analysis_R-GECO1_CDPK-FRET

Synchronisation analysis of R-GECO1 and CDPK-FRET in guard cells

Script for analysis of guard cells was adapted from Li et al. 2021 (Li, K., Prada, J., Damineli, D.S.C., Liese, A., Romeis, T., Dandekar, T., Feijó, J.A., Hedrich, R. and Konrad, K.R. (2021), An optimized genetically encoded dual reporter for simultaneous ratio imaging of Ca²⁺ and H⁺ reveals new insights into ion signaling in plants. New Phytol, 230: 2292-2310. https://doi.org/10.1111/nph.17202)

This script reads the excel file with the calcium and CPK-FRET traces from the guard cell and performs a synchronisation analysis of the signals

note: flg22 or ABA treatment after 3 min - data normalized 10 frames before treatment

1. Setting of the directory, installing and loading of packages, loading data from excel file and creating a results folder

rm(list = ls())

setwd("C:/Users/slederer/Desktop/ABA")  # adjusting working directory (folder with data file)
requirements=c("readxl","writexl","scales","baseline","pracma","pastecs","plotly","stats", 
               "ggplot2", "gridExtra", "forecast")
missing <- setdiff(requirements,
                   rownames(installed.packages()))

if (length(missing) != 0) {
  install.packages(missing, dependencies = TRUE)
}
invisible(lapply(requirements, require, character.only = TRUE))

dir.create(paste(getwd(),"/results",sep=""))
table <- read_excel("GC.xlsx")

2. Creating of a linear regression model with time (Time) as predictor variable and signal (RG or FRET_ratio) as response variable, assuming that the technical artefacts can be represented by a linear relationship

model_RG <- lm(RG ~ Time, data = table)
plot(table$RG ~ table$Time, xlab = "Time (min)", ylab = "F/F0")
abline(model_RG)

model_FRET <- lm(FRET_ratio ~ Time, data = table)
plot(table$FRET_ratio ~ table$Time, xlab = "Time (min)", ylab = "R/R0")
abline(model_FRET)

3. Normalization of the data by the linear regression model

lm_RG <- (table$Time * model_RG$coefficients[-c(1)]) + model_RG$coefficients[c(1)]
lm_FRET <- (table$Time * model_FRET$coefficients[-c(1)]) + 
            model_FRET$coefficients[c(1)]

table$normalized_RG <- table$RG/lm_RG
table$normalized_FRET <- table$FRET_ratio/lm_FRET

plot(table$normalized_RG ~ table$Time, xlab = "Time (min)", ylab = "Norm. F/F0")
plot(table$normalized_FRET ~ table$Time, xlab = "Time (min)", ylab = "Norm. R/R0")

4. Re-scaling between 0 and 1 after treatment application (frame 45) and smoothing of data

table$RG_rescaled <- NA
table$FRET_rescaled <- NA
smoothed_normalized_RG <- as.numeric(ma(table$normalized_RG, order = 8, 
                                        centre = TRUE))
smoothed_normalized_FRET <- as.numeric(ma(table$normalized_FRET, order = 8, 
                                          centre = TRUE))

table$RG_rescaled[45:(nrow(table) - 4)] <- rescale(smoothed_normalized_RG
                                                   [45:(nrow(table) - 4)], c(0,1))
table$FRET_rescaled[45:(nrow(table) - 4)] <- rescale(smoothed_normalized_FRET
                                                     [45:(nrow(table) - 4)], c(0,1))

plot(table$RG_rescaled[45:(nrow(table) - 4)] ~ table$Time[45:(nrow(table) - 4)], 
     xlab = "Time (min)", ylab = "Rescaled norm. F/F0")
plot(table$FRET_rescaled[45:(nrow(table) - 4)] ~ table$Time[45:(nrow(table) - 4)], 
     xlab = "Time (min)", ylab = "Rescaled norm. R/R0")

5. Cross correlation analysis between R-GECO1 signal and CDPK-FRET with (ccf_normalized) and without normalization by linear model (ccf)

max_lag = 50
cross_corr_matrix = matrix(0,nrow=105,ncol = 4)
colnames(cross_corr_matrix) <- c("lag", "Time", "Correlation coefficients", 
                                 "Correlation coefficients normalized")

ccf <- ccf(table$RG, table$FRET_ratio, lag.max = max_lag) 
ccf_normalized <- ccf(table$normalized_RG, table$normalized_FRET, lag.max = max_lag)
cross_corr_matrix[1:101,1] = ccf$lag
cross_corr_matrix[1:101,2] = ccf$lag * table$Time[2]
cross_corr_matrix[1:101,3] = ccf$acf
cross_corr_matrix[1:101,4] = ccf_normalized$acf
cross_corr_matrix[102,1] = "lag max Time ccf"
cross_corr_matrix[103,1] = "max correlation coefficient cff"
cross_corr_matrix[102,2] = ccf$lag[which.max(cross_corr_matrix[1:101,3])] * 
                           table$Time[2]
cross_corr_matrix[103,2] = ccf$acf[which.max(cross_corr_matrix[1:101,3])] 
cross_corr_matrix[104,1] = "lag max Time ccf normalized"
cross_corr_matrix[105,1] = "max correlation coefficient cff normalized"
cross_corr_matrix[104,2] = ccf_normalized$lag[which.max(cross_corr_matrix[1:101,4])] * 
                           table$Time[2]
cross_corr_matrix[105,2] = ccf_normalized$acf[which.max(cross_corr_matrix[1:101,4])] 

6. Output as excel files in result folder

write_xlsx(data.frame(cross_corr_matrix),path=paste(getwd(),"/results/cross correlation.xlsx",sep=""), col_names = TRUE)}
write_xlsx(data.frame(table),path=paste(getwd(),"/results/data.xlsx",sep=""), col_names = TRUE)