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这些回路的功能和失调至关重要 是疾病进展的基础然而，在识别细胞间回路方面存在技术差距，我们 建议通过单细胞测序与动态单细胞成像相结合来填补 转录活性单细胞测序能够鉴定大规模基因表达谱 和鉴定组织内的异质表型。然而，这些细胞和分子谱确实 不能有效地传达可能驱动特定细胞类型的响应的通信。我们提出了一个 环境分泌组与活细胞转录活性相结合的策略 测量（转录因子（TF），基因表达），并最终到单细胞表型， 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 采用转移小生境作为模型，并研究引导TC静止的细胞间回路， 以及自然杀伤（NK）细胞活性。转移性小生境由多种细胞类型组成，如支持性 免疫细胞和成纤维细胞，提供旁分泌和近分泌信号，促进随后的迁移， 转移部位的侵袭、增殖和血管生成。Shea和Jeruss实验室 开发了一种合成转移小生境（sMN），由招募转移性肿瘤的生物材料支架组成， 乳腺癌细胞在体内，导致减少肿瘤负荷和提高生存。这个sMN有很多 与自然转移小生境（nMN）相似，但差异导致不同的表型。我们因此 建议开发sMN和nMN的离体模型，并研究细胞间回路， 控制TC和NK细胞表型。具体目标1将研究TC表型异质性和回路 控制转移性小生境的静止。我们提出单细胞测序来表征细胞类型 以及sMN和nMN的表型，沿着离体转移性小生境培养物以鉴定TC表型 异质性（目标1.1）。然后，我们将应用活细胞报告基因测定将分泌组与TF、基因 表达和表型，重点是在sMN中差异观察到的静止表型， nMN（目标1.2）。具体目标2将研究与TC免疫逃避和NK细胞相关的机制 在转移灶激活。MN独特的微环境也影响免疫监视 通过NK细胞，其中sMN富集细胞毒性NK细胞。我们将研究如何离体sMN和nMN 改变NK细胞表型异质性（目的2.1）和控制NK细胞杀伤的细胞间回路（目的 2.2）。该研究小组包括乳腺癌生物学家和临床医生（Jeruss），组织工程师， 系统测量TF活性（Shea），NK免疫学家（Lowenstein）和计算生物学家， 将RNAseq与TF活性数据连接（RNAsekaran）。

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).