Kenny gets an R21 to study Sarbecovirus receptor interactions

The Matreyek lab is awarded a 2-year R21 grant to study how various Sarbecovirus (ie. SARS-like coronavirus) receptor binding domain sequences correspond to binding and infection with various ACE2 receptor sequences (eg. variants of human ACE2, or sequences of diverse ACE2 orthologs across mammals) to find the rules governing molecular compatibilities between these viruses and their potential hosts. More information here.

Quantitating cells via the plate reader

In moving down the floor and having some more space, we’ll be purchasing a plate reader. Based on previous experience / history, I’ve started by testing the BioTek Synergy H1. We’re going to put it through a number of the more traditional paces (such as DNA and protein quantitation), but we clearly have a bunch of cell-based experiments and potential assays that are worth testing on it. Since I’m curious about some of the possibilities / limitations here, I’ve taken a pretty active role in testing things out.

For these tests, I essentially did a serial dilution of various fluorescent protein expressing cells, to assess what the dynamic range of detection could be. Here, I did half log dilutions of the cells in a 96-well plate, starting with 75,000 cells (in 150uL) for the “highest” sample, and doing 6 serial dilution from there down to 75 cells per well, with a final row with media only to tell us background fluorescence. Everything was plated in triplicate, with analyses done on the average.

For the purposes of not having a ton of graphs, I’m only going to show the background subtracted graphs, where I’ve created a linear model based on the perceived area of dynamic range, and denote black dots showing datapoints that linear model predicts, to see how well it corresponds to the actual data (in color).

The above shows serial dilutions high UnaG expressing landing pad cells. Seems like a decent linear range of cell number -dependent fluorescence there, where we can see perhaps a little more than two orders of magnitude in predictable range. Of course, this is one of the least relevant (but easiest to measure) cases, so not super relevant to most of our experiments.

On the other hand, this is a situation that is far more relevant to most of our engineered cell lines. Here, it’s the same serial dilutions of cells, except we’re looking for histone 2A -fused mCherry. The signal here is going to be lower for three reasons: it’s behind an IRES instead of cap-dependent translation, red fluors are typically less bright than green fluors, and the histone fusion limits the amount of fluorescence to the nuclei, so the per-cell amount of fluorescence is decreased. Here, it was roughly a 20-fold range of the assay down from a confluent well. Maybe useful for some experiments quantitating effects of some pertubation on cell growth / survival (without exogenous indicators!) but obviously can’t expect to reliably quantitate more than that 20-fold effect.

Those are direct cell counting assays (or, well, relative counts based on total fluorescence), but what about enzymatic assays, such as common cell counting / titering assays based on dye conversion. I could certainly see these assays potentially having more sensitivity due to a “multiplier” effect through that enzymatic activity. Well, this is what it looks like for Resazurin / Alamar Blue / Cell Titer Blue.

The above is based on Resazurin fluorescence, where we have a pretty decent 2-log range, although even with these conditions, the confluent wells already saturated dye conversion and lost linearity.

Here, I’m testing a different cell titering dye, CCK-8 / WST-2, which only uses absorbance at A460 as its readout. Here, the linear range of the assay seemed to be a lot less; roughly 1 order of magnitude. I’m not showing resazurin conversion absorbance (A570, A600) here, but it looked pretty similar in overall range as CCK-8.

How about timing for the Resazurin and CCK8 assays? Well, here is what it looks like…. Note, I didn’t do the same linear modeling things, b/c I got lazy, but you can still tell what may be informative by eye:

So clearly, we lose accuracy at the top end but gain sensitivity at the bottom end by having the reaction run over night. This is looking at fluorescence. How about absorbance? That’s below:

Same shift toward increased sensitivity for looking at fewer cells, but same dynamic range window, so we lose accuracy in the more confluent wells as it shifts.

Here, the overnight incubation really didn’t do anything. Same linear range, really.

LP publications

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).

Manuscript acceptance timing

I’ve now been an author on enough papers to have a reasonable sampling of what the experiences can be like. In short, it minimally takes 2 months to go from initial submission to eventual manuscript acceptance. These experiences typically are those that require little to no experiments for revision. The process, especially when requiring hefty experiments or multiple rounds of revision, can easily stretch to half a year. In some cases it can take *much* longer (in one experience I’ve seen, a journal did the “rejected but amenable to resubmission if sufficient additional impact is added”, which resulted in an informal span of ~ 800 days!!! ). Anyway, at least for the manuscript submissions I was involved with where I had access to an author portal (or received emails when things happened), I noted when the reviews were returned and revisions submitted, to also keep track of how much of that time was technically under one’s control (manuscript with authors; red) or completely out of one’s control (manuscript with journal; blue). See below:

Also, part of the reason it makes sense to post manuscripts to bioRxiv; why have a completed manuscript that is essentially publication-worthy sit in the dark for half a year?

Note: Obviously this data is for manuscripts that eventually got accepted. Rather pleasantly, I’ve never been first or corresponding author for a paper that got rejected, so I don’t have nearly as good a sampling of that experience. But sitting on the sideline as a middle-author for a handful of such occasions, it would seem to take anywhere between a week (eg. immediate desk rejection) to a couple of months (eg. rejected upon peer review) per submission; when sequentially shopping across multiple journals to find a taker, this would seem to add up.

Lentivector supe collection

Most lentiviral production protocols (usually with VSV-G pseudotyped particles) tells the user to collect the supe at 48 or 72 hours. My protocols tend to say collect the supe twice a day (once when coming into the lab, and once when leaving) starting at 24 hours and ending a few days later (96 hours? more?).

This largely stems from a point in my PhD when I was generating a bunch of VSV-G pseudotyped lentiviral particles to study the “early stage” of the HIV life cycle (ie. the points preceding integration into the genome, such as the trafficking steps to get into the nucleus). After thinking about the protocol a bit, I realized that there’s really nothing stopping the produced VSV-G pseudotyped particles from attaching and re-entering the cells they emerged from, which is useless for viral production purposes. Even for the particles that are lucky enough not to re-enter the producer cells, they are going to be more stabler in a less energy environment (such as 4*C) than floating around in the supe at 37*C in the incubator.

But, well, data is always better to back such ideas. So back in April 2014 (I know this since I incorporated the date into the resulting data file name), I did an experiment where I produced VSV-G pseudotyped lentiviral particles as normal, and collected the supe at ~12 hour intervals, keeping them separate in the fridge. After they were all collected, I took ~ 10uL from each collected supe, put them on target cells, and measured luciferase activity a couple of days later (these particles had a lentiviral vector genome encoding firefly luciferase). Here’s the resulting data.

Some observations:

  • Particles definitely being produced by 24 hours, and seemingly reaching a peak production rate between 24 and 48 hours.
  • The producer cells kept producing particles at a reasonable constant rate. Sure, there was some loss between 48 and 72 hours, but still a ton being produced.
  • I stopped this experiment at 67 hours, but one can imagine extrapolating that curve out, and presumably there’s still ample production happening after 72 hours.

So yea, I suppose if the goal is to have the highest singular concentration, then taking a single collection at 48 or 72 hours will probably give you that. That said, if the goal is to have the highest total yield (which is usually the situation I’m in), then it makes much more sense to collect at various intervals, and then use the filtered, pooled supe in downstream experiments.

Also, I consider being able to dig up and discuss 10-year old data as a win!

Neutralizing Supe

Purchasing purified antibodies is expensive. Furthermore, purchased antibodies are a black box, from an amino-acid sequence perspective. For example, you may have a favorite anti-HA antibody from a company (my favorite from my PhD was an HRP direct conjugate of 3F10), but you likely have no clue what the sequence of that antibody is. Then again, if one had the sequence of the antibody, then they could order DNA encoding that antibody themselves, and then produce unlimited supplies of the antibody protein.

Well, I was curious enough to try this proof-of-principle. Thus, I ordered a DNA sequence encoding Bamlinivimab. It originally had an EUA to treat people infected with SARS-CoV-2, although the EUA eventually got pulled once variants resistant to it started circulating. Well, I engineered cells stably expressing it. Then to test it, I used it in a neutralization experiment, where I mixed SARS-CoV-2 spike pseudotyped lentiviral particles (encoding GFP) with high ACE2 expressing cells, and simultaneously added various amounts of the presumed Bamlinivimab-containing supernatant, or just supernatant from unmodified 293T cells as a control. Well, here are the results:

So definitely a dose-dependent decrease to pseudotyped virus infection, with the max amount used in this experiment (I believe 4 mLs supe out of 6 total mLs in the well, with the cells and virus each also taking a mL) giving a greater than 10-fold neutralizing effect. Cool.


So this doesn’t quite count as “synbiofun” since it didn’t work, so it’s not that fun. But figure I may as well post negative data here when we have it…

Based on this paper, I felt compelled to test some of the tricks they had published on that improved recombination efficiency. First up was DNA sequences that may help with nuclear targeting / import. Tried the NFkB DTS (DNA nuclear-targeting sequences) since that seemed to perform the best for them.

Didn’t exactly reproduce what they did, since I wanted to 1) Use my own construct that we normally use for recombination reactions, and 2) insert the sequence at a convenient location that I could put in with a single molecular cloning step and didn’t get in the way of any other elements we already had in the plasmid.

We cloned the sequences into the “G1180C_AttB_ACE2(del)-IRES-mScarletI-H2A-P2A-PuroR” backbone, where we could use the percentage of mScarlet fluorescent cells to tell us if recombination efficiency increased. Because of the repetitive nature of the NFkB DTS sequence, we ended up getting two different clones with the intended sequence: clone D had the indicated NFkB DTS sequence plus and additional repeat (for 5 repeats total), while clone E had the NFkB DTS sequence missing a repeat (for 4 repeats total).

Clone D
Clone E

Sarah recombined these into landing pad HEK 293T cells, and these were the results of red+ cells.
Negative control: 0.002%
G1180C (unmodified): 17.4%
Clone D (5 repeats): 4.78%
Clone E (3 repeats): 17.8%

So yea. Really didn’t seem to do anything. Not sure why Clone D is worse, although this is an n=1 experiment. If we really wanted to continue this, we would probably need to re-miniprep the plasmids to make sure it’s nothing about that specific prep. That said, nothing about the above results makes me optimistic that this will actually help our current system, so this avenue is likely going on ice.

Trimming and tabulating fastq reads

Anna has been testing her transposon sequencing pipeline, and needing some help processing some of her Illumina reads. In short, she needed to remove sequenced invariant transposon region (essentially a 5′ adapter sequence), trim the remaining (hopefully genomic) sequence to a reasonable 40nt, and then tabulate the reads since there were likely going to be duplicates in there that don’t need to be considered independently. Here is what I did.

# For removing the adapter and trimming the reads down, I used a program called cutadapt. Here's information for it, as well as how I installed and used it below.

## Run the commands below in Bash (they tell conda where else to look for the program)
$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge
$ conda config --set channel_priority strict

## Since my laptop uses an M1 processor
$ CONDA_SUBDIR=osx-64 conda create -n cutadaptenv cutadapt

## Activate the conda environment
$ conda activate cutadaptenv

## Now trying this for the actual transposon sequencing files
$ cutadapt -g AGAATGCATGCGTCAATTTTACGCAGACTATCTTTGTAGGGTTAA -l 40 -o sample1_trimmed.fastq sample1.assembled.fastq

This should have created a file called “sample1_trimmed.fastq”. OK, next is tabulating the reads that are there. I used a program called 2fast2q for this.

## I liked to do this in the same cutadaptenv environment, so in case it was deactivated, here I am activating it again.
$ conda activate cutadaptenv

## Installing with pip, which is easy.
$ pip install fast2q

## Now running it on the actual file. I think you have to already be in the directory with the file you want (since you don't specify the file in the command).
$ python -m fast2q -c --mo EC --m 2

## Note: the "python -m fast2q -c" is to run it on the command line, rather than the graphical interface. "--mo EC" is to run it in the Extract and Count mode. "--m 2" is to allow 2 nucleotides of mismatches.

Nanopore denovo assembly

Plasmidsaurus is great, but it looks like some additional companies aside from Primordium are trying to develop a “nanopore for plasmid sequencing” service. We just tried the Plasmid-EZ service from Genewiz / Azenta(?), partially b/c there’s daily pickup from a dropbox on our campus. At first glance, the results were rather mixed. Read numbers seemed decent, but 4 of the 8 plasmid submissions didn’t yield a consensus sequence. Instead, all we were given were the “raw” fastq files. To even see whether these reads were useful or not, i had to figure out how to derive my own consensus sequence from the raw fastq files.

After some googling and a failed attempt or two, I ended up using a program called “flye”. here’s what I did to install and use it, following the instructions here.

## Make a conda environment for this purpose
$ Conda create -n flye

## Pretty easy to install with the below command
$ Conda install flye

## It didn't like my file in a path that had spaces (darn google drive default naming), so I ended up dragging my files into a folder on my desktop. Just based on the above two commands, you should already be able to run it on your file, like so:
$ flye --nano-raw J202B.fastq --out-dir assembled

This worked for all four of those fastq files returned without consensus sequences. Two worked perfectly and gave sequences of the expected size, The remaining two returned consensus sequences that were 2-times as large as the expected plasmid. Looking at the read lengths, these plasmids did show a few reads that were twice as long as expected. That said, those reads being in there didn’t make the program return that doubly-long consensus sequence, as it still made that consensus seq even after the long reads were filtered out (I did this with fastq-filter; “pip install fastq-filter”, “fastq-filter -l 500 -L 9000 -o J203G_filtered.fastq J203G.fastq.gz”). So ya, still haven’t figured out why this happened and if it’s real or not, but even a potentially incorrectly assembled consensus read was helpful, as I could import it into Benchling and align it with my expected sequence, and see if there were any errors.

After this experience, I’ve come to better appreciate how well Plasmidsaurus is run (and how good their pipeline for returning data, is). We’ll probably try the Genewiz Plasmid-EZ another couple times, but so far, in terms of quality of the service, it doesn’t seem as good.

Plasmidsaurus fasta standardizer

I really like plasmidsaurus, and it’s an integral part of our molecular cloning pipeline. That said, I’ve found analyzing the resulting consensus fasta file to be somewhat cumbersome, since where they inevitable start their sequence string in the fasta file is rather arbitrary (which, I don’t blame them for at all, since these are circular plasmids with no particular starting nucleotide, and every plasmid they’re getting is unique), and obviously doesn’t match where my sequence starts on my plasmid map in Benchling.

For the longest time (the past year?) I dealt with each file / analysis individually, where I would either 1) reindex my plasmid map on Benchling to match up with how the Plasmidsaurus fasta file is aligning, or 2) Manually copy-pasting sequence in the Plasmidsaurus fasta file, after seeing hwo things match up after aligning.

Anyway, I got tired of doing this, so I wrote a Python script that standardizes things. This will still require some up-front work in 1) Running the script on each plasmidsaurus file, and 2) Making sure all of our plasmid maps in Benchling start at the “right” location, but I still think it will be easier than what I’ve been doing.

1) Reordering the plasmid map.

I wrote the script so that it reordering the Plasmidsaurus fasta file based on the junction between the stop codon of the AmpR gene, and the sequence directly after it. Thus, you’ll have to reindex your Benchling plasmid map so it exhibits that same break at that junction point. Thus, if your plasmid has AmpR in the forward direction, it should look like so on the 5′ end of your sequence:

And like this on the 3′ end of your sequence:

While if AmpR is in the reverse direction, it should look like this on the 5′ end of your sequence:

And like this on the 3′ end of your map:

Easy ‘nuf.

2) Running the Python script on Plasmidsauru fasta file.

The python script can be found at this GitHub link:

If you’re in my lab (and have access to the lab Google Drive), you don’t have to go to the GitHub repo. Instead, it will already be in the “[…additional_text_here…]/_MatreyekLab/Data/Plasmidsaurus” directory.

Open up Terminal, and go to that directory. Then type in “python3”. Before hitting return to run, you’ll have to tell it which file to perform this on. Because of the highly nested structure of how the actual data is stored, it will probably be easier just to navigate to the relevant folder in Finder, and then drag the intended file into the Terminal window. The absolute path of where the file sits in your directory will be copied, so the command will now look something like “python3 /[…additional_text_here…]/_MatreyekLab/Data/Plasmidsaurus/PSRS033/Matreyek_f6f_results/Matreyek_f6f_5_G1131C.fasta”

It will make a new fasta file suffixed with “_reordered” (such as “Matreyek_f6f_1_G1118A_reordered.fasta”), which you can now easily use for alignment in Benchling.

Note: Currently, the script only works for ampicillin resistant plasmids, since that’s somewhere between 95 to 99% of all of the plasmids that we use in the lab. That said, plasmidsaurus sequencing of the rare KanR plasmid won’t work with this method. Perhaps one day I’ll update the script for also working with KanR plasmids (ie. the first time I need to run plasmidsaurus data analysis on a KanR plasmid, haha).