Dr. Hisham Mazal

  • Postdoctoral Fellow
  • Room: A.3.246
  • Telephone: +49 9131 7133343
  • E-mail

My main research goal is to develop state-of-the-art single-particle cryogenic super-resolution fluorescence microscopy to uncover the intricate structures of soluble and membrane proteins in their native environments.

Additionally, our aim is to establish a streamlined workflow for freeze-preserved specimens enabling correlative structural biology studies using the two powerful microscopy approaches of cryogenic super-resolution light and electron microscopy.

2023

Insights into protein structure using cryogenic light microscopy

Hisham Mazal, Franz Wieser, Vahid Sandoghdar

Biochemical Society Transactions (2023) | Journal | PDF

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.

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

Nature Methods 20 442-447 (2023) | Journal | PDF

Interferometric scattering (iSCAT) microscopy is a label-free optical method capable of detecting single proteins, localizing their binding positions with nanometer precision, and measuring their mass. In the ideal case, iSCAT is limited by shot noise such that collection of more photons should extend its detection sensitivity to biomolecules of arbitrarily low mass. However, a number of technical noise sources combined with speckle-like background fluctuations have restricted the detection limit in iSCAT. Here, we show that an unsupervised machine learning isolation forest algorithm for anomaly detection pushes the mass sensitivity limit by a factor of 4 to below 10 kDa. We implement this scheme both with a user-defined feature matrix and a self-supervised FastDVDNet and validate our results with correlative fluorescence images recorded in total internal reflection mode. Our work opens the door to optical investigations of small traces of biomolecules and disease markers such as α-synuclein, chemokines and cytokines.<br><br>

Hisham Mazal studied Biotechnology Engineering (BSc) at ORT Braude Academic College of Engineering (Israel) from 2010 to 2013 and Chemical and Biological Physics (MSc) at Weizmann Institute of Science (Israel) from 2013 to 2015 as an undergraduate student. From 2016 to 2020 he continued at Weizmann Institute for his PhD thesis on “Single-molecule protein dynamics: From ligand binding effects on folding to function-related motions” and as a postdoc. In June 2020 Hisham Mazal joined the group of Prof. Vahid Sandoghdar at MPL as a postdoc.

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