We do a lot of molecular cloning in the lab. Standard practice in the workflow is to make your own home-made chemically competent NEB 10-beta bacteria to be used fresh the day of the transformation. This has worked surprisingly well, and we have made ~ 350 different plasmid constructs in the first ~ 600 days). Each time you do the transformation, it’s important to include a positive control (we use 40 ng of attB-mCherry plasmid) to make sure the transformation step was performing properly (to help interpret / troubleshoot what may have gone wrong if you had few / zero colonies in your actual molecular cloning transformation plates). I’ve done this enough times now to know what is “normal”. Thus, especially for new members in the lab, please reference this plot to see how your own transformation results compare to how it has worked for me, where I typically get slightly more than 10,000 transformants (Note: you may get numbers better than me, which is a good thing!).

Edit 2/24/2022: It recently dawned on me that we haven’t necessarily been using a standard number of bacteria per transformation (ie. we typically prep the same amount of bacteria, and that bacteria normally just gets split into however many transformations are being done at the time. Thus, if there are 10 plasmids being transformed, the number of bacteria per transformation is ~ one fifth of the number of bacteria for when two plasmids are being transformed.

Well, since I had a bunch of data recorded for our positive control transformations (40ng of attB-mCherry plasmid), I decided to see what number of bacteria we had used and if there were any patterns in the transformation efficiencies:

We generally talk about degenerate nucleotide positions in synthesized oligo (like primers) as if they are this automatic and even thing, but like everything else, things can be a lot more complicated. We recently ordered some primers with six degenerate positions (so NNNNNN) from ThermoFisher, and they were predominantly filled with T’s, while the same oligo ordered from IDT was a lot more balanced in terms of nucleotide composition. While this was done at really small (Sanger) scale and not quantitative, this reminded me of nucleotide biases I observed in libraries created in the Fowler lab using Inverse PCR (where the degenerate sequence is not hybridizing with a partner strand during initial hybridization, so no degenerate sequence should have a competitive edge over another, at least in terms of hybridization). These were all made with IDT primers. I dug up that slide, and have posted it below:

So in this analysis, you can see that there is a ubiquitous albeit somewhat variable G bias across the libraries. This often resulted in the presence of more glycines than expected. Overall, I don’t think this is ALL that bad; certainly still WAY better than the rampant (like, 86% T) bias with the ThermoFisher primer (When I emailed them about this, they told me “Unfortunately, we do know exactly what the issue is. This is an inherent instrument issue with how automated degeneracies are produced on the existing synthesizer. The only way to fix it from a customer perspective is to order using special instructions to “hand mix” the degeneracy in an optimal ratio to produce as close to a 25% balance as possible.”). So while I’ll still buy regular cloning primers from Thermo (b/c they are the cheapest for me), all degenerate primers will have to go through IDT (or maybe eventually EuroFins, as the Roth lab has noted good luck with them).

5/15/21 update: OK, got some Illumina sequencing back to actually quantitate this, and the ThermoFisher degenerate primer definitely suffered from too many T’s, with ~ 49% of all nucleotides being T’s. Actually, the IDT hand mixed did worse than IDT machine mixed, which had the closest to the target values. Interesting.

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’ve gone over how to make mutations of a plasmid before (since that’s the simplest molecular cloning process I could think of), but there will be many times we will need to make a new construct (via 2-part Gibson) by shuttling a DNA sequence from one plasmid to another. Here’s a tutorial describing how I do that.

12/2/24 update: Much like my SDM primer design post, I’ve also updated this strategy as well. See the relevant update at the bottom of the post.

First, open the maps for the two relevant plasmids on Benchling. Today it will be “G871B_pcDNA3-ACE2-SunTag-His6” serving as the backbone, and “A49172_pOPINE_GFP_nanobody” serving as the insert. The goal here will be to replace the ACE2 ectodomain in G871B with the GFP nanobody encoded by the latter construct.

Next, duplicate the map for the backbone vector. Thus, find the plasmid in the plasmid under the “Projects” tab in Benchling, right click on it to open up more options, and select “Copy to…”.

Then, make a copy into whatever directory you want to keep things organized. Today, I’ll be copying it into a directory called “Cell_surface_labeling”. A very annoying thing here is that Benchling doesn’t automatically open the copy, so if you start making edits to the plasmid map you already have open on the screen, you’ll be making edits to the *original*. Thus, make sure you go to the directory you had just located, and open duplicated file (it should start with “Copy of …”). Once open, rename the file. At this point, the plasmid won’t have a unique identifier yet, so I typically just temporarily prefix the previous identifier with “X” until I’m ready to give it it’s actual identifier. To rename the file, go to the encircled “i” icon on the far right of the screen, second from the bottom icon (the graduation cap). After you enter the new name (“XG871B_pcDNA3-Nanobody[GFP-pOPINE]-SunTag-His6” in this case), make sure you hit “Update Information” or else it will not save. Hurrah, you’ve successfully made your starting campus for in-silico designing your future construct.

Cool, now to the plasmid map with your insert, select the part you want, and do “command C” to copy it.

Now select the part of the DNA you want to replace. For this construct, we’re replacing the ACE2 ectodomain, but we want this nanobody to be secreted, so we need to keep the signal peptide. I didn’t already have the signal peptide annotated, so I’m going to have to do it now. When in doubt, one of the easiest things to do is to consult Uniprot, which usually has things like the signal peptide already annotated. Apparently it’s the first 17 amino acids, which *should* correspond to “MSSSSWLLLSLVAVTAA”. For whatever reason, the first 17 amino acids of the existing ACE2 sequence is “MSSSSWLLLSLVAVTTA”, so there’s a A>T mutation. It’s probably not a big deal, so I’ll leave it as it is. That said, to make my construct, I now want to select the sequence I want to replace, like so:

As long as you still have the nanobody sequence copied, you can now “Command V” paste it in place of this selection. The end result should look like so:

Great, so now I have the nanobody behind the ACE2 signal peptide, but before the GCN4 peptide repeats that are part of the Sun-tag. Everything looks like it’s still in frame, so no obvious screw-ups. Now time to plan the primers. I generally start by designing the primers to amplify the insert, shooting for enough nts to give me a Tm of slightly under 60*C. I also generally put in the ~ 17nt of homology appended onto these primers, thus. The fwd primer would look like this.

Same thing for the reverse primer, and it would look like this (make sure to take the reverse complement).

To recap, the fwd primer seq should be “ttgctgtTactactgctgttcaactggtggaaagcggc” and the reverse should be “ccgttggatccggtaccagagctcaccgtcacctgagt. Cool. Next, we need to come up with the primers for amplifying the vector. It could be worth checking to see if we have any existing primers that sit in the *perfect* spot, but for most construct, that’s likely not the case. Thus, design forward and reverse primers that overlap the homology segments, and have Tm’s slightly under 60*C. The forward primer will look like this:

And the reverse primer like this:

Hmmm. I just realized the Kozak sequence isn’t a consensus one. I should probably fix that at some point. But, that’s beyond the scope of this post. So again, to recap, the fwd and rev primers for the vector are “ggtaccggatccaacggtcc” and “agcagtagtAacagcaacaaggctg”. But now you’re ready to order the oligos, so go ahead and do this. As a pro-tip, I like to organize my primer order work-areas like this:

Why? Well, the left part can be copy-pasted into the “MatreyekLab_Primer_Inventory” google sheet, while the middle section can be copy-pasted into the template for ordering primers from ThermoFisher. The right part can be copy-pasted into the “MatreyekLab_Hifi_Reactions” google sheet in anticipation of setting up the physical reaction tubes once the primers come in.

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12/2/24 update: So the above instructions work fine, but I have since (as in like 3/4 years ago, haha) adopted a slightly different strategy. The original strategy prioritized having one primer (in the above example, the reverse primer, or KAM3686) being perfectly matching the WT sequence, so that it could double as a Sanger sequencing primer in other non-cloning circumstances. Well, we barely ever need these anymore, especially with the existence of whole-plasmid nanopore sequencing via Plasmidsaurus. Thus, I now:
1) Just design the primer pairs so that both the forward AND reverse primers are each encoding ~ 9 nt of “non-template matching” sequence at their 5′ ends. These “non-template matching” sequences won’t contribute to primer binding to the template, but will presumably contribute to primer binding to any amplicons that are being futher amplified during PCR, but their biggest importance are adding to the 18+ nt of homology necessary for Gibson.
2) I do shoot for the matching sequence on the 5′ end to still be somewhere in the 16 to 30 nt range, to get to the ~ 58*C Tm intended for the PCR reaction. Notably, as alluded to above, while this means that the Tm should be ~58*C when binding to and amplifying from the template, there will be 25 to 39 nt of matching sequence that occurs when the primer is binding to any further amplifying amplicons present in the PCR reaction (so not cycle 1 of the PCR, but presumably increasing in proportion in cycles 2+).

Thus, my updated primer strategy for the above reactions would look like these: