标题： 空间分辨基因表达从组织学图像的扩散生成建模 显示英文标题

标题： Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

摘要： 空间转录组学（ST）通过系统地将苏木精和伊红（H&E）染色组织学图像中描绘的细胞形态与空间解析的基因表达联系起来，实现了RNA序列丰度的高分辨率测量。ST是一种耗时、昂贵但强大的实验技术，它为在细粒度分子水平上理解癌症机制提供了新机会，这对于揭示疾病诊断和治疗的新方法至关重要。在这里，我们提出$\textbf{Stem}$ ($\textbf{S}$pa$\textbf{T}$ially resolved gene $\textbf{E}$xpression inference with diffusion $\textbf{M}$odel)，一种新颖的计算工具，该工具利用条件扩散生成模型，使从H&E染色图像中进行计算机基因表达推断成为可能。通过更好地捕捉ST数据中的固有随机性和异质性，$\textbf{Stem}$在空间基因表达预测方面达到了最先进的性能，并在测试时为新的H&E染色图像生成具有生物学意义的基因图谱。我们在具有各种组织来源和测序平台的数据集上评估了所提出的算法，其中它表现出对现有方法的明显改进。$\textbf{Stem}$生成的基因表达预测具有高保真度，其基因变异水平与真实数据相似，这表明我们的方法保留了潜在的生物学异质性。我们提出的流程打开了从基因组学角度分析现有、易于获取的H&E染色组织学图像的可能性，而无需实际进行基因表达分析，并从H&E染色组织学图像中推动潜在的生物学发现。 显示英文摘要

摘要： Spatial Transcriptomics (ST) allows a high-resolution measurement of RNA sequence abundance by systematically connecting cell morphology depicted in Hematoxylin and Eosin (H&E) stained histology images to spatially resolved gene expressions. ST is a time-consuming, expensive yet powerful experimental technique that provides new opportunities to understand cancer mechanisms at a fine-grained molecular level, which is critical for uncovering new approaches for disease diagnosis and treatments. Here, we present $\textbf{Stem}$ ($\textbf{S}$pa$\textbf{T}$ially resolved gene $\textbf{E}$xpression inference with diffusion $\textbf{M}$odel), a novel computational tool that leverages a conditional diffusion generative model to enable in silico gene expression inference from H&E stained images. Through better capturing the inherent stochasticity and heterogeneity in ST data, $\textbf{Stem}$ achieves state-of-the-art performance on spatial gene expression prediction and generates biologically meaningful gene profiles for new H&E stained images at test time. We evaluate the proposed algorithm on datasets with various tissue sources and sequencing platforms, where it demonstrates clear improvement over existing approaches. $\textbf{Stem}$ generates high-fidelity gene expression predictions that share similar gene variation levels as ground truth data, suggesting that our method preserves the underlying biological heterogeneity. Our proposed pipeline opens up the possibility of analyzing existing, easily accessible H&E stained histology images from a genomics point of view without physically performing gene expression profiling and empowers potential biological discovery from H&E stained histology images.