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

我们实验室的首要目标是了解细胞如何处理信号并在细胞中进行稳健的通信。 复杂的环境。我们将联合收割机实验、建模和技术开发相结合， 通量单细胞分析。在这里，我们建立在两个赠款，提出了基本的（R 01 GM 128042）和 免疫信号传导的技术（R 01 GM 127527）研究。我们的目标围绕两个关键问题： 细胞如何将信息编码成信号分子？环境输入检测 哨兵细胞，其作为响应产生细胞因子。最近的研究提出了细胞因子动力学 对来自环境的信息进行编码确定病原体和压力输入是如何编码的至关重要 对感染、自身免疫、败血症和癌症的理解。我们将使用单细胞蛋白质生产/分泌 检测和活细胞分析，以回答关键问题，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？细胞密度和协调性如何影响细胞因子 回应？髓系细胞或上皮细胞产生细胞因子变异性的机制是什么， 导致细胞因子过度产生/分泌？单细胞蛋白质分析技术 生产/分泌：我们将实现一种同时测量单细胞表达的 蛋白质、蛋白质复合物和mRNA，使用单细胞测序读数。高通量微流控 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。我们 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？由哨兵细胞产生的信号被处理 通过转录途径。NF-κB是一种炎症通路，控制对许多信号的反应， 这是一个创建微调响应的系统的例子。我们将NF-κB B作为一个模型系统来研究 单细胞信号处理中的问题：NF-κB如何处理组合信号？在感染期间， 在免疫和应激的情况下，细胞暴露于多种细胞因子的组合和时间序列。如何NF- κB处理这样的输入是不理解的。NF-κB动力学如何调节空间基因表达， 时间？虽然已经对信号的时间特性给予了很多关注，但对NF-κ B如何作用知之甚少。 κB解码在不同空间尺度上传播的信号。单细胞/群体如何响应 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中的一般问题，我们将开发广泛适用的活细胞分析 技术：我们将开发微流体系统，培养，跟踪和分析单细胞和群体 在预定的动态/组合信号下。我们将开发微流体技术， 活细胞和细胞信号事件的分析。我们将开发计算/统计方法， 用于图像分割、细胞/类器官跟踪和细胞结果预测的分析。

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.