I finished pooling my samples (lesson learned: don't use small volumes it evaporates as you pipette) a couple weeks ago and they've been sitting in the fridge ready to ship off just waiting for the finances to come through with the University of Utah. All the paperwork was finalized yesterday and we just missed the shipping time (dropped the package off at 4:05 for a 4pm pickup). We had to pick it back up and hold onto it until today. We'll add some more dry ice and send it off this afternoon.
It has been a ton of work to get that 30ul ready for shipping. Now we cross our fingers and hope that it comes out all right. In the mean time, I think I'll have a beer.
Showing posts with label RNAseq. Show all posts
Showing posts with label RNAseq. Show all posts
Tuesday, July 9, 2013
Tuesday, June 18, 2013
Diluting and pooling - last step
I have now prepared and amplified all my samples. I used 12 cycles and 8ul of product so that I wouldn't blow through the entire 20ul of library if something went wrong. Most of these had plenty of product, the couple that didn't I re-amplified for 14 cycles. For the dilution, I used the values from the nanodrop rather than the bioanalyzer as Sara has had some issues with pooling based on bioanalyzer values.
Here are the bioanalyzer traces of the completed pools:
The final concentration is a little low, but as the sequencing facility only needs ~10ul of 10nmol/L it should be fine. I actually ended up making 4 pools as the first two didn't stack up well on the qPCR. I think this was because I was pipetting such small volumes that the liquid was evaporating and changing my concentration. For the second set (Pools 3 and 4, seen above) I made sure that my sample volumes were never less than ~10ul. It seemed to work much better. Here's the qPCR trace of 5 of my samples. They should all be right on top of each other (except for the light blue one which is the no-template-control:
Here are the bioanalyzer traces of the completed pools:
The final concentration is a little low, but as the sequencing facility only needs ~10ul of 10nmol/L it should be fine. I actually ended up making 4 pools as the first two didn't stack up well on the qPCR. I think this was because I was pipetting such small volumes that the liquid was evaporating and changing my concentration. For the second set (Pools 3 and 4, seen above) I made sure that my sample volumes were never less than ~10ul. It seemed to work much better. Here's the qPCR trace of 5 of my samples. They should all be right on top of each other (except for the light blue one which is the no-template-control:
There is only about 1 cycle spread from the first one to the last, whereas the pools 1 and 2 had at least a 4 cycle spread.
S~ is now setting up the account at Utah and I'll be sending the samples as soon as all the paperwork goes through. (We've had some issues with sequencing at Berkeley and so we're gonna try a new facility.)
Wednesday, May 15, 2013
PCR amplification and the number of cycles
I had earlier decided that amplifying 8ul for 9 cycles would be good for my libraries. It turned out that 9 cycles was much to few and I increased the cycle number to 12 as per J~'s suggestion. I have now amplified all 40 samples at 12 cycles (still 8ul) and have just finished bioanalyzing the results. It seems that 12 has worked great for many of my samples, but has under-amplified a few (see below).
Of these samples, the first 4 are under-amplified, the middle 4 are fine, and the last 4 are perfect. None are over-amplified which is great.
Next I need to pool all of these samples into two lanes as per my earlier plans. The pooling will be done so that there are equal molar ratios of each sample in the final mix. The issue I am having is that J~ and I had talked earlier and said that I should amplify each sample the same number of cycles (12) but at this point, 12 cycles has under-amplified some of them and not others. One option is to amplify the low samples a couple times more to bring them up to par with the rest. I am unsure of whether treating some samples differently will cause any catastrophic failures of my experiment. It seems like it will not as the expression analysis will be standardized by the relative expression of a number of housekeeping genes. I would guess that this should remove any effects of cycle number, but I'm not sure. I'll talk to J~ and report back.
Below is a plot of all the samples spread along the X-axis and the final concentration (ng/ul) that the bioanalyzer reported on the Y. The different colors represent different batches of library prep (batch size = 8) and the different shapes represent the different RNA extraction batches (batch size = 4). The horizontal line is at 5ng/ul.
It looks like there are no batch effects for the most part. The one main exception is in the red batch where the squares (males) are lower than the circles (females). Earlier, I had started with unequal amounts of RNA for that batch and I ended up re-doing the males. It seems that something went wrong with the 2nd prep of those, and I need to re-prep them again. As long as I am at it I will also re-prep some of the others that are below 5ng/ul.
Monday, April 1, 2013
Cycle number in the TruSeq protocol
The qPCR results showed that 13-14 cycles would be excellent for my libraries (see this post). However, the amount of library that I started with was 1 40th of the total library (i.e. 0.5uL). If 0.5ul achieves the exponential phase in 13 cycles, then 8uL should achieve exponential phase in 9 cycles:
starting cycles to
ul exponential
0.5 13
1 12
2 11
4 10
8 9
16 8
Thus I used 8uL of my libraries and amplified them for 9 cycles. This resulted in some libraries that were perfectly amplified and some that were not amplified enough. The top sample is great, a small even peak (and no secondary hump under the 1500 marker). The second sample does have a peak in the right area, but hasn't been amplified enough - there is not enough library there to sequence.
starting cycles to
ul exponential
0.5 13
1 12
2 11
4 10
8 9
16 8
Thus I used 8uL of my libraries and amplified them for 9 cycles. This resulted in some libraries that were perfectly amplified and some that were not amplified enough. The top sample is great, a small even peak (and no secondary hump under the 1500 marker). The second sample does have a peak in the right area, but hasn't been amplified enough - there is not enough library there to sequence.
Fortunately, I only used 8uL of my pre-amplification library, not the entire 20uL just in case this issue arose. So now, instead of going back and re-preparing the entire library, I can just take and amplify the remaining parts.
I'll need to talk to J~, as there are some serious batch effects (the first 8 all look like the top image, while the second 8 all look like the bottom image) but it looks like 8ul at 11 or 12 cycles will work fine for most everything without over-amplifying the library. As I will be comparing transcript levels, it is important that all the libraries are amplified the same number of cycles.
Over-amplification is also bad because at that point in the PCR, the DNA molecules are being heated and cooled without being replicated. Thus they will fall apart and re-anneal repeatedly. They will often re-anneal with a DNA of a different sequence. This happens because the adapters have the same sequence, while the center sequences vary, two different molecules can meet eachother and the adapters on the ends will anneal while the middle region will for a big loop. This will mess up the sequencing machine and you'll get bad results. You know if you've over-amplified a library because there will be a low hump that around the 1500 marker. This is the weird hybridized molecules which travel slowly through a gel and so look quite large. In fact they are not any larger than the rest of the library and so can't be selectively removed via their size. The only thing that remains at that point is to re-amplify the library.
In short, I need my samples amplified more, but not too much more, so a cycle or two should be fine. J~ will be able to help me out with that.
Monday, March 18, 2013
qPCR and strand-specific TruSeq protocol
The qPCR results of my ssTruSeq prep (Sultan, 2012) showed up a little weird:
The group of 4 that amplified first was the females (prepared on 12-15 with []'s of 512, 367, 553, and 455ng/ul from the bioanalyzer) while the second group of 4, amplifying around 14 cycles is the males (prepared on 12-7 with []'s of 3086, 5631, 1783, 2363ng/ul from the bioanalyzer). The initial concentrations shouldn't matter much because I dilute them down to have 2ng of RNA entering the ssTruseq protocol. In this case though, it seems that I over-diluted the males causing them to amplify later. There is something really odd going on here because when I looked back through my lab book, the concentration of those samples from the nanodrop was much less: 1351, 1559, 1027, and 1276ng/ul. In fact these concentrations are ~4x less (i.e.: two pcr cycles different...). As they amplify on average 2 cycles later, I took a look back at the bioanalyzer results and found that the concentrations were slightly outside the optimal range and therefore likely a little spurious.
In the future I will dilute my samples down and then nano-drop them before I start the ssTruSeq protocol in order to avoid starting with different concentrations of RNA.
----------------------------------------------------------------------------------------------------------------------------
Sultan, M., S. Dökel, V. Amstislavskiy, D. Wuttig, H. Sültmann, H. Lehrach and M.-L. Yaspo. 2012. A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods. Biochemical and Biophysical Research Communications 422:643–646. Elsevier Inc.
The group of 4 that amplified first was the females (prepared on 12-15 with []'s of 512, 367, 553, and 455ng/ul from the bioanalyzer) while the second group of 4, amplifying around 14 cycles is the males (prepared on 12-7 with []'s of 3086, 5631, 1783, 2363ng/ul from the bioanalyzer). The initial concentrations shouldn't matter much because I dilute them down to have 2ng of RNA entering the ssTruseq protocol. In this case though, it seems that I over-diluted the males causing them to amplify later. There is something really odd going on here because when I looked back through my lab book, the concentration of those samples from the nanodrop was much less: 1351, 1559, 1027, and 1276ng/ul. In fact these concentrations are ~4x less (i.e.: two pcr cycles different...). As they amplify on average 2 cycles later, I took a look back at the bioanalyzer results and found that the concentrations were slightly outside the optimal range and therefore likely a little spurious.
In the future I will dilute my samples down and then nano-drop them before I start the ssTruSeq protocol in order to avoid starting with different concentrations of RNA.
----------------------------------------------------------------------------------------------------------------------------
Sultan, M., S. Dökel, V. Amstislavskiy, D. Wuttig, H. Sültmann, H. Lehrach and M.-L. Yaspo. 2012. A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods. Biochemical and Biophysical Research Communications 422:643–646. Elsevier Inc.
Monday, March 11, 2013
strand specific RNAseq: TruSeq Protocol + Sultan (2012)
I have begun preparing libraries for the sequencing. The Sultan (2012) protocol is nice and clear, but there are two buffers that it incorporates which are quite expensive. These buffers are parts of a second strand synthesis kit and it costs upwards of 1000$ for the kit - but the cost of the kit is due to the enzyme (which I don't use for Sultan's protocol) not the buffer. Instead of buying the entire kit, I ended up making the buffer myself (all the components cost less than 200$ total). Other than this issue with the buffer, I am quite pleased with the protocol in general.
the recipes I used are here:
5x second strand synthesis buffer invitrogen cat #11917-010
formula: 100 mM Tris-HCl (pH 6.9), 450 mM KCl, 23 mM MgCl2, 0.75 mM beta-NAD+, 50 mM (NH4)2SO4
for 10ml:
-1ml Tris HCl pH6.9
-0.3355g KCl
-0.23ml 23mM MgCl2
-0.0050g beta NAD+
-0.0661g (NH4)2SO4
bring to 10ml with DEPC H20
10x Reverse transcription buffer invitrogen cat #18080051
formula: 200 mM Tris-HCl, pH 8.4, 500 mM KCl
for 10ml:
-2ml 200mM Tris HCl pH 8.4
-0.3728g KCl
bring up to 10ml with DEPC H2O
As for the TruSeq protocol, the final step is a PCR enrichment of the adapter-ligated fragments. The protocol calls for 15 cycles to enrich the product, but that will likely place the reaction in the plateau phase (really bad because it results in "PCR duplicates" and reduces the overall complexity of the library). To optimize the number of cycles that I'll end up using I am running a qPCR. This will give me an understanding of how efficiently the library amplifies and therefore how many cycles to run so that my library is in the exponential phase and not plateau.
I started with 0.5ul of ligated product in the qPCR and the concentration was well below what the Nanodrop can read (not surprising as I haven't enriched it yet, but normally a bad sign). The qPCR results show that my library plateaued in 20 cycles, so I will be shooting for 14 cycles which should be mid-exponential phase. The next trick is that when I do the actual enrichment PCR I will have a much higher amount of starting DNA (~10ul instead of 0.5ul) and so plateau will be achieved much sooner. As I will be starting with 20x more DNA, I will need 4-5 fewer cycles (2^4 < 20 < 2^5). Assuming that the amplification will procede with the same efficiency (and that the efficiency is ~2, though often slightly lower, 1.8ish), this means that in my enrichment PCR I should use 10 cycles.
I will actually run two qPCRs to test the difference between 10 and 13 cycles. This should help me hone in on the exact number of cycles to use. I can afford to do this with this library as it is a "test." I won't be doing different cycles on the future libraries that I plan to sequence.
I will need to do this same qPCR experiment on each of my library samples and determine an "average" number of cycles to use. I could enrich each sample independently, but that is a bad idea since this is transcriptome work and the relative expression levels will depend on the number of cycles. I will probably do qPCR on the first 8 to get a good average, and then use that as the standard number of cycles for the entire experiment.
--------------------------------------------------------------------------------------------------------------------
Sultan, M., S. Dökel, V. Amstislavskiy, D. Wuttig, H. Sültmann, H. Lehrach and M.-L. Yaspo. 2012. A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods. Biochemical and Biophysical Research Communications 422:643–646. Elsevier Inc.
the recipes I used are here:
5x second strand synthesis buffer invitrogen cat #11917-010
formula: 100 mM Tris-HCl (pH 6.9), 450 mM KCl, 23 mM MgCl2, 0.75 mM beta-NAD+, 50 mM (NH4)2SO4
for 10ml:
-1ml Tris HCl pH6.9
-0.3355g KCl
-0.23ml 23mM MgCl2
-0.0050g beta NAD+
-0.0661g (NH4)2SO4
bring to 10ml with DEPC H20
10x Reverse transcription buffer invitrogen cat #18080051
formula: 200 mM Tris-HCl, pH 8.4, 500 mM KCl
for 10ml:
-2ml 200mM Tris HCl pH 8.4
-0.3728g KCl
bring up to 10ml with DEPC H2O
As for the TruSeq protocol, the final step is a PCR enrichment of the adapter-ligated fragments. The protocol calls for 15 cycles to enrich the product, but that will likely place the reaction in the plateau phase (really bad because it results in "PCR duplicates" and reduces the overall complexity of the library). To optimize the number of cycles that I'll end up using I am running a qPCR. This will give me an understanding of how efficiently the library amplifies and therefore how many cycles to run so that my library is in the exponential phase and not plateau.
I started with 0.5ul of ligated product in the qPCR and the concentration was well below what the Nanodrop can read (not surprising as I haven't enriched it yet, but normally a bad sign). The qPCR results show that my library plateaued in 20 cycles, so I will be shooting for 14 cycles which should be mid-exponential phase. The next trick is that when I do the actual enrichment PCR I will have a much higher amount of starting DNA (~10ul instead of 0.5ul) and so plateau will be achieved much sooner. As I will be starting with 20x more DNA, I will need 4-5 fewer cycles (2^4 < 20 < 2^5). Assuming that the amplification will procede with the same efficiency (and that the efficiency is ~2, though often slightly lower, 1.8ish), this means that in my enrichment PCR I should use 10 cycles.
I will actually run two qPCRs to test the difference between 10 and 13 cycles. This should help me hone in on the exact number of cycles to use. I can afford to do this with this library as it is a "test." I won't be doing different cycles on the future libraries that I plan to sequence.
I will need to do this same qPCR experiment on each of my library samples and determine an "average" number of cycles to use. I could enrich each sample independently, but that is a bad idea since this is transcriptome work and the relative expression levels will depend on the number of cycles. I will probably do qPCR on the first 8 to get a good average, and then use that as the standard number of cycles for the entire experiment.
--------------------------------------------------------------------------------------------------------------------
Sultan, M., S. Dökel, V. Amstislavskiy, D. Wuttig, H. Sültmann, H. Lehrach and M.-L. Yaspo. 2012. A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods. Biochemical and Biophysical Research Communications 422:643–646. Elsevier Inc.
Saturday, March 9, 2013
Indexing Samples
Here is my earlier plan for multiplexing the samples:
Lane 1: Lane 2:
BBM1+2+3 BBM3+4+5
BBF1+2+3 BBF3+4+5
BSM1+2+3 BSM3+4+5
BSF1+2+3 BSF3+4+5
SBM1+2+3 SBM3+4+5
SBF1+2+3 SBF3+4+5
SSM1+2+3 SSM3+4+5
SSF1+2+3 SSF3+4+5
Each of these numbers will have it's own unique barcode. But as I only have 24 barcodes, some will have to be repeated between the lanes. For example, BBM3 must go in both lanes and will have the same barcode. This will force BBM1 and BBM4 to share a barcode, same with BBM2 and BBM5. It will end up with barcodes 1-16 in each lane but on different samples, and barcodes 17-24 in each lane and on the same sample.
Here is a schematic of my plan:
Here is a schematic of my plan:
Sample ID
|
Illumina Barcode Number
|
Reference Number (above)
|
Lane
| |
1
|
BB.BB15.3M
|
1
|
BBM1
|
1
|
2
|
BB.SS70.3M
|
2
|
BSM1
|
1
|
3
|
SS.BB20.2M
|
3
|
SBM1
|
1
|
4
|
SS.SS82.1M
|
4
|
SSM1
|
1
|
5
|
BB.BB15.4F
|
5
|
BBF1
|
1
|
6
|
BB.SS70.2F
|
6
|
BSF1
|
1
|
7
|
SS.BB20.6F
|
7
|
SBF1
|
1
|
8
|
SS.SS82.2F
|
8
|
SSF1
|
1
|
9
|
BB.BB77.2M
|
9
|
BBM2
|
1
|
10
|
BB.SS70.5M
|
10
|
BSM2
|
1
|
11
|
SS.BB25.5M
|
11
|
SBM2
|
1
|
12
|
SS.SS87.1M
|
12
|
SSM2
|
1
|
13
|
BB.BB77.1F
|
13
|
BBF2
|
1
|
14
|
BB.SS70.4F
|
14
|
BSF2
|
1
|
15
|
SS.BB25.3F
|
15
|
SBF2
|
1
|
16
|
SS.SS87.3F
|
16
|
SSF2
|
1
|
17
|
BB.BB86.2M
|
17
|
BBM3
|
both
|
18
|
BB.SS72.2M
|
18
|
BSM3
|
both
|
19
|
SS.BB25.3M
|
19
|
SBM3
|
both
|
20
|
SS.SS88.1M
|
20
|
SSM3
|
both
|
21
|
BB.BB86.1F
|
21
|
BBF3
|
both
|
22
|
BB.SS72.1F
|
22
|
BSF3
|
both
|
23
|
SS.BB29.1F
|
23
|
SBF3
|
both
|
24
|
SS.SS88.3F
|
24
|
SSF3
|
both
|
25
|
BB.BB87.6M
|
1
|
BBM4
|
2
|
26
|
BB.SS73.3M
|
2
|
BSM4
|
2
|
27
|
SS.BB20.8M
|
3
|
SBM4
|
2
|
28
|
SS.SS89.2M
|
4
|
SSM4
|
2
|
29
|
BB.BB87.1F
|
5
|
BBF4
|
2
|
30
|
BB.SS73.1F
|
6
|
BSF4
|
2
|
31
|
SS.BB24.2F
|
7
|
SBF4
|
2
|
32
|
SS.SS89.1F
|
8
|
SSF4
|
2
|
33
|
BB.BB77.3M
|
9
|
BBM5
|
2
|
34
|
BB.SS71.3M
|
10
|
BSM5
|
2
|
35
|
SS.BB22.4M
|
11
|
SBM5
|
2
|
36
|
SS.SS91.1M
|
12
|
SSM5
|
2
|
37
|
BB.BB90.1F
|
13
|
BBF5
|
2
|
38
|
BB.SS73.2F
|
14
|
BSF5
|
2
|
39
|
SS.BB24.3F
|
15
|
SBF5
|
2
|
40
|
SS.SS91.4F
|
16
|
SSF5
|
2
|
Furthermore, I will need to prepare these in even batches to avoid any weird batch effects. I will therefore do them in 5 batches of 8. Each batch will have one male and one female from each cross type.
I'll start the first batch (numbers 1-8) tomorrow.
I'll start the first batch (numbers 1-8) tomorrow.
Tuesday, February 26, 2013
Insert length for RNAseq
For this big sequencing thing, I'm planning on using a 100bp PE protocol. This means that the Illumina machine will read the sequence from both ends and give me a sequence from each. The main question I'm working on now is how long should the insert size be?
If it is less than 200bp then I will get overlapping sequences (which is good according to J~, although I'm not clear as to exactly why). However Sara just got some sequencing back which had (we think) too much overlap and this diminished the total amount of data she has by about 10%. Her insert size was ~150bp which means the overlap was 50%. I think that what I should shoot for is closer to 20% but I can't seem to find any papers or seq-answer threads that address the issue.
I think that the reason to have some overlap is that the program I'll be using, Trinity (Grabherr 2011), will function better, but I'm not really sure.
Hopefully I can get this figured out soon so that I can start preparing my libraries. I have all the samples extracted so that's the final step before they get sent off.
3-10-13 update: The TruSeq protocol uses a enzyme to fragment the mRNA and then a bead-based size selection. I can modify this size-selection, but at this point it seems prudent to just go ahead and use what they give me.
----------------------------------------------------------------------------------------------------------------------------------
Grabherr, M. G., B. J. Haas, M. Yassour, J. Z. Levin, D. A. Thompson, I. Amit, X. Adiconis, L. Fan, R. Raychowdhury, Q. Zeng, Z. Chen, E. Mauceli, N. Hacohen, A. Gnirke, N. Rhind, F. Di Palma, B. W. Birren, C. Nusbaum, K. Lindblad-Toh, N. Friedman and A. Regev. 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology 29:644–652.
Wednesday, February 20, 2013
RNAseq Samples
Here is the running list of my samples for RNAseq, their RIN numbers, concentration, and library prep. I will keep updating this as I go until I have all 40 samples extracted and the libraries prepared.
#
|
Date
extracted
|
ID
|
RIN
|
[ ] ng/uL
|
Library
|
1
|
9-28-12
|
BB.BB15.3M
|
9.6
|
1071
|
Yes
|
2
|
9-28-12
|
BB.SS70.3M
|
9.6
|
1127
|
Yes
|
3
|
9-28-12
|
SS.BB20.2M
|
9.7
|
859
|
Yes
|
4
|
9-28-12
|
SS.SS82.1M
|
9.8
|
1543
|
Yes
|
5
|
9-28-12
|
BB.BB15.4F
|
9.4
|
1690
|
Yes
|
6
|
9-28-12
|
BB.SS70.2F
|
9.4
|
1427
|
Yes
|
7
|
9-28-12
|
SS.BB20.6F
|
9.6
|
1818
|
Yes
|
8
|
9-28-12
|
SS.SS82.2F
|
9.6
|
1808
|
Yes
|
9
|
9-29-12
|
BB.BB77.2M
|
9.3
|
3086
|
Yes
|
10
|
9-29-12
|
BB.SS70.5M
|
9.4
|
5631
|
Yes
|
11
|
9-29-12
|
SS.BB25.5M
|
9.7
|
1783
|
Yes
|
12
|
9-29-12
|
SS.SS87.1M
|
9.5
|
2363
|
Yes
|
13
|
12-15-12
|
BB.BB77.1F
|
9.4
|
512
|
Yes
|
14
|
12-15-12
|
BB.SS70.4F
|
9.3
|
367
|
Yes
|
15
|
12-15-12
|
SS.BB25.3F
|
9.4
|
553
|
Yes
|
16
|
12-15-12
|
SS.SS87.3F
|
9.6
|
455
|
Yes
|
17
|
12-15-12
| BB.BB86.2M | 9.4 | 449 |
Yes
|
18
|
12-15-12
|
BB.SS72.2M
|
9.6
|
483
|
Yes
|
19
|
12-15-12
|
SS.BB25.3M
|
9.3
|
326
|
Yes
|
20
|
12-15-12
|
SS.SS88.1M
|
9.6
|
440
|
Yes
|
21
|
12-15-12
|
BB.BB86.1F
| 9.1 | 235 |
Yes
|
22
|
12-15-12
|
BB.SS72.1F
|
8.9
|
354
|
Yes
|
23
|
12-15-12
|
SS.BB29.1F
|
9.1
|
266
|
Yes
|
24
|
12-15-12
|
SS.SS88.3F
|
9.5
|
507
|
Yes
|
25
|
12-17-12
|
BB.BB87.6M
|
9.1
|
466
|
Yes
|
26
|
12-17-12
|
BB.SS73.3M
|
8.6*
|
466
|
Yes
|
27
|
12-17-12
|
SS.BB20.8M
|
9.0
|
354
|
Yes
|
28
|
12-17-12
|
SS.SS89.2M
| 9.0 |
254
|
Yes
|
29
|
12-17-12
|
BB.BB87.1F
|
9.0
|
411
|
Yes
|
30
|
12-17-12
|
BB.SS73.1F
|
9.1
|
414
|
Yes
|
31
|
12-17-12
|
SS.BB24.2F
|
8.7
|
257
|
Yes
|
32
|
12-17-12
|
SS.SS89.1F
|
9.0
|
394
|
Yes
|
33
|
2-19-13
|
BB.BB77.3M
|
9.1
| 519 |
Yes
|
34
|
2-19-13
|
BB.SS71.3M
|
8.4
|
284
|
Yes
|
35
|
2-19-13
|
SS.BB22.4
|
8.5
|
273
|
Yes
|
36
| 2-19-13 |
SS.SS91.1M
|
9.5
|
557
|
Yes
|
37
|
2-19-13
|
BB.BB90.1F
|
8.3
|
448
|
Yes
|
38
|
2-19-13
|
BB.SS73.2F
|
8.4
|
318
|
Yes
|
39
|
2-19-13
|
SS.BB24.3F
|
8.1
|
281
|
Yes
|
40
|
2-19-13
|
SS.SS91.4F
|
8.5
|
563
|
Yes
|
12-17-12: I was going to try to finish everything up today or tomorrow, but I ran out of reagents. I ordered more but they won't get in for a little while. I'm really frustrated as I was hoping to finish everything up before I head home for the break. Now it looks like it won't get finished until February.
12-18-12: It's good that I didn't extract the other 8 yesterday, as the RIN numbers were all quite low. I'm not sure why, but I need to get that straightened out. I got one of the reagents in the mail today, now I'm just waiting on the other and I will be set to finish everything off.
2-20-13: Just bioanalyzed the last samples. Not great, but D~ says they are good enough - Illumina calls for >8. J~ was fine with it since there are only 8/40 that are between 8 and 9.
*There is one sample that was only 7.4 (BB.SS73.3M) and I re-bioanalyzed it today it went down to 5.6. This says to me that it has some RNase contaminant in it and is being actively degraded. I have some RNA partially extracted and archived that I will finish extracting, but most likely I will need to find a new sample. Though, having 1 out of 40 go bad is fine by me, that's pretty good odds.
*4-1-13 Upon re-extracting this sample the RIN was 8.6 and the concentration was a bit high (so the actual RIN is likely much higher) I can now finish up with the library preps.
12-18-12: It's good that I didn't extract the other 8 yesterday, as the RIN numbers were all quite low. I'm not sure why, but I need to get that straightened out. I got one of the reagents in the mail today, now I'm just waiting on the other and I will be set to finish everything off.
2-20-13: Just bioanalyzed the last samples. Not great, but D~ says they are good enough - Illumina calls for >8. J~ was fine with it since there are only 8/40 that are between 8 and 9.
*There is one sample that was only 7.4 (BB.SS73.3M) and I re-bioanalyzed it today it went down to 5.6. This says to me that it has some RNase contaminant in it and is being actively degraded. I have some RNA partially extracted and archived that I will finish extracting, but most likely I will need to find a new sample. Though, having 1 out of 40 go bad is fine by me, that's pretty good odds.
*4-1-13 Upon re-extracting this sample the RIN was 8.6 and the concentration was a bit high (so the actual RIN is likely much higher) I can now finish up with the library preps.
Monday, September 17, 2012
Experimental Design - Replicate, Randomize, Block
The design I had set up in the last post is no good. The problem is that I am confounding technical replicates and biological replicates and have no way to tease them apart: each lane includes biological replicates, but not technical ones, so I have no idea if a difference between the lanes is due to biology or to technical differences with the sequencing.
Here is my new solution.
I have 8 treatments:
P. campbelli male (BBM)
P. campbelli female (BBF)
P. campbelli X P. sungorus male (BSM)
P. campbelli X P. sungorus female (BSF)
P. sungorus X P. campbelli male (SBM)
P. sungorus X P. campbelli female (SBF)
P. sungorus male (SSM)
P. sungorus female (SSF)
Each of these has 5 biological replicates (#1-5)
I will use 2 lanes and I have 24 barcodes.
So, after going over the Auer (2010) paper again, here is the new set up:
Lane 1: Lane 2:
BBM1+2+3 BBM3+4+5
BBF1+2+3 BBF3+4+5
BSM1+2+3 BSM3+4+5
BSF1+2+3 BSF3+4+5
SBM1+2+3 SBM3+4+5
SBF1+2+3 SBF3+4+5
SSM1+2+3 SSM3+4+5
SSF1+2+3 SSF3+4+5
This is 24 individuals per lane, meaning I have enough barcodes. And, most importantly, there is a technical replicate for each treatment --> Biological Replicate #3 appears in both lanes. This way I can test for differences between the lanes (technical replicates) and ascribe those differences to the sequencing platform while still parsing out the biological variance as well.
Auer, P. L. and R. W. Doerge. 2010. Statistical Design and Analysis of RNA Sequencing Data. Genetics 185:405–416.
Saturday, September 15, 2012
Samples for RNAseq
One of the problems with past RNAseq studies of imprinting (such as the two Gregg (2010) science papers, see Proudhon (2011), and DeVeal (2012)) was an inadequate sample size and incorrect experimental set-up. This led to a large number of false-positives. To avoid these mistakes I am going to use 40 samples, 10 from each genotype (5 from each sex). Here is the set up:
P. campbelli 5 female, 5 male
P. campbelli X P. sungorus 5 female, 5 male
P. sungorus X P. campbelli 5 female, 5 male
P. sungorus 5 female, 5 male
I will be able to sequence 20 samples per lane, meaning that I can't split it perfectly equally between the lanes. In order to avoid batch effects (Auer, 2010), I will use the following set up:
Lane 1:
P. campbelli 3 female, 2 male
P. campbelli X P. sungorus 3 female, 2 male
P. sungorus X P. campbelli 3 female, 2 male
P. sungorus 3 female, 2 male
Lane 2:
P. campbelli 2 female, 3 male
P. campbelli X P. sungorus 2 female, 3 male
P. sungorus X P. campbelli 2 female, 3 male
P. sungorus 2 female, 3 male
Finally, each of the 5 female and 5 male placentas will be brother-sister pairs, from 5 different litters (this is the ideal, I'll have to do some more sex-typing to see if I can actually make it work - stay tuned. I will definitely assure that the 5 females are from 5 different litters (and the same for the males) but whether all those male-female pairs are brother/sister may be tricky). I am currently debating whether I will have the 3 females in lane 1 sister to the 3 males in lane 2, or whether there should be a mix. I need to go over the Auer (2010) paper again to see if I can glean any more insight from it.
#################################################################################
As for the exact brother-sister pairs:
P. campbelli:
1. BB15.3M BB15.4F
2. BB77.2M BB77.1F
3. BB86.2M BB86.1F
4. no male yet BB87.1F ---> male BB87.6M
5. no male yet BB90.1F ---> male 77.3M
Average placenta weight of these individuals is: 0.11783g
Overall average of P. campbelli is: 0.11122g
quite close-this should be fine.
I need to sex-type more offspring from families BB86 and BB90. There are two more unknowns from 86 and one from 90. Hopefully they will be males, otherwise I'll have to use a brother of 15.1M, 77.2M or 86.2M which would work, but is not ideal.
#################################################################################
P. campbelli X P. sungorus:
1. BS70.3M BS70.4F
2. BS72.2M BS72.1F
3. BS73.3M BS73.1F
4. BS71.3M BS70.2F
5. BS11.1M BS73.2F
You'll notice that for pairs 4 and 5, the individuals are not siblings (what is worse - the females are sibs of pairs 1 and 3) There is nothing else I can do here. The only option would be to set up more crosses. I'll consider this, but I need to do this sequencing sooner rather than later and I don't think that this little bit of my perfectionism is worth the wait. I'll talk to J~ and see what he says.
Average placenta weight of these samples: 0.12916g
Overall average for these hybrids: 0.10948g
Not as close as before, but not awful.
#################################################################################
P. sungorus X P. campbelli:
1. SB20.2M SB20.6F
2. SB22.4M SB22.5F
3. SB25.5M SB25.3F
4. SB29.4M SB29.1F
5. SB20.8M SB24.2F
Pair 5 are not sibs, but unless I set up more crosses this is the best I can do.
Average of these samples: 0.47890g
Overall average for these hybrids: 0.39388g
Pretty good.
#################################################################################
P. sungorus:
1. SS82.1M SS82.2F
2. SS87.1M SS87.3F
3. SS88.1M SS88.3F
4. SS89.2M SS89.1F
5. SS91.1M no female yet ----> female SS91.4F
6. no male yet SS93.2F ----> don't use these
I will sex-type the other individuals in SS91 and SS93. If I can find a female from 91 or a male from 93 I will be set. Otherwise, I will use the ones shown above.
Average of these samples: 0.14113
Overall average of SS: 0.14757
Great.
#################################################################################
Now off to sex-type, updates soon...
Here's the results:
lane: gender
1 Ladder
2 BB87.5 male !!
3 BB87.6 male !!
4 BB90.3 female*
5 SS91.3 female !!
6 SS91.4 female !!
7 SS91.5 male
8 SS93.3 female
9 SS93.4 male !!
10 SS93.5 male !!
11 Pos (male)
12 Neg (female)
13 Neg (dH2O)
14 Ladder
!! these are the correct gender that I needed. I'll probably use BB87.6M and SS91.4F (and not SS932F and SS93.4M). Finally to round out the P. campbelli I will use BB77.3M.
*this sample seemed very degraded, won't use for RNAseq regardless, but it may be "female" due to failure of the PCR (rather than no Y chromosome) as there was very little DNA in the extraction...
#################################################################################
Auer, P. L. and R. W. Doerge. 2010. Statistical Design and Analysis of RNA Sequencing Data. Genetics 185:405–416.
DeVeale, B., D. van der Kooy and T. Babak. 2012. Critical Evaluation of Imprinted Gene Expression by RNA–Seq: A New Perspective. PLoS Genet 8:e1002600.
Gregg, C., J. Zhang, B. Weissbourd, S. Luo, G. P. Schroth, D. Haig and C. Dulac. 2010. High-Resolution Analysis of Parent-of-Origin Allelic Expression in the Mouse Brain. Science 329:643–648.
Gregg, C., J. Zhang, J. E. Butler, D. Haig and C. Dulac. 2010. Sex-Specific Parent-of-Origin Allelic Expression in the Mouse Brain. Science 329:682–685.
Proudhon, C. and D. Bourc'his. 2011. Identification and resolution of artifacts in the interpretation of imprinted gene expression. Briefings in Functional Genomics 9:374–384.
P. campbelli 5 female, 5 male
P. campbelli X P. sungorus 5 female, 5 male
P. sungorus X P. campbelli 5 female, 5 male
P. sungorus 5 female, 5 male
I will be able to sequence 20 samples per lane, meaning that I can't split it perfectly equally between the lanes. In order to avoid batch effects (Auer, 2010), I will use the following set up:
Lane 1:
P. campbelli 3 female, 2 male
P. campbelli X P. sungorus 3 female, 2 male
P. sungorus X P. campbelli 3 female, 2 male
P. sungorus 3 female, 2 male
Lane 2:
P. campbelli 2 female, 3 male
P. campbelli X P. sungorus 2 female, 3 male
P. sungorus X P. campbelli 2 female, 3 male
P. sungorus 2 female, 3 male
Finally, each of the 5 female and 5 male placentas will be brother-sister pairs, from 5 different litters (this is the ideal, I'll have to do some more sex-typing to see if I can actually make it work - stay tuned. I will definitely assure that the 5 females are from 5 different litters (and the same for the males) but whether all those male-female pairs are brother/sister may be tricky). I am currently debating whether I will have the 3 females in lane 1 sister to the 3 males in lane 2, or whether there should be a mix. I need to go over the Auer (2010) paper again to see if I can glean any more insight from it.
#################################################################################
As for the exact brother-sister pairs:
P. campbelli:
1. BB15.3M BB15.4F
2. BB77.2M BB77.1F
3. BB86.2M BB86.1F
4. no male yet BB87.1F ---> male BB87.6M
5. no male yet BB90.1F ---> male 77.3M
Average placenta weight of these individuals is: 0.11783g
Overall average of P. campbelli is: 0.11122g
quite close-this should be fine.
I need to sex-type more offspring from families BB86 and BB90. There are two more unknowns from 86 and one from 90. Hopefully they will be males, otherwise I'll have to use a brother of 15.1M, 77.2M or 86.2M which would work, but is not ideal.
#################################################################################
P. campbelli X P. sungorus:
1. BS70.3M BS70.4F
2. BS72.2M BS72.1F
3. BS73.3M BS73.1F
4. BS71.3M BS70.2F
5. BS11.1M BS73.2F
You'll notice that for pairs 4 and 5, the individuals are not siblings (what is worse - the females are sibs of pairs 1 and 3) There is nothing else I can do here. The only option would be to set up more crosses. I'll consider this, but I need to do this sequencing sooner rather than later and I don't think that this little bit of my perfectionism is worth the wait. I'll talk to J~ and see what he says.
Average placenta weight of these samples: 0.12916g
Overall average for these hybrids: 0.10948g
Not as close as before, but not awful.
#################################################################################
P. sungorus X P. campbelli:
1. SB20.2M SB20.6F
2. SB22.4M SB22.5F
3. SB25.5M SB25.3F
4. SB29.4M SB29.1F
5. SB20.8M SB24.2F
Pair 5 are not sibs, but unless I set up more crosses this is the best I can do.
Average of these samples: 0.47890g
Overall average for these hybrids: 0.39388g
Pretty good.
#################################################################################
P. sungorus:
1. SS82.1M SS82.2F
2. SS87.1M SS87.3F
3. SS88.1M SS88.3F
4. SS89.2M SS89.1F
5. SS91.1M no female yet ----> female SS91.4F
I will sex-type the other individuals in SS91 and SS93. If I can find a female from 91 or a male from 93 I will be set. Otherwise, I will use the ones shown above.
Average of these samples: 0.14113
Overall average of SS: 0.14757
Great.
#################################################################################
Now off to sex-type, updates soon...
Here's the results:
lane: gender
1 Ladder
2 BB87.5 male !!
3 BB87.6 male !!
4 BB90.3 female*
5 SS91.3 female !!
6 SS91.4 female !!
7 SS91.5 male
8 SS93.3 female
9 SS93.4 male !!
10 SS93.5 male !!
11 Pos (male)
12 Neg (female)
13 Neg (dH2O)
14 Ladder
!! these are the correct gender that I needed. I'll probably use BB87.6M and SS91.4F (and not SS932F and SS93.4M). Finally to round out the P. campbelli I will use BB77.3M.
*this sample seemed very degraded, won't use for RNAseq regardless, but it may be "female" due to failure of the PCR (rather than no Y chromosome) as there was very little DNA in the extraction...
#################################################################################
Auer, P. L. and R. W. Doerge. 2010. Statistical Design and Analysis of RNA Sequencing Data. Genetics 185:405–416.
DeVeale, B., D. van der Kooy and T. Babak. 2012. Critical Evaluation of Imprinted Gene Expression by RNA–Seq: A New Perspective. PLoS Genet 8:e1002600.
Gregg, C., J. Zhang, B. Weissbourd, S. Luo, G. P. Schroth, D. Haig and C. Dulac. 2010. High-Resolution Analysis of Parent-of-Origin Allelic Expression in the Mouse Brain. Science 329:643–648.
Gregg, C., J. Zhang, J. E. Butler, D. Haig and C. Dulac. 2010. Sex-Specific Parent-of-Origin Allelic Expression in the Mouse Brain. Science 329:682–685.
Proudhon, C. and D. Bourc'his. 2011. Identification and resolution of artifacts in the interpretation of imprinted gene expression. Briefings in Functional Genomics 9:374–384.
Friday, September 14, 2012
RNAseq and Overgrown hamsters, experimental setup
Yesterday I ordered all the reagents that I need to prep the libraries, I'm super excited about it especially since some arrived this morning. I'll be using a protocol designed by Sultan et al. (Sultan 2012) which maintains the strand specificity of the sequences. This is important because most imprinted transcripts lie in clusters in the genome (Edwards 2007; Reik 1997) which seem to be more gene-dense than the rest of the genome and can have overlapping transcripts on the + and - strands (Ideraabdullah, 2008). Maintaining strand-specificity will allow me to keep overlapping genes separate in my analysis.
The Sultan (2012) protocol to maintain strand specificity is pretty slick. Here's how it goes:
1. Extract total RNA (tRNA). I'll use an Omega miRNA kit for this and also a genomic DNA kit (in case I want to go back later and look at methylation patterns of the DNA). As placenta is a fairly heterogeneous tissue I need to use the entire sample to assure an even sampling of each of the three main layers. This means that I need to collect archival quality DNA and RNA as I won't be able to go back later if I decide I need something extra. I will grind it with liquid nitrogen and take some of the homogenate for the RNA kit, and some for the DNA kit.
2. Purify the tRNA. There are many types of RNAs in a tissue. I am only interested in messenger RNAs (mRNA) which will all have a poly-A tail. To purify the mRNA from tRNA I will use magnetic beads that have a poly-T oligo attached to them. These will bind to the mRNAs, magnetically bind to the plate, and keep my mRNA from being washed away with all the rest of the material. Then I will elute the mRNA away from beads and collect it for the next step.
3. Fragment the mRNA. mRNAs are much to long to sequence on the Illumina machine, so I need to break them up. I'll use a solution that Illumina provided to fragment the RNA.
4. First-strand synthesis. We do not have the technology to sequence RNA and so to get around this, we instead convert the RNA into DNA and then sequence that. DNA that came from RNA is called complement DNA (cDNA). As DNA is double stranded, this takes two steps, the first strand synthesis (with a reverse-transcriptase) and the second strand synthesis (with a regular polymerase).
5. Second Strand synthesis. This is the first step where the Sultan (2012) protocol differs from the standard Illumina Hiseq. Sultan (2012) calls for using dUTP instead of dTTP when forming the second DNA strand. This way I can later use an enzyme which chops up DNA on a "U" to cut away the second strand, leaving only the actual strand I am interested in - super clever.
6. End repair. After the mRNA-->cDNA conversion there are a lot of overhangs. Here I chop off all the extra so that each fragment has blunt ends.
7. Adenylate 3` end. In order to add on the adapters (necessary for the Illumina machine can sequence my cDNA library) I need an overhanging "A" on each 3` end. Here I add that "A".
8. Adapter ligation. Here I will barcode and add the adapters onto each of my sequences. I'll be splitting my 40 samples into 2 lanes of HiSeq, so I need 20 different barcodes. Here I will also have to make sure and not introduce batch effects into my design (Auer, 2010). I'll probably have another post entirely about this.
9. U-digestion. Here I will digest out the cDNA with "U"s in it so that I only have DNA oriented in the correct way. I will of course amplify this DNA, but it will cause the adapters to be wrong for the Illumina platform, so while they will be present, they won't be sequenced. Clever, clever, clever.
10. Enrich DNA fragment. Here I will PCR the library so that I have many copies of each molecule. This way there will be plenty for the Illumina machine to sequence.
11. Pool libraries. I will combine 20 of the samples into one lane and the other 20 for the other lane. Then send them off for sequencing.
So in short, I can keep almost the entire Illumina HiSeq kit as per usual, with a couple minor changes to maintain strand specificity.
As you may have noticed my in-text citations, it's because I recently bought the program papers, and am now testing it out in Chrome. It's nice by the way, especially for pages which I've recently started using. However, it looks like it won't format the bibliography directly from in-text citations in chrome, though it will format them properly if you go through and select each reference you want to work with.
Auer, P. L. and R. W. Doerge. 2010. Statistical Design and Analysis of RNA Sequencing Data. Genetics 185:405–416.
Ideraabdullah, F. Y., S. Vigneau and M. S. Bartolomei. 2008. Genomic imprinting mechanisms in mammals. Mutat. Res. 647:77–85.
Edwards, C. A. and A. C. Ferguson-Smith. 2007. Mechanisms regulating imprinted genes in clusters. Current Opinion in Cell Biology 19:281–289.
Reik, W. and E. R. Maher. 1997. Imprinting in clusters: lessons from Beckwith-Wiedemann syndrome. Trends in Genetics 13:330–334.
Sultan, M., S. Dökel, V. Amstislavskiy, D. Wuttig, H. Sültmann, H. Lehrach and M.-L. Yaspo. 2012. A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods. Biochemical and Biophysical Research Communications 422:643–646. Elsevier Inc.
The Sultan (2012) protocol to maintain strand specificity is pretty slick. Here's how it goes:
1. Extract total RNA (tRNA). I'll use an Omega miRNA kit for this and also a genomic DNA kit (in case I want to go back later and look at methylation patterns of the DNA). As placenta is a fairly heterogeneous tissue I need to use the entire sample to assure an even sampling of each of the three main layers. This means that I need to collect archival quality DNA and RNA as I won't be able to go back later if I decide I need something extra. I will grind it with liquid nitrogen and take some of the homogenate for the RNA kit, and some for the DNA kit.
2. Purify the tRNA. There are many types of RNAs in a tissue. I am only interested in messenger RNAs (mRNA) which will all have a poly-A tail. To purify the mRNA from tRNA I will use magnetic beads that have a poly-T oligo attached to them. These will bind to the mRNAs, magnetically bind to the plate, and keep my mRNA from being washed away with all the rest of the material. Then I will elute the mRNA away from beads and collect it for the next step.
3. Fragment the mRNA. mRNAs are much to long to sequence on the Illumina machine, so I need to break them up. I'll use a solution that Illumina provided to fragment the RNA.
4. First-strand synthesis. We do not have the technology to sequence RNA and so to get around this, we instead convert the RNA into DNA and then sequence that. DNA that came from RNA is called complement DNA (cDNA). As DNA is double stranded, this takes two steps, the first strand synthesis (with a reverse-transcriptase) and the second strand synthesis (with a regular polymerase).
5. Second Strand synthesis. This is the first step where the Sultan (2012) protocol differs from the standard Illumina Hiseq. Sultan (2012) calls for using dUTP instead of dTTP when forming the second DNA strand. This way I can later use an enzyme which chops up DNA on a "U" to cut away the second strand, leaving only the actual strand I am interested in - super clever.
6. End repair. After the mRNA-->cDNA conversion there are a lot of overhangs. Here I chop off all the extra so that each fragment has blunt ends.
7. Adenylate 3` end. In order to add on the adapters (necessary for the Illumina machine can sequence my cDNA library) I need an overhanging "A" on each 3` end. Here I add that "A".
8. Adapter ligation. Here I will barcode and add the adapters onto each of my sequences. I'll be splitting my 40 samples into 2 lanes of HiSeq, so I need 20 different barcodes. Here I will also have to make sure and not introduce batch effects into my design (Auer, 2010). I'll probably have another post entirely about this.
9. U-digestion. Here I will digest out the cDNA with "U"s in it so that I only have DNA oriented in the correct way. I will of course amplify this DNA, but it will cause the adapters to be wrong for the Illumina platform, so while they will be present, they won't be sequenced. Clever, clever, clever.
10. Enrich DNA fragment. Here I will PCR the library so that I have many copies of each molecule. This way there will be plenty for the Illumina machine to sequence.
11. Pool libraries. I will combine 20 of the samples into one lane and the other 20 for the other lane. Then send them off for sequencing.
So in short, I can keep almost the entire Illumina HiSeq kit as per usual, with a couple minor changes to maintain strand specificity.
As you may have noticed my in-text citations, it's because I recently bought the program papers, and am now testing it out in Chrome. It's nice by the way, especially for pages which I've recently started using. However, it looks like it won't format the bibliography directly from in-text citations in chrome, though it will format them properly if you go through and select each reference you want to work with.
Auer, P. L. and R. W. Doerge. 2010. Statistical Design and Analysis of RNA Sequencing Data. Genetics 185:405–416.
Ideraabdullah, F. Y., S. Vigneau and M. S. Bartolomei. 2008. Genomic imprinting mechanisms in mammals. Mutat. Res. 647:77–85.
Edwards, C. A. and A. C. Ferguson-Smith. 2007. Mechanisms regulating imprinted genes in clusters. Current Opinion in Cell Biology 19:281–289.
Reik, W. and E. R. Maher. 1997. Imprinting in clusters: lessons from Beckwith-Wiedemann syndrome. Trends in Genetics 13:330–334.
Sultan, M., S. Dökel, V. Amstislavskiy, D. Wuttig, H. Sültmann, H. Lehrach and M.-L. Yaspo. 2012. A simple strand-specific RNA-Seq library preparation protocol combining the Illumina TruSeq RNA and the dUTP methods. Biochemical and Biophysical Research Communications 422:643–646. Elsevier Inc.
Monday, August 13, 2012
Sex-typing pure species embryos and the RNAseq experiment
Now that I have the gDNA, I've set up the PCR to test for sex. The PCR appears to have worked well. So far for P. campbelli (lanes 1-22) I have 10 males from 3 crosses and 12 females from 6 crosses. From P. sungorus (lanes 23-40) I have 10 males from 5 crosses and 8 females from 5 crosses. This should work well for the RNAseq experiment as I want 5 of each gender of each species. However, I will extract a few more P. campbelli males as it would be nice to have 5 litters instead of 3 that way every individual represents a different replicate cross.
Below is the gel. Positive bands mean that individual is a male, whereas negative bands indicate that the PCR failed and I am interpreting that to be a female. Alternatively failed reactions could be males and failed for some other reason - it's hard to tell the difference between these two alternatives. I am assuming that there will be some non-specific amplification in the females, so a smear indicates that the reaction would have worked had there been a Y chromosome (rather than failed outright) and is thus an actual female, whereas a completely blank lane indicates a likely failed reaction. I'll only use "females" who showed some non-specific binding rather than the "females" where there is no product in the well at all (fortunately, all the "females" have a smear, none failed completely).
As I have alluded to it numerous times, here is the outline of the experiment and the motivation for the sex-typing:
I want to compare the imprinting (and more generally, the entire expression profile) in Phodopus hybrids. As the hybrids show drastic parent-of-origin dependent growth differences, there should be some pretty striking parent-of-origin dependent differences in gene regulation (likely a species-specific breakdown of imprinting). The general approach is to compare the profile of the reciprocal hybrids with each other and then to expression of the parents. The between-hybrid comparison can tell me about imprinting and whether it is disrupted in any specific manner as well as whether there are any transcriptome-wide changes in gene expression. The hybrid-parent comparison can tell me about overall level of expression and if it is in fact different between hybrids, then I can use the parent species as a control for "normal" levels.
We are unsure of whether there will be sex-specific expression differences though and to account for that - there are genes that are predicted to show differences between the sexes during development - I will use 5 of each sex from each cross type. This results in 40 samples, 5 biological replicates per genotype, which will hopefully give me enough power to ask the questions I'm interested in.
The sex-typing from above will help me choose which samples will be the ones that I prepare.
Below is the gel. Positive bands mean that individual is a male, whereas negative bands indicate that the PCR failed and I am interpreting that to be a female. Alternatively failed reactions could be males and failed for some other reason - it's hard to tell the difference between these two alternatives. I am assuming that there will be some non-specific amplification in the females, so a smear indicates that the reaction would have worked had there been a Y chromosome (rather than failed outright) and is thus an actual female, whereas a completely blank lane indicates a likely failed reaction. I'll only use "females" who showed some non-specific binding rather than the "females" where there is no product in the well at all (fortunately, all the "females" have a smear, none failed completely).
As I have alluded to it numerous times, here is the outline of the experiment and the motivation for the sex-typing:
I want to compare the imprinting (and more generally, the entire expression profile) in Phodopus hybrids. As the hybrids show drastic parent-of-origin dependent growth differences, there should be some pretty striking parent-of-origin dependent differences in gene regulation (likely a species-specific breakdown of imprinting). The general approach is to compare the profile of the reciprocal hybrids with each other and then to expression of the parents. The between-hybrid comparison can tell me about imprinting and whether it is disrupted in any specific manner as well as whether there are any transcriptome-wide changes in gene expression. The hybrid-parent comparison can tell me about overall level of expression and if it is in fact different between hybrids, then I can use the parent species as a control for "normal" levels.
We are unsure of whether there will be sex-specific expression differences though and to account for that - there are genes that are predicted to show differences between the sexes during development - I will use 5 of each sex from each cross type. This results in 40 samples, 5 biological replicates per genotype, which will hopefully give me enough power to ask the questions I'm interested in.
The sex-typing from above will help me choose which samples will be the ones that I prepare.
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