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:

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.