iCasp9 as a negative selection cassette is amazing. Targeted protein dimerization with AP1903 / Rimiducid is super clean and potent, and the speed of its effect is a cell culturist’s dream (cells floating off the plate in 2 hrs!). It really works.

But when there are enough datapoints, sometimes it doesn’t. I have three recorded instances of email discussions with people that have mentioned it not working in their cells. First was Jeff in Nov 2020 with MEFs. Then Ben in June 2021 with K562s. And Vahid in July 2021 with different MEFs. Very well possible there’s one or two more in there I missed with my search terms.

Reading those emails, it’s clear that I had already put some thought into this (even if I can’t remember doing so), so I may as well copy-paste what some them were:

1) Could iCasp9 not work in murine cells, due to the potential species-based sequence differences in downstream targets? Answer seems to be no, as a quick google search yields a paper that says “Moreover, recent studies demonstrated that iPSCs of various origin including murine, non-human primate and human cells, effectively undergo apoptosis upon the induction of iCasp9 by [Rimiducid]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177583/

Separately, after the K562s (human-derived cells) came into the picture:

This is actually the second time this subject has come up for me this week; earlier, I had a collaborator working in MEF cells note that they were seeing slightly increased but still quite incomplete cell death. That really made me start thinking about the mechanism of iCasp9-based killing, which is chemical dimerization and activation of caspase 9, which then presumably cleaves caspases 3 and 7 to then start cleaving the actual targets actually causing apoptosis. So this is really starting to make me think / realize that perhaps those downstream switches aren’t always available to be turned on, depending on the cellular context. In their case, I wondered whether the human caspase 9 may not recognize the binding / substrate motif in murine caspase 3 or 7. In yours, perhaps K562’s are deficient in one (or both?) of those downstream caspases?

Now for the most recent time, which happened in the lab rather than by email: It was recently brought up that there is a particular landing pad line (HEK293T G417A1) which we sometimes use, that apparently has poor negative selection. John and another student each separately noticed it. Just so I could see it in a controlled, side-by-side experiment, I asked John if he’d be willing to do that experiment, and the effect was convincing.

So after enough attempts and inadvertently collecting datapoints, we see the cases where things did not go the way we expected. Perhaps all of these cases share a common underlying mechanism, or perhaps they all have unique ones; we probably won’t ever know. But there are also some potentially interesting perspective shifts (eg. a tool existing only for a singular practical purpose morphing into a potential biological readout), along with the practical implications (ie. if you are having issues with negative selection, you are not alone).

This is the post I will refer people to when they ask about this phenomenon (or what cell types they may wish to avoid if they want to use this feature).

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).

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.

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.
# https://cutadapt.readthedocs.io/en/stable/installation.html
# https://bioconda.github.io/

## 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.

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: https://github.com/MatreyekLab/Sequence_design_and_analysis/tree/main/Plasmidsaurus_fasta_reordering

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 Plasmidsaurus_fasta_standardizer.py”. 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 Plasmidsaurus_fasta_standardizer.py /[…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).