Hamidreza Heydarian

Hamidreza Heydarian

Postdoc researcher at TU Delft

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

First blog post

less than 1 minute read

Published:

I decided to maintain this personal website regularly and probably write about my work experiences here. Let’s see …

portfolio

publications

Template-free 2D particle fusion in localization microscopy

Published in Nature Methods, 2018

Methods that fuse multiple localization microscopy images of a single structure can improve signal-to-noise ratio and resolution, but they generally suffer from template bias or sensitivity to registration errors. We present a template-free particle-fusion approach based on an all-to-all registration that provides robustness against individual misregistrations and underlabeling. We achieved 3.3-nm Fourier ring correlation (FRC) image resolution by fusing 383 DNA origami nanostructures with 80% labeling density, and 5.0-nm resolution for structures with 30% labeling density.

Recommended citation: Heydarian, Hamidreza et al. (2018). "Template-free 2D particle fusion in localization microscopy." Nature Methods. 15. https://www.nature.com/articles/s41592-018-0136-6

Three dimensional particle averaging for structural imaging of macromolecular complexes by localization microscopy

Published in biorxiv, 2019

We present an approach for 3D particle fusion in localization microscopy which dramatically increases signal-to-noise ratio and resolution in single particle analysis. Our method does not require a structural template, and properly handles anisotropic localization uncertainties. We demonstrate 3D particle reconstructions of the Nup107 subcomplex of the nuclear pore complex (NPC), cross-validated using multiple localization microscopy techniques, as well as two-color 3D reconstructions of the NPC, and reconstructions of DNA-origami tetrahedrons.

Recommended citation: Hamidreza, Heydarian et al. (2019). "Three dimensional particle averaging for structural imaging of macromolecular complexes by localization microscopy." biorxiv. https://www.biorxiv.org/content/10.1101/837575v1

DATA FUSION IN LOCALIZATION MICROSCOPY

Published in Computational Modeling: From Chemistry to Materials to Biology, 2021

The following sections are included:

Recommended citation: HAMIDREZA HEYDARIAN, MARK BATES, FLORIAN SCHUEDER, RALF JUNGMANN, SJOERD STALLINGA, and BERND RIEGER, Computational Modeling: From Chemistry to Materials to Biology. February 2021, 201-204 https://doi.org/10.1142/9789811228216_0024

3D particle averaging and detection of macromolecular symmetry in localization microscopy

Published in Nature Communications, 2021

Single molecule localization microscopy offers in principle resolution down to the molecular level, but in practice this is limited primarily by incomplete fluorescent labeling of the structure. This missing information can be completed by merging information from many structurally identical particles. In this work, we present an approach for 3D single particle analysis in localization microscopy which hugely increases signal-to-noise ratio and resolution and enables determining the symmetry groups of macromolecular complexes. Our method does not require a structural template, and handles anisotropic localization uncertainties. We demonstrate 3D reconstructions of DNA-origami tetrahedrons, Nup96 and Nup107 subcomplexes of the nuclear pore complex acquired using multiple single molecule localization microscopy techniques, with their structural symmetry deducted from the data.

Recommended citation: Hamidreza, Heydarian et al. (2021). "3D particle averaging and detection of macromolecular symmetry in localization microscopy." Nature Communications. https://www.nature.com/articles/s41467-021-22006-5

Detecting structural heterogeneity in single-molecule localization microscopy data

Published in Nature Communications, 2021

Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.

Recommended citation: Huijben, T.A., Heydarian, H., Auer, A. et al. Detecting structural heterogeneity in single-molecule localization microscopy data. Nat Commun 12, 3791 (2021). https://www.nature.com/articles/s41467-021-24106-8

talks

Single Molecule Localization Microscopy Symposium (SMLMS) 2016

Published:

The 6th annual Single Molecule Localization Microscopy Symposium (SMLMS) will take place at the Ecole Polytechnique Fédérale de Lausanne 28th-30th August 2016 in Rolex Learning Center. This edition continues the successful SMLMS series Zurich 2011, Lausanne 2012, Frankfurt 2013, London 2014, and Bordeaux 2015. The scope of the meeting is to bring together scientists from Europe and abroad working in the field of single-molecule super-resolution imaging.
More information here

Quantitative BioImaging Conference 2017

Published:

The Quantitative BioImaging Society seeks to foster the scientific exchange of researchers with interest in quantitative imaging in biological and biomedical sciences. A particular emphasis is to promote interdisciplinary interactions between physicists, engineers, chemists, mathematicians, biologists, etc. One of the main activities to date has been the organization of the Quantitative BioImaging Conference that is held annually at different locations worldwide.

Quantitative BioImaging Conference 2018

Published:

The Quantitative BioImaging Society seeks to foster the scientific exchange of researchers with interest in quantitative imaging in biological and biomedical sciences. A particular emphasis is to promote interdisciplinary interactions between physicists, engineers, chemists, mathematicians, biologists, etc. One of the main activities to date has been the organization of the Quantitative BioImaging Conference that is held annually at different locations worldwide.

International Conference on Nanoscopy (ICON Europe) 2018

Published:

The second International Conference on Nanoscopy (ICON Europe), held 27 February–2 March, with more than 200 attendees from 21 countries. There were three keynote talks, 18 invited talks, 21 oral presentations and 40 poster presentations. I had an oral presentation at this conference. You can read news coverage about this conference here and here. Also More information here

teaching

Teaching assistant for Computational Science

Undergraduate course, Delft Dniversity of Technology, Imaging Physics Department, 2016

The aim of the course Computational Science is to introduce the student in the use of models and simulation techniques for research into physical phenomena and processes. First, the student is taught to program in a programming environment: MATLAB. The students then carry out a number of assignments that illustrate different aspects of the use of simulations within physics. Based on this, the student also becomes acquainted with a number of frequently used numerical techniques, the stability of the methods used and the error estimation.

Teaching assistant for Signals and Systems

Undergraduate course, Delft University of Technology, Imaging Physics Department, 2017

Bachelor course based on the famous book of Oppenheim. I was the tutor of this course.

Teaching assistant for Medical Imaging

Undergraduate course, Delft Dniversity of Technology, Imaging Physics Department, 2018

This course is part of the minor Biomedical Engineering. It consists of 3 parts, which are given simultaneously: Image acquisition, Image processing and Data analysis. I worked as TA for this course.

Teaching assistant for Advance Digital Image Processing

Master course, Delft Dniversity of Technology, Imaging Physics Department, 2020

The aim of this course is to teach the students to acquire in-depth knowledge of state-of-the-art image processing techniques. By the end of this course the students will be able to solve advanced problems addressing the theory of image processing by combining mathematical skills and physical insight. For this course, I designed the programming assignments. The programming assignments were written in MATLAB and used dipimage library for several tasks.