Cell Death probes

Let us first discuss some flow cytometric probes used in exploring the cellular death.

PI

Propidium iodide (PI) is a popular red-fluorescent nuclear and chromosome counterstain.
Since propidium iodide is not permeant to live cells, it is also commonly used to detect dead cells in a population.
PI binds to DNA by intercalating between the bases with little or no sequence preference.
In aqueous solution, the dye has excitation/emission maxima of 493/636 nm.
Once the dye is bound, its fluorescence is enhanced 20- to 30-fold, the fluorescence excitation maximum is shifted ~30–40 nm to the red and the fluorescence emission maximum is shifted ~15 nm to the blue, resulting in an excitation maximum at 535 nm and fluorescence emission maximum at 617 nm.

PI spectrum

PI structure

Annexin V

Fluorescent conjugates of annexin V are commonly used to identify apoptotic cells.

More about biophysical characters of Annexin V

The human vascular anticoagulant annexin V is a 35–36 kDa, Ca2+-dependent phospholipid-binding protein that has a high affinity for the anionic phospholipid phosphatidylserine (PS).
In normal healthy cells, PS is located on the cytoplasmic surface of the plasma membrane.
However, during apoptosis, the plasma membrane undergoes structural changes that include translocation of PS from the inner to the outer leaflet (extracellular side) of the plasma membrane. It has been reported that the translocated phosphatidylserine on the outer surface of the cell marks the cell for recognition and phagocytosis by macrophages

The Annexin V apoptotic scheme

The list of annexin conjugates

https://www.thermofisher.com/in/en/home/life-science/cell-analysis/cell-viability-and-regulation/apoptosis/annexin-v-staining.html?ef_id=Cj0KCQiAifz-BRDjARIsAEElyGI0uAvRJQlBJSl1C0GOnDIBtpWMfUYT-dAYu3fd_K9lZ-3AF8PSIkAaAne0EALw_wcB:G:s&s_kwcid=AL!3652!3!270996052709!e!!g!!annexin%20v&cid=bid_pca_frg_r01_co_cp1359_pjt0000_bid00000_0se_gaw_nt_pur_con&gclid=Cj0KCQiAifz-BRDjARIsAEElyGI0uAvRJQlBJSl1C0GOnDIBtpWMfUYT-dAYu3fd_K9lZ-3AF8PSIkAaAne0EALw_wcB

PE (Annexin conjugate)

Annexin V staining provides a very sensitive method for detecting cellular apoptosis, while propidium iodide (PI) is used to detect necrotic or late apoptotic cells, characterized by the loss of the integrity of the plasma and nuclear membranes.

Table Showing Classifier

Stain	Viable	Apoptotic	LateApoptotic	Necrotic
PI	0	0	1	1
PE	0	1	1	0

How it will look in the Scatter plot of Annexin V conjugate-PI

viable cells which are negative to both probes (PI/PE -/-; Q3)
apoptotic cells which are PI negative and Annexin positive (PI/PE -/+; Q1)
late apoptotic cells which are PI and Annexin positive (PI/PE +/+; Q2)
necrotic cells which are PI positive and Annexin negative (PI/PE +/-; Q4).

A set of FCS files

library(flowCore)
library(flowClust)
# read files with pattern dct
gh=read.flowSet(files=NULL, path="./isha/5thJan20",pattern="hct")
fnames.h=gh@phenoData@data$name
gc=read.flowSet(files=NULL, path="./isha/5thJan20",invert.pattern="hct")
fnames.c=gc@phenoData@data$name

doublet = function (a,h){
  x=log(a)
  y=log(h)
  l=lm(y~x)
  m=l$coefficients[2]
  c=l$coefficients[1]
  ik=which(y>m*x+c)
return(ik)}

i=4

f=fnames.h[i]
f= paste0('./isha/5thJan20/',sep='',f)
ff=read.FCS(f)
df=data.frame(ff@exprs[,1:6])
attach(df)
a=df$FSC.A
h=df$FSC.H
ik=doublet(a,h)
A=a[ik]
H=h[ik]
plot(a,h,pch=16,cex=0.5)
points(A,H,pch=16,cex=0.5,col="red")

library(flowClust)
# defining a singlet dataframe 
df.singlet=data.frame(sFSC.A=FSC.A[ik],sFSC.H=FSC.H[ik],sSSC.A=SSC.A[ik],sSSC.H=SSC.H[ik],sPE.A=PE.A[ik],sPI.A=PI.A[ik])
#plot(FSC.H,SSC.H,pch=16,cex=0.5)
res1 <- flowClust(df.singlet,
varNames=c("sFSC.H", "sSSC.H"), K=1) 
df2=Subset(df.singlet,res1)
res2<-flowClust(df2, varNames=c("sFSC.H", "sSSC.H"), K=1:3, B=100)
criterion(res2,"BIC")

[1] -129001.3 -123283.7 -122903.8

plot(res2[[3]], data=df2, level=0.8, z.cutoff=0)

Rule of identifying outliers: 80% quantile

ik1=which(res2[[3]]@label==1)
ik2=which(res2[[3]]@label==2)
ik3=which(res2[[3]]@label==3)

A visually interactive gate post-clustering

attach(df.singlet)
iik=which(sFSC.H<100 & sSSC.H<1560)
plot(sFSC.H,sSSC.H,pch=16,col="brown",cex=0.5)
points(sFSC.H[iik],sSSC.H[iik],pch=16,col="blue",cex=0.5)

Fishing the celluar debris out

df.singlet.nodebris = 
  data.frame(mFSC.A=sFSC.A[-iik],mFSC.H=sFSC.H[-iik],mSSC.A=sSSC.A[-iik],mSSC.H=sSSC.H[-iik],mPE.A=sPE.A[-iik],mPI.A=sPI.A[-iik])
plot(df.singlet.nodebris$mFSC.H,df.singlet.nodebris$mSSC.H,pch=16,cex=0.5)

Density plot

res2.den <- density(res2[[3]], data = df.singlet)
plot(res2.den,type="image")

Table showing the cluster population

table(Map(res2,echo=TRUE))


   1    2    3 
1644 2578 1352

Exploring individual clusers among singlet

attach(df.singlet)
plot(sFSC.H,sSSC.H,pch=16,cex=0.5)
points(sFSC.H[ik3],sSSC.H[ik3],pch=16,cex=0.5,col="blue")

Splitting the clusters

s1=split(df.singlet, res2)[[1]]
s2=split(df.singlet, res2)[[2]]
s3=split(df.singlet, res2)[[3]]
#plot(s1$sFSC.H,s1$sSSC.H,pch=19,col="red",cex=0.1)

plot(s3$sFSC.H,s3$sSSC.H,pch=19,col="green",cex=0.1)

#points(s3$sFSC.H,s3$sSSC.H,pch=19,col="blue",cex=0.1)

Working with the PI-PE

We now have a dataframe with the “iik” s which has actually gated out the singlet as well as the debris population.

attach(df.singlet.nodebris)
plot(mPE.A,mPI.A,pch=16,cex=0.5)

FlowCluster of the double split population

mres1 <- flowClust(df.singlet.nodebris,
varNames=c("mPE.A", "mPI.A"), K=1)
df2m=Subset(df.singlet.nodebris,mres1)
mres2<-flowClust(df2m, varNames=c("mPE.A", "mPI.A"), K=1:2, B=1000)
criterion(mres2,"BIC")

[1] -28032.00 -27490.53

plot(mres2[[2]], data=df2m, level=0.8, z.cutoff=0)

Rule of identifying outliers: 80% quantile

mres2.den <- density(mres2[[2]], data = df.singlet.nodebris)
plot(mres2.den)

Flow cytometry in Cell Death