Both Nidhi and Nisha receive travel awards to offset costs of attending and presenting at the Discover BMB 2023 in March in Seattle! Good job you two, and thanks ASBMB for providing these travel award opportunities to trainees!
Kind of an interesting observation, so I figured I’d post it.
Now that I’ve been here a little while and have gotten some research grants funded, I was curious how my research group was faring from the University’s finances perspective. Now of course this is going to be a gross oversimplification, but I figured a simple metric was taking the amount of money the University has gotten as indirects from my research grants (61% indirects rate; you can figure this out through a simple Google search), and subtract that by the amount of startup funds I’ve spent so far (note: this isn’t all the startup funds I have available; just what I’ve spent so far and is thus officially “gone”). Well, here’s what that looks like over the last two years:
My first pass at trying to analyze this data was incorrect, since my budget reports only list the total money spent (which includes directs and indirects), so I had to go back and extrapolate the direct and indirect funds out of that number for each budget number (with there being three classes: startup accounts with no indirects, the K award with 8% indirects, and the R awards with 61% indirects). So in actuality, I have not yet accrued more indirects than startup funds expended, although I do know that for the last 6 months I’ve been funded 100% off my NIH grants (I haven’t been spending my startup at all), so presumably the trend will keep trending toward the positive.
So I’ve long wanted finer control of protein abundance, and to date, have had the greatest success messing with the Kozak sequence to alter translation rate, thus modulating the amount of protein steady-state abundance. That said, I’ve wondered if there are other aspects that could be further manipulated to increase the dynamic range of the amount of protein steady-state abundance. This at some point led me to try playing around with upstream open reading frames (uORFs), that can interfere with the translation rate of the downstream protein (in my case, a green fluorescent reporter). We recently made a vector with one such uORF, so I looked at what effect having that uORF had on green fluorescence of the cells.
The actual vector plasmid name is “AttB_2xuORF_mGreenLantern-T2A-shBle-IRES-mCherry-P2A-PuroR”. As you can tell, red fluorescence is behind an IRES, and should be unaffected by the uORF. That’s indeed what we see, with the red distribution being a control construct without the uORF, and the blue distribution being the identical construct with a uORF immediately preceding mGreenLantern (Note: YL2-A is the fluorescence channel for red fluorescent emission).
Now if I gate on that bright red population, and look at the amount of green fluorscence, there is indeed a difference (BL1-A is the channel to look at for green fluorscence), although the effect isn’t huge. Looking at the distribution geometric means, it looks like the uORF construct is roughly 3.23-fold less bright than the control, so roughly a half-log less bright. Now the reason I’m underwhelmed is that I can get a roughly 1.5-fold difference in fluorescence using different Kozak sequences, so the uORF that I created doesn’t exhibit nearly the same magnitude of effect. Eh, that’s designing and testing constructs for you. That said, I suppose if I were to combine both, then I could likely get > 2 logs of dynamic range.
PS. Yes, I know there are simpler ways to modulate protein abundance, like transcriptionally through modulating the amount of expression. One day we’ll do more of that, but for now, translational control it’s been.
Every time I count cells, I not only write down the cell density (likely most relevant for the transfection i’m about to do), but I also write down the total volume of cells and the vessel the cells came from. Thus, I’ve essentially figured out how many total cells there were in the plate I was trypsinizing. I’ve mostly done this for T75 flasks, but I also have a handful of counts from 10cm plates as well:
So, in short, T75 flasks more or less max out around 20 million cells (hence the peak there, but the “confluent” flask), although I’ve gotten some larger counts before (maybe really packed in there, or maybe the result of counting error). 10cm, despite being slightly lower in surface area, has comparable counts, but that’s likely b/c I’ve tended to have consistently higher cell densities in there, since I’m usually doing end-point experiments in those and doing more routine passaging in T75s.
OK, so we all know that Plasmidsaurus nanopore sequencing isn’t perfect. Every time I see the mistake at the 5′ end of the IRES sequence I know to ignore it, but there are bunch of other ones that I still repeatedly run into (but not quite as frequently) such that I don’t have it memorized and am not sure if I should be ignoring it right off the bat. Thus, I’m going to keep a list of repeated erroneous calls here on this page so I’m reminded to ignore them in the future.
Visual evidence of individual example listed below. But here’s a summary of Plasmidsaurus errors to ignore:
- IRES – deletions near the 5′ end
- mCherry – W63R or Q114R
- mScarlet – errors at R71 (sometimes R71G) and S113/L114 (including L114P).
- mKG – L96P
- Puromycin resistance gene PAC – R18G or L125P
- shBleR – Q56R
- Silent or frameshift mutations at the NPGP motif at the 3’end of the P2A sequence
In fact, be very suspicious of any unexpected L -> P mutant through Plasmidsaurus seq. And maybe Q -> R muts too.
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!
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!
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 in that room 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.
When ready to start, I find this to be easiest order of events.
1. Go up to the podium. On Zoom, turn the speaker laptop off mute. Go to more > “Hide floating meeting controls”.
2. Turn on the microphone for the seminar room so people in the back can hear.
3. Go to the control panel on the stage and set the lighting to 4, which will dim the lights in the room.
4. Start talking!
Finishing up: Probably makes sense to go back to Lighting preset #2 during Q&A, so people can see each other talking easier.
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:
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…?
5/22 edit: We recently tested StayGold, and the results were rather underwhelming. In our n=1 experiment, it yielded a 6% increase over mGreenLantern in green MFI within stably expressing landing pad cells. If it were on the EGFP-normalized scale of the chart / experiment above, it would be a value of about 2.15. So ya, not that it’s not potentially better than mGreenLantern; it’s more that, not sure if it’s worth the effort for most applications.
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%).