The microfluidic chip is capable of discovering not only the existence of specific EGFR multi-mutations, but also other valuable single-cell-level information: on which specific cells the mutations occurred, or whether different mutations coexist on the same cells.
Discovering not only the existence of specific EGFR multi-mutations occurred in minority of EGFR-mutated cells which may be covered by the noises from majority of un-mutated cells, but also other valuable single-cell-level information: on which specific cells the mutations occurred, or whether different mutations coexist on the same cells.
Having established the primary driver of cell response at the individual cell level, this information can be used to construct a complete model of cell-nanoparticle interaction with an ab initio description of the population response based on single cell studies.
The ability to deliver spatially uniform, temporally synchronized, and dynamic signal patterns over a large cell array allows us to study a large population of cells at a single cell level, rendering information on the heterogeneity of subsets within the population.
For example, the time-series analysis of gene expression at the single cell level can provide information about the characteristics of intracellular dynamics.
Our lab and others have shown that performing gene expression analyses at the single cell level reveals useful information about disease states and conditional responses of both mammalian and bacterial cells [ 8- 11].
Although the work by Alonso-Simon and coworkers [ 40] provided detailed analysis of hemicelluloses degradation upon hydrothermal pretreatment, the study only reported the bulk chemical changes of wood meal, but not the cell wall-level information.
This selection was performed by aggregating non cell type specific pathway level information.
This enables investigating the process at the single cell level and provides important qualitative information; however, it requires examining cells one by one, which is very time-consuming and limits the sample size, whereas variation between cells may be high.
The experimental results indicate that SERS can be used to obtain molecular level information from cells, and has potential application in medicine and biotechnology.
