An Information Theoretic, Microfluidic-Based Single Cell Analysis Permits Identification of Subpopulations among Putatively Homogeneous Stem Cells (original) (raw)
Figure 1
Single cell gene expression analysis demonstrates transcriptional variation in murine LT-HSCs.
(A) Schematic of high throughput microfluidic chip-based single cell transcriptional analysis. A single cell is sorted by FACS into each well of a 96-well plate that has been preloaded with RT-PCR reagents (see methods for complete description). A low-cycle RT-PCR pre-amplification step creates cDNA for each gene target within each individual cell. Single cell cDNA is then loaded onto the microfluidics chip along with the primer-probe sets for each gene target. The BioMark machine performs qPCR for each cell across all 48 gene targets in parallel, resulting in 2,304 data points for each chip run. (B) FACS sorting parameters of two populations of HSCs isolated from primary murine bone marrow. All cells were LSK (Linneg Sca-1+ cKit+) CD48– CD135–CD150+ and were sorted into two distinct populations based on CD34 expression (CD34lo and CD34hi). SSC = side scatter. (C) Histogram presenting raw qPCR cycle threshold values for individual genes across 300 LT-HSCs. Each dot represents a single gene/cell qPCR reaction, with increased cycle threshold values corresponding to decreased mRNA content. Cycle threshold values of 40 were assigned to all reactions that failed to achieve detectable levels of amplification within 40 qPCR cycles. For convenience, genes that failed to amplify in the majority of cells have been omitted (see Figure S1 for complete dataset). (D) Single-gene coefficient of variance (COV) values for individual CD34lo HSCs. Error bars represent standard deviations derived through bootstrapping over 100,000 iterations as previously described [50].