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.

Now that my lab is fully equipped, I’m taking on rotation students. Unfortunately, with the pandemic, it’s harder to have one-on-one meetings where I can sit down and walk the new students through every method. Furthermore, why repeat teaching the same thing to multiple students when I can just make an initial written record that everyone can reference and just ask me questions about? Thus, here’s my instructional tutorial on how I design primers in the lab.

First, it’s good to start out by making a new benchling file for whatever you’re trying to engineer. If you’re just making a missense mutation, then you can start out by copying the map for the plasmid you’re going to use as a template. Today, we’ll be mutating a plasmid called “G619C_AttB_hTrim-hCPSF6(301-358)-IRES-mCherry-P2A-PuroR” to encode the F321N mutation the CPSF6 region. This should abrogate the binding of this peptide to the HIV capsid protein. Eventually every plasmid in the lab gets a unique identifier based on the order it gets created (this is the GXXXX name). Since we haven’t actually started making this plasmid yet, I usually just stick an “X” in front of the name of the new file, to signify that it’s *planned* to be a new plasmid, with G619C being used as the template. Furthermore, I write in the mutation that I’m planning to make in it. Thus, this new plasmid map is now temporarily being called “XG619C_AttB_hTrim-hCPSF6(301-358)-F321N-IRES-mCherry-P2A-PuroR”

That’s what the overall plasmid looks like. We’ll be mutating a few nucleotides in the 4,000 nt area of the plasmid.

I’ve now zoomed into the part of the plasmid we actually want to mutate. The residue is Phe321 in the full length CPSF6 protein, but in the case of this Trim-fusion, it’s actually residue 344.

I next like to “write in” the mutation I want to make, as this 1) makes everything easier, and 2) is part of the goal of making a new map that now incorporates that mutation. Thus. I’ve now replaced the first two T’s of the Phe codon “TTT” with two A’s, making the “AAT” codon which encodes Asn (see the image above)

Next is planning the primers. So there are a few ways one could design primers to make the mutation. I like to create a pair of overlapping (~ 17 nt), inverse primers, where one of the primers encodes the new mutation in it. PCR amplification with these primers should result in a single “around-the-circle” amplicon, where there is ~ 17 nt of homology on the terminal ends. These ends can then be brought together and closed using Gibson assembly.

So first to design the forward primer. This is the primer that will go [5’end] –[17 nt homology] — [mutated codon] — [primer binding region] — [3’end]. So the first step is to figure out the primer binding region.

In a cloning scheme like this, I like to start selecting the nucleotides directly 3′ of the codon to be mutated, and select enough nucleotides such that the melting temperature is ~ 55*C. In actuality, the melting temperature will be slightly higher, since 1) we will end up having 17 nt of matching sequence 5′ of the mutated codon, and 2) the 3rd nt in the codon, T, will actually be matching as well.

Now that I’ve determined how long I need that 3′ binding region to be, I select the entire set of nucleotides I want in my full primer. In this case, this ended up being a primer 36 nt in length (see below).

Since this is the forward primer, I can just copy the “sense” version of this sequence of nucleotides.

OK, so next to design the reverse primer. This is simpler, since it’s literally just a series of nucleotides going in the antisense orientation directly 5′ of the codon (as it’s shown in the sense-stranded, plasmid map). I shoot for ~ 55*C to 60*C, usually just doing a little bit under 60*C.

Since this is the reverse primer, we want the REVERSE COMPLEMENT of what we see on the plasmid map.

Voila, we now have the two primers we need. We just now need to order these oligos (we order from ThermoFisher, since it’s the cheapest option at CWRU, and can then perform the standard MatreyekLab cloning workflow).

I do a lot of molecular cloning, which means a lot of transformations of chemically competent e.coli. Using 50 uL of purchased competent bacteria would cost about $10 per transformation, which would be an AWFUL waste of money, especially with this being a highly recurring expense in the lab. I had never made my own competent cells before, so I had to figure this out shortly after starting my lab. It took a couple of days of dedicated effort, but it ended up being quite simple (I’ll link to my protocol a bit later on). Though my frozen stocks ended up working fine, I became quite used to creating fresh cells every time I need to do a transformation. The critical step here is taking a saturated overnight starter culture, and diluting it so you can harvest a larger volume of log-phase bacteria some short time later. A range of ODs [optical density here defined as absorbance at 600 nm] work, though I like to use bacteria at an OD around 0.2. I had gotten pretty good at being able to eyeball when a culture was ready for harvesting (for LB in a 250 mL flask, I found this was right when I started seeing turbidity), but I figured there was a better way to know when it’s worth sampling and harvesting.

I started keeping good notes about 1) the starting density of my prep culture (OD of the overnight culture divided by the dilution factor), 2) the amount of time I left the prep culture growing, and 3) the final OD the prep culture. I converted everything into cell density which is a bit more intuitive than OD (I found 1 OD[A600] of my bacteria roughly corresponded to 5e5 bacteria per mL), and worked in those units from there on out. Knowing bacteria exhibit exponential growth, I log base-10 transformed the counts. Much like the increasing number of COVID-19 deaths experienced by the US from early March through early April, exponential growth becomes linear in log-transformed space. I figured I could thus estimate the growth of my prep culture of competent cells by making a multi-variate linear model, where the final density of the bacteria was dependent on the starting bacterial density and how long I left it growing. I figured the lag-phase from taking the saturated culture and sticking it into cold-LB would end up being a constant in the model. Here’s my dataset, and here’s my R Markdown analysis script. My linear model seemed to perform pretty well, as you can see in the below plot. As of writing this, the Pearson’s r was 0.98.

The aforementioned analysis script has a final chunk that allows you to input the starting OD of your starter culture, and assuming a 1000-fold dilution, tells you how long you likely need to wait to hit the right OD of your prep culture. Then again. I don’t think anyone really wants to enter this info into a computer every time they want to set up a culture, so I made a handy little “look-up plot”, shown below, where a lab member could just look at their starter culture OD on the x-axis, choose the dilution they want to do (staying 2x within 1000-fold since I don’t know if smaller dilutions can affect bacterial competency), and figure out when they need to be back to harvest (or at least stick the culture on ice). I’ve now printed this plot out and left it by my bacterial shaker-incubator.

I’m still much more of a wet-lab scientist than a computational one. That said, god damn do I still think the moderate amount of computational work I can do is still empowering.

I only learned about Gibson when I started my postdoc, and it completely changed how I approached science. In some experiments with Ethan when I was in the lab, I was blown away when we realized that you don’t even need Gibson mix to piece a plasmid back together; this is something we were exploring to try to figure out if we could come up with an easier & more economical library generation workflow. I was disappointed but equally blown away when I realized numerous people had repeatedly “discovered” this fact in the literature already; the most memorable of the names given to it was IVA, or In-Vitro Assembly. Ethan had tried some experiments, and had said it worked roughly as well as with Gibson. Of course, I can’t recall exactly what his experiment was at this point (Although probably a 1-piece, DNA recircularization reaction, since this was in the context of inverse PCR-based library building, after all). So the take away I had was that it was a possible avenue for molecular cloning in the future.

We’ve done a fair amount of molecular cloning in the lab already, creating ~ 60 constructs in the first 4 months since Sarah joined. I forgot exactly the circumstances, but something was right where it made sense to try some cloning where we didn’t add in Gibson mix. I was still able to get a number of intended constructs on that first try, so I stuck to not adding Gibson mix for a few more panels of constructs. I’ve been trying to keep very organized with my molecular cloning pipelines and inventories, which included keeping track of how often each set of mol cloning reactions yielded correctly pieced-together constructs. I’ve taken this data, and broken it down based on two variables: whether it was a 1- or 2-part DNA combination (I hardly ever try more than 2 in a single reaction, for simplicities’ sake, and also because properly combined cloning intermediates may still be useful down the line, anyway), and whether Gibson mix was added or not. Here’s the current results:

So this was extremely informative. Some points
1) I’m willing to screen at least 4 colonies for a construct I really want. Thus, I’m counting a success rate > 0.25 as being a “successful” attempt at cloning a construct. In the above plot, that means any area above the dotted red line. Thus, 1-part DNA recircularizations have pretty decent success rates, since the area of the colored curve above the red dotted like >> the area below it. Sure, Gibson mix helps, but it’s not a night-and-day difference.
2) 2-part DNA combinations are a completely different story,. Lack of Gibson means that I have just as many failed attempts at cloning something as successful attempts. Those are not great odds. Adding Gibson mix makes a big difference here, since it definitely pushes things in favor of a good outcome. Thus, I will ALWAYS be adding GIbson mix before attempting any 2-part DNA combinations.

Other notes: I’m using home-grown NEB 10-beta cells, which give me pretty decent transformation rates (high-efficiency 1-part recircularization reactions can definitely yield many hundreds of colonies on the plate from a successful attempt), so there have been relatively few plates where I literally have ZERO colonies, where I’m more likely to have a few colonies that are just hard-to-remove residual template DNA).