Deep Learning of Mass Spectrometry Imaging

项目总结 摘要质谱学成像技术是一项发展迅速的技术，它为病理学家提供了许多新的类型。 转化型癌症研究评估的靶标(如代谢物和脂类)。然而，由此产生的数据 甚至比传统图像更复杂，因为它们维度很高，而且很大(每个图像大约100 GB 组织切片)。结果数据结构中的每个“像素”都包含一个由以下组成的二维质谱图 测量的离子质量和离子迁移率(m/z，1/k0)，每个光谱通常包含数百到 数以千计的单个离子(代谢物和脂质)。深度学习方法(机器学习)已经 包括大卫·芬约博士在内的几个实验室成功地应用于组织病理学数据 目前的建议是，这样的模型能够区分不同的癌症亚型和级别 例如。然而，大多数图像数据的机器学习模型都是围绕3数据通道(Red， 绿色、蓝色)，用于分析数字图像。因此，质谱学的n维数据结构 成像数据集不容易服从这些经过验证的机器学习工作流。我们将制作MSI数据 这些方法可通过扩展到n维“颜色通道”来访问，每个独特的代谢物或 作为个人数据输入的脂质图像。对于深度学习部分，我们将保持总体相同 Panoptes工具的架构和工作流程，由Fenyo et出版。Al.(细胞报告，医学，2021)，但是 我们将应用n维方法并在具有并行H&E的现有数据上测试数据结构 由病理学家注释的染色信息。这些挑战在本提案的目标1中得到了解决， 虽然目标2解决了检测这些图像内部和之间的图像相关性的密切相关的挑战 数据结构和其他成像模式。这类数据中的图像相关性更微不足道，但这些 由于计算量太大，现有的学术软件或供应商软件不能很好地支持分析 数百个数据维度所需的。我们进一步提出并测试了一种方法来将这些多维数据转换为 将三维数据转换成质心的单离子图像，然后对图像进行线性化，使之能够简单地 皮尔逊相关度量，从而使完整的相关矩阵可由比例因数n2访问 检测到的离子的数量。其次，为了处理MSI数据集和来自 其他方式，或相邻的组织切片，可能在大小和形状上不同，我们建议实施 相关分析和机器前质心图像数据的空间感知弹性变换 学习。

PROJECT SUMMARY Mass spectrometry imaging (MSI) is a rapidly developing technology which gives pathologists many new types of targets (e.g., metabolites and lipids) to assess for translational cancer research. However, the resulting data are even more complex than traditional images because they are highly-dimensional, and large (~100GB per tissue section). Each “pixel” in the resulting data structure contains a 2-dimensional mass spectrum made of both measured ion mass and ion mobility (m/z, 1/K0), and each spectrum typically contains hundreds to thousands of individual ions (metabolites and lipids). Deep-learning methods (machine learning) have been successfully applied to histopathology data by several laboratories including Dr. David Fenyo, Co-Investigator of the current proposal, with such models being able to discriminate between different cancer subtypes and grades for example. However, most machine learning models of image-data are designed around 3-data channels (Red, Green, Blue) for analysis of digital images. Therefore, the n-dimensional data structure of mass spectrometry imaging datasets is not easily amenable to these proven machine learning workflows. We will make MSI data accessible to these approaches by expanding to n-dimension “color-channels”, with each unique metabolite or lipid image serving as an individual data input. For the deep learning component, we will retain the same overall architecture and workflow of the Panoptes tool, published by Fenyo et. al., (Cell Reports, Medicine, 2021) but we will apply an n-dimensional approach and test the data structure on existing data which has parallel H&E stain information annotated by pathologists. These challenges are addressed in Aim1 of the current proposal, while Aim 2 addresses a closely related challenge of detecting image correlations both within and between these data structures and other imaging modalities. Image correlations within such data are more trivial, but these analyses are not well supported by existing academic or vendor software due to the amount of computation needed for hundreds of data dimensions. We further propose and test an approach for converting these multi- dimensional data into centroided single ion images, followed by linearization of the image to enable a simple Pearson correlation metric, thereby making a complete correlation matrix accessible by a scaling factor of n2 to the number of detected ions. Secondly, to deal with spatial correlations between MSI datasets and images from other modalities, or adjacent tissue sections which may be different in size and shape, we propose to implement a spatially aware elastic transform of the centroided image data prior to correlation analysis and machine learning.