Spec comparisons

Well, I was going to talk about some of these experiments during lab meeting, but why make a Powerpoint or Google CoLab link people won’t follow when I can write it as a blog post.

Regardless, we’ve recently been looking at how our various possible methods of spectrophotometry compare.

  1. Amazon Spec” purchased for $235 back in November 2020.
  2. ThermoFisher Nanodrop in a departmental common room (I don’t actually know what kind as I’ve never used it)
  3. BioTek Syngergy plate reader, either… A) with 200uL of bacteria pipetted into a flat-bottom 96-well plate, or B) using their “BioCell”, which is a $290 cuvette that fits onto one of their adapter plates. I mistakenly label this one as “BioCube” in the plots, but they probably should have just named it that in the first place so I don’t feel too bad.

To test the methods, Olivia sampled bacterial optical densities while a batch of e.coli were growing out to make competent cells. Thus, the different densities in the subsequent data will correspond to different timepoints of the same culture growing out. Each time point was measured with all four methods.

Well, all of the methods correlated pretty well, so no method was intrinsically problematic. I’m not sure if the settings for any automated calculation of absorbance values, but the BioCell numbers were just off by an order of magnitude (The BioCell data also had a clear outlier). The Amazon spec and Nanodrop generally gave similar values, although the nanodrop gave slightly higher numbers, comparatively.

The plate reader option was also perfectly fine, although it required more back-end math to convert the absorbance values to actual optical density. This is also not the raw data, as the media only absorbance has to be collected and subtracted to yield the below graph.

Rather than try to figure out the path length and try to calculate the formula, I just used the above dataset to create a calibration for “nanodrop-esque optical density”. (Note: There was a second independently collected set of data I added for this analysis). Here, the goal was to actually use the raw values from the plate reader output, so people could do the conversion calculation on the fly.

Say you have a particular nanodrop-esque A600 of 0.5 in mind. The formula to convert to plate reader units is 0.524 * [nanodrop value] + 0.123, or in this case, 0.385. Checks out with the linear model line shown above.

Or, if you already have raw platereader values and want to convert to nanodrop-esque values, the formula here is 1.79 * [biotekp value] – 0.2 to get the converted value. Here, let’s pretend we have an absorbance value of 0.3, which calculates to a nanodrop-esque value of 0.338. So perhaps that’s a decent raw plate reader value to get with nearly grown bacterial cultures during the chemically competent cell generation process.

Lastly, it’s worth noting how surprisingly large dynamic range there seems to be for spec readings of bacterial cultures. It’s likely largely because we’re used to handling either mid-to-late log phase growth or saturated / stationary cultures, but we’re used to dealing with values in the 0.2 to 1.2 range, although the log-scale plots above suggest that we can be detecting cultures reasonably well within the 0.01 to 0.1 range as well.

NSF? More like Not So Fast.

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.

Open Research Assistant Position

Our lab space issues were recently resolved, so I’m now able to spend some of my remaining start-up funds to hire more personnel. I also have a bunch of starts to various research directions, and nobody aside from me to push parts of them forward in various aspects (eg. tissue culture, data analysis, experimental planning). I’m curious to see if I can find a relatively recent college graduate that is interested in pursuing a PhD program, but wants to take a year or two beforehand to gain more hands-on research experience. Well, if so, here’s an open position on the CWRU hiring website that can be applied to (if you’re like me and already a CWRU employee, you may need to open that link in an incognito window since existing cookies can get in the way otherwise).

HEK cell small molecule toxicities

I’ve now done a *bunch* of kill curves with HEK cells in various forms (WT HEK cells, single or double landing pad cells). Here’s a compendium of observed toxicity of serial dilutions of various small molecules in HEK cells not engineered to be resistant in any way. (This is mostly for my own reference, when I’m in the TC room and need to check on some optimal concentrations).

Recombinastics paper in ACS Synth Biol

Our new paper, describing double landing pad cells and some other nifty tricks or assay configurations you can do with having orthogonal Bxb1 recombinase sites, is now published in ACS Synthetic Biology.

Old Lab Photos

As the lab finishes its 4th year of existence, I’m realizing that the older lab photos are going to eventually overwhelm the “personnel” page, but I still want to save them for posterity. Thus, I’ll start only keeping the most recent lab photos on the personnel page, while transferring older photos to this blog post.

Here’s the history of lab photos for the Matreyek and Bruchez labs, from (almost the) most recent to oldest:

September 2022: Matreyek and Bruchez lab picnic! From left to right: Sarah, Michelle, Nisha, Kenny, Alex, John*, Avery, Anna, Lane*, Nidhi, Olivia, Vidusha*. *[rotation student]
June 2021: Celebrating Drew’s last day, and Vini starting grad school at CWRU.
^ Geeks who (mostly do not) drink at Boss Dog Brewery, July 13, 2021.
“The Cloneheads” got 10th place out of 20 teams; a full 10 places higher than we expected to be!

Landing pad kill curves

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.

A personal clingen vignette

Back in 2017, when I was a postdoc and still first learning the human genetics field, I encountered a clinically relevant situation in my own family’s genetics. In short, my bloodline seemed to harbor a quite rare recessive allele in a gene encoding the “battery pack” for a set of critical hormone (and other small molecule) modifying / metabolizing enzymes. When two dysfunctional copies are harbored in the same individual, there are devastating consequences on the developing child.

Figuring this out ended up being an interesting academic exercise, since it was a case where I could synthesize a lot of the knowledge I had built up, from more recently acquired knowledge in human genetics, clinical genetics, genomic technologies, and genomic databases, to somewhat older knowledge I had on literature searches, molecular biology, cell biology, and protein structure-function. Here’s a report I had provided a family member upon embarking on my quest to understand the situation. Aside from my family, I had directly shared it with various friends / colleagues / trainees over the years, but a recent conversation with people in the lab made me realize that I may as well share it more broadly at this point (any lingering minor concerns about publicly sharing “private” health-related information – for both me and at least some of my family members – is offset by what I think is a nice case-study of the importance of science and genetics on understanding human disease.

Note: In the process of having Avery, we did get Anna tested, and she was not a carrier for any variants in POR thus making Avery’s risk of ABS essentially zero (rather than the number I had calculated at the end of the above document). That said, I remember that getting Anna tested was somewhat of a struggle, between it not being fully covered under insurance and there being some miscommunication between our hospital system and the external genetic testing lab, such that the test results were pretty delayed. Such is the US health care system…

Quantitating DNA via the plate reader

As another installment of the “demoing the plate reader” series, we’re also trying to see how well it quantitates DNA. Probably good that I’m actively involved in this process, as it’s making me learn some details about some of the existing and potential assays regularly used by the lab. Well, today, we’re going to look a bit at DNA binding dyes for quantitating DNA.

We have a qubit in the lab, which is like a super-simplified plate reader, in that it only reads a single sample at a time, it only does two fluorescent colors, and the interface is made very streamlined / foolproof, but in that way loses a bunch of customizability. But anyway, we started there, running a serial dilution of a DNA of known high concentration and running it on the qubit (200uL recommended volume). We then took that same sample and ran it on the plate reader. This is what the data looked like:

So pretty decent linearity of fluorescence between 1ug to 1ng. Both the qubit and plate reader gave very comparable results. Qubit readings missing on the upper range of things for the qubit since it said it was over the threshold upper limit of the assay (and thus would not return a value). This is reasonable, as you can see by the plate reader values that it is above the linear response of the assay.

So what if you instead run the same sample as 2uL on a microvolume plate instead of 200uL in a standard 96-well plate? Well, you get the above result in purple. The data is a bit more variable, which probably makes sense with the 100-fold difference in volume used. Also seems the sensitivity of the assay decreased some, in that the results became unreliable around 10ng instead of the 1ng for the full volume in plate format, although again, I think that makes sense with there being less sample to take up and give out light.

7/3/23 update:

Just tried AccuGreen from Biotium. Worked like a charm. They suggest mixing in ~200uL of DNA dye reagent (in this case, to 1uL of DNA already pipetted in), but I tried 100uL and 50uL as well, and if anything, 100uL arguably even looked the best.

Also, I just used the same plate as Olivia had run the samples on Friday. So in running these new samples, I ended up re-measuring those same samples 3 days later. And, well, it looked quite similar. So the samples for reading are quite stable, even after sitting on the bench for 3 days.

Oh, and lastly, this is what it looked like doing absorbance on 2uL samples on the Take3 plate. Looks perfectly fine, although as expected, sensitivity isn’t nearly as much as Accugreen. That said, you do get more linearity at really high concentrations (4000 ng/uL), so that’s kind of nice.