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

绘制空间和时间转移的单细胞状态基础

基本信息

批准号：
10738579
负责人：
Andrew Josef Ewald
金额：
$ 67.96万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-12 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10738579
关键词：
Age Algorithms Architecture Automobile Driving Behavior Biological Assay Biological Models Birth Rate Cancerous Cause of Death Cells Cellular Morphology Cessation of life Clustered Regularly Interspaced Short Palindromic Repeats Data Development Developmental Gene Disease Distant Ecosystem Environment Enzymes Epithelium Exhibits Genes Genetic Transcription Genetically Engineered Mouse Gland Goals Growth Hybrids Invaded Keratin Learning Link Literature Longevity Machine Learning Malignant Neoplasms Mammary Neoplasms Mammary gland Maps Measurement Modeling Molecular Morphogenesis Neoplasm Metastasis Noninfiltrating Intraductal Carcinoma Organ Patient-Focused Outcomes Patients Periodicity Pharmaceutical Preparations Primary Neoplasm Process Proteins Publishing RNA Regulator Genes Resolution Source Structure Systems Biology Tail Testing Time Tissues United States Veins Vimentin Weight cancer cell cancer site candidate validation cell type computerized tools differential expression high dimensionality high resolution imaging in vivo learning algorithm malignant breast neoplasm mammary epithelium migration neoplastic new therapeutic target patient derived xenograft model prevent programs single cell sequencing single-cell RNA sequencing small hairpin RNA small molecule spatiotemporal statistics three dimensional cell culture tool transcriptome transcriptomics transfer learning tumor tumor progression

项目摘要

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.

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的发育程序再激活与其时空之间的相互作用背景决定其转移潜力。我们将利用乳腺上皮的发育变化定义它们的细胞状态基础并绘制这些转录程序在转移性疾病中的异常重用。正常乳腺上皮和乳腺肿瘤在分化和分化方面都会发生巨大的变化。组织结构和分化丧失与患者预后不良相关。我们开发了3D文化概括上皮形态发生和癌症生长、侵袭和转移集落形成的测定。产生的关键概念是：（1）保守的去分化过程和极性丧失伴随着正常和肿瘤形态发生以及 (2) 管腔和基底乳腺癌中的癌细胞概括基底上皮和混合 EMT 程序。单细胞测序、空间测序的最新进展转录组学和机器学习能够在转录组范围内解决组织中的这些状态，定量正常细胞和癌细胞状态的比较，以及可靶向细胞状态调节剂的鉴定。目标 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。我们将从不同部位收集的正常腺体、导管原位癌和侵袭性肿瘤生成 scRNA-seq 数据年龄以及 3D 文化中的纵向。我们将使用 CoGAPS 算法来推断细胞状态及其时间进展。然后，我们将使用 PatternMarker2 统计数据来识别 MERSCOPE 的细胞状态制造者组织中的分析。我们将绘制正常腺体、原发性肿瘤和从肿瘤中分离出来的转移瘤中的这些状态。基因工程小鼠模型（GEMM）和患者来源的异种移植物（PDX）。目标 2：对发育和癌症进展过程中分化状态的动态进行建模。定义细胞状态对转移进展的影响，我们将构建一个生态系统式的多项多样性模型。我们将使用基于文献的参数值初始化模型，以预测细胞之间的相互作用类型和细胞状态。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驱动转移的细胞状态变化联系起来。目标 3：验证 3D 培养和体内转移细胞状态转变的候选调节因子。为了分离调节转移的基因，我们将使用我们的迁移学习算法 ProjectR 对每个基因进行评分癌细胞对 scRNA-seq 定义的分子程序的相对利用。然后我们将使用我们的投影驱动统计，用于识别癌症侵袭部位的差异表达（DE）基因，相对于肿瘤内部。 DE 基因将在模拟入侵和集落形成的 3D 培养测定中进行基因测试然后进行体内原位和尾静脉转移测定。