Spatial transcriptomics is an emerging technology that aligns histopathology
images with spatially resolved gene expression profiling. It holds the
potential for understanding many diseases but faces significant bottlenecks
such as specialized equipment and domain expertise. In this work, we present
SEPAL, a new model for predicting genetic profiles from visual tissue
appearance. Our method exploits the biological biases of the problem by
directly supervising relative differences with respect to mean expression, and
leverages local visual context at every coordinate to make predictions using a
graph neural network. This approach closes the gap between complete locality
and complete globality in current methods. In addition, we propose a novel
benchmark that aims to better define the task by following current best
practices in transcriptomics and restricting the prediction variables to only
those with clear spatial patterns. Our extensive evaluation in two different
human breast cancer datasets indicates that SEPAL outperforms previous
state-of-the-art methods and other mechanisms of including spatial context.

SEPAL 是一种新的模型，可以从组织外观直接预测基因组，通过利用生物学偏差来直接监督相对差异并通过图神经网络利用每个坐标的局部视觉上下文进行预测，该方法将当前方法中的完全局部性和完全全局性之间的差距缩小，并通过在转录组学中遵循当前最佳实践并限制预测变量仅为具有明确空间模式的变量来提出了一种旨在更好地定义该任务的新的基准，通过在两个不同的人类乳腺癌数据集中进行了广泛评估表明，SEPAL 优于之前的最先进方法和包括空间上下文的其他机制。

SEPAL：基于局部图的空间基因表达预测

SEPAL: Spatial Gene Expression Prediction from Local Graphs

Histology imaging is an important tool in medical diagnosis and research,
enabling the examination of tissue structure and composition at the microscopic
level. Understanding the underlying molecular mechanisms of tissue architecture
is critical in uncovering disease mechanisms and developing effective
treatments. Gene expression profiling provides insight into the molecular
processes underlying tissue architecture, but the process can be time-consuming
and expensive. In this study, we present BLEEP (Bi-modaL Embedding for
Expression Prediction), a bi-modal embedding framework capable of generating
spatially resolved gene expression profiles of whole-slide Hematoxylin and
eosin (H&E) stained histology images. BLEEP uses a contrastive learning
framework to construct a low-dimensional joint embedding space from a reference
dataset using paired image and expression profiles at micrometer resolution.
With this framework, the gene expression of any query image patch can be
imputed using the expression profiles from the reference dataset. We
demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking
its performance on a human liver tissue dataset captured via the 10x Visium
platform, where it achieves significant improvements over existing methods. Our
results demonstrate the potential of BLEEP to provide insights into the
molecular mechanisms underlying tissue architecture, with important
implications in diagnosis and research of various diseases. The proposed
framework can significantly reduce the time and cost associated with gene
expression profiling, opening up new avenues for high-throughput analysis of
histology images for both research and clinical applications.

该研究提出一种生物模态嵌入框架 Bi-modaL Embedding for Expression Prediction (BLEEP)，可以生成整张组织学图像中的基因表达文件，以此代替传统的时间与金钱代价高昂的基因表达分析方式进行医学诊断和研究。