The protocol is highly sensitive and allows high throughput capture of gene expression profile at single cell resolution. The technique provides both anatomical spatial and molecular specificity at single cell resolution. After harvesting the brain, selecting the single cells, and preparing the mRNA, inject control line fluid into the qPCR chip for priming.
Insert the qPCR chip into the microfluidic mixing device. Select the Prime script and run the program. After the completion of the program, remove the primed qPCR chip and pipette six microliters of the reaction from the PCR sample plate into the corresponding sample well in the primed qPCR chip.
Now, pipette six microliters of the reaction from the PCR assay plate into the corresponding assay well in the primed qPCR chip. Then insert the chip into the microfluidic mixing device. Select the Load Mix script and run the program.
Turn on the microfluidic RT-qPCR platform and warm up the bulb. Remove the qPCR chip from the microfluidic mixing device and peel the protective sticker from the bottom of the chip. Open the microfluidic RT-qPCR platform and load the qPCR chip into the platform.
Launch the data collection software by clicking on Start a New Run. Verify the chip barcode and chip type. Then click on next.
Select the chip run file and browse the file location for the data collection storage. Then click on Application Type and select Gene Expression. Select ROX for a passive reference, single probe, and EVAGreen for probe type.
Click to select the thermal cycling program and select Biomark HD GE:Fast 96x96 PCR+Melt v2. pcl file. Download the data analysis software.
Launch the software to analyze the microfluidic RT-qPCR experiment. Click Chip Run and open the ChipRun. bml file that was created by the experimenter.
A window displaying the experimental details including the passive reference, probe, and PCR thermal program will pop up. Under the Chip Explorer tab, click Sample Plate Setup and create a new sample plate template. Copy and paste the sample labels into the software spreadsheet as per the experimental design.
Enter the sample name and the RNA concentration used for the standard samples. Now, map the sample set up by clicking map from the task menu and selecting SBS96-Left.dsp. Next, select Detail Views to update these changes in the file and click Analyze.
Select Detector Plate Setup and create a new assay plate. Select the appropriate container type and format. Paste the assay names as per the experimental design and click on Analyze.
In the analysis settings pane, click the User and set the fit to auto. Now, manually review each reaction in the 96x96 chip. Visualize the amplification and melt curves to determine if each reaction followed the expected qPCR pattern.
If the amplification or melt curve does not match with what is expected, fail that reaction. Following QC, export the data by selecting file, clicking export, and saving the dataset as a csv file. As the exported csv file has both a pass fail matrix and a matrix with raw CT values, use the pass fail matrix to replace any failed cells in the dataset with NA.Download the recent version of open Source R software and then download the R Studio application.
For median centering, use the R software to calculate the median CT value calculated from all the CT values for an individual sample and then subtract all individual CT values from this median value to get a minus delta CT value. For housekeeping gene normalization, calculate the average expression of the housekeeping genes for each sample and use this value to subtract the individual CT values. To generate a minus delta delta CT value, run the code in the R software.
Upload the normalized dataset into R and analyze the data using the scale function, generate the scaled data, and then use the R heat map function or separate software to generate a heat map. To organize the data set for other functionality, calculate the Pearson correlations between each gene followed by the melt function. Export and upload this data into a gene correlation network software.
Neurons showed elevated expression of NeuN and microglial samples showed significant expression of the microglial markers Cd34 and Cx3xr1. The Th+neuronal samples demonstrated significantly elevated expression of Th compared to Th-neuronal and microglia samples. The Th-neurons displayed significant expression of Gcg, suggesting that Th-neuronal samples are enriched with neurons that use Glp1 as a neurotransmitter.
Linear discriminate analysis showed that these three cell types had differing gene expression profiles across all 65 genes. Cellular sub phenotypes of Glp1 enriched neuron samples through an alcohol withdrawal time series were represented using heat maps. The sub phenotype A, which highly expresses the inflammatory gene cluster one increases in ratio at the eight hour withdrawal time point and reaches a maximum at 32 hour withdrawal.
The inflammatory sub phenotype is normalized to control by 176 hour withdrawal. Sub phenotype B, which highly expresses GABA receptor gene cluster two demonstrates an overall suppression in the expression of this gene cluster by the 176 hour withdrawal condition. The gene expression of individual samples was combined into averages so that the expression of gene clusters and the protein location of that gene transcript could be visualized throughout the time series.
This technique can be applied to any biological system or tissue to understand the cellular response to a disease or a perturbation. That is to decipher the molecular response at single cell resolution relating to the spatial and the anatomical architecture of the tissue.
