Calf Thymus DNA (in vitro)

First we did some in vitro controls with calf-thymus DNA and the Ru / Rh complexes. We wanted to know how the data might look with the concentration of dsDNA we expected in the biofilm suspensions (~10uM).

Let’s read in the data for 10uM ctDNA bp + 1uM and the stepwise Rh additions. I’m attaching the conditions to the data based on Eric’s notes (see pdf).

df_3 <- read_csv("../data/80719_3_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 1uM Ru -Rh")

df_4 <- read_csv("../data//80719_4_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 1uM Ru + 0uM Rh")

df_5 <- read_csv("../data/80719_5_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 1uM Ru + 1uM Rh")

df_6 <- read_csv("../data/80719_6_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 1uM Ru + 2uM Rh")

df_7 <- read_csv("../data/80719_7_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 1uM Ru + 4uM Rh")

df_ctDNA <- bind_rows(df_4, df_5, df_6, df_7)

df_ctDNA %>% head() %>% kable(digits = 15) %>% kable_styling()

You can see that the text files are two columns with time and intensity. I added the condition column. Also note that the acquisition parameters are in the header of each file (hence the “skip = 16” lines).

Let’s see what the data look like:

ggplot(df_ctDNA, aes(x = time, y = intensity, color = condition)) + 
    geom_path() + scale_color_viridis_d()

Ok, it looks like one of the traces is obviously different from the others. Let’s zoom in.

ggplot(df_ctDNA, aes(x = time, y = intensity, color = condition)) + 
    geom_path() + xlim(NA, 1e-06) + ylim(NA, 0.02) + scale_color_viridis_d()

The one trace that is different from the others is the no Rh condition. However, it isn’t clear that there’s static quenching going on here. It may be useful to acquire an instrument response background scan with just buffer, so that we can background subtract these curves. It also looks like the curves may be saturated, so there may be an effect that’s just hard to see. Either way, the Rh gives an effect, but it does not increase in a stepwise manner. Not sure what to make of that.

One potential issue that Eric brought up was the stoichiometry of Ru/Rh to DNA bp. Not sure if that would explain the patterns, but let’s move forward to look at a lower stoichiometry with 10uM ctDNA and 0.1uM Ru.

First I’ll read in the data in the same way.

df_14 <- read_csv("../data/80719_14_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 0.1uM Ru -Rh")

df_15 <- read_csv("../data//80719_15_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 0.1uM Ru + 0.0uM Rh")

df_16 <- read_csv("../data/80719_16_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 0.1uM Ru + 0.1uM Rh")

df_17 <- read_csv("../data/80719_17_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 0.1uM Ru + 0.2uM Rh")

df_18 <- read_csv("../data/80719_18_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "10uM ctDNA + 0.1uM Ru + 0.4uM Rh")

df_ctDNA_lowRu <- bind_rows(df_14, df_15, df_16, df_17, df_18)

df_ctDNA_lowRu %>% head() %>% kable(digits = 15) %>% kable_styling()

The data structure is the same as before. Let’s look at it plotted:

ggplot(df_ctDNA_lowRu, aes(x = time, y = intensity, color = condition)) + 
    geom_path() + scale_color_viridis_d()

Ok, this looks a little weird. The one curve that looks like the highest intensity should have the most Rh quencher. Perhaps I added the wrong molecule (Ru and Rh look very similar…) or the scan was weird. That said part of the dataset makes sense if we just look at the 0uM - 0.2uM Rh conditions:

df_ctDNA_lowRu_2 <- bind_rows(df_15, df_16, df_17)

ggplot(df_ctDNA_lowRu_2, aes(x = time, y = intensity, color = condition)) + 
    geom_path() + scale_color_viridis_d() + facet_wrap(~condition) + 
    guides(color = "none")

Here you can see that the Ru only sample is quenched by the addition of the Rh. That said, it’s not a nice obvious stepwise thing. Eric mentioned that could also be because we are getting to concentrations that are probably below the Kd for Rh complex binding DNA…making it a much weaker quencher. In general though, it would be nice to background subtract out the instrument response to make sure that we are really only looking at the Ru emission…

\(\Delta phz*\) Biofilm

Now for the real deal. Here we basically just repeated the 1uM Ru experiment from above, but this time with my biofilm suspension added directly to the cuvette (instead of ctDNA).

Let’s read in the data:

df_19 <- read_csv("../data/80719_19_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "dPHZ -Ru -Rh")

df_20 <- read_csv("../data//80719_20_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "dPHZ + 1uM Ru -Rh")

df_21 <- read_csv("../data/80719_21_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "dPHZ + 1uM Ru 1uM Rh")

df_22 <- read_csv("../data/80719_22_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "dPHZ + 1uM Ru 2uM Rh")

df_23 <- read_csv("../data/80719_23_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "dPHZ + 1uM Ru 4uM Rh")

df_dphz <- bind_rows(df_19, df_20, df_21, df_22, df_23)

df_dphz %>% head() %>% kable(digits = 15) %>% kable_styling()

First let’s just look at the biofilm only scan compared to biofilm + 1uM Ru.

df_dphz %>% filter(condition %in% c("dPHZ -Ru -Rh", "dPHZ + 1uM Ru -Rh")) %>% 
    ggplot(data = ., aes(x = time, y = intensity, color = condition)) + 
    geom_path() + scale_color_viridis_d()

Here we can see two things. First, the biofilm by itself has significant fluorescence emission with these settings with a lifetime that happens on this timescale. Second, the Ru fluorescence / lifetime is actually visible above that background. So let’s look at the whole dataset:

ggplot(df_dphz, aes(x = time, y = intensity, color = condition)) + 
    geom_path() + scale_color_viridis_d()

# ggplot(df_dphz, aes(x = time, y = intensity, color =
# condition)) + geom_path() + xlim(NA, 1e-6)+ylim(NA,0.04) +
# scale_color_viridis_d()

Ok, it sort of look like something is happening there with the addition of the Rh. Let’s subtract the background of the biofilm only fluorescence and see how that looks. To do this we are just going to subtract the minimum value at each datapoint (which will be the biofilm only condition).

df_dphz_bg <- df_dphz %>% 
  group_by(time) %>% 
  mutate(min = min(intensity)) %>% 
  mutate(bg_sub = intensity - min)
  

ggplot(df_dphz_bg, aes(x = time, y = bg_sub, color = condition)) + geom_path() + #xlim(NA, 1e-6)+ylim(NA,0.04) +
  scale_color_viridis_d()

ggplot(df_dphz_bg, aes(x = time, y = bg_sub, color = condition)) + 
    geom_path() + xlim(0, 1e-06) + ylim(NA, 0.02) + scale_color_viridis_d()

df_dphz_bg_norm <- df_dphz_bg %>% 
  filter(time>0) %>% 
  group_by(condition) %>% 
  mutate(max = max(bg_sub)) %>% 
  mutate(norm_intensity = bg_sub / max)


ggplot(df_dphz_bg_norm, aes(x = time, y = norm_intensity, color = condition)) + geom_path() + #xlim(NA, 1e-6)+ylim(NA,0.04) +
  scale_color_viridis_d()

WT Biofilm

We had a little time leftover, so we decided to try a different condition - a WT biofilm. I reasoned that this biofilm may have more eDNA than the dPHZ…but it could also be more complicated if any of the phenazines quench the Ru fluorescence (instead of the Rh quenching).

Let’s read in the data:

df_25 <- read_csv("../data/80719_25_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "WT + 0uM Ru + 0uM Rh")

df_26 <- read_csv("../data/80719_26_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "WT + 1uM Ru + 0uM Rh")

df_27 <- read_csv("../data/80719_27_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "WT + 1uM Ru + 1uM Rh")

df_28 <- read_csv("../data/80719_28_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "WT + 1uM Ru + 2uM Rh")

df_29 <- read_csv("../data/80719_29_compressed.dat", skip = 16, 
    col_names = c("time", "intensity")) %>% mutate(condition = "WT + 1uM Ru + 4uM Rh")

df_wt <- bind_rows(df_25, df_26, df_27, df_28, df_29)

df_wt %>% head() %>% kable(digits = 15) %>% kable_styling()

Let’s look at all the data together:

ggplot(df_wt, aes(x = time, y = intensity, color = condition)) + geom_path() + xlim(NA, 1e-6)+ #ylim(NA,NA) +
  scale_color_viridis_d()

As for dPHZ, it is clear that the biofilm has a background fluorescence, but it’s also clear that we can see the Ru signal above that background. It’s not obvious if we are seeing any difference with the Rh added.

Let’s go ahead and background subtract out the biofilm only signal in the same was as above (since that was very useful).

df_wt_bg <- df_wt %>% group_by(time) %>% mutate(min = min(intensity)) %>% 
    mutate(bg_sub = intensity - min)


ggplot(df_wt_bg, aes(x = time, y = bg_sub, color = condition)) + 
    geom_path() + ylim(NA, 0.01) + scale_color_viridis_d()

Ok, the background subtraction seems to have worked well (biofilm is flat line). The Ru signal looks a little noisier than the dPHZ (it’s less than half the intensity), and that may be due to some effect of the phenazine? More importantly, it looks like there is an effect from adding the Rh here as well. Let’s zoom in a little more and smooth to see a prettier view:

ggplot(df_wt_bg, aes(x = time, y = bg_sub, color = condition)) + 
    geom_point(shape = 21) + geom_smooth(span = 0.1, se = F) + 
    ylim(NA, 0.0075) + xlim(0, 7.5e-07) + scale_color_viridis_d()

It’s still noisy, but it looks like the peaks for the 1 and 2uM Rh are lower than the 0uM Rh. The obvious decrease comes with the 4uM Rh. Surely we could get nicer fits by actual fitting a relevant model here…but that seems overkill for now.

The only other question for the WT data is - are the peaks the same shape? Let’s normalize as before and plot:

df_wt_bg_norm <- df_wt_bg %>% 
  filter(time>3e-8) %>% 
  group_by(condition) %>% 
  mutate(max = max(bg_sub)) %>% 
  mutate(norm_intensity = bg_sub / max)

ggplot(df_wt_bg_norm, aes(x = time, y = norm_intensity, color = condition)) + geom_path() + #xlim(NA, 1e-6)+ylim(NA,0.04) +
  scale_color_viridis_d()

When we normalize by the max in this time interval to focus on the peaks and not the noise beforehand we get this plot. This looks pretty good…for now I think it’s reasonable to think that these are the same shape and represent the same lifetime.