Mammalian cell biology is dynamic and responsive. Advances in genomic technology over the last decade made rich (eg multiplexed; genome-wide in the extreme) datasets accessible to individual biomedical investigators on a routine basis. But today's `omic approaches require destruction of cells to access their molecular contents and cannot resolve cellular diversity in situ. Researchers need tools that can probe living biology in tissue to produce rich time-series data specific to cells of interest that illuminate dynamic phenomena. The lack of such tools remains a major limitation in biological and biomedical research today. Here I propose a technology that allows cells to self-report their internal states in time series measurements by secreting a portion of their contents, which can be collected from the culture media for analysis by RNA-Seq and other methods without harming the cells. This capability will enable analysis of dynamic biological processes in heterogeneous cell populations, including tissues, where we can engineer the export apparatus to be selectively activated in the cells of interest. Obtaining time series data to follow dynamic processes simplifies experiments by eliminating the requirement that separate samples be prepared for each time point, enabling samples to serve as their own controls.
Many advanced technologies for reading out molecules in cells require killing the cells as part of the analysis. This program endeavors to establish a new technology that enables advanced readout from living cells. The new method will enable the collection of time-series data and follow-up analyses that are not currently possible, tremendously expanding our ability to understand what is happening in healthy and diseased cells.
