Correction to “FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data”

Supplemental method 1: Normalisation calculations: To calculate different types of normalisation the following equations were used, where Fcells(t) and Fneg(t) are the fluorescence of the sample of interest and non-fluorescent cells respectively at time t, and ODcell(t), ODneg(t) and ODblank(t) are the absorbance measurements of the sample of interest, non-fluorescent cells and average absorbance of the blank wells (containing only media) respectively at time t.


Supplemental method 1: Normalisation calculations:
To calculate different types of normalisation the following equations were used, where F cells (t) and F neg (t) are the fluorescence of the sample of interest and non-fluorescent cells respectively at time t, and OD cell (t), OD neg (t) and OD blank (t) are the absorbance measurements of the sample of interest, non-fluorescent cells and average absorbance of the blank wells (containing only media) respectively at time t.

Time based normalisation:
( ) − ( ) ( ) − ( ) Time based normalisation normalises the fluorescence of the fluorescent cells against the autofluorescence of the negative cells, and the absorbance of the fluorescent cells against the absorbance of the media, then takes the ratio of the normalised measurements to get fluorescence per OD.

Time OD based normalisation:
( Time OD based normalisation takes the ratio of the fluorescence vs OD at time t of both fluorescent cells and non-fluorescent cells first before using these ratios to normalise. This helps account for differences in OD between the two types of cells, but assumes a proportional relationship between OD and autofluorescence.
FlopR normalisation: FlopR creates a calibration curve using the non-fluorescent cells to get the expected autofluorescence as a function of OD, and uses this for normalisation, taking into account both differences in OD between the two types of cells, and the observed non-linear relationship between OD and autofluorescence.

Microspheres dilution:
Rows E-H on the plate. This is a 3:5 dilution done in 1.5 ml Eppendorf tubes and then pipetted into the plate (modified from the 1:2 dilution from the iGEM interlab and Beal et al 2019). This was done to avoid saturation issues that were observed with larger dilution steps, and to allow vortexing/good mixing of the tubes to minimize error due to microsphere settling (see Note2 below).
Note1: Microsphere manufacturer Cospheric listed ~1.5 trillion spheres per gram for 0.89 um diameter spheres and ~1.2 trillion spheres per gram for 0.961 um diameter spheres. Cospheric have now removed this information and replaced it with a value of ~2 g/cc for all sizes of microsphere. Note2: Microspheres settle very fast when diluted in a liquid, make sure to mix/vortex well before any steps.
1. Create microsphere stock solution A with 0.1 g of microspheres and 1.3 ml of deionized water 2. Make stock solution B by adding 300 µl of solution A to 200 µl of water. 3. Prepare eleven 1.5 ml Eppendorf tubes (numbered 1 to 11). 4. In tube number 1, make a 1 in 10 dilution of solution B to a final volume of 1.5 ml: add 150 µl of solution B to 1350 µl of water and mix well by pipetting up and down. This is the starting concentration of the dilution series. 5. Add 600 µl of water to each of the Eppendorf tubes numbered 2 to 11. 6. Add 900 µl from tube 1 to tube 2 and mix well by pipetting up and down. 7. Add 900 µl from tube 2 to tube 3 and mix well 8. Continue the serial dilution until tube 11. Tubes 1-10 should have 600 µl of diluted microspheres, tube 11 should have 1500 µl. 9. Pipette 125 µl of tube 1 into wells E1, F1, G1 and H1 10. Pipette 125 µl of tube 2 into wells E2, F2, G2 and H2 11. Continue until tube 11 into wells E11, F11, G11 and H11 12. Pipette 125 µl of molecular biology grade water into wells E12, F12, G12 and H12.

Plate reader measurement:
1. Measure the calibration plate on the plate reader using all intended experimental settings: -Excitation and emission settings identical to the green fluorescence channel of the flow cytometer that the plate reader data will be compared to. -Gain in steps of 10, from 40-120 (adjust range as necessary for your plate reader) -With and without a film 2. Save both files as .csv files (ex. Calibration_YYYY_MM_DD_film.csv and Calibration_YYYY_MM_DD_nofilm.csv).

Supplemental method 3: Sub-population Identification From Plate Reader Data
Plate reader experiments were carried out as detailed in the main methods section. Two cell type were used: the killer strain was E. coli JW3910 transformed with plasmids pMPES_AF01 (which provides bacteriocin production) and p63_AF043 (which provides GFP and mCherry fluorescence expression, and Gentamicin resistance), and the competitor strain (MG_Gm_CFP) was a strain of E. coli MG1655 with CFP and Gentamicin resistance incorporated into the genome using a Tn7 transposon. After initial growth, cultures of the two strains were mixed at various ratios (and for Figure S7 diluted to a variety of initial densities) and measured in the plate reader for 12 or 16 hours. For Figure 4, 1 µL samples were taken from the plate every hour and replaced with fresh media. The samples were diluted in 200 µL of PBS and measured in the flow cytometer, as detailed in the main methods.
The raw data was then normalised and calibrated using FlopR, with the competitor strain as the nonfluorescent control. The killer and competitor populations in the flow cytometry data were clustered using the flowClust package in R. Briefly, we attempt to fit t-mixture models with 1 and 2 clusters to the log10 GFP vs log10 mCherry measurements. The models are scored with an Integrated Complete-data Likelihood (ICL) criterion to identify whether the data is best described by 1 or 2 clusters. The clusters are then assigned to the killer or competitor population, determined by whether the mean fluorescences are above or below a threshold.
The plate reader populations are calculated, as described in the manuscript, by producing a calibration curve of normalised absorbance versus normalised fluorescence for a positive control population (consisting of entirely killer cells). This uses the same GAM model as is used for the autofluorescence normalisation. The calibration curve provides us with the expected fluorescence of an entirely killer population at any measured absorbance. As we have fully normalised the data, we also know that an entirely competitor population will have zero fluorescence at any measured absorbance. Using this knowledge we can say that, in a mixed culture, the fraction of killer cells at each time point is given by where ( ) is the normalized fluorescence of the sample at time , ( ) is the normalized absorbance of the sample at time , and uses the calibration curve to get the expected fluorescence at the given absorbance assuming an entirely killer population. The full dynamics of each subpopulation can then be reconstituted by multiplying this fraction (or 1 -fraction for the competitor) by the calibrated cell count.
The sensitivity of this method was explored computationally. We assume that there is an error in both the fluorescence and absorbance measurements which can be described by a normal distribution. If we make the simplifying assumption that the calibration curve produced by the positive control gives us a proportional relationship of the form where is the coefficient of proportionality. We can calculate the estimated fraction in a mixed culture by If we define sensitivity as the proportion of estimates that are within 5% of the true value, then = �0.95 ≤ ≤ 1.05�      . The start of growth rate was set to be when cells reach 1% above their N 0 (fitted starting population), and end of growth to be when cells reach 5% below their N max (fitted maximum population asymptote). These growth windows were used as the 'main growth phase' to calculate the Mean absolute error shown in Figure 2b.    Each point represents the sensitivity achieved given a parameter combination, where sensitivity is the proportion of samples (n=100) that fall within 5% of the true value for each combination of parameters.