E08_02 analysis

Experiment overview

The ccpA conidial protein was discovered through proteomic profiling of the conidial surface (Voltersen2018). This protein is suggested to contribute to properties of the conidial cell wall and to limit recognition of resting conidia.

ccpA knockout affects 4h swollen conidia activation of human moDCs as measured by TNF output.

Condition	Number
Media	1-3
ccpA 0h	4-6
ccpA 4h	7-9
alp1 0h	10-12
alp1 4h	13
W72310 0h	16-18
pyrG 0h fixed	19-21
DZym (10\(\mu\)g/ml)	22-23
Zym (10\(\mu\)g/ml)	24-25
LPS (250 ng/ml)	26-27

Flow notes

• Sample staining in 96 well plate
• Sample resuspended in 650 \(\mu\)l lung buffer then 400 \(\mu\)l sample run on Attune at 1000 \(\mu\)l/minute.
• FSC 120V, SSC 360 V, VL1 channel decreases to 360 V. No BV605/CD80 as run out!
• No higher than 10,000 events per second.

Staining and culture quality control

Example gating

• Cell numbers are reasonable - at least 5000 cDC2s.
• Percent cell death (although may be tricky due to spore/cell FSC/SSC separation) is clearly high
• Recompensated in flowjo with Autospill.

Analysis of gMFI

Begin by importing the gMFI table exported from flowjo.

#Import the dataset
df <- read_csv("C:/Users/gv272/University of Exeter/Cook, Peter - Projects/E08 Asp strains/E08 Data and analysis/E08_002/E08_02 gMFI.csv")

dfw <- df %>%
  pivot_longer(cols=c(2:9), names_to="Marker", values_to="gMFI")

dfw$Marker <- factor(dfw$Marker, levels = c("cDC1 CD40", "cDC1 CD86", 
                                            "cDC1 PDL2", "cDC1 MHCII",
                                            "cDC2 CD40", "cDC2 CD86", 
                                            "cDC2 PDL2", "cDC2 MHCII"))

dfw$Condition <- factor(dfw$Condition, levels=c("Media", "ccpA", "alp1", "W72310", 
                                                "pyrG", "ZymD", "Zym", "LPS"))
dfw$`Spore time` <- as.factor(dfw$`Spore time`)

dfw <- dfw %>%
  filter(FMO=="N")

ggplot(dfw, 
       aes(x=Condition, 
           y= gMFI/1000)) +
  ggbeeswarm::geom_beeswarm(aes(group=`Spore time`, 
                                colour=`Spore time`), 
                            dodge.width = 0.8, 
                            size=1.5, 
                            show.legend = T) +
  stat_summary(aes(group=`Spore time`, 
                   color=`Spore time`),
               geom="bar",
               fun = mean,
               position = position_dodge2(preserve = 'single', width=0.8),
               fill="NA",
               size = 1, width = 0.8,
               show.legend = T) +
  ylab(expression(paste("gMFI x ", 10^3))) +
  scale_y_continuous(expand = expansion(mult = c(0, .05))) +
  facet_wrap(vars(Marker), nrow=2, scales = "free_y") +
  theme_prism(base_line_size=0.2)  +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(legend.position = "bottom",
        text=element_text(face="plain"), 
        strip.text = element_text(face="bold"),
        axis.title.x=element_blank())

Spores <- c("Media", "ccpA", "alp1", "W72310", "pyrG")
Stimuli <- c("Media", "ZymD", "Zym", "LPS")

dfw_spores <- dfw[dfw$Condition %in% Spores ,]
dfw_stim <- dfw[dfw$Condition %in% Stimuli ,]

ggplot(dfw_spores, 
       aes(x=Condition, 
           y= gMFI/1000)) +
  ggbeeswarm::geom_beeswarm(aes(group=`Spore time`, 
                                colour=`Spore time`), 
                            dodge.width = 0.8, 
                            size=1.5, 
                            show.legend = T) +
  stat_summary(aes(group=`Spore time`, 
                   color=`Spore time`),
               geom="bar",
               fun = mean,
               position = position_dodge2(preserve = 'single', width=0.8),
               fill="NA",
               size = 1, width = 0.8,
               show.legend = T) +
  ylab(expression(paste("gMFI x ", 10^3))) +
  scale_y_continuous(expand = expansion(mult = c(0, .05))) +
  facet_wrap(vars(Marker), nrow=2, scales = "free_y") +
  theme_prism(base_line_size=0.2)  +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
  theme(legend.position = "bottom",
        text=element_text(face="plain"), 
        strip.text = element_text(face="bold"),
        axis.title.x=element_blank())

# Thoughts

• FSC/SSC voltages weren’t adjusted so it’s a bit tricky to gate spores vs. cells, but can be rectified by setting a stringent CD11c gate

• Seems to be quite a lot of death but this might be affected by the presence of debris in that gate which we would normally be able to gate out on FSC/SSC properties

• Seems to have a large double negative B220/CD11c population - perhaps the staining could be better? Seems to be present in all samples. But when gating on CD11c+B220- cells, there’s no evidence of a DN population for cDC1 vs cDC2 so I think the staining of those cells are fine and should be interpretable

• All the zero hour spores have a really stable signal which means they are well washed. Equal levels of activation with all strains. Crucially, lack of ccpA does not affect immunogenicity of 0 hour spore stages.

• we see an increase in the 0 vs 4 h ccpA sample. Unfortunately can’t compare this to 4 h Alp1 strain so hard to say about the role of ccpA in

• All the positive controls show definite increases in activation!