PDF(Pubmed)
Abstract：
Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.
摘要：
亚细胞成像转录组学平台的最新进展已经实现了基因表达的高分辨率空间定位，同时在准确识别细胞和分配转录本方面也带来了重大的分析挑战。现有的方法与细胞分割作斗争，经常导致片段化细胞或超大细胞捕获受污染的表达。为此,我们介绍BIDCell,基于自我监督的深度学习框架，具有生物学信息损失功能，可学习空间分辨基因表达与细胞形态之间的关系。BIDCell包含细胞类型数据，包括来自公共存储库的单细胞转录组学数据，具有细胞形态信息。使用由五个互补类别的指标组成的细胞分割性能的综合评估框架，根据各种组织类型和技术平台的许多指标，我们证明BIDCell优于其他最先进的方法。我们的发现强调了BIDCell显着增强单细胞空间表达分析的潜力，在生物学发现中发挥巨大潜力。
