We initially required the generation of nanobodies for numerous peptides that are present in more than one protein, to enable the combinatorial identification of large numbers of proteins. We injected alpacas with a fusion protein comprising 20 different target peptides, along with a high number of off-target and near-target peptides. A total of 46 non-redundant nanobody clones were identified.
We then proceeded to validate the nanobodies in an extensive fashion, by mass spectrometry.
As we suggested in the initial description of our project, the nanobodies should recognize different but overlapping proteins. This is precisely what we observed. The nanobodies have varying levels of overlap, with some recognizing more than 300 common proteins, while others overlap very little.
The protein identities are typically unique, meaning that one protein is observed by only one set of nanobodies, for more than 90% of all proteins. Proteins are typically bound by ~10 nanobodies, although a broad distribution is observed, as predicted in our initial grant application, with some proteins bound by all 42 nanobodies tested. Most erroneous identifications are observed for proteins that are only bound by one nanobody.
Even before the nanobodies are fully available, we needed to ensure that we are able to screen and characterize the nanobodies for the project. The BIU partners have therefore first developed a microfluidic device that can automate the screening and perform a binding assay to characterize the affinity of the different antibodies to their targets and their cross reactivity. The ultimate goal is to quantify the binding of each of the candidate nanobodies to a particular peptide.
As planned, we have tested the optimal conditions for this quantification, and we prepared the screening technology, based on microfluidic devices. We originally planned to use biotinylated peptides, immobilized to the device surface via Neutravidin, which are then detected using fluorescent nanobodies. Such a screen can be multiplexed easily by using small mixes of nanobodies, and later by iterating the screen on the best-binding mixes. We have finished establishing the assay in 2022.
To obtain higher resolution, we turned to expansion microscopy, where our progress has been far more than previously envisioned, as we (UMG team) have obtained resolutions of 1 nm or better. A major gap still exists in the imaging field, between the precise analysis of single proteins by cryo-EM, at Ångstrom resolutions, and the analysis of cellular samples by efficient optical super-resolution (≥10 nm). Optical microscopy should be able to fill this gap, but it is limited by two fundamental problems. First, the achievable structural resolution in biological samples is determined by the labeling density, which is limited by the size of the fluorescent probe (several nanometers) and by the labeling efficiency. Second, fluorophores can interact via energy transfer at sub-10 nm distances, which results in accelerated photoswitching (blinking) and photobleaching, and thus in substantially lower localization probabilities.
The simple solution to this question is to separate the fluorophores spatially, without changing the labeling efficiency. This is best achieved by the physical expansion of the specimen, after embedding it in a swellable gel, in ExM. We implemented this approach, which we termed optimized nanoscale expansion (ONE) microscopy. To improve ONE and ExM resolution, the KTH laboratory also introduced a setup capable of imaging large gels, using sample- and signa-adapted imaging approaches. The details of this work were published in several articles, throughout the project.
We next proceeded to the proof-of-principle demonstration of imaging all of our selected nanobodies. GFP-carrying nanobodies were used, were applied to expanded gels immobilized on coverslips, and resulted in a complex, combined image, as shown in the attached file.
