Cell based experiments to showcase LITOS

Introduction

This is the data analysis code of the cell-based experiment (Figure 4 and 5) to showcase LITOS with the optogenetic stimulation of the MAPK/ERK pathway. The methodological details about the experiments and the description of the results can be found in the main text of the paper.

Analyses of the experiments

Loading packages and data

The meta-package tidyverse is used to prepare (e.g. with dplyr) and to plot (with ggplot2) the data.

The first step is to load the data.

data_western_blot <- read_csv("data_WesternBlot.csv")
data_dose_response <- read_csv("data_DoseResponse.csv")
data_dynamics <- read_csv("data_Dynamics.csv")
data_high_resolution <- read_csv("data_HighResolution.csv")
data_uniform_activation <- read_csv("data_Uniform.csv")
data_mini_screen <- read_csv("data_MiniScreen.csv")

Uniform activation experiment

Plot the color-coded ERK activity (C/N - ratio) for each cell at its respective position in the well (Figure 4c).

data_plot <- data_uniform_activation %>%
  # Manually remove outliers
  filter(!(Image_Metadata_Well == "D09" & objNuc_ObjectNumber == 1) &
           !(Image_Metadata_Well == "D11" & objNuc_ObjectNumber == 207) & 
           !(Image_Metadata_Well == "D11" & objNuc_ObjectNumber == 2885)) %>% 
  
  # Rename columns
  mutate(X = objNuc_Location_Center_X, Y = objNuc_Location_Center_Y, 
         `C/N` = cytoVnuc.corr, 
         Timepoint = paste("t = ", Timepoint, "'", sep = ""), 
         Timepoint = factor(Timepoint, levels = c("t = 0'", "t = 3'", "t = 6'", "t = 9'", "t = 12'"))) %>% 
  
  # Limit the C/N to a min of 0.2 and a maximum of 1.5, so the color scale is more meaningful
  mutate(
    `C/N` = ifelse(`C/N` < 0.2, 0.2, `C/N`), 
    `C/N` = ifelse(`C/N` > 1.5, 1.5, `C/N`
  )) %>%
  ungroup()
          
# Plot the data^
ggplot(data_plot, aes(x = X, y = Y, col = `C/N`)) +
  geom_point(alpha = 0.85, shape=16, size =0.5) +
  scale_y_reverse() + 
  scale_color_gradient(high = "#f75555", low  = "#56B1F7") +
  theme_void() + theme(legend.position = "top") +
  facet_grid(. ~ Timepoint)

Western blot experiment

Plot the color-coded ERK activity (C/N - ratio) for each timepoint so that they can be compared with the western blot results (Figure 4d).

data_plot <- data_western_blot %>% 
  group_by(RealTime) %>%
  summarise(meanResponse = mean(Response), medianResponse = median(Response))

ggplot(data_plot, aes(x = RealTime, y = 1, fill = meanResponse)) + 
  geom_tile() +
  scale_fill_gradient(high = "#f75555", low  = "#56B1F7") +
  theme(legend.position = "bottom") +
  xlab(xlab) + ylab(ylab)

Dose-response experiment

Plot the dose response experiment (Figure 5a).

# Prepare data for plot
data_plot <- data_dose_response %>% 
  mutate(RealTime = factor(RealTime)) # change RealTime to a categorical variable

# Create data for blue square indicating stimulation
data_stim <- tibble(
  Cells = c("optoFGFR1", "optoFGFR1", "optoFGFR1", "optoFGFR1", "optoRAF", "optoRAF", "optoRAF", "optoRAF"),
  ExposureTime = c("0.5 min", "1 min", "2 min", "5 min", "0.5 min", "1 min", "2 min", "5 min"), 
  start = 0,
  end = c(0.5, 1, 2, 5, 0.5, 1, 2, 5),
  min = 0.2,
  max = 1.5
  ) 

# Plot 
ggplot(data_plot) +
  geom_boxplot(aes(x = RealTime, y = Response), outlier.shape = NA) +
  geom_rect(data=data_stim, aes(xmin = start+1, xmax = (end/3)+1, ymin = min, ymax = max), fill = "blue", alpha = 0.2) +
  geom_boxplot(aes(x = RealTime, y = Response), outlier.shape = NA) + # put boxplot over rectangles
  coord_cartesian(ylim = c(0.27, 1.3)) +
  facet_grid(Cells ~ ExposureTime) +
  theme_bw() + theme(legend.position = "bottom", text = element_text(family = "sans-serif", size = 8)) +
  xlab(xlab) + ylab(ylab)

Mini drug screen

Plot the results of the mini drug screen which was performed with LITOS (Figure 5b).

# Calculate mean per time point
data_plot <- data_mini_screen %>%
  group_by(Drug, Concentration, RealTime) %>% 
  summarise(err = sd(Response)/sqrt(n()), Response = median(Response), .groups = "drop")

# Generate data to indicate stimulations
data_stim <- tibble(
  Drug = c("RAF709", "UO126", "SCH772984")
  ) 

ggplot(data_plot) +
  geom_rect(data = data_stim, aes(xmin = 0.3, xmax = 0.7, ymin = 0.2, ymax = 0.7), fill = "blue", alpha = 0.2) +
  geom_point(aes(x = RealTime, y = Response, col = Concentration)) +
  geom_line(aes(x = RealTime, y = Response, col = Concentration, group = paste(Concentration, Drug))) +
  coord_cartesian(ylim = c(0.44, 0.65)) +
  theme(text = element_text(family = "sans-serif", size = 8), legend.position = "top") +
  facet_wrap(~Drug) +
  xlab(xlab) + ylab(ylab)

High temporal resolution experiment

Plot a single ERK activity pulse with high temporal resolution (Figure 5c).

data_plot <- data_high_resolution

# Generate data to indicate stimulations
dp_stim <- tibble(
  Drug = c("RAF709")
  ) 

ggplot(data_plot) + 
  geom_boxplot(aes(x = factor(Timepoint), y = cytoVnuc.corr), outlier.shape = NA) + 
  geom_rect(data=dp_stim, aes(xmin = 1.3, xmax = 1.5, ymin = 0, ymax = 1.6), fill = "blue", alpha = 0.2) +
  geom_boxplot(aes(x = factor(Timepoint), y = cytoVnuc.corr), outlier.shape = NA) +  # put boxplot over rectangles
  coord_cartesian(ylim = c(0.15, 1.438)) +
  theme(text = element_text(family = "sans-serif", size = 8)) +
  xlab(xlab) + ylab(ylab)

Induction of temporal dynamics

Plot the dynamics which was generated with LITOS (Figure 5d).

# Calculate mean and SEM per time point
data_plot <- data_dynamics %>% 
  # mutate(RealTime = as.numeric(as.character(RealTime))) %>%
  group_by(RealTime, PulseInterval) %>%
  summarise(mean = mean(Response), sd = sd(Response), sem = sd(Response)/sqrt(n()), .groups = "drop")

# Generate data to indicate stimulations
min = min(data_plot$mean)
max = max(data_plot$mean)

data_stim <- tibble(
  PulseInterval = c("sustained", "every 20 min", "every 20 min", "every 20 min", "every 20 min"),
  start = c(4, 4, 24, 44 , 64),
  end = c(84, 4.4, 24.4, 44.4, 64.4),
  min = min-0.1,
  max = max+0.1
  ) 

# Plot 
ggplot(data_plot) +
  geom_rect(data=data_stim, aes(xmin = start, xmax = end, ymin = min, ymax = max), fill = "blue", alpha = 0.2) +
  geom_line(aes(x = RealTime, y = mean, group = PulseInterval)) +
  geom_errorbar(aes(x = RealTime, y = mean, ymin = mean - sem, ymax = mean + sem)) +
  facet_grid(PulseInterval ~ .) +
  theme(text = element_text(family = "sans-serif", size = 8)) +
  xlab(xlab) + ylab(ylab)