Workday accounting

Rather facetiously got a suggestion to keep track of how my workdays are spent, but that did prompt me to start keeping track since I have gotten into the phase of my job where I’m feeling somewhat burdened by non-research responsibilities and I like having data in hand. As I’ve noted on my other website, my workdays are now largely constrained by daycare hours. Thus, I do have pretty limited hours in a day to get everything done, requiring a fair amount prioritization; doing one things often means not doing something else.

I’ll sporadically hit “run” on my analysis script and the below plot will update. The n values are currently pretty small, but I plan to keep doing this indefinitely.

Keys for the above plot:
Red dashes are mean values across all days. Gray dots are values for individual days.
Research_internal” denotes activities that directly impact my research group (eg. meetings with personnel, data analysis, benchwork).
Research_external” denotes research activities that don’t have to do with my group (eg. Science-centric meetings with other faculty, emails to people requesting reagents).
Administrative_internal” denotes general paperwork (eg. Filling out my annual performance reviews)
Seminar_director” denotes work related to running the immunology portion of the Dept seminar series (eg. More emails…)
Postdoc_affairs” denotes work related to trying to manage postdoc affairs for the dept (and in some ways, by extension, the SOM).
Other_service” denotes other service activities for the school (eg. Corresponding with CWRU undergrads not in my group).

But I can break down some of these activities further. For example, for the the “Research_internal” section where I’m handling things directly related to my research lab, it can be further broken down as follows:

Most of the categories here are self explanatory. “DNA_construct_stuff” is planning out primers or checking plasmid associated sequencing reads. “Labwork” is mostly tissue culture, since I think that’s where my direct efforts are most valuable (in contrast to using a DNA extraction kit, for example). “Literature” is either doing literature searches or reading papers.

And, well, since so much time seems to be spent writing emails nowadays, this how much time I spend writing emails each day (note: I do all internal communication with lab members via Slack, so this is mostly administrative matters):

Codon cheat sheet

Like many people, I have an amino acid / codon cheat sheet posted around my desk that I can look at whenever I need to quickly design a missense mutation into a construct, or get the sense of the relative size differences between two different amino acid side chains. Well, I recently scribbled on the one I had hanging on my desk from when I started, so I had to replace it. But, I took a little time to customize it with information I would find the most useful (eg. reminding me which amino acids were encoded by 6 codons instead of the usual 2 or 4, which is the most frequent codon per amino acid that isn’t ridiculously GC rich). It’s meant for double-sided printing.

Net University Funds

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:

So in short, at least according to this metric, I had (very understandably) started out in the red as I used startup funds to get things going before I got government grants to fund my work, but as of the last month or so I’ve crossed over into the black, where I’ve brought the university more money through indirect cost recovery than they’ve spent paying for things out of my startup account. Presumably this trend will continue, where I’ll be very slowly spending money out of my startup / discretionary funds and having that really get offset by the amount I’m spending through extramural research funds. Interesting.

Synthetic uORF construct

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.

TC cell numbers

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.

Common Plasmidsaurus Errors

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:

  1. IRES – deletions near the 5′ end
  2. mCherry – W63R or Q114R
  3. mScarlet – errors at R71 (sometimes R71G) and S113/L114 (including L114P).
  4. mKG – L96P
  5. Puromycin resistance gene PAC – R18G or L125P
  6. shBleR – Q56R
  7. 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.

Since almost all of my plasmids have this IRES sequence in it, I almost always run across this error (although it’s usually a 1nt miscall rather than 2nt like this example).
This Puromycin R18G error is annoying b/c it looks like it could be really problematic.
I don’t use mScarlet-I all that often, but when I do, Plasmidsaurus sometimes gives me this L114P erroneous call.
Here it gets screwed up in the same area but mysteriously called an A insertion, making it an S113fs.
It also has issues with mScarlet-I R71. Sometimes it calls it a silent mutation, but other times it calls it as R71G.
An insertion (which, if true, would make a frameshift) in the NPGP motif toward the 3’end of P2A.
Puro L125P
mCherry W63R
mCherry Q114R
mKG L96P
shBleR Q56R.

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 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.