Mapping the single cell state basis of metastasis in space and time

我们建议利用机器学习和系统生物学的最新进展来实现高 驱动转移的动态细胞状态转变的维度分子评估。我们假设 癌细胞内在重新激活发育程序与其时空的相互作用 背景决定了它的转移潜力。我们将利用乳腺上皮的发育变化 以确定它们的细胞状态基础，并绘制这些转录程序在转移疾病中的异常重复使用图。 正常乳腺上皮和乳腺肿瘤在分化和分化过程中都发生了巨大的变化 组织结构和分化丧失与患者预后不良相关。我们开发了3D文化 分析上皮细胞形态发生和癌细胞生长、侵袭和转移集落形成。 产生的关键概念是：(1)伴随着一个保守的去分化和极性丧失的过程 正常和肿瘤性形态发生；(2)腔上皮性和基底性乳腺癌的癌细胞 概述基础上皮性和混合型EMT计划。单细胞测序、空间分析的最新进展 转录学和机器学习使这些状态在组织中的转录组范围内得到解析， 正常和癌变细胞状态的比较，以及靶向细胞状态调节因子的识别。 目的1：绘制细胞在发育、肿瘤形成和转移过程中的空间和时间状态图。我们会 从正常腺体、导管原位癌和不同来源的浸润性肿瘤中产生scRNA-seq数据 年龄和3D文化的纵向也是如此。我们将使用CoGAPS算法来推断细胞状态及其 时间进程。然后，我们将使用patternMarker2统计信息来识别MERSCOPE的细胞状态标记 组织中的分析。我们将绘制正常腺体、原发肿瘤和分离出来的转移瘤的状态图。 基因工程小鼠模型(GEMM)和患者来源的异种移植(PDX)。 目的2：模拟发育和肿瘤进展过程中分化状态的动态变化。要定义 细胞状态对转移进展的影响，我们将构建生态式多项式多样性 模特。我们将使用基于文献的参数值来初始化模型以预测细胞之间的相互作用 类型和单元格状态。然后我们将扩展模型以使用CoGAPS分配给每个单元格的权重，从而 将基因调控程序与细胞状态联系起来，改变了驱动转移的因素。 目的3：验证3D培养和体内转移细胞状态转换的候选调节因子。 为了分离调控转移的基因，我们将使用我们的转移学习算法project tR对每个基因进行评分 肿瘤细胞对其相对利用scRNA-seq定义的分子程序。然后，我们将使用我们的 项目驱动统计以确定癌症侵袭部位的差异表达(DE)基因，相对于 肿瘤内部。De基因将在3D培养试验中进行遗传学测试，以模拟入侵和克隆形成 然后在体内进行原位和尾静脉转移实验。

We propose to leverage recent advances in machine learning and systems biology to enable high dimensional molecular assessment of the dynamic cell state transitions driving metastasis. We hypothesize that the interaction between a cancer cell's intrinsic reactivation of developmental programs with its spatiotemporal context determines its metastatic potential. We will exploit developmental changes in the mammary epithelium to define their cell state basis and map the aberrant reuse of these transcriptional programs in metastatic disease. Both normal mammary epithelium and breast tumors undergo dramatic changes in differentiation and tissue architecture, and loss of differentiation correlates with poor patient outcomes. We developed 3D culture assays that recapitulate epithelial morphogenesis and cancer growth, invasion, and metastatic colony formation. The key concepts arising are that: (1) a conserved process of dedifferentiation and loss of polarity accompanies both normal and neoplastic morphogenesis and (2) the cancer cells in luminal and basal breast cancer recapitulate basal epithelial and hybrid EMT programs. Recent advances in single cell sequencing, spatial transcriptomics, and machine learning enable transcriptome-wide resolution of these states in tissue, quantitative comparison of normal and cancerous cell states, and the identification of targetable cell state regulators. Aim 1: Map cell states in space and time during development, tumor formation, and metastasis. We will generate scRNA-seq data from normal glands, ductal carcinoma in situ, and invasive tumors collected at different ages and also longitudinally in 3D culture. We will use our CoGAPS algorithm to infer cell states and their temporal progression. We will then use our patternMarker2 statistic to identify cell state makers for MERSCOPE analysis in tissue. We will map these states in normal glands, primary tumors, and metastases isolated from genetically engineered mouse models (GEMM) and patient derived xenografts (PDX). Aim 2: Model the dynamics of differentiation state during development and cancer progression. To define the effect of cell state on metastatic progression, we will construct an ecosystem-style multinomial diversity model. We will initialize the model with literature-based parameter values to predict the interactions between cell type and cell state. We will then extend the model to use the weights assigned by CoGAPS to each cell, thereby linking gene regulatory programs to the cell state changes driving metastasis. Aim 3: Validate candidate regulators of metastatic cell state transitions in 3D culture and in vivo. To isolate the genes regulating metastasis, we will use our transfer learning algorithm, projectR, to score each cancer cell for its relative utilization of scRNA-seq-defined molecular programs. We will then use our projectionDriver statistic to identify differentially expressed (DE) genes at sites of cancer invasion, relative to the tumor interior. DE genes will be tested genetically in 3D culture assays modeling invasion and colony formation and then in orthotopic and tail vein metastasis assays in vivo.