So many of my experimental readouts in my scientific career have been fluorescence-based (and for good reason), but especially as we keep doing pseudotyped virus infection assays, it’s becoming really prohibitive to continue reading out infection by flow cytometry (b/c of cost, availability of the instruments, etc), so we’ve recently shifted over to luminescence. As part of this, I wanted to create a recombination vector construct that encodes firefly luciferase, so we can use it as a control when needed. (I also just remembered the other reason we made this plasmid, and it was also to have a luminescence version of testing recombination efficiencies).

Anyway, I recently recombined and selected cells with two independently generated constructs (clones G1402C and G1402D), and determined how many recombined (fLuc-expressing) cells need to be in the well to be detectable. Here’s the resulting plot.

The datapoints on the y-axis/ left edge of the plot are media only, where there is no luciferase enzyme (thus helping to establish the background of the assay). You can tell based on the plot that we start getting detectable values around 10 cells per well, and we’re clearly in the linear range by ~ 100 cells per well. Above that value, it’s clearly in the linear range at least through 250,000 fLuc expressing cells (it’ll be nice to see if we can ever max out the linear range, but that’s probably best done with high MOI pseudotyped virus transductions). The variability between G1402C and G1402D may be error in cell counts, since we know there’s possible (slight) error in those estimates.

Perhaps one day we’ll also start playing around with renilla luciferase and Nanoluc, but for now, we’ll keep playing around with fLuc only just to keep things simple. But ya, now with this plasmid in hand, we can start more comprehensively testing recombination protocols for efficiency without having to book a ton of time at the flow cytometer…

I’ve been thinking about budgets, partially b/c I just went through a protracted experience of getting the school to give me access to the remaining part of my startup (from almost 5 years ago! And while I was repeatedly told the remaining amount was non-expiring, it was just given to me in an account that expires 5 years from now…). Regardless, the goal is to spend down these remaining institutional funds while bolstering my research group, largely through personnel additions and management.

For that reason, I created a simulation of how personnel salary + other operational costs would exhaust my current funding portfolio (currently one R21 that ends in a year, an R35 that ends in 2 years, and the aforementioned remaining startup account). The black line in the plot below shows this happening, with the line terminating around the 3 year mark. This is presumably around when I would expect to run out of money, if zero future action were taken.

Now, keep in mind that there are some MAJOR assumptions going on here:
1. This simulation assumes that I DO NOT receive another grant in the next 5 years. Of course that will not be the case.
2. Aside from Nisha’s intended graduation date, the rest of the end dates are very roughly estimated. In the case of research staff, this is assumed to be indefinite for two of the individuals. Of course, if money ends up getting tight, A) my salary will end up going down some, which will help alleviate costs, and B) I’ll let go of research staff as necessary, and well before there are impacts on students (to which there is a larger commitment).
3. Everything is modeled as a daily recurring expenditure. In real life, I think everything behaves more like discrete sums that are added into the account yearly (like NIH budgets) or monthly / bimonthly (like salaries).

Note: It goes into the negative values since it’s allowing the startup not spent by the end of R35 in two years to subsequently exhaust (that’s when the line ends).

Now, I’m actively trying to recruit more personnel to the lab, at this point largely in the form of PhD students. That’s what the additional colors on the plot are. A singular PhD student would be the orange line, and two new PhD students would be the red line.

Especially in light of the fact that I *have* to spend that startup (or it presumably goes *poof* into the administrative ether), looks like I’m going to be good for at least a couple of years in the worst-case scenario. Regardless, this really does help me frame what I need to be doing at a given time. If financial situations were dire or particularly worrisome, I would be focusing on writing grant applications right now. Based on the above plot, I think it makes a lot more sense for me to devote focus on publishing existing projects and further developing preliminary evidence to increase the success rates of grant applications I could just as well submit in a year.

1/25/25 Update:

Well, with the future now a bit clearer in terms of personnel (and a recent grant award), this is what the current projection looks like:

Not terrible, but almost any projection can still be anxiety-provoking in the context of the current administration (eg. destabilization of the NIH).

2/8/25 Update:

That previous analysis did the simulation from scratch, using total award amounts and monthly personnel and supply costs to perform the calculation. I realized at some point that a more directly relevant analysis would simply use my existing unspent funds and would just see when that is used up (based on the same above monthly personnel and supply costs). Anyway, this is what that plot looks like.

Well, this gives me almost the exact same answer. That gives me some confidence on the accuracy of the estimate, at least.

When I was an early PhD student, I barely knew about the NSF GRFP, and it wouldn’t matter if I did since I felt too overwhelmed / behind on everything to consider applying. Fast forward to my postdoc, where I was in a lab that expected you to write for postdoc fellowships (it’s a good policy, btw), in a department where half the grad students seemed to *win* an NSF GRFP any given year; this experience made me realize just how useful of a training opportunity writing fellowship applications can be, as well as helping me to realize that there can be a bit of a feed-forward effect for success, where being awarded a competitive fellowship early on opens up a small number of additional possibilities, which can compound over time until the final result ends up being a large effect.

Well, simply bringing this ethos with me when starting my own lab seemingly isn’t enough; there are apparently entire institutional administrative systems that can get in the way. I’ve already talked about a previous experience, where the school grants offices wouldn’t allow a student’s application to be sent since they weren’t in the internal grants management system yet, despite the fact that NSF GRFP applications are supposed to be sent in by the student. At least that application got saved at the 11th hour, and was actually successfully submitted and reviewed, even though it went unfunded.

This year, we had two students put together well-prepared, complete applications, only to be disqualified on the same technicality. Turns out, sometime within the last two years, the school started putting a summer class on the transcripts of incoming biomedical PhD students; in short, instead of the first entry of their transcript being the fall quarter, there is a summer quarter listed on the transcript preceding it. In actuality, the only thing that is happening during this time is rotations, which typically start in July or August here. There may be a handful of workshop type things, but nothing that counts as an academic course that is a requisite for eventually graduating. It’s effectively the same situation as most other PhD programs in terms of timing of official instruction.

Well, in the case of 2nd year grad students, NSF considers that summer transcript entry as indication that they have completed 1 year of graduate coursework prior to submission, disqualifying them. Each of the two students got an email from NSF saying so, and each student tried to get a statement from the university administration clarifying the situation. In neither case was the school willing to deviate from whatever legal wording they already had, so the disqualification appeal filed by the student was denied by NSF.

So two applications (and probably more) where, in retrospect, the student was writing the application documents, as well as corralling letter writers, with a 0% chance of success. You’d think the school would want to change their transcript policies to not unnecessarily disqualify their biomedical PhD students from the NSF GRFP, but there was seemingly little indications that the contacted administrators will do anything about it. So essentially, no CWRU School of Medicine PhD student in their second year should submit an NSF GRFP application, unless something changes. Maybe (hopefully), this isn’t the case with other CWRU schools, like those overseeing biology or bioengineering.

Well, the biomedical PhD students can still apply in their first year, right? I suppose so, although if they’re new to the campus, they probably aren’t writing their application based on a 4 to 6 week rotation (the expected rotation duration in our school). Only those that were in the know and had strong, supportive research environments prior to grad school would be in the running, and I suspect that isn’t a large fraction of our incoming student populations.

So a school that simultaneously bemoans a lack of student fellowships, yet through lack of experience and rigid / misguided administration, manifests the same situations it bemoans, with little indication that this will ever change even when notified of the problem. Institutions, man.

I previously did some recombined cell enrichment curves using flow cytometry of mixed cells populations (containing both recombined and unrecombined cells) upon treatment with various selections (Blast, Puro, Hygro, Zeo), but my recent experience testing out the various plate readers got me in the mode of doing serial dilutions with various cell viability / (relative) cell counting assays, so I did that for our mostly commonly used Single Landing Pad HEK cells (LLP-Int-BFP-IRES-iCasp9-Blast) as well as our still unpublished Double Landing Pad HEK cells. Here are what those curves generally look like.

Note: The Double Landing Pad cells encode hygromycin resistance within the second landing pad, so the difference in hygromycin IC90’s in the SLP and DLP cells is expected. Once we have the plate reader purchased and back in the lab, I’ll probably do some other conditions (eg. unmodified 293Ts, recombined SLP cells, recombined DLP cells) for some of those common cell culture antibiotics.

Note 2: Also, now that I know the general effective concentrations of some of these chemotherapeutics (eg. cyclophosphamide, gemcitibine, vincristine; although I did completely overshoot the concentrations for gemcitibine), I’m somewhat set up to see how the IC90 changes when I overexpress various transgenes that should increase resistance to those drugs.

For my own curiosity (as well as for some data in case I want to show to granting agencies that I’m not hoarding the research tools / materials that I make), I keep track of the papers that have used the landing pad cells / materials in their work. Here’s a chart of publications using some version of the LP cells over time.

The first few papers were mine, but from there, there have been roughly 10 or more papers per year. It’s seemingly been a little higher in the most recent couple of years, although the 2023 date has a dotted line projection for the end of year number (in the instance of my originally writing this paragraph, it was early June).