ASC Speck formation

Posted on by kmatreyek

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

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.

Green FPs in HEK293T cells

Posted on by kmatreyek

At one point, I was a doe-eyed postdoc reading about new fluorescent proteins (FPs) with improved brightness and thinking it was potentially important to incorporate new FPs into my constructs for cell culture work. I have since come to realize that the intrinsic gains to fluorescence published in those papers do not necessarily translate to brightness in my experiments. The reason are probably multifactorial, including:
1. Commonly accessible equipment is generally optimized for EGFP (or similar FPs), so newer FPs with slightly different excitation and emission spectra may not be captured well with existing microscopes or flow cytometers.
2. The FP brightnesses are typically assess in-vitro, and there may be other factors in eukaryotic cell cytoplasms that may affect the FP brightness (eg. FP half-life).

Well, I’ve still ordered a handful of different green fluorescent proteins anyway, and figured it was worth doing a side-by-side comparison in the transgenic system we use in the lab. This was all done with the HEK293T G542Ac3 (LLP-Tet-Bxb1attP_Int-BFP_IRES-iCasp9-BlastR) cells, which were stably recombined with a single copy of each fluorescent protein at a common genomic locus. The construct organization was: Bxb1attB_[Green FP]_IRES-mCherry-2A-PuroR. Olivia did these recombinations, selected the cells, and ran the cells on the ThermoFisher Attune Flow cytometer, with Sarah’s help. This is what the results look like:

Some interpretations:
1. Rather minimal (~3-fold) difference between mGreenLantern and UnaG. I suppose if we were ever in a situation where we needed every unit of green brightness possible, that we would go with mGreenLantern. That said, UnaG has some benefits; namely, it’s 58% the size of EGFP/mGreenLantern/mNeonGreen, and it lacks the VERY ANNOYING identical sequences at the N- and C-termini (MVSKG … DELYK) which makes molecular cloning a potential pain.
2. Conceptually, I like the idea of fuGFP, but that 20-fold diminished green fluorescence compared to EGFP is potentially problematic. Who knows, maybe I’ll turn this into a target of directed evolution at some point…?

5/22 edit: We recently tested StayGold, and the results were rather underwhelming. In our n=1 experiment, it yielded a 6% increase over mGreenLantern in green MFI within stably expressing landing pad cells. If it were on the EGFP-normalized scale of the chart / experiment above, it would be a value of about 2.15. So ya, not that it’s not potentially better than mGreenLantern; it’s more that, not sure if it’s worth the effort for most applications.

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%).

ACE2 dependency paper in PLoS Biology

Posted on by kmatreyek

Our paper, entitled “Expanded ACE2 dependencies of diverse SARS-like coronavirus receptor binding domains” (Roelle et al), is published in PLoS Biology!

Ordering oligos at CWRU

Posted on by kmatreyek

Here’s a price comparison I did back in 2019 (presumably still correct?). But in short, per nt price was cheapest through ThermoFisher.

Thus, we’ve been almost exclusively buying oligos from them, with $7,220 spent (as of June 2022) since our first orders starting December 2019.

Here’s what the histogram of oligo costs have shaped up as.

But, well, don’t order degenerate nucleotides oligos from them as they’ll likely be T biased.

If anyone sees anything better on campus, let me know!

Consistent Plasmidsaurus sequencing miscalls

Posted on by kmatreyek

As I noted in this Twitter exchange, plasmid nanopore sequencing via Plasmidsaurus is great, but not perfect. For example, there seem to be some “achilles heal” sequences, where nanopore reproducibly (like 100% of the time with different plasmid submissions) miscalls certain parts of our plasmids. How do we know they’re miscalls? B/c the Sanger traces of the same exact plasmids show the expected sequence very clearly. Here are two that we commonly see:

A single deleted C nucleotide in the beginning of our IRES sequence:

A phantom T>C base miscall that incorrectly tells us we have a W566R nonsynonymous change in every single one of our human ACE2 constructs.

Both are related to C repeats, but there are plenty of other C repeats in the plasmids we submit and it’s ALWAYS these sequences that give Plasmidsaurus problems. Once I figured this one, it’s really NBD, since I know to ignore these changes, although it did inform our current molecular biology workflow in the lab of 1) Screen colony minipreps via Sanger -> 2) Sequence candidate good constructs with Plasmidsaurus / nanopore -> 3) Sanger to resolve unexpected discrepancies between the expected / intended and Plasmidsaurus sequences.

Command line BLAST

Posted on by kmatreyek

One of the pseudo-projects in the lab requires looking for a particular peptide motif in genomic data. While small scale searches can be done using the web interface, the idea is to do this in a pretty comprehensive / high throughput manner, so shifting to the command line makes sense for this work. I last did this back in 2018 for some preliminary studies, so I’m going to have to re-install the software on my new computer and re-run some of those analyses. I figure I’ll write down my notes as I re-do this, so that I (and others) can use this post as a reference.

Installing BLAST+

The instructions on how to download the program can be found here. I’m on a mac, so I downloaded “ncbi-blast-2.13.0+.dmg” and double clicked and ran the package installer.

Assuming it’s been correctly installed, writing the command …

blastp -task blastp-short -query <(echo -e ">Name\nAAWLIEKGVASAEE") -db nr -remote -outfmt 1

… into the terminal should actually reveal some BLAST-specific output, rather than throw an error.

Running protein motif-specific blast searches

Type in the following into your terminal:

psiblast -phi_pattern PHI-Blast_2A_pattern.txt -db nr -remote -query <(echo -e ">Name\nGATNFSLLKQAGDVEENPGP") -max_hsps 1 -max_target_seqs 10000 -out phi_blast_output.csv -outfmt 10

Note: The above command will require having a text file specifying the pattern constraint (“PHI-Blast_2A_pattern.txt” above), which can be found here. This should yield a 25 KB file csv output, like so.

Extracting just the accession numbers

I don’t remember if there are other BLAST+ outputs that give you the full hit sequence. If so, the method I ended up taking back in 2018 would seem to be unnecessarily roundabout. But, until I figure that out, I’ll follow the old method. As you can see in the aforementioned output format, it doesn’t output the hit protein sequence, and instead just gives the accession number. Thus, the next step is using the accession number to actually figure out the protein sequence. To do this, we’ll use Entrez Direct. To install Entrez Direct, follow the instructions here. Briefly, type in the following into the terminal:

sh -c "$(curl -fsSL ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh

In order to complete the configuration process, execute the following:

echo "source ~/.bash_profile" >> $HOME/.bashrc
echo "export PATH=\${PATH}:/Users/kmatreyek/edirect" >> $HOME/.bash_profile

OK, now that it’s installed, here’s how I’ve used it:

First, the output file above has more info than the accession number. To have it pare down to only the accession number, I used this script, which can be run by entering the following into the terminal, assuming you have the previous output csv file somewhere in the directory with the script (can even be in other folders within that directory):

python3 3_Blast_to_accession.py

This will create a file called “3A_prot_accession_list_complete.txt” (example output file here) which will be the unique-ified list of accession numbers to give to Entrez Direct. (Uniquifying is important if you have multiple .csv outputs you wanted to compile into a single master list).

This can be fed into Entrez Direct using this shell script, which you can run by typing in:

sh 4_Accession_to_fasta.sh

You should now have an output file called “4A_prot_fasta.txt” with the resulting protein sequences in fasta format, like so.

Now you can search for your desired sequence (in its full protein context) within the resulting file.

To be continued…

Are there other steps in this process related to this project? Sure. Like what do you do with all of these full sequences containing the hits? Well, that’s beyond the scope of this post.

ODs on the spec and nanodrop

Posted on by kmatreyek

So there are two ways to measure bacterial culture ODs in the lab. The first is to use the nearby ~ $10,000 Thermofisher Nanodrop One (no cuvette option). The second option is to use a relatively cheaply made cuvette-based spectrophotometer I bought off of Amazon for ~ $100. To make it clear, this comparison is not a statement about the value of a Nanodrop (though I will say that having an instrument like a Nanodrop is essentially a must in a mol biol lab). This is more about if the Nanodrop is already being used by someone and waiting would get in the way of some bacterial speccing timepoints, can I purchase a $100 piece of equipment to relieve such a conflict? Especially for bacterial cultures, where volume isn’t really an issue and the measurement is simply the reading at 600 nm, not even requiring some algebra to make a conversion to more practical units (like ng/uL for DNA).

So to do this comparison, over a number of independent instances, I took the same bacterial culture and put 1mL into a cuvette and ran it on the old spec, and took 2 uL and put it on the Nanodrop pedestal and measured there. I made a table of the results, and graphed it in the plot below.

So the readings on the two instruments certainly correlate (that’s good), although it’s not an exact 1:1 relationship. In fact, the nanodrop gave numbers roughly 1.5 times higher than the spec. But if the two instruments give two different readings, then the question becomes “which is right?”

And to that, I essentially say there is no right answer. Each is a proxy for bacterial cell density (ie. Billions of bacteria / mL), but there’s no “absolute” information encoded in the OD number that tells us that specifically for our bacteria, and we’d still have to come up with a conversion factor either way (ie. my doing limiting dilutions of specc’d cultures and counting colonies), and once we have that, both will be right with that context. Sure, it would be nice if we had a method that was the most in-line with whatever ODs that were being described by various papers in the literature, but who knows what they used (recent papers may be using ODs from the nanodrop [with some perhaps using the cuvette option but many others not], while the older publications certainly didn’t have and instead likely used some old-school form of spec). But even that’s going to be heterogeneous, and will only give limited information anyway.

Well, good record-keeping to the rescue. We’ve transformed the positive control plasmid enough times to sample a range of various ODs just by chance, to see if certain bacterial ODs correlate with transformation efficiency. And boy, there’s been a whole lot of nothing there so far (which is actually quite notable; see below).

(FYI: I don’t remember which instrument I used to measure the OD A600 readings. Probably mostly the old spec, tho).

So yea, I’ve generally used cultures with ODs at the time of collection between 0.1 and 0.45, and they’ve collectively given me transformation rates of ~ 20,000 using our standard “positive control” plasmid. So there seems to be a pretty wide window of workable ODs. But generally speaking, I see no issue with having a culture of 0.1 to 0.4 OD as measured with either machine for use with chemical transformation.