Ultra-Sensitive Multiplexed Quantification of Proteins Secreted from Single-Cells This proposal aims to develop a powerful new technology for ultra-sensitive quantification of multiple proteins secreted from single-cells. Secreted protein signals coordinate cellular and organism-level functions across all biological systems, and their accurate quantification is crucial for understanding biological information processing in general. Unfortunately, signaling studies have been hindered by poor sensitivity and limited multiplexing ability of current proteomic methods, and the confusion of results due to pooling of multiple cell readouts when conducting population-averaged assays. To address the need to quantify single-cell secreted proteins across different signaling systems, we will develop a new microfluidic technology for ultra-sensitive, multiplexed protein quantification at the single-cell level. We will use our wide-ranging expertise in single-cell assay and microfluidic development and our extensive preliminary data to develop a user-friendly system that will measure multiplexed protein signals from a wide range of cell types, in a time-dependent manner. We will validate our method by quantifying signaling time courses of diverse systems including immune cells, cancer secretion, and hormone signaling. This method will, for the first time, provide the means to gather sensitive, multiplexed single-cell secreted protein measurements, and will be indispensable for understanding signaling in immunity, metabolism, development and cancer.
We propose an ultrasensitive detection method for protein quantification that will enable a new standard for investigating signaling pathways. This high-sensitivity, amplified detection will offer the ability to dynamically detect the full complement of signals used by single cells during information transfer. Once realized, this technique will enable detailed investigations into a variety of signaling pathways, which are a fundamental aspect of biological life, but have so far remained poorly studied due to limitations in the ability to study signaling events.
