The dark corners of the plasmid

Posted on by kmatreyek

I was trying to streamline our existing attB vector. I was prompted to do this for a few reasons: 1) I recently identified a previously unappreciated T7 (bacteriophage) promoter and potential cryptic bacterial promoter in our standard plasmid, 2) There are presumably some weak cryptic eukaryotic promoters hidden somewhere in the plasmid too, and 3) I was trying to “domesticate” the plasmid to get rid of some Type II and Type IIS restriction enzyme sites.

As part of the most recent plan, I decided to delete two different sections of the plasmid; one was the segment of DNA between the attB site and the Amp promoter driving the AmpR gene, and the second was the segment between the origin of replication and the SV40 PolyA signal. First one worked fine (which I knew, since I eventually remembered that I had previously done this back in like….2015, but never used it again for some reason). The second one proved very problematic. Here’s the section in question:

So I had seen those annotations for the lac promoter and lac operon, but assuming the directionality of the map was true, it seemed like they weren’t really pointed at enough bacterial sequence to matter so I just assumed they were vestiges of something. Well, this is what happened in terms of my plasmid yields in this lineage of plasmid.

I’m not going to read into the slightly higher concentration of L036 too much, but man, what really smacks you in the face is just how bad yields became with L048 (a derivative of L036). Well, so I tested the panel for their ability to recombine into landing pad cells, and the phenotype there was obvious as well; all plasmids up to and including L036 recombined at high rates, whereas L048 and one of its sibling plasmids with the same deletion had nonzero but *severely* diminished recombination. So not only is the DNA yield bad, but the “quality” in some sense seems to be much worse in that the DNA that is there is not resulting in good recombination.

I’ve learned my lesson, and I’m now just trying to take out that last BspQI/SapI site with a nucleotide substitution.

But still, this begets the question: so what in the world is in that DNA section, and why is it so important for plasmid propagation? I’m sure some bacteriologists and perhaps some old-school molecular biologists should know, but I’ve always lamented how much of a black box the bacterial portions of plasmids are (my expertise is in eukaryotic / mammalian cell biology). Will I ever figure this out?

Well, I will first try with pLannotate. That’s what told me there was a T7 primer site in this plasmid after all.

Well, so that didn’t really uncover anything new. Hmmm…

Pymol figures

Posted on by kmatreyek

I end up having to Google search the commands the relevant commands every time I need to make publication-quality figures in Pymol, so I’m just going to note them here to save myself some time.

  1. set bg_rgb, white

    This sets the background to white. Usually safe bet for any image meant for a normal presentation (ie. slides with white backgrounds), or a publication (where it’s text and images against a white page).

  2. set ray_trace_mode, 1

    This allows it to have the “black outline” representations, which I think look a little nicer than the default.

  3. ray [Some number, usually between 900 and 1500]

    This makes a static fully-rendered image that is much higher quality than the fast render in the interactive interface.

See above for an example of what a resulting image looks like.

When iCasp9 doesn’t kill

Posted on by kmatreyek

iCasp9 as a negative selection cassette is amazing. Targeted protein dimerization with AP1903 / Rimiducid is super clean and potent, and the speed of its effect is a cell culturist’s dream (cells floating off the plate in 2 hrs!). It really works.

But when there are enough datapoints, sometimes it doesn’t. I have three recorded instances of email discussions with people that have mentioned it not working in their cells. First was Jeff in Nov 2020 with MEFs. Then Ben in June 2021 with K562s. And Vahid in July 2021 with different MEFs. Very well possible there’s one or two more in there I missed with my search terms.

Reading those emails, it’s clear that I had already put some thought into this (even if I can’t remember doing so), so I may as well copy-paste what some them were:

1) Could iCasp9 not work in murine cells, due to the potential species-based sequence differences in downstream targets? Answer seems to be no, as a quick google search yields a paper that says “Moreover, recent studies demonstrated that iPSCs of various origin including murine, non-human primate and human cells, effectively undergo apoptosis upon the induction of iCasp9 by [Rimiducid]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177583/

Separately, after the K562s (human-derived cells) came into the picture:

This is actually the second time this subject has come up for me this week; earlier, I had a collaborator working in MEF cells note that they were seeing slightly increased but still quite incomplete cell death. That really made me start thinking about the mechanism of iCasp9-based killing, which is chemical dimerization and activation of caspase 9, which then presumably cleaves caspases 3 and 7 to then start cleaving the actual targets actually causing apoptosis. So this is really starting to make me think / realize that perhaps those downstream switches aren’t always available to be turned on, depending on the cellular context. In their case, I wondered whether the human caspase 9 may not recognize the binding / substrate motif in murine caspase 3 or 7. In yours, perhaps K562’s are deficient in one (or both?) of those downstream caspases?

Now for the most recent time, which happened in the lab rather than by email: It was recently brought up that there is a particular landing pad line (HEK293T G417A1) which we sometimes use, that apparently has poor negative selection. John and another student each separately noticed it. Just so I could see it in a controlled, side-by-side experiment, I asked John if he’d be willing to do that experiment, and the effect was convincing.

So after enough attempts and inadvertently collecting datapoints, we see the cases where things did not go the way we expected. Perhaps all of these cases share a common underlying mechanism, or perhaps they all have unique ones; we probably won’t ever know. But there are also some potentially interesting perspective shifts (eg. a tool existing only for a singular practical purpose morphing into a potential biological readout), along with the practical implications (ie. if you are having issues with negative selection, you are not alone).

This is the post I will refer people to when they ask about this phenomenon (or what cell types they may wish to avoid if they want to use this feature).

Firefly Luciferase

Posted on by kmatreyek

So many of my experimental readouts in my scientific career have been fluorescence-based (and for good reason), but especially as we keep doing pseudotyped virus infection assays, it’s becoming really prohibitive to continue reading out infection by flow cytometry (b/c of cost, availability of the instruments, etc), so we’ve recently shifted over to luminescence. As part of this, I wanted to create a recombination vector construct that encodes firefly luciferase, so we can use it as a control when needed. (I also just remembered the other reason we made this plasmid, and it was also to have a luminescence version of testing recombination efficiencies).

Anyway, I recently recombined and selected cells with two independently generated constructs (clones G1402C and G1402D), and determined how many recombined (fLuc-expressing) cells need to be in the well to be detectable. Here’s the resulting plot.

The datapoints on the y-axis/ left edge of the plot are media only, where there is no luciferase enzyme (thus helping to establish the background of the assay). You can tell based on the plot that we start getting detectable values around 10 cells per well, and we’re clearly in the linear range by ~ 100 cells per well. Above that value, it’s clearly in the linear range at least through 250,000 fLuc expressing cells (it’ll be nice to see if we can ever max out the linear range, but that’s probably best done with high MOI pseudotyped virus transductions). The variability between G1402C and G1402D may be error in cell counts, since we know there’s possible (slight) error in those estimates.

Perhaps one day we’ll also start playing around with renilla luciferase and Nanoluc, but for now, we’ll keep playing around with fLuc only just to keep things simple. But ya, now with this plasmid in hand, we can start more comprehensively testing recombination protocols for efficiency without having to book a ton of time at the flow cytometer…

Budget Estimations

Posted on by kmatreyek

I’ve been thinking about budgets, partially b/c I just went through a protracted experience of getting the school to give me access to the remaining part of my startup (from almost 5 years ago! And while I was repeatedly told the remaining amount was non-expiring, it was just given to me in an account that expires 5 years from now…). Regardless, the goal is to spend down these remaining institutional funds while bolstering my research group, largely through personnel additions and management.

For that reason, I created a simulation of how personnel salary + other operational costs would exhaust my current funding portfolio (currently one R21 that ends in a year, an R35 that ends in 2 years, and the aforementioned remaining startup account). The black line in the plot below shows this happening, with the line terminating around the 3 year mark. This is presumably around when I would expect to run out of money, if zero future action were taken.

Now, keep in mind that there are some MAJOR assumptions going on here:
1. This simulation assumes that I DO NOT receive another grant in the next 5 years. Of course that will not be the case.
2. Aside from Nisha’s intended graduation date, the rest of the end dates are very roughly estimated. In the case of research staff, this is assumed to be indefinite for two of the individuals. Of course, if money ends up getting tight, A) my salary will end up going down some, which will help alleviate costs, and B) I’ll let go of research staff as necessary, and well before there are impacts on students (to which there is a larger commitment).
3. Everything is modeled as a daily recurring expenditure. In real life, I think everything behaves more like discrete sums that are added into the account yearly (like NIH budgets) or monthly / bimonthly (like salaries).

Note: It goes into the negative values since it’s allowing the startup not spent by the end of R35 in two years to subsequently exhaust (that’s when the line ends).

Now, I’m actively trying to recruit more personnel to the lab, at this point largely in the form of PhD students. That’s what the additional colors on the plot are. A singular PhD student would be the orange line, and two new PhD students would be the red line.

Especially in light of the fact that I *have* to spend that startup (or it presumably goes *poof* into the administrative ether), looks like I’m going to be good for at least a couple of years in the worst-case scenario. Regardless, this really does help me frame what I need to be doing at a given time. If financial situations were dire or particularly worrisome, I would be focusing on writing grant applications right now. Based on the above plot, I think it makes a lot more sense for me to devote focus on publishing existing projects and further developing preliminary evidence to increase the success rates of grant applications I could just as well submit in a year.

PhD Student Rotations

Posted on by kmatreyek

This post was originally from June 2022, but I’m reposting now ahead of the 2024 incoming PhD class rotations.
It’s PhD student rotation season again at CWRU, so I figured I may as well put this post on the lab website to 1) inform any prospective PhD students that may be perusing through the lab website, and 2) remind me of the things I like to bring up before people rotate.

  1. If you’re interested in rotating, we should definitely schedule a meeting so I can get a sense of your background and interests, so I can tailor the rotation appropriately (and screen out people who are likely to be really poor fits; see point 3 below). It will also give me the opportunity to talk through some of the other points listed below.
  2. Rotations are suuuuper short here (Generally 4 to 6 weeks). Thus, there is ZERO expectation on my end to get any “publication quality” experiments done. My main goal is to make sure you’re familiar with some of the bread-and-butter methods in the lab (eg. molecular cloning, landing-pad -centric tissue culture, script-based data analysis). Failed experiments are fine, since it gives us the opportunity to talk about the data and troubleshoot together. The main thing I’ll be looking for is how well we’re able to communicate and work together, since that’s arguably the most important thing we can learn from that rotation that could be extrapolated to predict how good of a dissertation work environment it would be for the specific individual.
  3. There isn’t really any prerequisite experience for rotation students. Yea, it would be helpful if you know how to pipet, have done some basic tissue culture work of any kind, and have designed and interpreted some experiments before. Being housed in a wet-lab department, I have very little expectation of computational experience. That said, wet-lab people that have zero interest in learning computational biology and data analysis are probably not great fits, since all projects in the lab will always have hefty data analysis components. Conversely, computation-only people with zero interest (and maybe even experience) in wet-lab research is also likely a bad fit, since all projects in the lab will also always have hefty wet-lab components.
  4. The lab is pretty interdisciplinary. Like, some people work on virology, while other people work on proteins related to clinical genetics. Thus, you’ll have to be generally interested in science / biology / medicine to enjoy your time here. In contrast, if you only care about subject XXXX or subject YYYY and nothing else, then lab meetings are going to be really boring to you. There’s always talk about (practical) statistics, molecular biology, synthetic biology, cell engineering, assay development, and high throughput sequencing; thus, if you’re into those things at some level, then you’re probably fine!
  5. There are three very different options in terms of dissertation projects. There are some “ready-to-go” project ideas, where I’ve already crafted a grant application very clearly explaining the project scope, or there is no grant yet but the ideas are straightforward and all of the assays are already in place. These are currently listed on this Google Sheet. There are also some projects where I’ve played around a bit with some ideas / preliminary data, but it’s not really clearly written out anywhere and things will need to be hashed out. Both of these types of projects should be listed in this “Research Directions” network graph. Then again, there are probably some really great projects that I haven’t thought of yet, that A) are in line with the student’s interests, and B) can be tackled with the techniques / perspectives that the lab is good at. If it’s a decent idea that has links between cell culture assays, cell engineering, genetics, proteins, cell biology, and pathological consequences, I’m sure I’ll find it interesting and get on board. Highest potential risk, but also highest possible reward for the student (at least from a training for independent thinking perspective).
  6. Rotation projects don’t have to be on the same topic as potential thesis projects. In my opinion, it’s oftentimes best to separate them, since potential thesis projects likely don’t have any DNA constructs made for it already, so working on it means only doing (likely failed) cloning during the rotation, which is no fun and not particularly informative.
  7. I’ll only ever take one student any given year. So while it’s not a competition, some people who may want to join may not be able to. Something to keep in mind!
  8. I expect every student to give an “end of rotation” presentation during lab meeting. The main reasons are A) So I can get a sense of where you’re starting in terms of presentation skills, and B) so we can go through the process of giving feedback on a presentation, since that’s an important part of doing a PhD in the lab (giving and receiving critiques / constructive feedback). It’s OK if you didn’t really generate any real data during the rotation; pretty hard to generate data in such a short rotation, and as I note in point 2 above, it’s not really the goal of the rotation anyway. Instead, what I would be looking more for would be signs of understanding the concepts behind the project and the techniques, and thoughtfulness in organizing the presentation for clarity.
  9. While I suppose I’ll have the final word into who is potentially offered a spot in the lab, I will still be soliciting opinions on rotating students from existing lab members. The idea isn’t that it’s a “popularity contest” in any sense; it’s more, I want to make sure that all full-time personnel that join the lab are able to get along with the people already there, to avoid potentially problematic or toxic situations.

Pseudotyped Virus Entry Assay with Multiplexed Receptor Libraries

Posted on by kmatreyek

May 30th, 2024 update:
Now published in PLoS Pathogens! Find it at this link: https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1012044

Originally posted February 25th 2024:

Spec comparisons

Posted on by kmatreyek

Well, I was going to talk about some of these experiments during lab meeting, but why make a Powerpoint or Google CoLab link people won’t follow when I can write it as a blog post.

Regardless, we’ve recently been looking at how our various possible methods of spectrophotometry compare.

  1. “Amazon Spec” purchased for $235 back in November 2020.
  2. ThermoFisher Nanodrop in a departmental common room (I don’t actually know what kind as I’ve never used it)
  3. BioTek Syngergy plate reader, either… A) with 200uL of bacteria pipetted into a flat-bottom 96-well plate, or B) using their “BioCell”, which is a $290 cuvette that fits onto one of their adapter plates. I mistakenly label this one as “BioCube” in the plots, but they probably should have just named it that in the first place so I don’t feel too bad.

To test the methods, Olivia sampled bacterial optical densities while a batch of e.coli were growing out to make competent cells. Thus, the different densities in the subsequent data will correspond to different timepoints of the same culture growing out. Each time point was measured with all four methods.

Well, all of the methods correlated pretty well, so no method was intrinsically problematic. I’m not sure if the settings for any automated calculation of absorbance values, but the BioCell numbers were just off by an order of magnitude (The BioCell data also had a clear outlier). The Amazon spec and Nanodrop generally gave similar values, although the nanodrop gave slightly higher numbers, comparatively.

The plate reader option was also perfectly fine, although it required more back-end math to convert the absorbance values to actual optical density. This is also not the raw data, as the media only absorbance has to be collected and subtracted to yield the below graph.

Rather than try to figure out the path length and try to calculate the formula, I just used the above dataset to create a calibration for “nanodrop-esque optical density”. (Note: There was a second independently collected set of data I added for this analysis). Here, the goal was to actually use the raw values from the plate reader output, so people could do the conversion calculation on the fly.

Say you have a particular nanodrop-esque A600 of 0.5 in mind. The formula to convert to plate reader units is 0.524 * [nanodrop value] + 0.123, or in this case, 0.385. Checks out with the linear model line shown above.

Or, if you already have raw platereader values and want to convert to nanodrop-esque values, the formula here is 1.79 * [biotekp value] – 0.2 to get the converted value. Here, let’s pretend we have an absorbance value of 0.3, which calculates to a nanodrop-esque value of 0.338. So perhaps that’s a decent raw plate reader value to get with nearly grown bacterial cultures during the chemically competent cell generation process.

Lastly, it’s worth noting how surprisingly large dynamic range there seems to be for spec readings of bacterial cultures. It’s likely largely because we’re used to handling either mid-to-late log phase growth or saturated / stationary cultures, but we’re used to dealing with values in the 0.2 to 1.2 range, although the log-scale plots above suggest that we can be detecting cultures reasonably well within the 0.01 to 0.1 range as well.