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使高高动态细胞状态转变的尺寸分子评估驱动转移。我们假设这一点癌细胞对发育程序的内在重新激活与其时空之间的相互作用上下文决定其转移潜力。我们将利用乳腺上皮的发育变化定义其细胞状态并绘制转移性疾病中这些转录程序的异常重复使用。正常乳腺上皮和乳腺肿瘤都会在分化和组织结构和分化的丧失与差的患者结局相关。我们开发了3D文化概括上皮形态发生和癌症生长，侵袭和转移性菌落形成的测定。产生的关键概念是：（1）伴随的推论和丧失的保守过程正常和肿瘤形态发生，以及（2）腔内和基础乳腺癌中的癌细胞概括基础上皮和混合EMT程序。单细胞测序，空间的最新进展 transcriptomics, and machine learning enable transcriptome-wide resolution of these states in tissue, quantitative 正常和癌细胞态的比较，以及可靶向细胞状态调节剂的鉴定。 AIM 1：在发育，肿瘤形成和转移过程中的时空中的地图细胞状态。我们将从正常腺体，原位导管癌和在不同年龄，在3D文化中也纵向。我们将使用我们的Cogaps算法来推断细胞状态及其时间进展。然后，我们将使用我们的tattermarker2统计量来识别Merscope的单元状态制造商组织中的分析。 We will map these states in normal glands, primary tumors, and metastases isolated from 基因工程的小鼠模型（GEMM）和患者衍生的异种移植物（PDX）。目标2：在发育和癌症进展过程中分化状态的动力学建模。定义细胞状态对转移性进展的影响，我们将构建生态系统风格的多项式多样性模型。我们将使用基于文献的参数值初始化模型，以预测单元格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，从而将基因调节程序与细胞状态联系起来改变转移。目标3：验证3D培养和体内转移性细胞状态转变的候选调节剂。为了隔离调节转移的基因，我们将使用转移学习算法（Projectr）进行评分癌细胞相对利用SCRNA-SEQ定义的分子程序。然后我们将使用我们的相对于癌症侵袭部位鉴定差异表达的（DE）基因的投射驱动器统计量相对于肿瘤内部。 DE基因将在3D培养试验中进行遗传测试，以建模浸润和菌落形成然后在体内进行原位和尾静脉转移测定。