I still intend to get around to posting most if not all landing pad plasmids to Addgene, but it’s taken me forever to get around to it. Actually, the institution takes just as forever to do it, since apparently they have to get permissions to post any plasmid with parts I may have amplified from another source(eg. the puromycin resistance gene)? Once those are there, the plasmid sequences will obviously be publicly available. In the mean time, I figure I’d post some of the most common plasmid maps here, so other people can benefit form them (or I don’t have to send specific emails to each person who asks for them). So here are some of the most popular, published plasmids, in GenBank (.gb) format.

G542A_pLenti-Tet-coBxb1-2A-BFP_IRES_iCasp9-2A-Blast_rtTA3 (Addgene #171588)

G698C_AttB_ACE2_IRES_mCherry-H2A-P2A-PuroR (Addgene #171594)

G758A_AttB_ACE2(del)-IRES-mCherry-H2A-P2A-PuroR (Addgene #171596)

p0489_AttB_EGFP-Link-PTEN-IRES-mCherry_Bgl2.gb

p0669_AttB_sGFP-PTEN-IRES-mCherry-P2A-bGFP_Bgl2

G384A_LLP-iCasp9-Blast

G273A_AttB_sGFP-PTEN-IRES-mCherry-P2A-HygroR

G274A_attB-sGFP-PTEN_IRES-mCherry-P2A-PuroR

attB-EGFP (Addgene #171597)

attB-mCherry (Addgene #171598)

dAAVS1_TetBxb1_mTagBFP2_rtTa3

dAAVS1_CMV-rtTA3-Term

dAAVS1_Dummy

G371A_LLP-rEF1a

G375F_attB-eGFP-rIRES-mCherry

G382C_attB-eGFP-rIRES-mCherry-2A-PuroR

G163A/B_AttB_PuroR-P2A-mCherry_Bgl2

G310A_pLenti-TetBxb1BFP-2A-coBxb1-2A-Blast_rtTA3

pLenti-TetBxb1BFP_coBxb1-rtTA3_Blast

pLenti-TetBxb1BFP-rtTA3_Blast

G57A_attB_TP53-link-EGFP-IRES-mCherry

AttB_sGFP-VHL-IRES-mCherry-P2A-bGFP_Bgl2

AttB_VHL-sGFP_IRES-mCherry-P2A-bGFP_Bgl2

Positive selection for recombined selection is a pretty useful technique when working with the HEK 293T landing pads, but people might not know what concentrations are best. Well, here is that info, all in one place. In each case, the antibiotic resistance gene had been placed after an IRES element in an attB recombination plasmid, and was linked to mCherry using a 2A stop-start sequence. Thus, the Y axis is showing how well the mCherry+ cells were enriched at various concentrations of antibiotic.

Consistent with the above plots, I generally suggest people use 1 ug/mL Puro, 10 ug/mL Blast, 100 ug/mL Hygro, and 100 ug/mL Zeocin for selections.

11/19/2024 Update: I was recently reminded that I had since created this data a bit over year ago using resazurin dye on our plate reader. Gosh, now that I’m looking at this data side-by-side, I’m finding that they correspond with each other even better than I expected. How lovely.

Almost everybody hates doing Western blots since they’re so labor intensive (and somewhat finicky), but they are undeniably useful and will forever have a place in molecular biology / biomedical research. We’re currently putting together a manuscript where we express and test variants of ACE2, which requires some Western blotting quantitation. Since I’m about to do some of the quantitation now, I figured I’d just record the steps I do it so trainees in the lab have a basic set of instructions they can follow in the future.

This already assumes you have a “.tif” file of western blot. While I someday hope to do fluorescent westerns, this will likely be a chemiluminescent image. Hopefully this isn’t a scan of a chemiluminescent image captured on film, b/c film sux. So you presumably took it on a Chemidoc or an equivalent piece of equipment. Who knows; maybe I finally found the time to finish / standardize my chemiluminescent capture using a standard mirrorless camera procedure (unlikely). I digress; all that matters right now is you already have such an image file ready to quantitate.

My favorite method for Western blot quantitation is to use “Image Lab” from BioRad. You know, I like BioRad. And I definitely like the fact that they provide this software for free. Anyway, download and install it as we’ll be using it.

Once you have it installed, start it up and open your image file of interest. The screen will probably look something like this:

First off, stop lying to yourself. By default, the imagine is going to be auto-scaled so that the darkest grey values in your image get turned to black, while the lightest grey values get turned white. But in actuality, the image you see is not going to be the “raw” range of your image, so you may as well turn off the auto-scaling so you see your image for what it really is. Thus, press that “Image Transform” button…

… and get a screen that looks like below:

See where those low and high values are? Awful. Set the low value to 0 and the high value to the max (65535).

And now the image looks like this, which is great, since this is what the actual greyscale values in your image actually look like.

OK, now we can actually start quantitating the bands in the plot. First off, don’t expect your western blot to look 100% clean. Lysates are messy, and you can’t always get pure, discrete bands. Sure, some of the lower-sized bands may be degradation products that happened when you accidentally left the lysates out of ice (you should avoid that, of course). Then again, you may have done everything perfectly bench-wise, and the lower-sized bands may be because you’re overexpressing a transgene, and proteins naturally get degraded, and overexpressed proteins may tax the normal machinery and get degraded more obviously. I say the best thing is to acknowledge that happens, show all your results / work, and make the more educated interpretations that you can. Regardless, in that above plot, we’re going to try to quantitate the density of the highest molecular weight band, since that should be the full-length protein. To do that, first select the “Lane and bands” button on the left.

I then press the “Manual” lane annotation button.

In this case, I know I have 11 lanes, so I enter that and get something that looks like this:

Clearly that’s wrong, so grab the handles on the edges and move the grid such that it actually falls more-or-less on the actual lanes.

Sure the overall grid is pretty good, but maybe it’s not perfect for every single individual lane. The program also lets you adjust those. To do that, click off the “Resize frame” button on the left so it’s no longer blue…

And then adjust the individual lanes so they fit the entire band as your human eyes see them, resulting in an adjust grid that looks like this:

Nice. Now go to the left and select the tab at the top that says “Bands”, and then click on the button that says “Add”.

Once you do that, start clicking on the bands you want to quantitate across all of the lanes. You may have to grab the dotted magenta lines in each lane to adjust them so that the actual band is within them (and presumably somewhere near the solid magenta line which should be somewhere in between them). This is what it looks like after I do that:

It’s good to check how well the bands are being seen by the program. Go to the top and press the “Lane profile” button. It should give you a density plot. This is also the window where you can do background subtraction. Find a number that seems sensible (in this case, a disk size of 20 mm seems reasonable), and make sure you hit the “apply to all lanes” button so it propagates this across lanes. While I’m only showing the picture for lane 5, it’s probably worth scanning across the lanes to make sure the settings are sensible.

Now with those settings fine, close out, and then click on “Analysis table” at the top. Once that is open, go to the bottom and click on “lane statistics”. These should be the numbers you’re looking for.

Now export the statistics (either pressing the “copy analysis table to clipboard” button and pasting in an spreadsheet you want to use, or the “export analysis table to spreadsheet”). The number you’ll be looking to analyze will be those in the “Adj. Total Band Vol” column.

Note: Now that I’m doing this, the “standard curve” button is now ringing a bell. I’m fairly certain that in my PhD work, when I ran a ton of Western blots or just straight up protein gels stained with coomassie, that I would run dilutions of lysates / proteins to make a standard curve of known proteins amounts that I could calibrate the densitometry against. We obviously didn’t do that here, since we didn’t have the space, so these numbers aren’t going to be quite as accurate if we had done so. Still, getting some actual numbers we can compare across replicates is still a major step up than not quantitating and having everything be even more subjective.

I try to be as deliberate as I can be about designing my synthetic protein-coding constructs. While I’ve largely viewed splicing as an unnecessary complication and have thus left it out of my constructs (though, who knows; maybe transgenes would express better with splicing, such as supposedly happens with the chicken ß-actin intron present in the pCAGGS promoter/5’UTR combo), there’s still a very real possibility that some of my constructs encode cryptic splice sites that could be affecting expression. In a recent conversation with Melissa Chiasson (perhaps my favorite person to talk syn-bio shop with), she noted that there is actually a way to use SpliceAI to predict splicing signals in synthetic constructs, with a nice correspondence here showing what the outputs mean. Below is my attempt to get this to work in my hands:

First is installing Splice AI, following the instructions here.

I started by making a new virtual environment in anaconda:
$ conda create --name spliceai

I then activated the environment with:
$ conda activate spliceai

Cool, next is installing spliceai. Installing tensorflow the way I have written below got me around some errors that came up.
$ conda install -c bioconda spliceai
$ conda install tensorflow -c anaconda

OK, while trying to run the spliceAI custom sequence script, I got an error along the line of “Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized…”. I followed the instructions here, and typed this out into my virtualenv:
$ conda install nomkl

Alright, so that was fixed, but there was another error about no config or something (“UserWarning: No training configuration found in save file: the model was not compiled. Compile it manually….”). So I got around that by writing a new flag into the load_module() function based on this response here.

OK, so after that (not so uncommon) struggling with dependencies and flags, I’ve gotten things to work. Here’s the result when I feed it a construct from the days when I was messing around with consensus splicing signals early during my postdoc. In this case, it’s a transcript that encodes the human beta actin cDNA, with its third intron added back in. It’s also fused to mCherry, found on the C-terminal end.

And well, that checks out! The known intron is clearly observed in the plot. The rest of Actin looks pretty clean, while there seems to be some low-level splicing signals within mCherry. That said, the fact that they’re in the wrong order probably means it isn’t really splicing, and I’m guessing the signals are weak and far away enough that there isn’t much cross-splicing with the actin intron.

Oh, and now for good measure, here’s the intron found in transcripts made from the pCAGGS vectors, with this transcript belonging to this plasmid from Addgene encoding codon optimized T7 polymerase.

Nice. Now to start incorporating it into analyzing some of the constructs I commonly use in my research…

Illumina sequencing of barcoded amplicons is going to be large factor in the work we’ll be doing. Going to a completely new place, I now need to explain to people how it works. To keep this post simpler, I’m skipping all of the landing pad details (hopefully you already understand it). Let’s just start with what was genomically integrated after the recombination reaction. In the case of the PTEN library, it looks like this:

As you can see, the barcode is the blue “NNNNNNNNNNNNNNNNNN” region at the bottom of the above image. We can’t directly sequence it with primers directly flanking it, since those sequences will also be present in the unexpressed plasmid sequences likely contaminating our genomic DNA. Thus, we first have to create an amplicon containing our barcode of interest, but spanning the recombination junction (“Recomb jxn” above).

For the PTEN library, the barcode is located in back of the EGFP-PTEN ORF, so we have to amplify across the whole thing. We did this by using a forward primer located in the landing pad prior to recombination (such as KAM499 found in the Tet-inducible promoter and shown in red as the “Forward “Primer), as well as a reverse primer located behind the barcode associated with the PTEN coding region (KAM501 in this case).

The above is a simplistic representation. The forward primer / KAM499 is indeed just the sequence needed for hybridization, since we can’t going to do anything else with this end of the amplicon. On the other hand, we’ll add some more sequence to the reverse primer to help us with the next steps. In this case, this is a nucleotide sequence that wasn’t present before, so that we can amplify this specific amplicon in the next step. The actual amplicon will thus look something like below:

OK, so the above amplicon is huge; too big to form efficient cluster generation. Thus, we’ll now use that amplicon to make a much smaller, Illumina compatible second amplicon. We’ll also include an index of degenerate nucleotides “nnnnnnnn” that can be used to distinguish different amplicons from each other when we mix multiples samples together before doing the actual sequencing step. This second amplicon looks something like this:

This time, the forward primer has both the hybridizing portion (in the blue-purple) as well as one of the cluster generators, shown as that light blue. At the complete other end, you can see the “nnnnnnnn” index sequence in indigo, followed by the other cluster generator sequence in orange. Please note: Here, the index is a KNOWN sequence, like GTAGCTAC, or GATCGAGC. It’s just that each sample will have a different KNOWN sequence,. so it was simpler just to denote it with “n”s for the purpose of this explanation.

There’s more in the above map though. We’ll likely want to do paired sequencing of the barcode, so we’ll give the Illumina sequencer two read primers: read 1 (in red) which reads through the barcode in the forward direction, as well as read 2 (in green), which goes through the barcode in the reverse direction. We will also need to sequence the index, though we’ve tended to only do that with one primer. This primer is Index 1, and is colored in yellow.

For the actual primer sequences and everything, look at Supplementary Table 7 of my 2018 Nature Genetics paper.