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:

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.

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.

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.

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.

COVID cases at CWRU

I’ve been keeping track of what the COVID situation has been like at Case since they first started posting the data every week, back in the fall of 2020 ( Whenever the cases seem to be higher than usual, I’ve been messaging the below graph out to my group, so they can be informed and make the best risk assessments about their activities on campus.

Anyway, figured other people may be interested in this information too, and I’m getting kind of tired of sending the same exact message out like the last four weeks, so I figured I’d just post the plot here so people can see the current stats.

As of writing this (first week of May), cases have been the highest they’ve ever been, although at least almost everyone should be vaccinated and perhaps even boosted. Still, would certainly be nice to see that number come down some…

Where lab funds go

As you can tell from the above graph, the people in the lab (including me) are by far its most costly resource, accounting for the majority of all lab expenditures. Thus, while there are other important reasons, there’s always this very “bottom line” reason for me wanting to minimize how much personnel time and effort is wasted by confusion and mismanaging!

Some Expected Yields

Here is some real-world data describing expected yields we may expect from some of these routine lab procedures or services.

Obviously the above plot is about how much total plasmid DNA we get from the miniprep kit we use in the lab.
The plot above show the expected total yields of DNA based on the extraction type / method
And this is the pretty wide range of reads we’ve gotten from submitting plasmids to plasmidsaurus
The above graph shows how many (raw) reads we’ve gotten from Azenta / Genewiz Amplicon-EZ.

Oh, and this is a good one:

How well my determination of flask “confluency” actually correlated with cell counts. I mean, sure, there must be some error being imparted by the actual measurement of the cells when counting, but I think we all know it’s mostly that my estimate really isn’t precisely informative.

Identity matrix of indices used in the lab

We’ll be doing a lot of multiplex amplicon-based Illumina sequencing, which means we’ll eventually have a lot of different indices (I think some people refer to these as barcodes) used to multiplex the samples. I’m doing everything as 10 nt indices, so theoretically there is 4^10 or slightly over one million unique nucleotide combinations that could be made with an index of that length. I don’t intend of having anywhere close to 1 million different primers, so I think we’re pretty safe.

That said, I’d like to ensure our indices are of sufficient distance away from each other such that erroneous reads don’t result in switching of one index for another. Anh has come up with a way that we can make sure our randomly generated indices don’t overlap with previous indices, but still useful for me to keep track and make sure things are running smoothly. Thus, I generated an identity matrix of all of the indices we have in the lab right now.

In a sense, the diagonal is a perfect match, and serves as a good positive control for the ability to see what close matches look like. By eye, the closest matches between any two unique indices seem to be 70% identity, which I can live with.

Vacuum Concentration

I hate the high cost of research lab materials / equipment, especially when the underlying principles are pretty simple and mundane. For example, I’ve used blue LEDs and light-filtering sunglasses to visualize DNA with SYBR Safe. And I’ve used a mirrorless digital camera paired with a Python script to visualize Western blots.

Well, this time around I was thinking about vacuum concentration. Many of the lab-spaces I’ve been around have had speed-vacs accessible, though I’ve never really used them since I don’t ever really need to lyophilize or concentrate aqueous materials. Though the other day, we had some DNA that was 1.5 to 2-fold less concentrated then we needed for submission to a company, and I was reluctant to ethanol precipitate or column-concentrate the sample at the risk of losing some of the total yield. Thus, became curious about taking advantage of vacuum concentration.

So the lab already has built-in vacuum lines, so I just needed a vessel to serve as a vacuum chamber. I bought this 2-quart chamber from Amazon for $40, and started seeing what rates of evaporation I see if I leave 200uL of ddH2O in an open 1.5mL tube out on the bench, or if I instead leave it in the vacuum chamber.

The measurements of vacuums are either in “inches of mercury”, starting at 0″ Hg, which is atmospheric pressure, to 29.92″ Hg, which is a perfect vacuum (so no air left). As you can see, the built in vacuum lines at work top out at ~ 21″ Hg, so somewhat devoid of air, yes, but far from a perfect vacuum. I even did a test where I put in a beeping lab timer into it, and while the vacuum chamber did make it a lot quieter, it was far from completely silent, like the vacuum chamber exhibit at the Great Lakes Science Center achieves (here’s the Peeps version). But what does it do for vacuum concentrating liquid? Here’s a graph of the results, when performed at room temperature.

So the same sample in the vacuum is clearly evaporating much faster. I can make a linear model of the relationship between time and amount of sample lost (which is the line in the above plot), and it looks like the water is evaporating at about 1% (or 2 uL) per hour in atmospheric conditions (oh the bench), while it’s evaporating at about 2% (or 4 uL) per hour in the vacuum chamber. Thus, leaving the liquid in the vacuum chamber for 24 hours resulted in half the volume, or presumably, a 2-fold concentration of the original sample.

Clearly, this is not a speedvac. If I understand it correctly, speedvacs also increase temperature to speed up the evaporation process. I could presumably recreate that by putting a heating block under the vacuum chamber, but I haven’t gotten around to trying that yet. There also is no centrifuge. While I could probably modify and fit one of my Lego minicentrifuges inside, the speed of evaporation at room temp has been slow enough that everything has stayed on the bottom of the tube anyway, so it’s not really a worry so far. At some point, I’ll also perform a number of comparison at 4*C as well (since the vacuum chamber is so small, I can just put it in my double-deli lab fridge), which may make more sense for slowly concentrating more sensitive samples.

Overall, for a $40 strategy to achieve faster evaporation, this doesn’t seem too bad. In the future, if we need to concentrate a DNA sample 2-fold or so, maybe it’s worth just leaving it in the vacuum chamber overnight. Furthermore, the control sample is kind of interesting to consider, as it’s now defined how fast samples left uncapped on the bench may evaporate (I suppose I’ll try this with capped samples at some point as well, which will presumably evaporate a little bit slower). Same thing with samples kept in the fridge, which are also evaporating at a slow but definable rate. After all, “everything is quantifiable“.

1/25/2023 Update: In explaining this as a potential option, I used the word “slow-vac” which is good name for this. Time to trademark it! Though other people were onto this name a while back so maybe they did (obviously they didn’t).