Opticon2 Calibration

Jake and Steven recently noticed localized “hot spots” on most of Jake’s recent qPCRs, where higher levels of fluorescence were consistently showing up in interior portions of the plates than the outer portion of the plates.

Ordered 5nmol of 6-FAM T10 Calibration Standard from Biosearch Technologies and resuspended it in 50μL of 1x dilution buffer (10mM Tris-HCl pH8.0, 50mM NaCl, 5mM MgCl2) to make a 100μM solution. Buffer and dye were stored @ -20C after use.

Buffer calculations: Total Volume = 15mL

  • 1.5mL of 100mM Tris-HCl
  • 150μL of 5M NaCl
  • 750μL of 100mM MgCl2

Made a working dilution of the 6-FAM dye of 300nM in 5mL of 1x dilution buffer (15uL of 100uM dye in 5mL of buffer).

Ran the calibration protocol on the Opticon2 (BioRad) using 50μL of dye in all wells when required by the calibration protocol.

 

Results:

EMPTY PLATE MEASUREMENTS

Empty Plate – Channel 1 voltage measurements

 

Empty Plate – Channel 2 voltage measurements

 

Empty Plate – Ratio of Channel 1 to Channel 2 voltage measurements.

 

The empty plate measurements above show the expected low voltage measurements, but also show a  ~5-fold difference in min/max voltages in each channel. Additionally, the voltage ratios (the third image above) show a wavy pattern, but a smooth, even level from well-to-well is what would be expected if the Opticon was in measuring things properly.

 

DYE PLATE MEASUREMENTS

Dye Measurements – Channel 1 voltage measurements

 

Dye Measurements – Channel 2 voltage measurements

 

Dye Measurements – Channel 1 to Channel 2 voltage measurement ratios.

 

The voltages measured in each channel show the expected increase in voltages relative to the empty plate (> 10x voltage than empty plate). However, the spread between the min/max voltages in both channels is ~4-fold. Additionally, the ratio between the two channels still shows the wavy pattern across all the wells instead of the expected even ratio from well-to-well that should result from the calibration.

It appears the calibration has not resolved the issue.

 

To verify that calibration has failed, I ran two sets of qPCR “protocols” that simply read the dye plate to measure fluorescence across the plate in two plate orientations.

Original Orientation Data File (TAD): 20150630_162622_calibration_test.tad
180 degree rotation Data File (TAD): 20150630_162622_calibration_test_180.tad

 

Dye Fluorescence – Original Orientation

 

Dye Fluorescence – Original Orientation with Fluorescence Graph

 

Dye Fluorescence – 180 Degree Rotation

 

Dye Fluorescence – 180 Degree Rotation with Fluorescence Graph

 

 

First thing to notice is that there’s clearly uneven fluorescence detection across the plate. Viewing the images that also contain the fluorescence graphs reveals a spread of ~8-fold between the highest and lowest fluorescence detection.

The second thing to notice is that, despite rotating the plate 180 degrees, the rotation has no effect on the fluorescence detected in each block location.

Both of these taken together provide strong evidence that there’s an issue with the machine.

RNAseq Data Receipt – Geoduck Gonad RNA 100bp PE Illumina

Received notification that the samples sent on 20150601 for RNAseq were completed.

Downloaded the following files from the GENEWIZ servers using FileZilla FTP and stored them on our server (owl/web/nightingales/P_generosa):

Geo_Pool_F_GGCTAC_L006_R1_001.fastq.gz
Geo_Pool_F_GGCTAC_L006_R2_001.fastq.gz
Geo_Pool_M_CTTGTA_L006_R1_001.fastq.gz
Geo_Pool_M_CTTGTA_L006_R2_001.fastq.gz

Generated md5 checksums for each file:

$for i in *; do md5 $i >> checksums.md5; done

Made a readme.md file for the directory.

Reverse Transcription – O.lurida DNased RNA Controls and 1hr Heat Shock

Performed reverse transcription on the Olympia oyster DNased RNA from the control samples and the 1hr heat shock samples of Jake’s project. To accommodate the large numbers of anticipated genes to be targeted in subsequent qPCRs, I prepared 100μL reactions (normally, 25μL reactions are prepared) using 250ng of each DNased RNA. A 1:10 dilution of the oligo dT primers (Promega) was prepared to improve pipetting accuracy. All incubations were performed in a thermal cycler without using a heated lid.

DNased RNA was combined with NanoPure H2O and oligo dT primers in 48 wells of a PCR plate, heated @ 70C for 10mins and immediately placed on ice. After 5mins, the plate was spun 2000g @ RT for 2mins and returned to ice.

25.25μL of a master mix containing 5x M-MLV Buffer (Promega), dNTPs (10mM each; Promega), and M-MLV Reverse Transcriptase (50U/rxn; Promega) was distributed to each well and mixed via pipetting. The plate was heated @ 42C for 1hr, 95C for 3mins. The plate was spun 2000g @ RT for 2mins and then stored @ -20C.

Plate layout and all calculations can be found here (Google Sheet): 20150616_Jake_Oly_cDNA_Calcs

Sample Submission – Olympia oyster & Sea Pen PCRs Sanger Sequencing

Prepared two DNA plates and corresponding primer plates for sequencing at the UW HTGC from the purified gel-purified PCRs from yesterday. Primer plates were prepared by adding 7μL of NanoPure H2O to each well and then adding 3μL of 10μM primer to the appropriate wells. For the DNA plates, added 10μL of DNA to the appropriate wells.

NOTE: The H2A_ST1 samples had insufficient volume of DNA for all four sequencing reactions. Added 30μL of NanoPure water to purified DNA, mixed and distributed to the appropriate wells.

Sequencing plates layouts can be seen here (Google Sheet): sequence_log.

Submitted the plates to the UW HTGC for Sanger sequencing.

Gel Purification – Olympia Oyster and Sea Pen PCRs

Purified DNA from the remaining PCR bands excised by Jake on 20150609 and 20150610, as well as Jonathan’s sea pen PCRs from 20150604, using Ultrafree-DA spin columns (Millipore). Transferred gel pieces from storage tubes (1.5mL snap cap tubes) to spin columns. Spun 10,000g, 5mins @ RT. Transferred purified DNA back to original storage tubes. See the sequence_log (Google Sheet) for a full list of the samples and the sequencing plates layouts. Purified DNA was stored @ 4C O/N. Will prepare and submit plates for Sanger sequencing tomorrow.

Sample Submission – Geoduck Gonad for RNA-seq

Prepared two pools of geoduck RNA for RNA-seq (Illumina HiSeq2500, 100bp, PE) with GENEWIZ, Inc.

I pooled a set of female and a set of male RNAs that had been selected by Steven based on the Bioanalyzer results from Friday.

The female RNA pool used 210ng of each sample, with the exception being sample #08. This sample used 630ng. The reason for this was due to the fact that there weren’t any other female samples to use from this developmental time point. The two other developmental time points each had three samples contributing to the pool. So, three times the quantity of the other individual samples was used to help equalize the time point contribution to the pooled sample. Additionally, 630ng used the entirety of sample #08.

The male RNA pool used 315ng of each sample. This number differs from the 210ng used for the female RNAs so that the two pools would end up with the same total quantity of RNA. However, now that I’ve typed this, this doesn’t matter since the libraries will be equalized before being run on the Illumina HiSeq2500. Oh well. As long as each sample in each pool contributed to the total amount of RNA, then it’s all good.

The two pools were shipped O/N on dry ice.

  • Geo_pool_M
  • Geo_pool_F

Calculations (Google Sheet): 20150601_Geoduck_GENEWIZ_calcs

Goals – June 2015

Before we check out this month’s goals, let’s have a quick review of last month’s goals and which, if any, I was able to accomplish.

From May 2015:

Geoduck Reproductive Development Transcriptomics

Goal(s): Isolate RNA from geoduck histology blocks

Status: Accomplished!

BS-Seq Illumina Data Assembly/Mapping

Goal(s): Glean additional info about this data set and our ability/inability to create our own BS-seq libraries.

Status: Still a mystery. Currently reaching out to Doug Turnbull at the Univ. of Oregon Genomics Core Facility to see if he can provide any insight as to why our data looks the way it does, which might help us figure out why we’re having such difficulty mapping our reads to the C.gigas genome…

C.gigas Heat Stress MeDIP/BS-Seq

Goal(s): BS-seq Claire’s samples.

Status: Untouched. Is dependent upon whether or not we can successfully create our own high-throughput sequencing libraries (see above).

Miscellany

Goal(s):

  • Migrate Wikispaces notebook to this notebook
  • Add to GitHub code pages
  • Flesh out/create README files on server(s)
  • Lab cleanup tasks
  • Assist on Jonathan’s Capstone project

Status:

  • Migrate Wikispaces notebook to this notebook – Minimal progress
  • Add to GitHub code pages – Updated “Commercial Protocols”, added wget command for offline notebook backups
  • Flesh out/create README files on server(s) – No progress
  • Lab cleanup tasks – No progress
  • Assist on Jonathan’s Capstone project – Things still not working. Had Jonathan isolate gDNA for proper testing of primers.

 

Geoduck Reproductive Development Transcriptomics

This project is progressing relatively smoothly. Finished RNA isolations from all samples and checked their qualities via Bioanalyzer. Steven and Brent selected samples of males and females to pool for RNA-seq. Goal is to have these two pools sent off to GENEWIZ, Inc. for RNA-seq. Currently awaiting a quote adjustment as well as an answer regarding sample quantity requirements. Hope to have these sent off later today and data back by the end of the month. This data will be used alongside proteomics data that Emma is currently generating.

BS-Seq Illumina Data Assembly/Mapping – C.gigas larvae OA

The troubleshooting for the data from these “homemade” libraries continues. We’ve tried various approaches to trimming the data, but Steven’s mapping attempts are still not yielding great results. I’ve contacted Univ. of Oregon Genomics Core Facility to see if they can provide insight, but haven’t gotten a response. Will hit them up again to see if I can get a response (and some help).

Geoduck & Olympia Oyster Genome Sequencing

We have quotes from BGI Americas for genome sequencing for both of these organisms. Currently, we’re awaiting for funding to be processed, but expect it to be available this month. Hope to send out samples this month.

C.gigas Heat Stress MeDIP/BS-Seq

This is still dependent upon our ability to make our own BS-seq libraries. Until, then, this project will likely be on the back burner for awhile.

Miscellany

I’d like to continue to contribute to our GitHub code repository with various command line tips and tricks. Additionally, I do need to actually spend some time creating/updating README files for our servers. We have a ton of folders that need some sort of descriptor file in them so users know what to expect to find in those folders. Additionally, we have a ton of data that needs descriptions and/or links to the projects from which the data was generated to serve as a means for people to know how/why/from what the data was generated. This has been done for newer data sets, but there’s a tremendous amount of data sets that have no information about them available in the README files. Also along the data management front, I’d like to tackle a bit of a reorganization, particularly re-establishing the go-to resource for lab members to find “stuff.” For example, Jake recently needed to know where/if we had some software and had to ask about it. Better organization on our part would eliminate him wasting time trying to track down this sort of thing. Part of the organizational issue is that we’ve partially transitioned over to using GitHub instead of Wikispaces. However, the transition hasn’t been fully realized/implemented and the result is fragmentation and confusion on where to find lab info. Oh, one last “digital” note. I’ll be teaching the Unix Shell lesson at Software Carpentry on June 25 – 26, so I have to get prepped for that (not on work time, of course).

In the lab, I still need to tackle some lab cleanup tasks that I neglected to deal with last month (autoclaving, glass disposal). Additionally, I need to continue helping Jonathan with his Capstone project, but I need to manage my time with him better.

 

 

 

Bioanalyzer – Geoduck Gonad RNA Quality Assessment

Before proceeding with transcriptomics for this project, we need to assess the integrity of the RNA via Bioanalyzer.

RNA that was previously isolated on 20150508, 20150505, 20150427, and 20150424 (those notebook entries have been updated to report this consolidation and have a link to this notebook entry) were consolidated into single samples (if there had been multiple isolations of the same sample) and spec’d on the Roberts Lab NanoDrop1000:

Google Sheet: 20150528_geoduck_histo_RNA_ODs

NOTE: Screwed up consolidation of Geoduck Block 03 sample (added one of the 04 dupes to the tube, so discarded 03).

RNA was stored in Shellfish RNA Box #5.

RNA was submitted to to Jesse Tsai at University of Washington Department of Environmental and Occupational Health Science Functional Genomics Laboratory for running on the Agilent Bioanalyzer 2100, using either the RNA Pico or RNA Nano chips, depending on RNA concentration (Pico for lower concentrations and Nano for higher concentrations – left decision up to Jesse).

 

Results:

Bioanalzyer 2100 Pico Data File (XAD): SamWhite_Eukaryote Total RNA Pico_2015-05-28_12-50-00.xad
Bioanalzyer 2100 Nano Data File (XAD): SamWhite_Eukaryote Total RNA Nano_2015-05-28_13-22-53.xad

 

Pico Gel Representation

 

Pico Electropherogram

 

Nano Gel Representation

 

Nano Electropherogram

 

Jesse alerted me to the fact that they did not have any ladder to use on the Nano chip, as someone had used the remainder, but failed to order more. I OK’d him to go ahead with the Nano chip despite lacking ladder, as we primarily needed to assess RNA integrity.

 

Bad Samples:

  • Geo 04 – No RNA detected
  • Geo 65, 67, 68 – These three samples show complete degradation of the RNA (i.e. no ribosomal band present, significant smearing on the gel representation).

All other samples look solid. Will discuss with Steven and Brent on how they want to proceed.

Full list of samples for this project (including the Block 03 sample not included in this analysis; see above). Grace’s notebook will have details on what the numbering indicates (e.g. developmental stage).

  • block 02
  • block 03 (no RNA)
  • block 04 (no RNA)
  • block 07
  • block 08
  • block 09
  • block 34
  • block 35
  • block 38
  • block 41
  • block 42
  • block 46
  • block 51
  • block 65 (degraded RNA)
  • block 67 (degraded RNA)
  • block 68 (degraded RNA)
  • block 69
  • block 70