Project Leads: Sui Huang, John Aitchison

The Single Cell facility houses the Fluidigm system, mainly consisting of the C1 and the Biomark, and the nanostring nCounter system. The Fluidigm C1 functions to isolate single cells and create pre-amplified cDNA for further downstream analysis. The Fluidigm Biomark produces high-throughput gene expression data. The nanostring nCounter system is capable of generating sensitive single cell transcript counts with minimal amplification based on, up to, 800 genes.

Current single cell projects are:

. Identification of regulators that orchestrate post-differentiation transitions. A) Basic concept of our study design: combine population dynamics with gene regulatory modules to understand how cell-to-cell variability and “quantized” populations are regulated through the Gene Regulatory Network. B) Heatmap of the gene expression levels of 34 differentially regulated transcripts in post-differentiation day 7 and 21

Identification of regulators that orchestrate post-differentiation transitions. Basic concept of our study design: combine population dynamics with gene regulatory modules to understand how cell-to-cell variability and “quantized” populations are regulated through the Gene Regulatory Network.

Development of automated single-cell analysis of induced pluripotent stem cells assay.  How pluripotent stem cells differentiate into various cell types (e.g., cardiomyocytes, hepatocytes, and neurons).  Stem cell differentiation is hypothesized to occur through the interaction of quantized cell subpopulations within the cell culture where groups of cells are composed of similar but not identical cells.

Development of a scalable, automated, multiparameter solid medium, colony growth rate analysis platform.  Throughout the Center projects, cell growth is used as a complex phenotype to gauge the impact of genetic and environmental perturbations. Ultimately, our goal is to reveal networks through which genomic and environmental information flow and which can be perturbed to control biological systems and cure or modulate disease.  This, of course, depends critically on developing technologies and computational approaches to:  1. reveal molecular networks, their interactions and their dynamics; and 2. quantitative and reliable measurement of complex phenotypic traits.