI on spatially resolved gene expression profiles of the developing retina, acquired using multiplexed single molecule Fluorescence In Situ Hybridization (smFISH). SEFI is publicly available at https://github.com/eduardchelebian/sefi.
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