Chen Jiayuan, Wang Yu, Deng Ruining, Liu Quan, Cui Can, Yao Tianyuan, Liu Yilin, Zhong Jianyong, Fogo Agnes B, Yang Haichun, Zhao Shilin, Huo Yuankai
Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12933. doi: 10.1117/12.3006318. Epub 2024 Apr 3.
Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features. The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The toolkit's implementation and its complete source code are made openly accessible at the GitHub repository: https://github.com/hrlblab/spatial_pathomics.
足细胞是包裹肾小球毛细血管的特殊上皮细胞,在维持肾脏健康方面发挥着关键作用。目前对病理切片特征的描述和量化有限,这促使需要创新解决方案来全面评估全切片图像(WSIs)中的各种表型属性。特别是,了解足细胞(终末分化的肾小球上皮细胞)的形态特征对于研究肾小球损伤至关重要。本文介绍了空间病理组学工具包(SPT)并将其应用于足细胞病理组学。SPT由三个主要组件组成:(1)实例对象分割,能够精确识别足细胞核;(2)病理组学特征生成,从识别出的细胞核中提取一系列全面的定量特征;(3)强大的统计分析,有助于全面探索形态学和空间转录组学特征之间的空间关系。SPT成功地从足细胞核中提取并分析了形态和纹理特征,通过统计分析揭示了大量足细胞形态组学特征。此外,我们展示了SPT揭示足细胞分布固有空间信息的能力,阐明了与肾小球损伤相关的空间模式。通过推广SPT,我们的目标是为研究界提供一个强大且用户友好的资源,推动肾脏病理学中的细胞空间病理组学发展。该工具包的实现及其完整源代码可在GitHub仓库中公开获取:https://github.com/hrlblab/spatial_pathomics 。