Understanding robust cellular information processing in complex environments and development of enabling single-cell analysis technologies

了解复杂环境中强大的细胞信息处理以及单细胞分析技术的开发

• 批准号: 10552335
• 负责人: Savas Tay
• 金额: $ 66.26万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552335
• 关键词: Attention; Autoimmunity; Biological Assay; Biological Models; Biomedical Engineering; Cell Signaling Process; Cell physiology; Cells; Characteristics; Coculture Techniques; Communication; Complex; Computer Models; Development; Disease; Environment; Epithelial Cells; Event; Exposure to; Gene Expression; Goals; Grant; Health; Immune signaling; Immunity; Individual; Infection; Inflammatory; Laboratories; Malignant Neoplasms; Measurement; Messenger RNA; Methods; Microfluidics; Modeling; Myeloid Cells; Organoids; Outcome; Pathway interactions; Population; Process; Production; Protein Secretion; Proteins; Science; Sentinel; Sepsis; Signal Pathway; Signal Transduction; Signaling Molecule; Statistical Methods; Stimulus; Stress; System; Technology; Time; Transcription Process; automated image analysis; biological information processing; combinatorial; cytokine; density; experimental study; high throughput technology; improved; information processing; memory encoding; microfluidic technology; new technology; pathogen; protein complex; response; segmentation Image analysis; signal processing; single cell analysis; single cell proteins; single cell sequencing; technology development

The overarching goal of our laboratory is to understand how cells process signals and communicate robustly in complex environments. We combine experiments, modeling, and development of technologies for high- throughput single-cell analysis. Here, we build upon two grants that proposed fundamental (R01GM128042) and technological (R01GM127527) studies of immune signaling. Our goals are integrated around two key questions: How do cells encode information into signaling molecules? Environmental inputs are detected by sentinel cells, which in response produce cytokines. Recent studies led to the hypothesis that cytokine dynamics encode information from the environment. Determining how pathogen and stress inputs are encoded is crucial for understanding infection, autoimmunity, sepsis and cancer. We will use single-cell protein production/secretion assays and live-cell analysis to answer key questions including: How do cells exposed to multiple sequential stimuli encode the memory of prior stimuli? How does cellular density and coordination influence cytokine responses? What are the mechanisms of cytokine production variability by myeloid or epithelial cells, and what causes excessive cytokine production/secretion? Technologies to analyze single-cell protein production/secretion: We will realize a method for simultaneous measurement of single-cell expressed proteins, protein complexes and mRNA using single-cell sequencing readout. High-throughput microfluidic technologies for time-dependent measurement of proteins secreted by single live-cells will be developed. We will also develop microfluidic co-culture systems for creating controlled dynamic microenvironments. How do cells process combinatorial/dynamic signals? Signals generated by sentinel cells are processed by transcriptional pathways. NF-κB, an inflammatory pathway that controls responses to many signals, is a prime example of a system that creates fine-tuned responses. We will study NF-κB as a model system to answer key questions in signal processing in single-cells: How does NF-κB process combinatorial signals? During infection, immunity and stress, cells are exposed to combinations and temporal sequences of multiple cytokines. How NF- κB processes such inputs is not understood. How does NF-κB dynamics regulate gene expression in space and time? While much attention has been given to temporal characteristics of signaling, little is known about how NF- κB decodes signals propagating over different spatial scales. How do single cells/populations respond to dynamic (increasing, decreasing, oscillating) inputs? High-throughput live-cell analysis technologies: To better answer these and other general questions in signaling, we will develop broadly applicable live-cell analysis technologies: We will develop microfluidic systems that culture, track and analyze single-cells and populations under predetermined dynamic/combinatorial signals. We will develop microfluidic technologies for spatial analysis of live cells and cell signaling events. We will develop computational/statistical methods for automated analysis for image segmentation, cell/organoid tracking, and prediction of cellular outcomes.

我们实验室的首要目标是了解细胞如何处理信号并在 我们将实验、建模和技术开发结合起来，以实现高水平的环境。 在这里，我们基于提出的两项资助（R01GM128042）和 免疫信号传导的技术（R01GM127527）研究我们的目标围绕两个关键问题： 细胞如何将信息编码到信号分子中？ 最近的研究提出了细胞因子动力学的假设。 确定来自环境的信息的编码方式至关重要。 为了了解感染、自身免疫、败血症和癌症，我们将使用单细胞蛋白质产生/分泌。 测定和活细胞分析来回答关键问题，包括：细胞如何暴露于多个连续的 刺激编码先前刺激的记忆，细胞密度和协调如何影响细胞因子？ 骨髓细胞或上皮细胞产生细胞因子变异的机制是什么？ 导致细胞因子过度产生/分泌？ 分析单细胞蛋白质的技术 生产/分泌：我们将实现一种同时测量单细胞表达的方法 使用单细胞测序读出蛋白质、蛋白质复合物和 mRNA。 我们将开发对单个活细胞分泌的蛋白质进行时间依赖性测量的技术。 还将开发微流体共培养系统，用于创建受控的动态微环境。 细胞如何处理组合/动态信号？前哨细胞产生的信号是如何处理的？ NF-κB 是一种控制多种信号反应的炎症途径。 创建微调响应的系统示例 我们将研究 NF-κB 作为模型系统来回答关键问题。 单细胞信号处理问题：NF-κB 在感染过程中如何处理组合信号？ 免疫和压力，细胞暴露于多种细胞因子的组合和时间序列。 NF-κB 动力学如何调节空间和基因表达尚不清楚。 虽然人们对信号传导的时间特征给予了很多关注，但对于 NF-如何发生却知之甚少。 κB 解码在不同空间尺度上传播的信号如何响应。 动态（增加、减少、振荡）输入？ 高通量活细胞分析技术： 为了更好地回答这些和其他信号传导方面的一般问题，我们将开发广泛适用的活细胞分析 技术：我们将开发用于培养、跟踪和分析单细胞和群体的微流体系统 我们将在预定的动态/组合信号下开发空间微流体技术。 我们将开发自动化的计算/统计方法。 图像分割分析、细胞/类器官跟踪和细胞结果预测。