My lab works on studying microRNAs and we have known for decades that microRNAs are depleted in cancer. However, the mechanism as to how these microRNAs are lost has not been actively understood. Our recent work on extracellular vesicles suggests that microRNAs that are depleted in cancer are being actively loaded into these extracellular vesicles.
Our goal now is to really get to the mechanism as to how these RNAs are actively loaded into these extracellular vesicles in the process of tumorigenesis. The protocol helps understand and capture the dynamic process of microRNA release into extracellular vesicles, which is one of the challenging areas in studying extracellular vesicle biology. Our findings provide a tool to advance the study of pathways that regulate depletion of microRNAs in cancer.
We plan on identifying functional and phenotypic consequences of the loss of specific subsets of microRNA via extracellular vesicles in cancer. Once we identify the factors that are involved in loading these microRNAs into extracellular vesicles, our goal is to downregulate these factors and evaluate the phenotypic consequence. Moreover, because we identified a motif that's contained in these small RNAs, we're curious to know whether this motif is both necessary and sufficient for loading these RNAs into the extracellular vesicles.
To begin, take Calu-6 cells transfected with fluorophore-conjugated EV-microRNA and cell-microRNA. Filter the cell suspension through a 35 micrometer strainer cap, included with the round bottom polystyrene FACS tubes. Then set up the flow cytometry instrument, switch on the flow cytometer and allow it to warm up for 30 minutes before use.
Prime the fluidics system with sheath fluid. After that, verify and adjust the laser alignment. For quality control, load ultrapure filtered water into the flow cytometer and run it at an appropriate flow rate, ensuring an adequate acquisition time.
Then set the threshold for capturing the signal from Calu-6 cells at FSC 5, 000. To account for spectral overlap between fluorophores, prepare compensation controls using untransfected cells and cells transfected with only fluorophore. Perform expression analysis in transfected cells using the FSC, SSC, FITC, and PE channels set at 258, 192, 269, and 423 voltage units respectively.
Then introduce the negative control sample into the flow cytometer. Capture cell signals after defining FSC and SSC intensities in a custom worksheet. Select FSC for the x-axis and SSC for the y-axis in the plot.
Draw a polygon around the population of interest to create a gate on the axis. To create a new plot using the gated population, set the x-axis to fluorescent signal one for cell-microRNA detection and the y-axis to fluorescence signal two for EV-microRNA detection. Establish compensation parameters for the individual fluorophores using cells independently transfected with each of the fluorescently-tagged microRNAs.
Flow cytometry analysis of cells transfected with cell-microRNA and EV-microRNA revealed a sequential decrease of fluorescent signal corresponding to EV-microRNA. In contrast, the signal corresponding to the cell-microRNA was retained in the cells, indicating that the fluorophore does not impact the retention of microRNAs by the cells. To begin, take EVs isolated from Calu-6 cells transfected with fluorophore-conjugated EV-microRNA and cell-microRNA.
Dilute the EVs in an appropriate volume of ultra filtered PBS to prepare the extracellular vesicle suspension for flow cytometry analysis. Use the nano flow cytometry to analyze nano-sized particles. Launch the software and configure the instrument according to the recommended calibration instructions.
Using performance nano beads, conduct the performance test. The instrument's performance is deemed optimal if the nano beads are detected within the standard size range. Determine the suitable flow rate for EV particle detection.
Now perform a quality control check using ultra pure filtered water to verify the instrument's fluidics and assess the performance of detectors and lasers. Configure the parameters to evaluate the sensitivity and resolution of different populations in the bead mix as per the manufacturer's protocol. Then apply appropriate gating to distinguish the bead population from instrument noise.
Next, thoroughly vortex the samples to ensure the even distribution EVs before loading them for analysis. Launch the flow cytometry data analysis software and introduce ultra filtered PBS. Using ultra filtered PBS, set up gating for EV particles, ensuring that the gating corresponds to the appropriate size of particles based on the calibration.
Take EVs that have been transferred and vortexed in an Attune compatible tube and load the tube into the instrument. Capture EV signals while plotting FSC on the x-axis and SSC on the y-axis. For cell-microRNA detection, on the x-axis, select fluorescent signal one, and for EV-microRNA detection on the y-axis, choose fluorescent signal two.
Using the EV samples isolated from cells transfected with a single microRNA, establish compensation parameters for the fluorophores. Flow cytometry revealed a time-dependent accumulation of miR-451a Alexa Fluor 488 in EVs supporting efficient packaging and release. In contrast, the fluorescence of cell-miR-67 was not observed in EVs.
