Page 148 - Read Online

Maiocchi et al. Vessel Plus 2023;7:27 https://dx.doi.org/10.20517/2574-1209.2023.69 Page 11 of 19

thresholds to classify droplets into positive and negative populations: positive droplets contain the target of
interest and negative droplets do not. Finally, review the gating strategy by inspecting the data to ensure
accurate identification of the positive, dual positive, and negative droplet populations.

Figure 4 contains all representative 2-dimensional plots for each of the following cardiovascular-related
miRNA targets. The gating strategy is defined on the X and Y axis of each purple threshold line. These
values are held constant within each reaction and are: miR-1 FAM 1300 VIC 1500, miR-133a FAM 1500
VIC 1700, miR-143 FAM 1600 VIC 700, miR-145 FAM 1600 VIC 1300, miR-16 FAM 1600 VIC 1600, miR-
193a FAM 1600 VIC 800, miR-21 FAM 1600 VIC 800, miR-29a FAM 1600 VIC 1000, miR-30b FAM 1300
VIC 1700, miR-574 FAM 1500 VIC 500, miR-147a FAM 1500 VIC 688, miR-486 FAM 1500 VIC 744,
RNU6B FAM 1500 VIC 3500.

Supplementary Figure 2 contains all representative 2-dimensional plots of each miRNA specific NTC and
NC control. Utilizing the gating strategy described above should result in little to no positive droplets being
detected in the FAM or VIC fluorescence channels.

For data analysis: Confirm that the number of droplets exceeds 10,000 events and there is little to no
fluorescence signal in any of the NTC or NC internal controls. If under 10,000 droplets, repeat from step 6:
droplet generation. If a signal appears in the NC and not the NTC, repeat the process from step 5: cDNA
generation. If a fluorescence signal is detected in both the NTC and NC, repeat the process from step 3:
small RNA quantification and concentration normalization.

After confirming the above and adjusting the thresholds to the described settings, record the copy numbers
for each miRNA target and their respective miR-39 values. Apply the Ratio Scale to all miR-39 copy
numbers using the below equation, then divide the individual target copy number by its respective ratio
scale-corrected miR-39 value. Perform a congruence transformation to normalize miR-39 values by dividing
each miR-39 value by the square root of the sum of squares of all the values obtained from each assay (at
least three values are required). Below is a representative equation for a congruence transformation of a
matrix of values.

An example using representative data is: Patient 1 miR-X: 100; miR-39: 1,010 / Patient 2 miR-X: 94; miR-39:
2
2
1,203 / Patient 3 miR-X: 114; miR-39: 973. The first step is to square all miR-39 values (1,010 , 1,203 ,
973 ) = (1,020,100, 1,447,209, 946,729). Next, perform the sum of the squares (1,020,100 + 1,447,209 +
2
946,729) = (3,414,038). Then calculate the square root of the sum of squares: √ (3,414,038) = (1,847.71).
Divide each value by the square root of the sum of squares (1,010/1,847.71, 1,203/1,847.71, 973/
1,847.71) = (0.546, 0.651, 0.526). Finally, divide each microRNA target value (miR-X) by its respective ratio
scale corrected miR-39 value (100/0.546, 94/0.651, 114/0.526) = (183.15, 144.39, 216.73). These are the
miRNA values to report for each patient.

Ratio Scale normalization is a method used to correct target values to a common scale . This is because the
[10]
ratios among the intervals between numbers are not affected by congruence transformations, which makes
it useful for comparing values from different datasets using different plates or miR-39 spike-in lots.