Tag Archives: Pacific oyster

qPCRs – Ronit’s C.gigas ploidy/dessication/heat stress cDNA (1:5 dilution)

IMPORTANT: The cDNA used for the qPCRs described below was a 1:5 dilution of Ronit’s cDNA made 20181017 with the following primers! Diluted cDNA was stored in his -20oC box with his original cDNA.

The following primers were used:

18s

  • Cg_18s_F (SR ID: 1408)

  • Cg_18s_R (SR ID: 1409)

EF1 (elongation factor 1)

  • EF1_qPCR_5′ (SR ID: 309)
  • EF1_qPCR_3′ (SR ID: 308)

HSC70 (heat shock cognate 70)

  • Cg_hsc70_F (SR ID: 1396)
  • Cg_hsc70_R2 (SR ID: 1416)

HSP90 (heat shock protein 90)

  • Cg_Hsp90_F (SR ID: 1532)
  • Cg_Hsp90_R (SR ID: 1533)

DNMT1 (DNA methyltransferase 1)

  • Cg_DNMT1_F (SR ID: 1511)
  • Cg_DNMT1_R (SR ID: 1510)

Prx6 (peroxiredoxin 6)

  • Cg_Prx6_F (SR ID: 1381)
  • Cg_Prx6_R (SR ID: 1382)

Samples were run on Roberts Lab CFX Connect (BioRad). All samples were run in duplicate. See qPCR Report (Results section) for plate layout, cycling params, etc.

qPCR master mix calcs (Google Sheet):


RESULTS

No analysis here. Will analyze data and post in different notebook entry. This entry just contains the qPCR setup, resulting data, and a glimpse of how each primer performed.

Nothing is broken down based on sample ploidy or experimental conditions.

18s

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Amplication Plots

Melt Curves


DNMT1

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Amplication Plots

Melt Curves


EF1

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Amplication Plots – Manual Threshold (Linear)

Amplication Plots – Manual Threshold (Log)

Amplication Plots – Automatic Threshold (Linear)

Amplication Plots – Automatic Threshold (Log)

Melt Curves


HSC70

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Amplication Plots

Melt Curves


HSP90

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Amplication Plots

Melt Curves


Prx6

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Amplication Plots

Melt Curves

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Reverse Transcription – Ronit’s C.gigas DNased ctenidia RNA

Proceeded with reverse transcription of Ronit’s DNased ctenidia RNA (from 20181016).

Reverse transcription was performed using 100ng of each sample with M-MMLV Reverse Transcriptase from Promega.

Briefly, 100ng of DNased RNA was combined with oligo dT primers and brought up to a final volume of 15uL. Tubes were incubated for 5mins at 70oC in a PTC-200 thermal cycler (MJ Research), using a heated lid. Samples were immediately placed on ice.

A master mix of buffer, dNTPs, water, and M-MMLV reverse transcriptase was made, 10uL of the master mix was added to each sample, and mixed via finger flicking. Samples were incubated for 1hr at 42oC in a PTC-200 thermal cycler (MJ Research), using a heated lid, followed by a 5min incubation at 65oC.

Samples were stored on ice for use later this afternoon by Ronit.

Samples will be stored in Ronit’s -20oC box.

Reverse transcription calcs (Google Sheet):

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qPCR – Ronit’s DNAsed C.gigas Ploidy/Dessication RNA with elongation factor primers

After I figured out the appropriate DNA and primers to use to detect gDNA in Crassostrea gigas samples, I checked Ronit’s DNased ctenidia RNA (from 20181016) for residual gDNA.

Elongation factor primers:

  • EF1_qPCR_5′ (SRID 309)
  • EF1_qPCR_3′ (SRID 310)

BB16 from 20090519 was used as a positive control.

Samples were run on Roberts Lab CFX Connect (BioRad). All samples were run in duplicate. See qPCR Report (Results section) for plate layout, cycling params, etc.

qPCR master mix calcs (Google Sheet):


Results

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

In the plots below, green is the positive control, blue are the samples, and red is the no template control (NTC).

Everything looks great! Nice, clean, gDNA-free RNA! Will proceed with reverse transcription.


Amplification Plots


Melt Curves

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qPCR – C.gigas primer and gDNA tests with 18s and EF1 primers

The [qPCR I ran earlier today to check for residual gDNA in Ronit's DNased RNA] turned out terribly, due to a combination of bad primers and, possibly, bad gDNA.

I tracked down some different primers for testing:

  • Cg_18s_1644_F (SRID 1168)
  • Cg_18s_1750_R (SRID 1169)
  • EF1_qPCR_5′ (SRID 309)
  • EF1_qPCR_3′ (SRID 310)

In addition to BB15 from 20090519, I decided to test out BB16 from 20090519 as a positive control.

Samples were run on Roberts Lab CFX Connect (BioRad). All samples were run in duplicate. See qPCR Report (Results section) for plate layout, cycling params, etc.

qPCR master mix calcs (Google Sheet):


Results

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Looks like the elongation factor (EF1) primers and BB16 gDNA as a positive control are the way to go.

In the plots below, the black lines are BB16, the green lines are BB15, and the red lines are no template controls (NTC).

The amplification plots show that the EF1 primers do not amplify with BB15, but do amplify with BB16 (black lines Cq ~34). The 18s primers amplify with both BB15 & BB16 (Cq ~16 & ~18, respecitively), but produce primer dimers (red lines in amplification and melt curve plots).


Amplification Plots


Melt Curves

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qPCR – Ronit’s DNAsed C.gigas Ploidy/Dessication RNA with 18s primers

After DNasing Ronit’s RNA earlier today, I needed to check for any residual gDNA.

Identified some old, old C.gigas 18s primers that should amplify gDNA:

  • gigas18s_fw (SRID 157)
  • gigas18s_rv (SRID 156)

Used some old C.gigas gDNA (BB15 from 20090519) as a positive control.

Samples were run on Roberts Lab CFX Connect (BioRad). All samples were run in duplicate. See qPCR Report (Results section) for plate layout, cycling params, etc.

qPCR master mix calcs (Google Sheet):


Results

qPCR Report (PDF):

qPCR File (PCRD):

qPCR Data (CSV):

Well, this primer set and/or the gDNA is not good. In the plots below, the positive control gNDA is in green, samples in blue, and no template controls (NTC) are in red.

Poor performance is most easily noticed when looking at the melt curves. They have multiple peaks, suggesting non-specific amplification, even in the positive control.

Additionally, although less evident from just looking at the plots, is the replicates are highly inconsistent. Although it’s possible that might be due to poor technique, it’s very unlikely.

Will have to identify different primers and/or positive control DNA.


Amplification Plots


Melt Curves

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DNase Treatment – Ronit’s C.gigas Ploiyd/Dessication Ctenidia RNA

After quantifying Ronit’s RNA earlier today, I DNased them using the Turbo DNA-free Kit (Ambion), according to the manufacturer’s standard protocol.

Used 1000ng of RNA in a 50uL reaction in a 0.5mL thin-walled snap cap tube. Samples were mixed by finger flicking and then incubated 30mins @ 37oC in a PTC-200 thermal cylcer (MJ Research), without a heated lid.

DNase inactivation was performed (0.1 volumes of inactivation reagent; 5uL), pelleted, and supe transferred to new 1.7mL snap cap tube.

Samples were stored on ice in preparation for qPCR to test for residual gDNA.

DNase calculations are here:

Samples will be permanently stored here (Google Sheet):

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RNA Quantification – Ronit’s C.gigas Ploidy/Dessication RNA

Last Friday, Ronit quantified 1:10 dilutions of the RNA I isolated on 20181003 and the RNA he finished isolating on 20181011, but two of the samples (D11-C, T10-C) were still too concentrated.

I made 1:20 dilutions (1uL RNA in 19uL 0.1% DEPC-treated H2O) and quantified them using the Roberts Lab Qubit 3.0, with the RNA HS assay. Used 1uL of the diluted RNA.


RESULTS

Qubit data (Google Sheet):

Everything looks good. Added final concentration values (Qubit data x 20, to account for dilution factor) to Ronit’s master sheet (Google Sheet):

Will proceed with DNasing.

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RNA Isolation – Ronit’s C.gigas diploid/triploid dessication/heat shock ctenidia tissues

Isolated RNA from a subset of Ronit’s Crassostrea gigas ctenidia samples (see Ronit’s notebook for experiment deets):

  • D01 C

  • D02 C

  • D19 C

  • D20 C

  • T01 C

  • T02 C

  • T19 C

  • T20 C

RNA was isolated using RNAzol RT (Molecular Research Center) in the following way:

  • Unweighed, frozen tissue transferred to 500uL of RNAzol RT and immediately homogenized with disposable pestle.
  • Added additional 500uL of RNAzol RT and vortexed to mix.

  • Added 400uL of 0.1% DEPC-treated H2O, vortexed and incubated 15mins at RT.

  • Centrifuged 12,000g for 15mins at RT.

  • Transferred 750uL of supernatant to clean tube (discarded remainder), added 1 volume (750uL) of isopropanol, vortexed, and incubated at RT for 10mins.

  • Centrifuged 12,000g for 10mins at RT.

  • Discarded supernatant.

  • Washed pellet with 75% ethanol (made with 0.1% DEPC-treated H2O).

  • Centrifuged 4,000g for 2mins at RT.

  • Discarded supernatant and repeated wash steps.

Pellet was resuspended in 50uL of 0.1% DEPC-treated H2O and stored @ -80oC in Ronit’s temporary box.

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Samples Received – Triploid Crassostrea gigas from Nisbet Oyster Company

Received a bag of Pacific oysters from Nisbet Oyster Company.

Four oysters were shucked and the following tissues were collected from each:

  • ctenidia
  • gonad
  • mantle
  • muscle

Utensils were cleaned and sterilized in a 10% bleach solution between oysters.

Tissues were stored briefly on wet ice and then stored at -80C in Rack 2, Column 3, Row 1

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Morphometrics – Crassostrea gigas OA Selection Bags Using ImageJ

Due to some sort of data mis-handling, morphometric data that was previously taken for thousands (seriously, THOUSANDS) of Pacific oysters in 2014 was found to be incorrect. Unfortunately, there’s not enough of a “paper trail” to back track to see what/where things might have gone wrong to try to fix the issues. Essentially, they all had to be re-measured!

The one good thing is that all of the oysters were photographed at the time of sampling (along with a ruler), which allows us to go back and measure them.

I re-measured them all using the free imaging software ImageJ.

Oyster measurements taken were length and width. For length, the oyster was measured from hinge to the leading edge of the shell, attempting to measure as close to the theoretical center line of the oyster as possible, while also capturing the two points furthest from each other. The width was measured at the apparent widest part of the oyster and attempted to be perpendicular to the length measurement line.

Each image with the measurement lines was saved as a .tif file and the filename appended with “measured”. Additionally, each image produced a corresponding Excel file named CgOA_measurements_bag_info.xls, where “bag_info” contains information regarding the oysters in that set.

Images were measured by setting the pixel scale using 100mm (10cm) measurement on each image via the ruler in the image. Images were greatly enlarged when setting the scale to improve scale accuracy. Some images did not contain a ruler. Instead, the scale was set using the length of a weigh boat: 89mm (8.9cm). Weigh boat size was gathered from manufacturer specs: VWR Cat#89106-768 (8.9cm x 8.9cm x 2.5cm). Files corresponding to these sets of measurements are appended with “no_ruler” in the filename. The sample sets that were measured in this fashion were oyster bags:

  • 492
  • 530

The measured images and the individual Excel files were uploaded to the following Dropbox location: Dropbox/Friedman Lab/Carolyn Lab/Manuscripts/2016/Cg OA selection/Data/Sam DATA.

Data from the individual files was aggregated in the following spreadsheet in Dropbox: Dropbox/Friedman Lab/Carolyn Lab/Manuscripts/2016/Cg OA selection/Data/Sam DATA/files to merge/Cg OA selection 9mo sampling All 3 sites_survival data_ FOR SAM to add L and W data.xlsx

Data is still missing (i.e. no labelled image file was present) for the following oysters:

  • 458 21-37
  • 486 1-20
  • 556 20-45
  • 588 1-19

Here’s a quick summary of the amount of data I gathered. I’ll provide details of how I used ImageJ to not only measure the samples, but also create a more reproducible means of following the data acquisition process so that we can improve our ability to follow the “paper trail” from who acquired the data, how they acquired it, and allow people to easily review that data. This way, once all this data is transposed to some master spreadsheet, it will still be granularly accessible for any future troubleshooting that might be needed. I’ll do this in a separate post .

So, what did this work produce and how did I determine this information?

Using Bash (i.e. command line in Terminal):

Count the number of image files analyzed (i.e. saved) by ImageJ:

ls -1 *.tif | wc -l

163

 

Count the number of spreadsheet files produced by ImageJ:

ls -1 CgOA*.xls | wc -l

164

Well, there’s an odd discrepancy. These should be the same number. However, if anything were off, I’d expect the number of images to be greater in number than the Excel files. That would indicate I went through the measuring process, but neglected to save the data. However, this suggests that there’s an “extra” Excel file. It’s possible that I accidentally saved the image to a different location by accident. Will look into this…

Count the number of measurements taken. This will be a two step process.

First, aggregate all the data from the individual data files into a single file:

for i in CgOA*.xls; do awk 'NR>1' "$i" >> all_measures.csv; done

The code above uses a for loop to look at each Excel file (files beginning with “CgOA” and ending with “.xls”). Each file ($i) is passed to the program awk, which concatenates/appends the contents of the file (excluding the header line; NR>1) to a new file (all_measures.csv).

Next, count the number of lines (i.e. measurements) in the all_measures.csv file:

wc -l < all_measures.csv

5251

Whoa! That’s pretty remarkable. Over 5000 individual measurements were recorded (length and width for each oyster). That means there were over 2600 oysters!!!

Hopefully we won’t have to re-measure these guys a third time!

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