We’re planning on submitting some dual indexed, paired read, amplicon sequencing samples, and depending on how many we have, we may submit for sequencing on a Miseq or Nextseq. Since this is all custom (and we generated the indices), I had to figure out how exactly how the process plays out for the two instruments to see what we need to the sample sheet for demultiplexing. I figure I’d illustrate it out for people in the lab so they understand the process as well.

The common steps

Everything starts with bridge amplification, where both the forward and reverse strands are physically bound to the flow cell by their 5′ ends. I’m denote where the DNA strands are physically bound to the flow cell with the grey circles on the ends.

Next is denaturing the strands so they are no longer bound, and also cleaving the strand that is bound to the flow cell via the p5 sequence. The result is something that looks like this.

Now the single stranded molecule is ready for sequencing from the read 1 primer. The light blue box is showing the nucleotides we’ll be reading in this particular library.

Once that read is done, next is reading the first index.

We didn’t do a ton of dual indexing of the libraries in my postdoc (and since Jason used to run all of the kits anyway), I didn’t really need to know these steps, but I’ve had to figure them out now setting everything up in my own lab. I’ll go over what happens with the Nextseq first, since that’s conceptually a bit easier. Here’s what the Illumina docs say.

The Nextseq way

So this setup allows for dual indexing read off of two custom primers. So for this specific library, this means that the complementary sequence is going to be synthesized, making a double-stranded bridged molecule again.

And then after everything is denatured and the original template strand removed (presumably by cleaving at the p7 sequence this time), the second index can now be sequenced.

Followed by sequencing of the second read.

The Miseq way

Looking at what Illumina says in their documents for dual indexing on the Miseq, it looks a little different:

Note: I didn’t realize this until we did the exercise, but the p5 cluster generator we used to use in the Fowler lab is longer than the actual p5 sequence Illumina gives in their manuals. Not quite sure for this discrepancy, though I’m assuming that may mean that the sequences immediately after the p5 oligo during this step won’t be our rather variable index sequences, and instead may be some constant bases preceding the indexes. I’m guessing this is not a deal-killer, but something we’ll still have to be cognizant of when determining the run programming.

After that, is the complete second strand synthesis, resulting in a double stranded bridged molecule again.

Followed by denaturing and cleavage (again, presumably of p7 sequence) as described above, followed by annealing of the read2 primer.

So why does this matter?

Well, I think the practical implications are a few-fold. Firstly, Miseq dual indexing won’t need that second custom index read primer, since it will be reading off of the p5 sequence. And this is further complicated by the fact that the p5 adapter sequence we added onto the amplicons may perhaps be a bit longer than what’s actually on the chip, so we may have to factor this into the run parameters. Secondly, the strand that is reading that second index is different. With the Miseq, it’s being read off that first strand and thus in the same orientation as index 1. On the Nextseq, it’s being read on the second strand, so it will be read in the opposite order as index 1. Thus, I think this matters in terms of whether we’re putting the forward or reverse complement in the sample sheet, which will differ depending on whether we’re going Miseq or Nextseq with these samples.

Due to popular request, I’m going to put my *most current* version of the HEK 293T Landing Pad recombination protocol here for others’ benefit. Much of the credit goes to Sarah Roelle, who wrote up this current version of the protocol.

Recombination:

[This protocol is for a 24-well plate:]
Day 1:

1) Make 2 transfection mixtures per sample:
Tube 1: 23 μL Opti-MEM + 1 μL Fugene6

Tube 2 (If using cells that don’t already express the Bxb1 recombinase enzyme): μL of DNA corresponding to 16 ng Bxb1 plasmid + μL corresponding to 224 ng attB plasmid and OPTI-MEM to a final tube volume of 24 μL.
-OR-
Tube 2 (If using cells that already express the Bxb1 recombinase enzyme): μL of DNA corresponding to 240 ng attB plasmid and OPTI-MEM to a final tube volume of 24 μL.

2) Mix Tube 2 into Tube 1 for each sample. Mix up and down a couple times with pipet. Then let the mixtures sit for 15-30 min while you get the cells ready (Unless you trypsinized and counted the cells first, to know how many you had in case they were limiting).

4) [Meanwhile] Trypsinize and count the cells. Add 120,000 cells per well in a final volume of 300 μL media to each well.

5) Once at least 15 minutes have passed since mixing, add the mixtures dropwise throughout the well of cells being transfected.

Day 2:
Add at least 500 μL media to each well.

Notes about scaling up / down:
We typically pilot new plasmids in a 24-well scale, and transfect larger populations of cells by transfecting 6-wells and increasing the number of plates / wells as needed.

Negative selection (if applicable):

Negative selection with iCasp9 is wonderful, and hopefully you are using one of those landing pad versions. I typically use 10nM final concentration of AP1903, although I’m fairly certain 1nM is just as effective. Death occurs in a couple of hours, so you can come back to your plate / flask later on in the day and change media to get rid of the dying cells. I typically wait until at least 72 hours after recombination to perform this step.

Important note: If you’re doing a library-based experiment, then make sure you leave some cells aside (even 100k to 500k cells will be plenty) that DO NOT go through any selection steps, since this will allow us to estimate the number of recombined cells (and thus estimate the coverage of your library; see below for the calculation). If you’re just doing individual samples, this isn’t nearly as big of a deal, since you’ll be able to visually inspect the number of recombinants (ie. do you see only a handful of surviving cells, or is it 100+ individual cells surviving?).

Positive selection (if applicable):

I tend to do the positive selection step AFTER negative selection, since the negative selection step has usually thinned the number of cells down enough such that the positive selection is going to be most effective (I find that over-confluent wells tend not to do super-grant with positive selection). Thus, while it can be done as soon as 72 hours post recombination, I tend to do this a week or later after recombination. You can find effective concentrations for positive selection of landing pad HEK 293T cells here.

Some additional notes:

1. Due to a phenomenon of (annotated) promoter-less expression from the thousands of un-recombined plasmids that remain in the cell following transfection, I typically wait ~ 5 to 7 days before running any cells in the flow cytometer, to wait for that background signal to die down.
2. Other transfection methods may work, but will need to be optimized. For example, I have observed transfection with lipofectamine 3000 to result in prolonged promoter-less expression, probably due to increased transfection and greater toxicity preventing cell division and thus plasmid dilution.
3. Calculating the number of recombinants. OK, so as I alluded to above, it’s definitely worth estimating the number of recombinants of any library-based experiments. To do this, take your observed recombination rate from running flow on your unselected cells (say, you see 5% of cells as being mCherry+ / recombined when you ran flow on unselected cells 7 days after transfection), and multiply that fraction with the number of cells you transfected. So, if we pretend that you transfected 20 million HEK 293T cells and you had observed 5% of cells recombined in your unselected well, then the rough calculation is 2e7 cells * 0.05, amounting to roughly 1 million cells estimated to have been recombined. Of course, directly sequencing what’s there in the recombined library is a more direct measurement of library coverage at the recombined cell step, but doing this calculation is still usually worthwhile as another line of evidence.

We recently got some Illumina sequencing back from GeneWiz, and I realized that this is good opportunity to show people in the lab how to do some really basic operations to handle such types of sequencing data, so I’ll write those instructions here. Since essentially everybody in the lab uses a Mac as their primary computer, these instructions will be directly related to performing these steps on a Mac, though the same basic steps can likely be applied to PCs. Also, since these files are small, everything will be done locally; once the files get big enough and the analyses more complicated, we’ll start doing things on a computer cluster. Now to get to the actual info:

1. First, find the data we’ll be using for practice today. If you’re in the lab, you can go to the lab GoogleDrive into the Data/Illumina/Amplicon_EZ/30-507925014/00_fastq directory to find the files.

We won’t need to analyze everything there for this tutorial; instead, let’s focus on the “KAM-IDT-Std_R1_001.fastq.gz” and “KAM-IDT-Std_R2_001.fastq.gz” files.

2. Copy the files to a directory on your local computer. You can do the old “drag and drop” using the GUI, or you can do it in the command line like so, once you adjust the paths for your own computer:

$ cp /Volumes/GoogleDrive/My\ Drive/MatreyekLab_GoogleDrive/Data/Illumina/Amplicon_EZ/30-507925014/00_fastq/KAM-IDT-Std_R1_001.fastq.gz /Users/kmatreyek/Desktop/Illumina_data

$ cp /Volumes/GoogleDrive/My\ Drive/MatreyekLab_GoogleDrive/Data/Illumina/Amplicon_EZ/30-507925014/00_fastq/KAM-IDT-Std_R2_001.fastq.gz /Users/kmatreyek/Desktop/Illumina_data

3. Un-gzip the files. You can do this in the GUI by double-clicking the files, or you can do it in the terminal (if you’re now in the right directory) like so.

$ gzip -dk KAM-IDT-Std_R1_001.fastq.gz

$ gzip -dk KAM-IDT-Std_R2_001.fastq.gz

Optional: Take a look at your fastq files. You won’t want to open the files in their entirety, so what makes more sense it just looking at the first 4 or so lines of the file, corresponding to the first read. To do this, type:

$ head -4 KAM-IDT-Std_R1_001.fastq

And you should get an output that looks like so:

4. They won’t always be paired reads, but this time it is. So we’ll pair them. I you don’t already have a method for doing this, then download PEAR and install it like I described here. Once you have it installed, you can type in a command like so:

$ pear -f KAM-IDT-Std_R1_001.fastq.gz -r KAM-IDT-Std_R2_001.fastq.gz -o IDT_HM

It took my desktop a couple minutes for this process to complete. You’ll get an output that looks like this.

Your directory should now have all of these files:

You can look at the first read again (now that it’s been paired), using the following line, it should look like so:

$ head -4 IDT_HM.assembled.fastq

As you can tell, the quality scores in the first line went from mostly F’s (Q-Score of 37) to almost all I’s (Q-Score of 40).

5. Now that we’ve prepped the Illumina data, it’s time for the downstream analysis. This will be far more project or experiment specific, so these next steps won’t apply for every situation. But in this case, we made a library of Kozak variants to try to get a range of expression levels of the protein of interest. Furthermore, the template DNA used for the PCR lacked a Kozak sequence and a start codon, and these will inevitably be in the sequencing data also. So the goal of this next step is to identify the reads that are template vs those that have the Kozak sequence, and if it does have a Kozak and ATG introduced, to extract the Kozak sequence from the read.

I went ahead and wrote a short python script that achieves this. So, grab that file (ie. Right click, and it “Save link as…”), stick it in the same directory as the data you want to analyze, and run it with the following command.

$ python3 Extract_Kozak.py IDT_STD.assembled.fastq

The script should then create a file called “IDT_STD.assembled.tsv” that should look like this:

This can now be easily analyzed with whatever your favorite data analysis language is, whether it’s R or Python. Huzzah!

I still intend to get around to posting most if not all landing pad plasmids to Addgene, but it’s taken me forever to get around to it. Actually, the institution takes just as forever to do it, since apparently they have to get permissions to post any plasmid with parts I may have amplified from another source(eg. the puromycin resistance gene)? Once those are there, the plasmid sequences will obviously be publicly available. In the mean time, I figure I’d post some of the most common plasmid maps here, so other people can benefit form them (or I don’t have to send specific emails to each person who asks for them). So here are some of the most popular, published plasmids, in GenBank (.gb) format.

G542A_pLenti-Tet-coBxb1-2A-BFP_IRES_iCasp9-2A-Blast_rtTA3 (Addgene #171588)

G698C_AttB_ACE2_IRES_mCherry-H2A-P2A-PuroR (Addgene #171594)

G758A_AttB_ACE2(del)-IRES-mCherry-H2A-P2A-PuroR (Addgene #171596)

p0489_AttB_EGFP-Link-PTEN-IRES-mCherry_Bgl2.gb

p0669_AttB_sGFP-PTEN-IRES-mCherry-P2A-bGFP_Bgl2

G384A_LLP-iCasp9-Blast

G273A_AttB_sGFP-PTEN-IRES-mCherry-P2A-HygroR

G274A_attB-sGFP-PTEN_IRES-mCherry-P2A-PuroR

attB-EGFP (Addgene #171597)

attB-mCherry (Addgene #171598)

dAAVS1_TetBxb1_mTagBFP2_rtTa3

dAAVS1_CMV-rtTA3-Term

dAAVS1_Dummy

G371A_LLP-rEF1a

G375F_attB-eGFP-rIRES-mCherry

G382C_attB-eGFP-rIRES-mCherry-2A-PuroR

G163A/B_AttB_PuroR-P2A-mCherry_Bgl2

G310A_pLenti-TetBxb1BFP-2A-coBxb1-2A-Blast_rtTA3

pLenti-TetBxb1BFP_coBxb1-rtTA3_Blast

pLenti-TetBxb1BFP-rtTA3_Blast

G57A_attB_TP53-link-EGFP-IRES-mCherry

AttB_sGFP-VHL-IRES-mCherry-P2A-bGFP_Bgl2

AttB_VHL-sGFP_IRES-mCherry-P2A-bGFP_Bgl2

Positive selection for recombined selection is a pretty useful technique when working with the HEK 293T landing pads, but people might not know what concentrations are best. Well, here is that info, all in one place. In each case, the antibiotic resistance gene had been placed after an IRES element in an attB recombination plasmid, and was linked to mCherry using a 2A stop-start sequence. Thus, the Y axis is showing how well the mCherry+ cells were enriched at various concentrations of antibiotic.

Consistent with the above plots, I generally suggest people use 1 ug/mL Puro, 10 ug/mL Blast, 100 ug/mL Hygro, and 100 ug/mL Zeocin for selections.

11/19/2024 Update: I was recently reminded that I had since created this data a bit over year ago using resazurin dye on our plate reader. Gosh, now that I’m looking at this data side-by-side, I’m finding that they correspond with each other even better than I expected. How lovely.