Cell surface FP brightness

When doing the Green FPs in HEK 293T cells experiment, we noticed how the same fluorescent protein, EGFP, could have vastly different brightness depending on the construct we were using.

Put another way, cytoplasmic EGFP gave us really high green fluorescence intensity, but a different construct wherein that same EGFP sequence was preceded by a signal sequence (thus causing it to become a secreted / extracellular protein), and succeeded by a transmembrane domain (thus causing the extracellular EGFP molecule to be anchored to the plasma membrane) gave us cells that were roughly 30 to 100-fold less bright. The amount of fluorescence of the transmembrane version was further susceptible to other sequence considerations; for example, addition of a his tag right after the signal sequence (such that the his tag is the most distal sequence on the protein, with the tag flapping around as part of a flexible N-terminus), resulted in a ~ 3-fold reduction in fluorescence as compared to an untagged version. My guess is that this repetitive, pseudo-charged region was interfering with efficient translation into the rough ER, but who knows.

Still, how do I explain this result? Well, I have no evidence-supported answer, but my guess is that it’s about translation process bandwidth and overall real-estate. I’m guessing that cytoplasmic translation and accumulation has pretty high bandwidth, where there are plenty of ribosomes to translate cytoplasmic protein, and there’s plenty of space to accommodate them. In contrast, I’m assuming there are comparatively fewer ribosomes capable of translating transmembrane proteins at the rough ER, and that the overall real-estate on cell-associated membranes (particularly in the vesicular pathway leading up to and including the plasma membrane) is also less (while an imperfect approximation, I’m thinking of it kind of like a difference in surface area or volume of a sphere, type of thing). Although who knows; maybe that’s all incorrect, and it’s more about the signal sequence (possibly from CD8?) and the transmembrane domaine sequence (seemingly from PDGFR-beta) that I used.

Edit 1: Although again, I need to keep reminding myself. Since these are cell surface proteins on adherent cells, some of the reduced signal with the transmembrane protein may be due to some proportion of proteins getting cleaved off the cells during routine trypsinization. I’ve talked to Olivia about trying a side-by-side experiment of resuspending the cells with trypsinization or Versene with gentle agitation. Stay tuned to see if I need to update the above plot or not!

ASC Speck formation

One of the possible projects is understanding how protein sequence variants impact inflammasome formation. There are multiple possible assays for this, but a classic one is observing the formation of ASC specks, where a nucleation of activated sensor (say, MEFV / Pyrin or NLRP3) nucleates the oligomerization of all of the ASC in the cell into a single gigantic filament.

To try to assess this, we have a construct where ASC is directly fused to mCherry. There is clear speck formation by microscopy, as you can see here.

Clearly there is a bunch of really bright puncta when MEFV and ASC-mCherry is present (right), very few such puncta when no MEFV is there (middle), and no puncta at all when there is no mCherry-tagged ASC (left).

That said, any high-throughput compatible experiment can’t simply rely on microscopy (without a bunch of extra infrastructure), and it’s easier to make it a FACS compatible assay. Thus, the big question was whether we could see a difference in flow. Here are the results:

As you can tell, the diffuse cells (left) have a streak of points below the slope = 1 diagonal,  the ASC only cells (diffuse with a handful of puncta) have a similar streak of points but with a small shadow of points along the slope = 1 diagonal (probably the completely spontaneous ASC speck formation cells; middle), and the MEFV and ASC-mCherry overexpressing cells are almost exclusively puncta forming and also almost exclusively have points running down that slope = 1 diagonal. Thus, it does seem we can distinguish ASC speck formation using flow cytometry.

To make things even easier, one can turn it into a ratiometic density plot, where I’ve divided the red fluorescence height by the red fluorescence area. The differences are relatively subtle, so you have to make sure your axes are zoomed into the right region, but once you do, you can definitely see that there is a different distribution for the ASC speck cells. Cool!

Using the projector in the WRB auditorium

I’m now in charge of running a decent chunk of the departmental seminars. Thus. it’s behooved me to figure out how to handle the A/V as well, since I don’t want seminars ruined by technical problems. So here are my notes for handling them:

Initial lighting: Preset 2 makes sense for people first walking in. That said, if that seems too dark for the stage, then the full “on” position is fine (preset 1 on the elevator-side panel, or just “on” in the main entrance panel).

I’m going to suggest using your own laptop. The computer there is fine (you’ll have to log in using your Case ID and passphrase) but I still think it’s going to go way smoother with your own laptop. I’m going to suggest any time I’m in control, that we use the “MatreyekLab” laptop for this.

Use the touchpad on the right to wake the projector. Then, click on “Laptop” so that it knows to use the external VGA (which, I’m assuming, usually has the HDMI adapter plugged into it). Note: The cord is *very* finnicky. Like, don’t touch the cord at all, or leave it in a position where it may hang a bit. I’ve tried to tighten it as much as possible, and that seems to keep it somewhat resistant to disconnecting, at least for a while).

If you want to have presenter tools, you’ll have to be in “Extended Desktop” mode. For the LCD_VGA projector, make sure it is in 720p mode or else it may look awful. No underscanning necessary. This is all when on a Mac. To get access to those settings, go to “System Preferences” > “Displays”.

The mic electronics should be on by default, but there is a “on / off” switch at the base on the mic. Once that light comes on, you know it’s on. I haven’t been able to find a volume knob for it or anything. Probably makes sense to just turn it away if it is too loud.

What I like to do, is to have my personal laptop log in as my actual Case Zoom account (the one where I’m presumably host or co-host). After the meeting is started / set, using the Meeting ID and Password, I log into Zoom as a “guest” user on the MatreyekLab laptop. Once logged in, don’t forget to rename yourself to be “Speaker” or whoever the actual speaker’s name is, to make it clear which Zoom window is actually the presenter. Then, using the personal laptop with the host account, make the “Speaker” account a co-host (for this session), so they can easily share their screen. I then just share my screen, and use “Desktop 2” as the screen being shared, and it should be more-or-less set.

I have found that while the built in mic can be OK for this, a cheap $30 mic off Amazon may give you better sound for the speaker’s voice, and help pick up on the sound from audience questions as well. Probably makes sense to at least move the Zoom bar on the presenting computer away from the top, since it’s going to obscure the slide titles. Even better if you change the settings in Zoom (on the presenter computer, not on the personal laptop) to get rid of the floating Zoom bar, so that it doesn’t start taking up a bunch of slide space when people start asking questions

I can monitor how things look and sound from my main laptop, although there is a half-second lag between the real-life voice and the captured voice transmitted through Zoom, so it’s only possible to check in on the sound periodically for short amounts of time.

Finishing up: Probably makes sense to go back to Lighting preset #2 during Q&A, so people can see each other talking easier.

Green FPs in HEK293T cells

At one point, I was a doe-eyed postdoc reading about new fluorescent proteins (FPs) with improved brightness and thinking it was potentially important to incorporate new FPs into my constructs for cell culture work. I have since come to realize that the intrinsic gains to fluorescence published in those papers do not necessarily translate to brightness in my experiments. The reason are probably multifactorial, including:
1. Commonly accessible equipment is generally optimized for EGFP (or similar FPs), so newer FPs with slightly different excitation and emission spectra may not be captured well with existing microscopes or flow cytometers.
2. The FP brightnesses are typically assess in-vitro, and there may be other factors in eukaryotic cell cytoplasms that may affect the FP brightness (eg. FP half-life).

Well, I’ve still ordered a handful of different green fluorescent proteins anyway, and figured it was worth doing a side-by-side comparison in the transgenic system we use in the lab. This was all done with the HEK293T G542Ac3 (LLP-Tet-Bxb1attP_Int-BFP_IRES-iCasp9-BlastR) cells, which were stably recombined with a single copy of each fluorescent protein at a common genomic locus. The construct organization was: Bxb1attB_[Green FP]_IRES-mCherry-2A-PuroR. Olivia did these recombinations, selected the cells, and ran the cells on the ThermoFisher Attune Flow cytometer, with Sarah’s help. This is what the results look like:

Some interpretations:
1. Rather minimal (~3-fold) difference between mGreenLantern and UnaG. I suppose if we were ever in a situation where we needed every unit of green brightness possible, that we would go with mGreenLantern. That said, UnaG has some benefits; namely, it’s 58% the size of EGFP/mGreenLantern/mNeonGreen, and it lacks the VERY ANNOYING identical sequences at the N- and C-termini (MVSKG … DELYK) which makes molecular cloning a potential pain.
2. Conceptually, I like the idea of fuGFP, but that 20-fold diminished green fluorescence compared to EGFP is potentially problematic. Who knows, maybe I’ll turn this into a target of directed evolution at some point…?

CWRU financial docs

Gotta say; one of the hardest things to deal with in this job are all of the minutiae that come along with the administrative aspects. The topic of today’s post is me keeping notes of my observations with the CWRU financial docs, since 1) I’m just going to forget them otherwise, and 2) it may help other new PIs here.

Salary & Fringe costs terminology: In the summary section for each grant / speedtype / account, personnel costs are summarized. While people’s names are shown in the itemized costs part, the summary section uses vaguer / more confusing language. Here’s the translation:
1. “Faculty Control” <- PI
2. “Academic Support Staff Control” <- Grad students
3. “Research Personnel Control” <- Postdocs
4. “Student Control” <- Not sure yet, since grad students apparently don’t go here?
5. “Non-Academic Professional Control” <- Research Assistant (RA1 and RA2 for me)

Additional personnel costs:
1. Fringe Benefits: Only applies to the faculty (eg. PI) and staff (eg. RAs). As of July 2022, it is 30% for grant accounts, but 34% from startup. Had no clue that difference existed.
2. Postdoc insurance: Shows up under “Insurance Control”, and appears to be 12.22% of salary as of July 2022.
3. Apparently there are no additional costs for grad students, as far as I can tell.

Encumbrances: Things that have been charged / ordered, but haven’t been fulfilled yet. My lab has a bunch of backordered items on here.

Core service costs:
We routinely use 1) The CWRU flow cytometry core, and 2) The CWRU genomics core. The charges from them are listed as COR####### numbers billing to Journal numbers, both of which change every month, so there’s no static identifier that can be used to distinguish which is which.

Spent and unspent funds: Probably the clearest place to find these values will be the “contr_summ_by_pi” document. Importantly, the “budget”, “TTD expense”, and “balance” columns have values which are the combination of both direct and indirect cost values. But, as a PI trying to run the lab, I think more in terms of direct costs; both for my personnel salaries and lab purchases, but also for the yearly grant budget. Thus, to convert the “direct+indirect cost” values in the pdf into useful values for lab budgeting, you’ll want to multiply the “direct+indirect” number by 0.625 to get the “direct only” value (at least as of 8/11/2022, when the indirect cost rate here is 61%).

PhD Student 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 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, 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. 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 curtail potentially problematic or toxic situations.

Ordering oligos at CWRU

Here’s a price comparison I did back in 2019 (presumably still correct?). But in short, per nt price was cheapest through ThermoFisher.

Thus, we’ve been almost exclusively buying oligos from them, with $7,220 spent (as of June 2022) since our first orders starting December 2019.

Here’s what the histogram of oligo costs have shaped up as.

But, well, don’t order degenerate nucleotides oligos from them as they’ll likely be T biased.

If anyone sees anything better on campus, let me know!

Consistent Plasmidsaurus sequencing miscalls

As I noted in this Twitter exchange, plasmid nanopore sequencing via Plasmidsaurus is great, but not perfect. For example, there seem to be some “achilles heal” sequences, where nanopore reproducibly (like 100% of the time with different plasmid submissions) miscalls certain parts of our plasmids. How do we know they’re miscalls? B/c the Sanger traces of the same exact plasmids show the expected sequence very clearly. Here are two that we commonly see:

A single deleted C nucleotide in the beginning of our IRES sequence:

A phantom T>C base miscall that incorrectly tells us we have a W566R nonsynonymous change in every single one of our human ACE2 constructs.

Both are related to C repeats, but there are plenty of other C repeats in the plasmids we submit and it’s ALWAYS these sequences that give Plasmidsaurus problems. Once I figured this one, it’s really NBD, since I know to ignore these changes, although it did inform our current molecular biology workflow in the lab of 1) Screen colony minipreps via Sanger -> 2) Sequence candidate good constructs with Plasmidsaurus / nanopore -> 3) Sanger to resolve unexpected discrepancies between the expected / intended and Plasmidsaurus sequences.