Evaluating the in vitro tumorigenicity of CSC candidate using the 3D multi-spheroid model by determining spheroid number using 3D-SiSP analytical method.
Krittiyabhorn Kongtanawanich
2025-02-25
Overview of R analysis for evaluating the in vitro tumorigenicity of CSC candidate using the 3D multi-spheroid model by determining spheroid number using 3D-SiSP analytical method.
The in vitro tumorigenic assay is a crucial step in verifying CSC
properties. In our study, we focus on the usage of the CSC biosensor
SORE6, however, other CSC markers can also be utilized with our analysis
platform. SORE6 serves as the CSC biosensor in this example, with a
green fluorescence protein (GFP) acting as the biosensor protein in this
system. Cells containing the SORE6 biosensor were sorted into
GFP-positive (POS) and GFP-negative (NEG)
populations using fluorescence-activated cell sorting, expected to
represent CSCs and non-CSCs, respectively. Then, the expected CSC and
non-CSC populations were separately cultured in the 3D multi-spheroid
model at various initial seeding densities. According to the 3D-SiSP
analytical method, spheroids which were determined by a custom area
cut-off, were counted and compared between the CSC and non-CSC
groups.
According to image analysis, all cells in each well were nuclear stained, enabling the measurement of the area of all objects within the well (Figure 1). To evaluate the in vitro tumorigenicity of cancer stem cell (CSC) candidates, we developed a high-throughput analysis. In our analysis pipeline, objects were selected based on the area cut-off to be considered as spheroids, which was converted from the published spheroid diameter cut-off. Subsequently, the spheroids in each initial cell seeding condition were counted and compared between the GFP-positive and GFP-negative groups. Statistical tests were performed by comparing the CSC and non-CSC groups in each cell seeding density. Additionally, box plots were generated for data visualization.
Figure 1: Image analysis of each 3D object. (Created by BioRender.com / Mahidol University)
1. Analysis overview
Here, the analysis composed of 4 main sections below.
- Section 1: Define information of the dataset.
- Part 1.1: Define input/output information.
- Part 1.2: Define a spheroid area cut-off.
1.2.1 Convert spheroid diameter to spheroid area.
1.2.2 Define a spheroid area cut-off.
- Part 1.1: Define input/output information.
- Section 2: Create analysis pipeline.
- Part 2.1: Create a function (
Count.Spheroids) for spheroid determination (Spheroid) and spheroid count (Spheroid_Count).
- Part 2.2: Perform the analysis for all conditions using the created
function (
Count.Spheroid).
- Part 2.3: Create summary analysis
- Part 2.1: Create a function (
- Section 3: Statistic test
- Part 3.1: Normality test
- Part 3.2: Statistical test
- Part 3.1: Normality test
- Section 4: Data visualization.
2. Inputs
The plate map was arranged as shown in the picture below. Biosensor
groups (POS and NEG) and initial cell seeding
density were arranged accordingly. According to the image analysis, the
area of all objects were measured and stored in a
WellName.csv file, well by well.
In the WellName.csv files contains 4 columns, which are
described as below.
Object_Area_um2= Area of each object (μm²)
CellSeedingDensity= Initial cell seeding density
BiosensorGroup= CSC biosensor groups (POSorNEG)
Replicate= Technical replicate of the experiment
Here, we also provide a
set of example inputs, where each WellName.csv file contains
information about the objects in the respective well.
Figure 2: Plate map of in vitro tumorigenic assay. (Created by
BioRender.com / Mahidol University)
Analysis for evaluating the in vitro tumorigenicity of CSC candidate using the 3D multi-spheroid model by determining spheroid number using 3D-SiSP analytical method.
Import R library
library(dplyr)
library(ggplot2)
library(ggpubr)
library(rstatix)Section 1: Define information of the dataset.
Part 1.1: Define input/output information
To define information of the dataset.
1.1 Define working directory: Output_Directory
1.2 Define input directory: Input_Directory
1.3 Define the cell line name: CellLine_Name
# Dataset example
## Output directory
Output_Directory <- 'path/to/directory'
setwd(Output_Directory)
## Input directory
Input_Directory <- 'path/to/directory'
CellLine_Name <- "CellLineX"Part 1.2: Define a spheroid area cut-off.
Length is mostly used as spheroid determination cut-off. However, It
is inapplicable for spheroid with non-spherical shape (Figure 3). To
fairly determine the spheroid in all shapes, Area will be used for
further analysis.
Figure 3: Comparing spheroid length and area in spheroid with spherical
(left) and non-spherical shape (right)
The cut-off by area was calculated based on the length. Length
cut-offs between 50 and 200 μm are commonly used for determining
spheroids in the CSC field.
Here, we provide an R code for converting the length (diameter) to
area by assuming that the spheroid is a circular shape in 2
dimensions.
1.2.1 Convert object diameter to area.
# Function to calculate spheroid area from spheroid diameter (length)
Area.from.Diameter <- function(Diameter)
{
Radius <- Diameter / 2
Area <- pi * Radius^2
return(Area)
}
# Example usage:
Diameter <- 100 # Replace with your desired diameter.
Circle_Area <- Area.from.Diameter(Diameter)
print(paste("From spheroid diameter equals to", Diameter,"μm, spheroid area is", Circle_Area, "μm²."))## [1] "From spheroid diameter equals to 100 μm, spheroid area is 7853.98163397448 μm²."1.2.2 Define a spheroid area cut-off.
CutOff <- 7853.98Section 2: Create analysis pipeline.
Part 2.1: Create a function (Count.Spheroids) for
spheroid determination (Spheroid) and spheroid count
(Spheroid_Count).
Count.Spheroids <- function(Well_FileName, Input_Directory, CellLine_Name, CutOff)
{
# Import each `.csv` file.
Well_FilePath <- file.path(Input_Directory, Well_FileName)
Well_File <- read.csv(Well_FilePath)
# Extract information (Initial cell seeding density, Biosensor group) from the file.
CellSeedingDensity <- Well_File$CellSeedingDensity[1]
BiosensorGroup <- Well_File$BiosensorGroup[1]
# Extract area of all objects from the file.
EachWell_ObjectArea <- Well_File$Object_Area_um2
EachWell_ObjectArea <- as.vector(EachWell_ObjectArea)
# Determine which object is a spheroid by the custom area cut-off
Spheroid <- which(EachWell_ObjectArea >= CutOff)
# Count number of the spheroids.
Spheroid_Count <-length(Spheroid)
# Create a data frame to store spheroid count information.
Result <- data.frame(FileName = Well_FileName,
CellSeedingDensity = CellSeedingDensity,
BiosensorGroup = BiosensorGroup,
CellLine = CellLine_Name,
SpheroidCount = Spheroid_Count
)
return(Result)
}Part 2.2: Perform the analysis for all conditions using the created
function (Count.Spheroid)
# Create a blank data frame to store `EachWell_Spheroid_Count` information.
AllCondition_Spheroid_Count <- data.frame()
# Load the input data.
FileName_list <- list.files(Input_Directory)
FileName_list <- as.list(FileName_list)
for (Well in FileName_list) {
# Calculate the spheroid count in each well by `Count.Spheroid` function.
EachWell_Spheroid_Count <- Count.Spheroids(Well, Input_Directory, CellLine_Name, CutOff)
# Store `EachWell_Spheroid_Count` into the creted data frame (`AllCondition_Spheroid_Count`).
AllCondition_Spheroid_Count <- rbind(AllCondition_Spheroid_Count,
EachWell_Spheroid_Count
)
}
# Reorder the data according to `BiosensorGroup` and `CellSeedingDensity`.
AllCondition_Spheroid_Count <- with(AllCondition_Spheroid_Count,
AllCondition_Spheroid_Count[order(BiosensorGroup, CellSeedingDensity), ]
)
# Write the `AllCondition_Spheroid_Count` data into a `.csv` file.
write.csv(AllCondition_Spheroid_Count,
paste0(CellLine_Name, "_Raw_SpheroidCount_CutOff", CutOff, ".csv"), row.names = FALSE)
print(paste0("The .csv file was written as '", CellLine_Name, "_Raw_SpheroidCount_CutOff", CutOff, ".csv'"))## [1] "The .csv file was written as 'CellLineX_Raw_SpheroidCount_CutOff7853.98.csv'"Observe the AllCondition_Spheroid_Count data.
head(AllCondition_Spheroid_Count)## FileName CellSeedingDensity BiosensorGroup CellLine SpheroidCount
## 2 B11.csv 63 NEG CellLineX 7
## 12 C11.csv 63 NEG CellLineX 5
## 22 D11.csv 63 NEG CellLineX 1
## 32 E11.csv 63 NEG CellLineX 1
## 42 F11.csv 63 NEG CellLineX 3
## 52 G11.csv 63 NEG CellLineX 0Part 2.3: Create summary analysis.
For the summary analysis of the spheroid count
(AllCondition_Spheroid_Count), the statistics comprising
mean and standard deviation were calculated and written as a
.csv file.
Summary_SpheroidCount <- AllCondition_Spheroid_Count%>%
group_by(CellLine,
BiosensorGroup,
CellSeedingDensity) %>%
summarise_at(vars(SpheroidCount),
list(Mean_SpheroidCount = mean,
SD_SpheroidCount = sd))
# Write the `Summary_Result` into a `.csv` file.
write.csv(Summary_SpheroidCount,
paste0(CellLine_Name, "_Summary_SpheroidCouint_CutOff", CutOff, ".csv"), row.names = FALSE)
print(paste0("Summary of the spheroid count analysis ", "was written as '", CellLine_Name, "_Summary_SpheroidCount_CutOff", CutOff, ".csv'"))## [1] "Summary of the spheroid count analysis was written as 'CellLineX_Summary_SpheroidCount_CutOff7853.98.csv'"Observe the Summary_SpheroidCount data.
head(Summary_SpheroidCount)## # A tibble: 6 × 5
## # Groups: CellLine, BiosensorGroup [2]
## CellLine BiosensorGroup CellSeedingDensity Mean_SpheroidCount SD_SpheroidCount
## <chr> <chr> <int> <dbl> <dbl>
## 1 CellLin… NEG 63 2.83 2.71
## 2 CellLin… NEG 125 4 3.16
## 3 CellLin… NEG 250 3.83 1.72
## 4 CellLin… NEG 500 20.2 5.78
## 5 CellLin… NEG 1000 60.5 5.43
## 6 CellLin… POS 63 1.67 1.03Section 3: Statistical test
3.1 Normality test
To determine whether the data follows a normal distribution, the normality test should be performed.
shapiro.test(AllCondition_Spheroid_Count$SpheroidCount)##
## Shapiro-Wilk normality test
##
## data: AllCondition_Spheroid_Count$SpheroidCount
## W = 0.78259, p-value = 5.1e-08For the example dataset, the result showed that the data do not follow a normal distribution.
3.2 Statistical test
According to the non-normal distribution of the data, the Wilcoxon
rank-sum test was used for statistical testing by comparing the spheroid
count between the biosensor groups in the same cell seeding
density.
If your data follows a normal distribution, a parametric test like
the “T-test” should be used instead.
Here, we also provide the choice of statistical tests as shown
below.
# T-test
Stat_SpheroidCount_Ttest <- compare_means(SpheroidCount ~ BiosensorGroup,
data = AllCondition_Spheroid_Count,
group.by = "CellSeedingDensity",
method = "t.test"
)
# Wilcoxon rank-sum test
Stat_SpheroidCount_Wilcox <- compare_means(SpheroidCount ~ BiosensorGroup,
data = AllCondition_Spheroid_Count,
group.by = "CellSeedingDensity",
method = "wilcox.test"
)
# Write the `Stat_SpheroidCount` into a `.csv` file.
write.csv(Stat_SpheroidCount_Wilcox,
paste0(CellLine_Name, "_Stat_SpheroidCount_CuOff", CutOff, ".csv"), row.names = FALSE)
print(paste0("Statistical test of the spheroid count was written as '", CellLine_Name, "_Stat_SpheroidCount_CuOff", CutOff, ".csv'"))## [1] "Statistical test of the spheroid count was written as 'CellLineX_Stat_SpheroidCount_CuOff7853.98.csv'"Observe the Stat_SpheroidCount_Wilcox data.
Stat_SpheroidCount_Wilcox ## # A tibble: 5 × 9
## CellSeedingDensity .y. group1 group2 p p.adj p.format p.signif method
## <int> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 63 Spher… NEG POS 0.684 0.74 0.6836 ns Wilco…
## 2 125 Spher… NEG POS 0.368 0.74 0.3682 ns Wilco…
## 3 250 Spher… NEG POS 0.00492 0.02 0.0049 ** Wilco…
## 4 500 Spher… NEG POS 0.00216 0.011 0.0022 ** Wilco…
## 5 1000 Spher… NEG POS 0.00813 0.024 0.0081 ** Wilco…Section 4: Data visualization
Data visualization is presented as a faceted box plot in an object
named Tumorigenicity_BoxPlot and saved as a
.png file.
# Define `CellSeedingDensity` and `BiosensorGroup` as factors.
AllCondition_Spheroid_Count$CellSeedingDensity <- factor(AllCondition_Spheroid_Count$CellSeedingDensity,
levels = c("1000", "500", "250", "125", "63")
)
AllCondition_Spheroid_Count$BiosensorGroup <- factor(AllCondition_Spheroid_Count$BiosensorGroup,
levels = c("POS", "NEG")
)
# Create a box plot which faceted by `CellSeedingDensity`.
Tumorigenicity_BoxPlot <- ggboxplot(AllCondition_Spheroid_Count,
x = "BiosensorGroup",
y = "SpheroidCount",
color = "BiosensorGroup",
fill = "BiosensorGroup",
facet.by = "CellSeedingDensity",
short.panel.labs = TRUE,
add = "jitter") +
scale_color_manual(values = c("#154360",
"#9A7D0A")) +
scale_fill_manual(values = c("#0073C2FF",
"#EFC000FF")) +
labs(x = "GFP expression",
y = "Number of spheroids",
title = "In vitro tumorigenicity") +
theme(plot.title = element_text(hjust = 0.5)) +
guides(fill = guide_legend(title = NULL)) +
facet_wrap(~CellSeedingDensity) +
facet_grid(rows = NULL,
cols = vars(CellSeedingDensity)) +
stat_compare_means(label = "p.format",
label.x.npc = 0.8, label.y.npc =0.9) +
theme(legend.position = "none")
# Save the box plot as a `.png` file.
ggsave(paste0(CellLine_Name, "_Plot_SpheroidCount_CutOff", CutOff, ".png"),
width = 10 , height = 3.5,
dpi = 300, units = "in")
print(paste0("The .png file was saved as '", CellLine_Name, "'plot_SpheroidCount_CutOff", CutOff, ".png'"))## [1] "The .png file was saved as 'CellLineX'plot_SpheroidCount_CutOff7853.98.png'"
Figure 4: In vitro tumorigenic assay of cell line X. The spheroids were
determined by area cut-off 8,000 μm².