Identifying intercellular circuits driving cell phenotypes within a niche

组织由多种细胞类型组成，这些细胞类型协同工作以指导细胞反应。这种细胞间 组织内细胞类型之间的通讯对于功能和这些回路的失调是至关重要的 是疾病发展的基础。然而，在识别细胞间电路方面存在技术差距，我们 建议通过单细胞测序和动态单细胞成像相结合的方法来填补 转录活性。单细胞测序能够识别大规模的基因表达谱 以及组织内异质表型的鉴定。然而，这些细胞和分子图谱 没有有效地传达可能正在驱动特定细胞类型的响应的通信。我们提出了一个 环境分泌体与活细胞转录活动相结合的策略 测量(转录因子(TF)、基因表达)，并最终测量单细胞表型和 表达谱，以确定驱动细胞和组织表型的关键细胞间回路。我们建议 使用转移的生态位作为模型，并研究引导TC静止的细胞间回路，如 以及自然杀伤(NK)细胞活性。转移的小生境由多种类型的细胞组成，如支持性 免疫细胞和成纤维细胞，提供旁分泌和旁分泌信号，促进随后的迁移， 转移部位的侵袭、增殖和血管生成。谢伊和杰鲁斯实验室已经 开发了一个由生物材料支架组成的合成转移瘤壁龛(SMN)，该支架招募转移瘤患者 体内的乳腺癌细胞，从而减轻肿瘤负担，提高存活率。这个SMN有很多 与自然转移生态位(NMN)相似，但不同导致不同的表型。因此，我们 建议建立SMN和NMN的体外模型，并研究 控制TC和NK细胞表型。具体目标1将研究TC表型异质性和电路 在转移的利基市场上保持沉默。我们建议用单细胞测序来表征细胞类型 和SMN和NMN的表型，以及体外转移的生态位培养来鉴定TC表型 异质性(目标1.1)。然后，我们将应用活细胞报告分析将分泌体与Tf基因相关联 表达和表型，集中在SMN和SMN中不同观察到的静止表型 NMN(目标1.2)。特定目标2将研究与TC免疫逃避和NK细胞相关的机制 转移壁龛的激活。MN独特的微环境也影响免疫监视 通过NK细胞，SMN富含细胞毒性NK细胞。我们将研究体外SMN和NMN是如何 改变NK细胞表型异质性(AIM 2.1)和支配NK细胞杀伤的细胞间回路(AIM 2.2)。研究团队包括乳腺癌生物学家和临床医生(Jeruss)、组织工程师和Unique 用于测量TF活性的系统(SHEA)、NK免疫学家(Lowenstein)和计算生物学家 将RNAseq与TF活动数据(Chandrasekaran)连接。

Tissues are comprised of multiple cell types that work in concert to direct cellular responses. This intercellular communication amongst the cell types within the tissue is critical to function, and dysregulation of these circuits underlies disease progression. However, a technology gap exists in identifying intercellular circuits, one we propose to fill through the combination of single cell sequencing with dynamic single cell imaging of transcriptional activity. Single cell sequencing enables the identification of large-scale gene expression profiles and identification of heterogeneous phenotypes within a tissue. However, these cell and molecular profiles do not effectively convey the communication that may be driving the response of specific cell types. We propose a strategy in which the secretome of the environment is combined with live cell transcriptional activity measurements (transcription factors (TFs), gene expression) and ultimately to single cell phenotypes and expression profiles to determine the key intercellular circuits driving cell and tissue phenotypes. We propose to employ a metastatic niche as a model, and to investigate the intercellular circuits guiding TC quiescence, as well as natural killer (NK) cell activity. Metastatic niches consist of multiple cell types, such as supportive immune cells and fibroblasts, that provide paracrine and juxtacrine signals that facilitate subsequent migration, invasion, proliferation, and angiogenesis at the metastatic site. The Shea and Jeruss laboratories have developed a synthetic metastatic niche (sMN) consisting of a biomaterial scaffold that recruits metastatic breast cancer cells in vivo, resulting in decreased tumor burden and enhancing survival. This sMN has many similarities to the natural metastatic niche (nMN), yet differences that lead to distinct phenotypes. We thus propose to develop an ex vivo model of the sMN and nMN and to investigate the intercellular circuits that govern TC and NK cell phenotype. Specific Aim 1 will investigate TC phenotypic heterogeneity and the circuits governing quiescence at the metastatic niche. We propose single cell sequencing to characterize cell types and phenotypes at the sMN and nMN, along with ex vivo metastatic niche cultures to identify TC phenotypic heterogeneity (Aim 1.1). We will then apply live cell reporter assays to correlate secretome to TF, gene expression, and phenotype, focusing on the quiescent phenotype that is differentially observed in the sMN and nMN (Aim 1.2). Specific Aim 2 will investigate mechanisms associated with TC immune-evasion and NK cell activation at the metastatic niche. The unique microenvironment of the MN also influences immunosurveillance by NK cells, with the sMN enriched for cytotoxic NK cells. We will investigate how the ex vivo sMN and nMN alters NK cell phenotyping heterogeneity (Aim 2.1) and the intercellular circuits that govern NK cell killing (Aim 2.2). The research team includes breast cancer biologist and clinician (Jeruss), tissue engineer with unique system for measuring TF activity (Shea), a NK immunologist (Lowenstein), and a computational biologist to connect RNAseq with TF activity data (Chandrasekaran).