chipPCR (version 0.0.8-10)

C85: Helicase Dependent Amplification of Vimentin using the VideoScan Platform

Description

A Helicase Dependent Amplification (HDA) of Vimentin (Vim) was performed. The VideoScan Platform (Roediger et al. (2013)) was used to monitor the amplification. The HDA was performed at 65 degrees Celsius. Three concentrations of input DNA (D1, D2, D3) were used.

Usage

data(C85)

Format

A data frame with 301 observations on the following 5 variables.

Cycle

Cycles HDA measurements.

t.D1

Dilution 1, elapsed time during HDA in seconds.

MFI.D1

Dilution 1, fluorescence.

t.D2

Dilution 2, elapsed time during HDA in seconds.

MFI.D2

Dilution 2, fluorescence.

t.D3

Dilution 3, elapsed time during HDA in seconds.

MFI.D3

Dilution 3, fluorescence.

Source

Claudia Deutschmann & Stefan Roediger, BTU Cottbus - Senftenberg, Senftenberg, Germany

Details

To perform an isothermal amplification in VideoScan, standard conditions for the IsoAmp(R) III Universal tHDA Kit (Biohelix) were used. Primers and templates are described in Roediger et al. (2013). The reaction was composed of reaction mix A)10 micro L A. bidest, 1.25 micro L 10xbuffer, 0.75 micro L primer(150nM final), 0.5 micro L template plasmid. Preincubation: This mixture was incubated for 2 min at 95 degree. Celsius and immediately placed on ice. Reaction mix B) 5 micro L A. bidest., 1.25 micro L 10x buffer, 2 micro L NaCl, 1.25 micro L MgSO4, 1.75 micro L dNTPs, 0.25 micro L EvaGreen, 1 micro L enzyme mix. The mix was covered with 50 micro L mineral oil. The fluorescence measurement in VideoScan HCU started directly after adding buffer B at 65 degrees Celsius. A 1x (D1), a 1:10 dilution (D2) and a 1:100 (D3) dilution were tested. Temperature profile (after Preincubation): - 60 seconds at 65 degrees Celsius - 11 seconds at 55 degrees Celsius && Measurement

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33--74, 2013. http://www.ncbi.nlm.nih.gov/pubmed/22437246

Examples

data(C85)
# First example
plot(NA, NA, xlim = c(0,85), ylim = c(0,1), xlab = "Time [min]", 
      ylab = "Fluorescence", main = "HDA amplification")
points(C85[, 2]/60, C85[, 3], type = "b", col = 1, pch = 20)
points(C85[, 4]/60, C85[, 5], type = "b", col = 2, pch = 20)
points(C85[, 6]/60, C85[, 7], type = "b", col = 3, pch = 20)
legend(40, 0.5, c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
	pch = rep(20,3))

# Second example
plot(NA, NA, xlim = c(0,30), ylim = c(0,0.8), xlab = "Time [min]", 
      ylab = "Fluorescence", main = "HDA amplification")
points(C85[, 2]/60, C85[, 3], type = "b", col = 1, pch = 20)
points(C85[, 2]/60, smoother(C85[, 2]/60, C85[, 3], 
      method = list("savgol")), type = "b", col = 2, pch = 20)
points(C85[, 2]/60, smoother(C85[, 2]/60, C85[, 3], 
      method = list("smooth")), type = "b", col = 3, pch = 20)
points(C85[, 2]/60, smoother(C85[, 2]/60, C85[, 3], 
      method = list("mova")), type = "b", col = 4, pch = 20)

legend(1, 0.8, c("D1, raw", "D1, savgol", "D1, smooth", "D1, mova"), 
	col = c(1:4), pch = rep(20,4))

# Third example
# Comparison of Lowess, Moving average and splines to smooth amplification 
# curve data of
# a HDA using the VideoScan HCU for amplification and monitoring.

xrange <- 2:2400
plot(NA, NA, xlim = c(0,85), ylim = c(0.4, 0.8), xlab = "Time [min]", 
      ylab = "RFI", main = "Raw data")
points(C85[, 2]/60, C85[, 3], type = "b", col = 1, pch = 20)
points(C85[, 4]/60, C85[, 5], type = "b", col = 2, pch = 20)
points(C85[, 6]/60, C85[, 7], type = "b", col = 3, pch = 20)
legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
	pch = rep(20,3))

mtext("A", cex = 2, side = 3, adj = 0, font = 2)

plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
      ylab = "RFI", main = "Moving average")
movaww <- seq(1,17,4)
for (i in 1:length(movaww)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(mova = list(movaww = movaww[i])), 
			bg.outliers = TRUE)
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
	  col = i)
    }
}
mtext("B", cex = 2, side = 3, adj = 0, font = 2)
legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
	pch = rep(20,3))
legend("bottomright", 6, paste("movaww : ", movaww), pch = 20, lwd = 2, 
	col = 1:length(movaww))
    
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
      ylab = "RFI", main = "Cubic Spline")
df.fact <- seq(0.5,0.9,0.1)
for (i in 1:length(df.fact)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], 
		  method = list(smooth = list(df.fact = df.fact[i])), 
		  bg.outliers = TRUE)
    
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, 
    cex = 0.5, col = i)
    }
}
    
mtext("C", cex = 2, side = 3, adj = 0, font = 2)
legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
pch = rep(20,3))
legend("bottomright", paste("df.fact : ", df.fact), pch = 20, lwd = 2, 
col = 1:length(df.fact))
    
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
ylab = "RFI", main = "Lowess")
f <- seq(0.01,0.2,0.04)
for (i in 1:length(f)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(lowess = list(f = f[i])), 
		  bg.outliers = TRUE)
    
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
    col = i)
    }
    }
    
    mtext("D", cex = 2, side = 3, adj = 0, font = 2)
    legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
    pch = rep(20,3))
    legend("bottomright", paste("f : ", f), pch = 20, lwd = 2, col = 1:length(f))
	
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
ylab = "RFI", main = "Friedman's\n''super smoother''")
span <- seq(0.01,0.05,0.01)
for (i in 1:length(span)) {
  for (j in c(2,4,6)) {
    tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
    tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list(supsmu = list(span = span[i])), 
	  bg.outliers = TRUE)
    
    lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
col = i)
    }
    }
    
    mtext("E", cex = 2, side = 3, adj = 0, font = 2)
    legend("topleft", c("D1, 1x", "D2, 1:10", "D3, 1:100"), col = c(1:3), 
pch = rep(20,3))
    legend("bottomright", paste("span : ", span), pch = 20, lwd = 2, col = 1:length(span))
	  
plot(NA, NA, xlim = c(0,40), ylim = c(-0.05, 0.3), xlab = "Time [min]", 
ylab = "RFI", main = "Savitzky-Golay")

for (j in c(2,4,6)) {
  tmp <- data.frame(na.omit(C85[xrange, j])/60, na.omit(C85[xrange, j + 1]))
  tmp.out <- smoother(tmp[, 1], tmp[, 2], method = list("savgol"), 
	bg.outliers = TRUE)
  
  lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20, cex = 0.5, 
col = 1)
  }
  
  mtext("F", cex = 2, side = 3, adj = 0, font = 2)
  legend("bottomright", paste("/ : ", NULL), pch = 20, lwd = 2, col = 1:length(span))