5 5 2015 310 Heat Shock RNA Isolation / qPCR

Today I process the heat shock samples from the Fish 310 lab. I isolated the RNA, DNase treated the sample, and ran a qPCR for quality control. I changed a few things from the previous protocols to make things run smoother and more efficient today. I've also taken several of Sam's suggestions on how I can improve my notebook.

RNA Isolation Protocol.

1. 100 mg tissue homogenized in 1 ml RNAzol RT at room temp. 
2. Added 400 ul 0.1% DEPC water to homogenate.
3. Incubated 15 minutes at room temp
4. Centrifuged 15 minutes at 11,400 rpm (~12,000 g)
5. Transferred 1 ml supernatant to fresh tube
6. Added 400 ul 75% EtOH
7. Mixed via inversion for 15 seconds
8. Incubated samples 10 minutes at room temp
9. Centrifuged 8 minutes at 11,400 rpm (~12,000 g)
10. Discarded supernant
11. Added 600 ul 75% EtOH
12. Vortexed 15 seconds
13. Centrifuged 3 minutes at 7,400 rpm (~8000 g)
14. Repeated steps 10 - 13. 
15. Removed supernatant
16. resuspended RNA in 50 ul 0.1% DEPC water

Instead of nanodropping the RNA, I immediately Turbo DNase Treated the samples to remove any DNA contamination. 

DNase Treatment Protocol:

1. Added 5 ul DNase 10X Buffer to each 50 ul of RNA.
2. Added 1 ul Turbo DNase to each RNA.
3. Incubated at room temp for 30 minutes
4. Added 5 ul DNase inhibitor to each sample
5. Incubated at room temp for 5 minutes, flicking occasionally
6. Centrifuged at 9400 rpm (~10,000 g) for 1.5 minutes
7. Decanted the top 50 ul of supernatant to fresh tube

8. Nanodropped

Nanodrop Results (including positive control):

Sample ID	Date	Time	ng/ul	A260	A280	260/280	260/230
C+	5/5/2015	4:17 PM	167.3	4.182	2.092	2	2.34
42715HT1	5/5/2015	4:18 PM	238.58	5.965	3.366	1.77	0.66
42715HT2	5/5/2015	4:19 PM	422.45	10.561	5.431	1.94	1
42715HT3	5/5/2015	4:21 PM	175.57	4.389	2.26	1.94	1.79
42715HT4	5/5/2015	4:22 PM	251.19	6.28	3.532	1.78	0.68
42715HT5	5/5/2015	4:23 PM	391.29	9.782	5.133	1.91	0.87
42715ST1	5/5/2015	4:24 PM	690.94	17.274	10.258	1.68	0.49
42715ST2	5/5/2015	4:25 PM	393.97	9.849	5.181	1.9	0.91
42715ST3	5/5/2015	4:25 PM	365.73	9.143	4.917	1.86	0.99
42715ST4	5/5/2015	4:26 PM	426.33	10.658	5.407	1.97	0.99
42715ST5	5/5/2015	4:27 PM	343.49	8.587	4.622	1.86	0.81

For the qPCR I used Actin primers and a positive control from Fidalgo seed oysters extracted on 3/23/2015. My negative control contained no DNA as to show there was no contamination in the Master Mix. I also bumped up the volume of sample used to 0.5 ul due to issues with the pipetter not being able to accurately pipette 0.2 ul. This is about 2.5 X the concentration of contamination gDNA that the cDNA will have in it. I also bumped down the amount of water by 1 ul per reaction so as to ensure proper reaction mixture ratios were maintained. 

qPCR Reagent Table:

	Volume	Reactions X12
Ssofast Evagreen MM	10	140
FWD Primer	0.5	7
REV Primer	0.5	7
Nuclease Free H2O	8.8	123.2
RNA	0.5

qPCR protocol:

1. Added each from greatest volume to least to make the master mix. 

2. Pipetted 20 ul master mix into each well of a qPCR partial plate

3. Added 0.5 ul sample to each tube

qPCR plate lay out:

	11	12
C	C-	C+
D	42715HT1	42715ST1
E	42715HT2	42715ST2
F	42715HT3	42715ST3
G	42715HT4	42715ST4
H	42715HT5	42715ST5

This layout can be seen in the top left corner of the qPCR image below. 

qPCR program:

Sybr New Plate+Sybr cDNA 55 melt 1 Read
Step	Temperature	Time
Initiation	95 C	10 min
Elongation	95 C	15 sec
	55 C	15 sec
Read
	72 C	15 sec
Repeat Elongation 40 times
Termination	95 C	1 min
	55 C	1 sec
Melt Curve Manual ramp 0.2C per sec Read 0.5 C	65 - 95 C	30 sec
	21 C	10 min
End

Results:

It looks like the DNase treatment removed some but not all the DNA. The DNA replicates much much earlier in the cycles. Overall I think these samples are good samples with high concentrations and mild DNA contamination. You can see the data files for the qPCR here and here. 

Sometime this week I will begin the process of creating cDNA to use with qPCR to test for HSP70 and other RNA markers. 

5 4 2015 DNase treatment

Today I ran the DNase treatment on the samples to remove any excess DNA from the samples. The treatment was pretty simple but I don't think it helped much as their was amplification at roughly the sample cycle as there was in the last quality check. The DNase treatments were old but I don't think that would cause an issue. I also had to mix kits as there wasn't enough reagents in a single kit to do the treatments on 10 samples.

Protocol for treatment:

1. Added 10 ul DNase 10X Buffer to each 100 ul of RNA.
2. Added 2 ul Turbo DNase to each RNA.
3. Incubated at 37 C for 30 minutes
4. Added 10 ul DNase inhibitor to each sample
5. Incubated at room temp for 5 minutes, flicking occasionally
6. Centrifuged at 10,000 g for 1.5 minutes
7. Decanted the top 100 ul of supernatant to fresh tube
8. Placed in -80 until I could run qPCR

Later this afternoon I ran the qPCR on the treatment.

Reagent work sheet:

	Volume	Reactions X12
Ssofast Evagreen MM	10	140
FWD Actin Primer	0.5	7
REV Actin Primer	0.5	7
Nuclease Free H2O	9.8	137.2
RNA	0.2

1. Added each from greatest volume to least to make the master mix. 

2. Pipetted 21 ul master mix into each well of a qPCR partial plate

3. Added 0.2 ul sample to each tube

4. Positive control was DNA extract from Oly seed oysters

5. Negative control had nothing added to the master mix

I ran the following program:

Sybr New Plate+Sybr cDNA 55 melt 2
Step	Temperature	Time
Initiation	95 C	10 min
Elongation	95 C	15 sec
	55 C	15 sec
Read
	72 C	15 sec
Read
Repeat Elongation 40 times
Termination	95 C	1 min
	55 C	1 sec
Melt Curve Manual ramp 0.2C per sec Read 0.5 C	65 - 95 C	30 sec
	21 C	10 min
End

You can see the results below

bright green = positive control, red = negative control, light blue down to brown = HM1-5, dark blue to yellow = SM1-5

The positive control began amplifying at the 21 cycle marks. One sample began at 30 cycles the rest were between 34 and 36. 

You can see the raw datafile here.

Tomorrow I will isolate the RNA from the 310 heat shock samples, treat with DNase, and qPCR for quality. 

5 1 2015 qPCR Quality Check

Today I ran a quality check on the samples that I isolated yesterday. Instead of creating cDNA before doing the qPCR I ran just the RNA with Actin primers to determine if there was any genomic contamination in the RNA samples. I diluted the RNA to levels similar to that which would have gone from the cDNA process to the qPCR process. This was to ensure that any genomic dna would be at levels similar to that from from the cDNA process and wouldn't over estimate the concentration of them in the sample.

The qPCR protocol was as follows

Master Mix

	Volume	Reactions X12
Ssofast Evagreen MM	10	120
FWD Primer	0.5	6
REV Primer	0.5	6
Nuclease Free H2O	9.8	117.6

1. Added the reagents from highest volume to lowest to the master mix

2. Vortexed

3. pipetted into .5 ml qPCR tubes

4. Added 0.2 ul RNA to each tube from each isolation

5. Placed into the Opticon 2 qPCR machine

6. Ran the program "Sybr New Plate + Sybr cDNA 55 melt 2 reads"

Results

As you can see, the samples began amplifying at 27-30 cycles which is indicative of a genomic DNA contamination. The negative control did not begin amplifying until 37 cycles. 

For future work, I need to use a DNase treatment to remove genomic DNA before I create cDNA. Next week I will isolate the 310 heat shock animals, DNase treat everything, and produce cDNA. 

11 24 2014 Non Parametric Test for Differences in Size

y1boxplot.R

Jake H

Wed Nov 26 15:18:25 2014

require(ggplot2)

## Loading required package: ggplot2

require(plyr)

## Loading required package: plyr

require(splitstackshape)

## Loading required package: splitstackshape
## Loading required package: data.table

require(nparcomp)

## Loading required package: nparcomp
## Loading required package: multcomp
## Loading required package: mvtnorm
## Loading required package: survival
## Loading required package: splines
## Loading required package: TH.data

y1size=read.csv('Y1size.csv')
#creates dataframe and reads in the CSV file for sizes
View(y1size)
#check data
y1size$Date<-as.Date(y1size$Date, "%m/%d/%Y")
#make R understand dates
y1meansize<-ddply(y1size,.(Date,Site,Pop),summarise, mean_size=mean(Length.mm,na.rm=T))
#create table of ave size for outplant and year one for each pop at each site
#print it out
print(y1meansize)

##          Date       Site Pop mean_size
## 1  2013-08-16    Fidalgo  2H     10.67
## 2  2013-08-16    Fidalgo  2N     11.60
## 3  2013-08-16    Fidalgo  2S     11.25
## 4  2013-08-16 Manchester  4H     10.53
## 5  2013-08-16 Manchester  4N     13.40
## 6  2013-08-16 Manchester  4S     11.30
## 7  2013-08-16 Oyster Bay  1H     10.49
## 8  2013-08-16 Oyster Bay  1N     10.90
## 9  2013-08-16 Oyster Bay  1S     12.15
## 10 2014-09-19 Oyster Bay  1H     27.96
## 11 2014-09-19 Oyster Bay  1N     34.65
## 12 2014-09-19 Oyster Bay  1S     27.98
## 13 2014-10-17    Fidalgo  2H     24.40
## 14 2014-10-17    Fidalgo  2N     29.10
## 15 2014-10-17    Fidalgo  2S     28.91
## 16 2014-10-24 Manchester  4H     21.49
## 17 2014-10-24 Manchester  4N     24.37
## 18 2014-10-24 Manchester  4S     23.99

#now we need to create subsets for each site for out plant and end of year 1
outmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Manchester")
outfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Fidalgo")
outoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Oyster Bay")
endmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-24"&Site=="Manchester")
endfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-17"&Site=="Fidalgo")
endoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-09-19"&Site=="Oyster Bay")

ggplot()+
  geom_boxplot(data=outmany1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"))+
  scale_fill_manual(values=c("blue","purple","orange"))

ggplot()+
  geom_boxplot(data=endmany1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
  scale_fill_manual(values=c("blue","purple","orange"), guide=F)+
  ylim(c(0,50))+
  labs(x="Population",y="Average Length (mm)")+
  scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
  annotate("text", x=c("4N","4H","4S"),y=50, label=c("A","B","A"),size=10)+
  theme_bw()+
  theme(axis.text.x=element_text(size=20),
        axis.title.x=element_text(size=25),
        axis.title.y=element_text(size=25, vjust=2),
        axis.text.y=element_text(size=20))

ggplot()+
  geom_boxplot(data=outfidy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"))+
  scale_fill_manual(values=c("blue","purple","orange"))

ggplot()+
  geom_boxplot(data=endfidy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
  scale_fill_manual(values=c("blue","purple","orange"),guide=F)+
  ylim(c(0,50))+
  labs(x="Population",y="Average Length (mm)")+
  scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
  annotate("text", x=c("2N","2H","2S"),y=50, label=c("A","B","A"),size=10)+
  theme_bw()+
  theme(axis.text.x=element_text(size=20),
        axis.title.x=element_text(size=25),
        axis.title.y=element_text(size=25, vjust=2),
        axis.text.y=element_text(size=20))

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

ggplot()+
  geom_boxplot(data=outoysy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"))+
  scale_fill_manual(values=c("blue","purple","orange"))

ggplot()+
  geom_boxplot(data=endoysy1,aes(x=Pop,y=Length.mm,fill=Pop))+
  scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
  scale_fill_manual(values=c("blue","purple","orange"),guide=F)+
  ylim(c(0,50))+
  labs(x="Population",y="Average Length (mm)")+
  scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
  annotate("text", x=c("1N","1H","1S"),y=50, label=c("B","A","A"),size=10)+
  theme_bw()+
  theme(axis.text.x=element_text(size=20),
        axis.title.x=element_text(size=25),
        axis.title.y=element_text(size=25, vjust=2),
        axis.text.y=element_text(size=20))

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

normality<-ddply(y1size,.(Date,Site,Pop),summarize,n=length(Length.mm),sw=shapiro.test(as.numeric(Length.mm))[2])
y1size$Pop2<-y1size$Pop
y1size$Pop2<-revalue(y1size$Pop2,c("1H"="H","2H"="H","4H"="H","1N"="N","2N"="N","4N"="N","1S"="S","2S"="S","4S"="S"))
#Here we subset the data set to only include data from the end of year 1
endy1<-ddply(y1size,.(Length.mm,Site,Pop,Tray,Sample,Area,Pop2),subset,Date>="2014-09-19")
normality<-ddply(endy1,.(Date,Site,Pop),summarize,n=length(Length.mm),sw=shapiro.test(as.numeric(Length.mm))[2])
endy1$log<-log2(endy1$Length.mm)
endy1$asin<-asin(sign(endy1$Length.mm)*sqrt(abs(endy1$Length.mm)))

## Warning: NaNs produced

normality<-ddply(endy1,.(Date,Site,Pop),summarize,n=length(log),sw=shapiro.test(as.numeric(log))[2])

sizekw<-kruskal.test(endy1$Length.mm~endy1$Site,endy1)
print(sizekw)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  endy1$Length.mm by endy1$Site
## Kruskal-Wallis chi-squared = 426.2, df = 2, p-value < 2.2e-16

sizekwpop<-kruskal.test(endy1$Length.mm~endy1$Pop2,endy1)
print(sizekwpop)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  endy1$Length.mm by endy1$Pop2
## Kruskal-Wallis chi-squared = 230, df = 2, p-value < 2.2e-16

require(PMCMR)

## Loading required package: PMCMR

sizenemenyi1<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Site, method="Tukey")

## Warning: Ties are present, p-values are not corrected.

sizenemenyi1

## 
##  Pairwise comparisons using Tukey and Kramer (Nemenyi) test  
##                    with Tukey-Dist approximation for independent samples 
## 
## data:  endy1$Length.mm and endy1$Site 
## 
##            Fidalgo Manchester
## Manchester < 2e-16 -         
## Oyster Bay 1.1e-12 < 2e-16   
## 
## P value adjustment method: none

sizenemenyi2<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Pop2, method="Tukey")

## Warning: Ties are present, p-values are not corrected.

sizenemenyi2

## 
##  Pairwise comparisons using Tukey and Kramer (Nemenyi) test  
##                    with Tukey-Dist approximation for independent samples 
## 
## data:  endy1$Length.mm and endy1$Pop2 
## 
##   H       N      
## N < 2e-16 -      
## S 3.4e-14 4.9e-08
## 
## P value adjustment method: none

sizenemenyi3<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Site:endy1$Pop2, method="Tukey")

## Warning: Ties are present, p-values are not corrected.

sizenemenyi3

## 
##  Pairwise comparisons using Tukey and Kramer (Nemenyi) test  
##                    with Tukey-Dist approximation for independent samples 
## 
## data:  endy1$Length.mm and endy1$Site:endy1$Pop2 
## 
##              Fidalgo:H Fidalgo:N Fidalgo:S Manchester:H Manchester:N
## Fidalgo:N    < 2e-16   -         -         -            -           
## Fidalgo:S    7.8e-14   0.9995    -         -            -           
## Manchester:H 2.7e-07   < 2e-16   < 2e-16   -            -           
## Manchester:N 1.0000    9.2e-14   1.1e-13   7.3e-06      -           
## Manchester:S 0.9786    < 2e-16   8.5e-14   0.0004       0.9880      
## Oyster Bay:H 3.1e-10   0.2781    0.6352    1.2e-14      2.4e-08     
## Oyster Bay:N < 2e-16   1.5e-11   7.2e-13   < 2e-16      < 2e-16     
## Oyster Bay:S 1.8e-09   0.6813    0.9255    9.1e-14      5.7e-08     
##              Manchester:S Oyster Bay:H Oyster Bay:N
## Fidalgo:N    -            -            -           
## Fidalgo:S    -            -            -           
## Manchester:H -            -            -           
## Manchester:N -            -            -           
## Manchester:S -            -            -           
## Oyster Bay:H 9.5e-12      -            -           
## Oyster Bay:N < 2e-16      1.0e-13      -           
## Oyster Bay:S 6.0e-11      1.0000       6.9e-12     
## 
## P value adjustment method: none