Dr. Hisham Mazal

Deciphering a hexameric protein complex with Angstrom optical resolution

Hisham Mazal, Franz Wieser, Vahid Sandoghdar

Cryogenic optical localization in three dimensions (COLD) was recently shown to resolve up to four binding sites on a single protein. However, because COLD relies on intensity fluctuations that result from the blinking behavior of fluorophores, it is limited to cases, where individual emitters show different brightness. This significantly lowers the measurement yield. To extend the number of resolved sites as well as the measurement yield, we employ partial labeling and combine it with polarization encoding in order to identify single fluorophores during their stochastic blinking. We then use a particle classification scheme to identify and resolve heterogenous subsets and combine them to reconstruct the three-dimensional arrangement of large molecular complexes. We showcase this method (polarCOLD) by resolving the trimer arrangement of proliferating cell nuclear antigen (PCNA) and the hexamer geometry of Caseinolytic Peptidase B (ClpB) of Thermus thermophilus in its quaternary structure, both with Angstrom resolution. The combination of polarCOLD and single-particle cryogenic electron microscopy (cryoEM) promises to provide crucial insight into intrinsic, environmental and dynamic heterogeneities of biomolecular structures. Furthermore, our approach is fully compatible with fluorescent protein labeling and can, thus, be used in a wide range of studies in cell and membrane biology.<br><br>Significance statement Fluorescence super-resolution microscopy has witnessed many clever innovations in the last two decades. Here, we advance the frontiers of this field of research by combining partial labeling and 2D image classification schemes with polarization-encoded single-molecule localization at liquid helium temperature to reach Angstrom resolution in three dimensions. We demonstrate the performance of the method by applying it to trimer and hexamer protein complexes. Our approach holds great promise for examining membrane protein structural assemblies and conformations in challenging native environments. The methodology closes the gap between electron and optical microscopy and offers an ideal ground for correlating the two modalities at the single-particle level. Indeed, correlative light and electron microscopy is an emerging technique that will provide new insight into cell biology.

Insights into protein structure using cryogenic light microscopy

Hisham Mazal, Franz Wieser, Vahid Sandoghdar

Fluorescence microscopy has witnessed many clever innovations in the last two decades, leading to new methods such as structured illumination and super-resolution microscopies. The attainable resolution in biological samples is, however, ultimately limited by residual motion within the sample or in the microscope setup. Thus, such experiments are typically performed on chemically fixed samples. Cryogenic light microscopy (Cryo-LM) has been investigated as an alternative, drawing on various preservation techniques developed for cryogenic electron microscopy (Cryo-EM). Moreover, this approach offers a powerful platform for correlative microscopy. Another key advantage of Cryo-LM is the strong reduction in photobleaching at low temperatures, facilitating the collection of orders of magnitude more photons from a single fluorophore. This results in much higher localization precision, leading to Angstrom resolution. In this review, we discuss the general development and progress of Cryo-LM with an emphasis on its application in harnessing structural information on proteins and protein complexes.<br>

Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa

Mahyar Dahmardeh, Houman Mirzaalian Dastjerdi, Hisham Mazal, Harald Köstler, Vahid Sandoghdar

Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizing<br>their binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noise<br>so that collection of more photons should allow its detection sensitivity to biomolecules of arbitrarily low mass. However, a<br>number of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit in<br>iSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes the<br>mass sensitivity limit by a factor of four to below 10 kDa. We implement this scheme both with a user-defined feature matrix<br>and a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internal<br>reflection mode. Our work opens the door to the optical detection of small traces of disease markers such as alpha-synuclein,<br>chemokines, and cytokines.