Illumina sequencing of barcoded amplicons is going to be large factor in the work we’ll be doing. Going to a completely new place, I now need to explain to people how it works. To keep this post simpler, I’m skipping all of the landing pad details (hopefully you already understand it). Let’s just start with what was genomically integrated after the recombination reaction. In the case of the PTEN library, it looks like this:

As you can see, the barcode is the blue “NNNNNNNNNNNNNNNNNN” region at the bottom of the above image. We can’t directly sequence it with primers directly flanking it, since those sequences will also be present in the unexpressed plasmid sequences likely contaminating our genomic DNA. Thus, we first have to create an amplicon containing our barcode of interest, but spanning the recombination junction (“Recomb jxn” above).

For the PTEN library, the barcode is located in back of the EGFP-PTEN ORF, so we have to amplify across the whole thing. We did this by using a forward primer located in the landing pad prior to recombination (such as KAM499 found in the Tet-inducible promoter and shown in red as the “Forward “Primer), as well as a reverse primer located behind the barcode associated with the PTEN coding region (KAM501 in this case).

The above is a simplistic representation. The forward primer / KAM499 is indeed just the sequence needed for hybridization, since we can’t going to do anything else with this end of the amplicon. On the other hand, we’ll add some more sequence to the reverse primer to help us with the next steps. In this case, this is a nucleotide sequence that wasn’t present before, so that we can amplify this specific amplicon in the next step. The actual amplicon will thus look something like below:

OK, so the above amplicon is huge; too big to form efficient cluster generation. Thus, we’ll now use that amplicon to make a much smaller, Illumina compatible second amplicon. We’ll also include an index of degenerate nucleotides “nnnnnnnn” that can be used to distinguish different amplicons from each other when we mix multiples samples together before doing the actual sequencing step. This second amplicon looks something like this:

This time, the forward primer has both the hybridizing portion (in the blue-purple) as well as one of the cluster generators, shown as that light blue. At the complete other end, you can see the “nnnnnnnn” index sequence in indigo, followed by the other cluster generator sequence in orange. Please note: Here, the index is a KNOWN sequence, like GTAGCTAC, or GATCGAGC. It’s just that each sample will have a different KNOWN sequence,. so it was simpler just to denote it with “n”s for the purpose of this explanation.

There’s more in the above map though. We’ll likely want to do paired sequencing of the barcode, so we’ll give the Illumina sequencer two read primers: read 1 (in red) which reads through the barcode in the forward direction, as well as read 2 (in green), which goes through the barcode in the reverse direction. We will also need to sequence the index, though we’ve tended to only do that with one primer. This primer is Index 1, and is colored in yellow.

For the actual primer sequences and everything, look at Supplementary Table 7 of my 2018 Nature Genetics paper.

Anna was using the Nikon microscope a couple of days ago, and noticed some level of bleed-over of mCherry fluorescence into the CY5 filter-set channel. I previously played around with making a bespoke script for figuring out the optimal fluorescent protein : laser : detector combinations on the two flow cytometers we routinely use at the core here, and figured I could modify the script to understand how our existing Nikon filter sets work with the various fluorescent proteins we use here. It took me a couple hours, but I made a script posted on my GitHub. Here’s a figure that script produces:

Based on the filter sets we currently have, looks like it may be near impossible to avoid bleedover of a near infra-red FP fluorescence into the red channel. Also, this seems to reproduce / explain the effect Anna was seeing with mCherry fluorescence bleedover into the NIR channel. Seems this problem doesn’t happen with mScarlet.I, so maybe I’ll slowly shift toward using that FP more.

This is going to be a very lab-specific instructional post, since it’s literally only going to talk about how we normally order primers here in the lab.

So as I mentioned in this previous post, at least for us here at CWRU, primers are cheapest when ordered from ThermoFisher. There’s an unfortunate delay on how long you have to wait until you can receive the primers, but for the most part it isn’t a big problem. Regardless, I’ve essentially streamlined how we order and organize primers as much as possible, so here are the steps.

Any permanent member of the lab should have their own tab (titled with their initials) in the “MatreyekLab_Primer_Inventory” sheet. I also have my own tab, which is called “KAM”. Every primer you order should have its own unique identifier (like KAM2199). Since you’re the only one adding to your own tab, there is no reason you should ever have duplicate identifiers. You want to do this step first, so you know what identifier to give the primer you intend on ordering now (The most recent primer I ordered was KAM3495, so the primer I’ll order today is KAM3496).

I usually keep a separate “scratch” google sheet where I write down things I’m in the process of doing. Here, I like to make a set of fields like this (I usually just copy-paste from the last time I ordered primers, actually). But, if you don’t already have one of these sheets already, I just made a “Scratch_area” tab in the google sheet.

Here, I like to list a few things. There’s an area I intend to later copy-paste into the primer inventory, which lists the date, the length of the oligo (using the =LEN() function of the actual primer sequence), the primer identifier, the actual primer sequence, and then a short description for what the primer is for.

Next, I like to have another section which repeats a subset of the information, but in a slightly different order. The reason for this is that the ThermoFisher website requires the primer ordering sample sheets to have the data entered in a specific order (sequence, identifier, Name of person ordering). Since this is the same information as in some of the previous columns, I just have those cells point to each of the relevant cells so that I don’t need to fill in that same info again.

If you’re ordering primers for sequencing, then you’re all set. But if you’re ordering primers for some cloning, I like to also write a short section that writes down which primers are supposed to be paired with which other primers to make a particular plasmid, also listing the template plasmid that will be used for the reaction. It’s nice to write this info now, so you don’t have to try to remember everything / redo the effort you just did to remember how you need to pair things to actually perform the PCR when you receive the primer. I usually copy-paste these cells into the “MatreyekLab_Hifi_Reactions” google sheet, into an area below where I have written “** denotes primers we may not have yet“.

So next is actually ordering the primers. There is a folder in the lab google drive where all of the primer order sheets are kept (MatreyekLab_GoogleDrive/Primer_order_templates). I usually just find a recent file, duplicate it, and rename it for today’s date and whoever is ordering (eg. KAM).

Once I copy-paste the new info in, it should look like this in the end.

K, great. Now to actually order it. Log into our thermofisher account by going to www.thermofisher.com, hitting “Sign In” at the top right. Lab account email is “[email protected]” and the password is the lab password (which you should already have memorized!). Then I hover over “Popular” at the top left, and click on “Oligos, Primers, & Probes”.

Then hit “Order now” under “Custom made to order”

Then I always do the “Bulk Upload” button:

Next hit “choose file”:

Then click on the samplesheet you want to upload. Your primer should now be entered.

Cool, so now is actually checking out. Hover over the cart and then click ‘View cart & check out”.

Next is a series of somewhat annoying confirmation screens. Scroll down and hit “Begin Checkout”

Default settings are fine, so scroll down and hit “Continue to Payment”

Default settings (including the speed type) are still fine, so scroll down and hit “Continue to Review Order”

You’re almost done! Now scroll down, click on the “You agree to the thermofisher.com terms and conditions…” and then click “Submit Order”.

You should now see a screen that says “Thank you for your order!” at the top. The “[email protected]” account will also have received a confirmation email (but you don’t need to check this or anything). You are FINALLY done. Log out of the account and go on with your day.

TL;DR -> If you’re doing infection assays, it’s worth sticking to < 20% EGFP positive, and depending on how many cells you count, > 0.03% EGFP positive cells.

Can’t say I expected it, but the SARS CoV-2 pandemic has brought virology back into my sights. This includes some familiar tools, such as lentiviral pseudovirus infection assays which I had used a lot of in graduate school. It’s the same basic process as using lentiviral vectors for cell engineering, except you’re changing out the transgene of interest for a reporter protein, like EGFP. If the pseudovirus is able to (more-or-less) accurately model an aspect of the infection process, then you now have yourself a pretty nice molecular / cell biology surrogate to learn some biology with. Essentially, it tells you how efficiently the virus is getting into cells. Since all of the “guts” can look like HIV, you can us it to study parts of the HIV life cycle after it’s entered the cell but before its integrated into the host cell genome (this is what my PhD was in). It’s arguably more common use is instead as a vehicle for studying the protein interactions that various viruses make to enter the cell cytoplasms.

The loveliness of the assay (especially the fluorescent protein version) is that it is inherently a binary readout (infected or uninfected) at a per-cell level. But, since there are *MANY* cells in the well, it becomes a probability distribution, where the number of infected cells captures how efficient the conditions of entry / infection were.

But of course there is also some nuance to the stats here. For example, if you mixed a volume containing 100,000 cells with another volume containing 100,000 viral particles (and thus a multiplicity of infection (MOI) of 1), 100% of the cells don’t get infected. In actuality, you would only get about 63% of cells getting infected. That’s b/c each virus isn’t a heat-seeking missile capable of infecting the only remaining uninfected cells around; instead, it’s because each infection event is (*more or less) an independent event. Thus, cells can be multiply infected. Notably, based on an EGFP readout, you can’t really figure that out (ie. cells that are doubly infected are essentially indistinguishable from singly infected cells based on their fluorescence levels). But, since we know how the system should function, we know that it should largely follow a Poisson distribution.

Here’s a plot I made with this script to demonstrate what that relationship is between the MOI and the % of green cells you should expect to see:

Things look nice and linear at low MOIs, but as soon as you start getting close to an MOI of 1 then the number of multiply infected cells start taking on a bigger role, until eventually every cell becomes multiply infected as the last few “lucky” uninfected cells get so overrun that there is no escaping infection. In terms of trying to get nice quantitative data, that saturation of the assay is kind of problematic. Generally speaking I find somewhere about 20-30% of GFP positive cells where you’re really started losing that linearity (shown by the dotted line).

Why not just always do small numbers, like 1% or even 0.1%? Well, b/c you’re kind of caught between a rock and a hard place: I guess the rock is the unbreakable boundary of 100% infected cells, while if you go too low it gets hard to sample enough events to accurate quantitate something that’s really rare.

I do think there’s still a lot of meat in that assay though, as long as you count sufficient cells. I generally quantitate the number of infected + uninfected cells using flow cytometry. Being able to count 10,000+ events a second mean you can run through all of the cells of a 24-well in under a minute. That means you can pretty reasonably sample 100,000 cells per condition. Assuming that’s the case, how does our ability to accurately quantitate our sample drop as the number of infected cells fall?

IMO, it begins to become unusable when you get to roughly 0.03 percent GFP positive cells. That’s because past that, the amount of variability you get in trying to measure the mean is roughly about the size of the mean itself. Furthermore, your chances for encountering replicate experiments where you get *zero* GFP positive cells really starts to increase. And those zeros a pretty annoying, since they are quite uninformative.

So just understanding the basic stats, it looks like my informative range is going to be ~ 0.03 percent positive to ~ 30 percent positive. So roughly 3 orders of magnitude. Ironically, that just about exactly what I saw when I ran serial dilutions of a SARS CoV pseudovirus on ACE2 overexpressing cells.

So there you have it. The theoretical range of the assay, backed experimentally by what I saw in real life. The wonderfulness of science. You can find the script I used to these these estimations here.