In GranderLab/miR34a_asRNA_project: The miR34AasRNAproject package facilitates the transparency and reproducibility of the miR34a asRNA project and associated publication.

packages <- c(
    "tidyverse",
    "printr",
    "ggthemes",
    "readr",
    "miR34AasRNAproject",
    "grid",
    "gtable"
)
purrr::walk(packages, library, character.only = TRUE)
rm(packages)

projectUrl <- "https://github.com/GranderLab/miR34a_asRNA_project/raw/master/inst"
dataUrl <- "https://github.com/GranderLab/miR34a_asRNA_project/raw/master/"

Introduction

miR34a is known to regulate cell cycle and, specifically, induce G1-phase arrest. Due to the increase observed in miR34a expression levels in cell lines over-expressing miR34a asRNA, we investigated if the cell cycle was perturbed in these cells.

Methods

Cell culture and cell cycle analysis

All cell lines were cultured at 5% CO2 and 37° C with Skov3 and PC3 cells in RPMI (Hyclone) and 2 mM L-glutamine. All growth mediums were supplemented with 10% heat-inactivated FBS and 50 μg/ml of streptomycin and 50 μg/ml of penicillin. 1x10^5 cells per well in a 6-well plate and harvested after 24hrs. Cells were washed in PBS and fixed in 4% PFA at room temperature overnight. PFA was removed, and cells were resuspended in 95% EtOH. The samples were then rehydrated in distilled water, stained with DAPI and analyzed by flow cytometry on a LSRII (BD Biosciences) machine.

Analysis

The cell cycle phases ModFit software (Verity Software House) was used to quantify the percentage of cells in each cell cycle phase. Percentages were converted to log2 fractions for each sample. Student's t-test was used for statistical analysis comparing the mock vs miR34a asRNA overespression conditions using five (PC3) or three (Skov3) independant experiments.

Results

Representative result

url <- fileMap(type = "png")["Supplementary Figure 4b"][[1]]
knitr::include_graphics(file.path(projectUrl, url))

Representative flow cytometry analysis from the cell cycle analysis in Figure 3B.

data <- getData("Figure 3b")

#convert the value (precent) to log2(fraction)
data <- mutate(data, value = log2(value / 100))

#qqplot shows that the data is reasonably normal. The shapiro test is also used below. Note that a p < 0.05 indicates non-normality.
#qqnorm(data$value)

#calculate stats
stats <- data %>%
  full_join(
    filter(data, condition == "mock"), 
    by = c("Biological Replicate", "Cell line", "phase"), 
    suffix = c("", ".y")
  ) %>%
  group_by(`Cell line`, condition, phase) %>%
  summarize(
    n = n(),
    mean = (2^mean(.data$value)),
    max = (2^max(.data$value)),
    CI95l = (2^t.test(.data$value)$conf.int[1]),
    CI95h = (2^t.test(.data$value)$conf.int[2]),
    pValue = t.test(.data$value, .data$value.y)$p.value,
    shapiroTest = shapiro.test(value)$p.value
  ) %>%
  ungroup() %>%
  pFormat(.)

#prepare for plotting
#change value back to percent
data <- data %>%
  mutate(value = 2^value)

#calculate start and end for horizontal plot lines
stats <- stats %>%
  group_by(phase, `Cell line`) %>%
  mutate(y = max(c(.data$CI95h, .data$max))) %>%
  ungroup() %>%
  arrange(`Cell line`, phase) %>%
  mutate(
    x = rep(sort(rep(1:3, 2)), 2) - 0.25,
    xend = rep(sort(rep(1:3, 2)), 2) + 0.25
  )

p <- ggplot(data = NULL) +
  geom_dotplot(
    data = data, 
    aes(
      x = phase,
      y = value,
      fill = condition
    ),
    binwidth = 1/100,
    colour = NA,
    #alpha = 0.5,
    position = position_dodge(width = 0.9), 
    binaxis = 'y', 
    stackdir = 'center',
    dotsize = 1,
    show.legend = TRUE
  ) +
  facet_grid(
    . ~ `Cell line`, 
    scale = "free"
  ) +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(
    x = "Cell cycle phase",
    y = "",
    title = "Cell cycle"
  )

markdown <- p +
  geom_linerange(
    data = stats,
    aes(
      x = phase, 
      ymin = CI95l, 
      ymax = CI95h, 
      group = interaction(phase, condition)
    ),
    position = position_dodge(width = 0.9),
    colour = "grey25",
    show.legend = FALSE
  ) +
  geom_point(
    data = stats,
    aes(
      x = phase, 
      y = mean, 
      group = condition
    ), 
    colour = "black",
    position = position_dodge(width = 0.9),
    shape = 95,
    size = 6,
    show.legend = FALSE
  ) +
  geom_segment(
    data = stats,
    aes(
      x = x,
      y = y + 0.01, 
      xend = xend, 
      yend = y + 0.01
    ),
    colour = "grey43",
    show.legend = FALSE
  ) +
  geom_label(
    data = stats,
    aes(
      x = phase, 
      y = y + 0.02, 
      label = pFormat
    ),
    label.size = 0,
    label.padding = unit(0.01, "lines"),
    show.legend = FALSE,
    fill = "white",
    size = 3,
    family = "Arial Unicode MS"
  ) +
  guides(
    fill = guide_legend(
      title = "Condition", 
      override.aes = list(size = 6)
    )
  )

plotRmarkdown(markdown)

pdf <- p +
  geom_linerange(
    data = stats,
    aes(
      x = phase, 
      ymin = CI95l, 
      ymax = CI95h, 
      group = interaction(phase, condition)
    ),
    position = position_dodge(width = 0.9),
    colour = "grey30",
    size = 0.3,
    show.legend = FALSE
  ) +
  geom_point(
    data = stats,
    aes(
      x = phase, 
      y = mean, 
      group = condition
    ), 
    colour = "grey30",
    position = position_dodge(width = 0.9),
    shape = 95,
    size = 4,
    show.legend = FALSE
  ) +
  geom_segment(
    data = stats,
    aes(
      x = x,
      y = y + 0.02, 
      xend = xend, 
      yend = y + 0.02
    ),
    colour = "grey30",
    size = 0.3,
    show.legend = FALSE
  ) +
  geom_label(
    data = stats,
    aes(
      x = phase, 
      y = y + 0.038, 
      label = pFormat
    ),
    colour = "grey30",
    label.size = 0,
    label.padding = unit(0.01, "lines"),
    show.legend = FALSE,
    fill = "white",
    size = 2.5,
    family = "Arial Unicode MS"
  ) +
  guides(
    fill = guide_legend(
      title = "Condition", 
      override.aes = list(size = 4)
    )
  )

figure <- plotPDF(pdf)

# path <- file.path("~/GitHub/miR34a_asRNA_project/inst", fileMap(type = "pdf")["Figure 3b"][[1]])
# ggsave(
#    plot = figure,
#    filename = path,
#    device = cairo_pdf,
#    height = 60,
#    width = 93,
#    units = "mm"
# )

Cell cycle analysis in miR34a asRNA over-expressing PC3, Skov3, and Saos2 stable cell lines compared to their respective mock control. The points represent the values obtained from each independant experiment (PC3 n = 5; Skov3 n = 3). 95% confidence interval (vertical black lines), mean (horizontal black lines), and p-values are shown with the horizontal line under the p-value indicating the comparison that was tested.

Conclusion

An increase in the percentage of cells in G1-phase can be seen in cell lines overexpressing miR34a asRNA. In addition, a concominant decrease of cells in G2-phase can be observed.

sessionInfo()

GranderLab/miR34a_asRNA_project documentation built on May 26, 2019, 7:26 a.m.