(53a) Increasing the Dimensionality of Single Cell Transcriptomics to Address Systems Biology: Proteins, Imaging, and More

Systems biology represents a comprehensive approach to measure the many dimensions of biology, and discover how the sum of fundamental parts becomes a complex whole. Technology development is our cure for complexity, with powerful techniques emerging in sequencing, gene editing, remote cell manipulation, and more. Single cell resolution in particular has been transformative: by unveiling cellular heterogeneity we can understand fundamental mechanisms like cancer drug resistance, and our biggest challenge is now to determine just how deeply biology varies from cell to cell.

Here I present approaches to expand our understanding of heterogeneity by combining the incredibly rich data sets of single cell transcriptomics with complementary measurements like protein analysis to provide illuminating context, all in single cells. The critical issue here is that a single measurement of a single cell is not necessarily representative of that single cell’s role in the native tissue and active biology. To understand biology with the sophistication needed to provide therapies and improve health, we will have to understand still more about the internal biology of each cell – it’s mRNA, proteins, metabolites, and more – and we will have to recapitulate local cell environments and cell-material interactions to measure more realistic cell behaviors.

First, I will show that a microfluidic chip can integrate proteomic and transcriptomic measurements by embedding location information within transcriptomic measurements. This is enabled by a location encoding strategy and microfluidic flow patterning, linking single cell transcriptomics captured by Dropseq-style bead methods to single cell protein measurements made using DNA-Encoded Antibody Libraries. With this method, I measure full transcriptomes as well as proteins localized to the cytosol, mitochondria, and nucleus within the same single cells.

Next, I will demonstrate a technology framework to integrate the myriad tools of biology into the rich datasets of single cell transcriptomics all within the same cells. This generalized strategy can be applied to augment single cell transcriptomics with completely orthogonal measurements, such as fluorescent measurements of glucose uptake and imaging analyses. The core of this technology is to provide mappings between transcriptomic measurements and a physical chip at high throughput. This is done with location, spectral, and temporal encoding for thousands of cells at once. Once the link between a cell’s transcriptome and location is established, useful experimental conditions like engineered local microenvironments or light-based manipulations can be mapped to transcriptomics, to extend the reach of multi-modal single cell measurements in new directions. These technologies represent a unified approach to systems biology, to provide relevant biological context to high dimensional data and link previously established biology to powerful modern experiments.