Super-resolution imaging of proteins in live cells using reversibly interacting peptide pairs

Super-resolution techniques have revolutionised our ability to observe cellular structures with significantly higher resolution than traditional microscopy. Despite the number of super-resolution microscopy techniques available, live cell super-resolution imaging remains challenging. For example, while Photo-activated localisation microscopy (PALM) can be used in vivo, it necessitates the direct fusion of a fluorophore to the protein of interest. This approach can be problematic because a direct fusion to a fluorescent protein can disrupt the normal function and localisation of the protein being studied. Moreover, once the fluorescent protein is photobleached, no more data can be collected from that molecule. In this thesis, I describe the development and use of LIVE-PAINT, a novel live-cell super-resolution microscopy technique. In LIVE-PAINT, a peptide-protein or peptidepeptide pair, one fused to the protein of interest and the other to a fluorescent protein, reversibly interact. When the peptide pair bind, a blink is observed, and the precise location can be determined. In a few minutes, enough binding events occur to generate an image of the protein of interest with a resolution of around 20 nanometres. Initially, this work optimises and applies LIVE-PAINT for diffraction-limited and super-resolution imaging of proteins within live budding yeast cells. I then demonstrate that the small peptide tag used to label the protein of interest makes LIVE-PAINT a valuable tool for imaging proteins that are sensitive to direct fusions to fluorescent proteins. In addition, I validate that LIVE-PAINT enables replenishment of signal throughout imaging. This is because the imaging peptide, the peptide-labelled fluorescent protein, is expressed separately from the target protein, creating a pool of imaging peptides within the cell that can replenish those that are photobleached during imaging. I utilise this property of LIVE-PAINT to track moving proteins over long periods of time. Subsequently, I describe how I adapted the LIVE-PAINT system to apply this technique to the more complex environment of live mammalian cells. I show that LIVE-PAINT successfully yields diffraction-limited and super-resolution images of proteins located in various organelles. This is the first time that interacting peptide pairs have been used to facilitate point accumulation for imaging in nanoscale topography (PAINT) based super-resolution imaging in live mammalian cells. These results are obtained through both transient transfections of labelled proteins and stably integrated versions. Through this work I generate several new cell lines which can be shared with other researchers allowing them to use this technique to gain new insights into the proteins they study. Furthermore, this thesis explores improvements to the LIVE-PAINT method. I demonstrate that peptides as small as 5 residues can be used for LIVE-PAINT imaging. This will broaden the applicability of LIVE-PAINT to a wider range of proteins that cannot tolerate modifications. To harness the increased brightness of synthetic fluorescent dyes compared to fluorescent proteins, I developed mammalian cell lines expressing a HaloTag fused to a LIVE-PAINT peptide. I show that the exogenous addition of the binding partner to HaloTag, HaloLigand, labelled with a synthetic dye, to these cells, enables LIVE-PAINT imaging with synthetic dyes. Lastly, I validate that LIVE-PAINT can be multiplexed by using orthogonal peptide-protein pairs to image two proteins concurrently in live cells. In summary, this thesis presents the development and optimisation of LIVE-PAINT, an innovative peptide-based super-resolution imaging technique tailored for live cell imaging. While this work explores select applications of LIVE-PAINT, it is anticipated that this novel technique will have a broad spectrum of applications

Self-Assembling Protein Surfaces for In Situ Capture of Cell-Free-Synthesized Proteins

We present a new method for the surface capture of proteins in cell-free protein synthesis (CFPS). We demonstrate the spontaneous self-assembly of the protein BslA into functionalizable surfaces on the surface of a CFPS reaction chamber. We show that proteins can be covalently captured by such surfaces, using “Catcher/Tag” technology. Importantly, proteins of interest can be captured either when synthesised in situ by CFPS above the BslA surfaces, or when added as pure protein. The simplicity and cost efficiency of this method suggest that it will find many applications in cell-free-based methods

Live‐cell super‐resolution imaging of actin using LifeAct‐14 with a PAINT‐based approach

We present direct‐LIVE‐PAINT, an easy‐to‐implement approach for the nanoscopic imaging of protein structures in live cells using labeled binding peptides. We demonstrate the feasibility of direct‐LIVE‐PAINT with an actin‐binding peptide fused to EGFP, the location of which can be accurately determined as it transiently binds to actin filaments. We show that direct‐LIVE‐PAINT can be used to image actin structures below the diffraction‐limit of light and have used it to observe the dynamic nature of actin in live cells. We envisage a similar approach could be applied to imaging other proteins within live mammalian cells

Imaging Proteins Sensitive to Direct Fusions Using Transient Peptide–Peptide Interactions

Fluorescence microscopy enables specific visualization of proteins in living cells and has played an important role in our understanding of the protein subcellular location and function. Some proteins, however, show altered localization or function when labeled using direct fusions to fluorescent proteins, making themdifficult to study in live cells. Additionally, the resolution of fluorescence microscopy is limited to ∼200 nm, which is 2 orders of magnitude larger than the size of most proteins. To circumvent these challenges, we previously developed LIVE-PAINT, a live-cell superresolution approach that takes advantage of short interacting peptides to transiently bind a fluorescent protein to the protein-ofinterest. Here, we successfully use LIVE-PAINT to image yeastmembrane proteins that do not tolerate the direct fusion of a fluorescent protein by using peptide tags as short as 5-residues. We also demonstrate that it is possible to resolve multiple proteins at the nanoscale concurrently using orthogonal peptide interaction pairs.KEYWORDS: membrane protein, protein−protein interaction, super-resolution microscopy, live-cell imaging, LIVE-PAINT, yeas

Python code to total localizations per user defined number of frames from SMLM data

Python code that reads GDSCSMLM FitResults csv files and totals localisaitons in user specified frame divisions. This code was written as part of ongoing research of the LIVE-PAINT super-resolution imaging technique in the Regan and Horrocks Labs at the University of Edinburgh

Python code to identify septum specific localisations and calculate the mother:daugter cell ratio and septum width

<p>Python code that reads GDSCSMLM FitResults csv files from images of Cdc12 in budding yeast and runs DBSCAN cluster analysis on the data to find the septum specific localisations as well as cellular localisations. The code then calculates the mother cell:daughter cell diameter ratio and the average width of the septum. This code was written as part of ongoing research of the LIVE-PAINT super-resolution imaging technique in the Regan and Horrocks Labs at the University of Edinburgh.</p&gt

Cluster analysis of SMLM data and analysis of the distances between nearest neighbouring clusters

Python code that reads GDSCSMLM FitResults csv files and runs DBSCAN cluster analysis on the data. A nearest neighbour algorithm is then applied to identify and calculate the distance to the nearest neighbouring cluster to each cluster. The distances between clusters identified for each file and for all the files in a folder are displayed in histograms. In addition, a confidence ellipse is fitted to each cluster to enable the calculation of the eccentricity and the maximum length of the cluster. This code was written as part of ongoing research of the LIVE-PAINT super-resolution imaging technique in the Regan and Horrocks Labs at the University of Edinburgh

Python code to calculate the average FRC resolution, precision, signal, SBR, and number of localisations for each file in a folder

<p>Python code that reads GDSCSMLM FitResults csv files and calculate the average FRC resolution, precision, signal, SBR, and number of localisations for each file and produces a summary spreadsheet for all the files analysed. The code was adapted from a script written by Dr Mathew Horrocks. </p> <p>This code was written as part of ongoing research of the LIVE-PAINT super-resolution imaging technique in the Regan and Horrocks Labs at the University of Edinburgh.</p&gt

LIVE-PAINT Supporting Datasets

We present LIVE-PAINT, a new approach to super-resolution fluorescent imaging inside live cells. In LIVE-PAINT only a short peptide sequence is fused to the protein being studied, unlike conventional super-resolution methods, which rely on directly fusing the biomolecule of interest to a large fluorescent protein, organic fluorophore, or oligonucleotide. LIVE-PAINT works by observing the blinking of localized fluorescence as this peptide is reversibly bound by a protein that is fused to a fluorescent protein. We have demonstrated the effectiveness of LIVE-PAINT by imaging a number of different proteins inside live S. cerevisiae. Not only is LIVE-PAINT widely applicable, easily implemented, and the modifications minimally perturbing, but we also anticipate it will extend data acquisition times compared to those previously possible with methods that involve direct fusion to a fluorescent protein.Oi, Curran; Horrocks, Mathew; Gidden, Zoe; Regan, Lynne. (2020). LIVE-PAINT Supporting Datasets, [dataset]. University of Edinburgh. Quantitative Biology, Biochemistry and Biotechnology. https://doi.org/10.7488/ds/2859

Cerebrovascular amyloid Angiopathy in bioengineered vessels is reduced by high-density lipoprotein particles enriched in Apolipoprotein E

Background: Several lines of evidence suggest that high-density lipoprotein (HDL) reduces Alzheimer’s disease (AD) risk by decreasing vascular beta-amyloid (Aβ) deposition and inflammation, however, the mechanisms by which HDL improve cerebrovascular functions relevant to AD remain poorly understood. Methods: Here we use a human bioengineered model of cerebral amyloid angiopathy (CAA) to define several mechanisms by which HDL reduces Aβ deposition within the vasculature and attenuates endothelial inflammation as measured by monocyte binding. Results: We demonstrate that HDL reduces vascular Aβ accumulation independently of its principal binding protein, scavenger receptor (SR)-BI, in contrast to the SR-BI-dependent mechanism by which HDL prevents Aβ-induced vascular inflammation. We describe multiple novel mechanisms by which HDL acts to reduce CAA, namely: i) altering Aβ binding to collagen-I, ii) forming a complex with Aβ that maintains its solubility, iii) lowering collagen-I protein levels produced by smooth-muscle cells (SMC), and iv) attenuating Aβ uptake into SMC that associates with reduced low density lipoprotein related protein 1 (LRP1) levels. Furthermore, we show that HDL particles enriched in apolipoprotein (apo)E appear to be the major drivers of these effects, providing new insights into the peripheral role of apoE in AD, in particular, the fraction of HDL that contains apoE. Conclusion: The findings in this study identify new mechanisms by which circulating HDL, particularly HDL particles enriched in apoE, may provide vascular resilience to Aβ and shed new light on a potential role of peripherally-acting apoE in AD.Applied Science, Faculty ofMedicine, Faculty ofScience, Faculty ofOther UBCNon UBCBiomedical Engineering, School ofMedicine, Department ofNeurology, Division ofPathology and Laboratory Medicine, Department ofPhysics and Astronomy, Department ofReviewedFacult