Submitted DNA amounts and reads returned

Posted on by kmatreyek

In this previous post, I showed how many reads we’ve gotten from our Plasmidsaurus and AMP-EZ submissions. Well, now’s also time to see whether the amount of DNA that we gave correlated with the number of reads we got back.

Submissions to Plasmidsaurus. Red vertical line denotes the minimum value asked for submission (>= 10uL at 30 ng/uL). Blue line is a linear model based on the datapoints.

As you can see above, since this is miniprepped DNA, it’s usually quite easy to reach the 300 ng needed for submission. One time, when we submitted closer to 200ng, it worked perfectly fine. One other time, when we submitted ~ 100ng, it did not, albeit this was not plasmid DNA and instead was a PCR product, so it’s an outlier for that reason as well.

Submissions to Genewiz / Azenta AMP-EZ. Red vertical line is the minimum amount of DNA asked for, while the horizontal red line is the number of reads they “guarantee” returned. Blue line is a linear model based on the data.

This is the more important graph though, since all of our AMP-EZ submissions are from gel extracted PCR amplifications, and it can be quite difficult to do it in such a way that we have the 500 ng of total qubitted DNA available for submission. Well, turns out that it’s probably not all that important for us to hit 500 ng of DNA, since it’s worked perfectly fine in our attempts between 200 and 500 ng. I imagine people in my lab will simultaneously be happy (knowing they don’t have to hit 500 ng) and sad (knowing they had spent a bunch of extra effort in the past unnecessarily trying to reach that number) seeing the above data, but hey, it’s good to finally know this and better late than never!

Workday accounting

Posted on by kmatreyek

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

Posted on by kmatreyek

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.

Click here

Net University Funds

Posted on by kmatreyek

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.

11/9/23 update: Well, so the trend did continue until I reached dead even, although I’ve since started spending on my discretionary accounts because I’m tired of it just sitting there and losing value due to inflation. Now my short-term goal is to keep finding good ways to spend my discretionary funds to help the research projects in the lab while I get more space (been waiting for years…) so I can potentially hire more people and buy more equipment with remaining funds later…

TC cell numbers

Posted on by kmatreyek

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

Posted on by kmatreyek

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

Posted on by kmatreyek

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. (2024 update; now the rates are 28% and 34%, respectively)
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%).

COVID cases at CWRU

Posted on by kmatreyek

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 (https://case.edu/covid19/health-safety/testing/covid-19-testing-vaccination-and-case-data). 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

Posted on by kmatreyek

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

Posted on by kmatreyek

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.