Tissue samples frequently contain multiple cell types. New methods (e.g. laser capture microdissection) enable the segregation of small quantities of individual cells and cell types, but the current methods to analyze global gene expression do not provide highly efficient profiling, leading to incomplete, and even empty, expression profile maps, especially from low- to moderate-expressed genes.
Linear amplification is the method of choice for amplifying gene expression profiles due to the fidelity of amplification of individual transcript concentrations relative to each other unlike polymerase chain reaction (PCR) amplification. However, this method is limited in that the enzymes used in the process have complex mechanisms and require minimum concentrations to operate efficiently. By immobilizing the target mRNA onto functionalized microbeads packed into nL-sized columns, mRNA concentration can be effectively increased to the recommended levels for enzymatic processing. The use of constant perfusion so fresh reagents are introduced to the sample and byproducts that inhibit or deactivate the enzymes flow away maintains near-ideal reaction conditions.
Recent advances in single-cell RNA capture and processing using microfluidics to manipulate the samples suggest order-of-magnitude improvements can be made in several steps of the amplification process. These improvements correlate to higher efficiency, leading to more complete expression profiling through positive identification of on the order of 20% more gene transcripts, or approximately 1000 genes.
Perform research to address the current and long-term needs for gene expression pro-filing of small biological samples, and to anticipate future directions in the field of bio-logical and biomedical analysis of gene expression.
- Enable robust and reproducible measurements from biological samples at the single cell level
- Optimize mRNA amplification process steps for sample amount, quality, and source
- With NCI, analyze rare cells from extracted tissues and cultures to identify gene ex-pression signatures and quantify differential gene expression from the corresponding normal cells
- Examine the feasibility of employing high throughput sequencing technology for massively parallel single-cell gene expression profiling