Biology subjects

Zehtabian, A.

Publications and source records attributed to Zehtabian, A..

7 recordsLinked to original sources

bioRxiv2025

Multiscale imaging of cytokinesis

After separation of duplicated genetic material, animal cells divide by cytokinesis. This process involves several simultaneous and successive steps of molecular reorganization at cell membrane, cortex and the spindle apparatus as the cleavage furrow ingresses and the spindle is transformed into the intercellular bridge. Dozens of proteins play a role in cytokinesis. However, a comprehensive view of their successive nanoscale reorganizations over cytokinesis remains untractable with current microscopy methods. Here, we developed a multimodal imaging and analysis framework to investigate the relative organization of key cytokinetic molecules over time. We extract cytokinetic hallmarks from live-cell lattice light-sheet and expansion microscopy (ExM) to computationally define pseudotime and align nanoscale ExM images of cytokinesis. We discover a cytokinetic substep and create nanoscale maps of the intercellular bridge and the midbody in 6 stages for 20 involved molecules. We thus provide a framework for nanoscale analysis of cytokinesis, a process fundamental to life.

cell biology↗

bioRxiv2025

T cell protrusions enable fast, localised initiation of CAR signalling

Actin-rich protrusions densely cover the surface of T cells and are well characterised for their role in cell migration. However, recent studies have uncovered their role in antigen surveillance and immune signalling initiation. To investigate how membrane protrusions initiate and contribute to signalling, from the first cell-cell contact to immunological synapse formation, we performed dynamic imaging experiments of endogenously tagged signalling proteins in T cells. To quantitatively capture the early dynamics of cell-cell interactions, we employed HER2-CAR-expressing T cells targeting HER2+breast cancer cells. By harnessing live-cell imaging and super-resolution stimulated emission depletion (STED) microscopy we were able to capture topological membrane changes and their correlation with mesoscale protein rearrangements over time. Our findings indicate that, prior to activation, key molecular players in T cell activation, including the kinase Lck, the phosphatase CD45 and the adaptor LAT, as well as the exogenously expressed CAR, lack any enrichment in actin-rich protrusions. However, upon initial contact of the T cell with the target cell, a dynamic and fast rearrangement of the surface receptors, phosphatases, and kinases occurs within the protrusions, ensuring a rapid and effective initiation of the immune signalling cascade. The rapid clustering of the HER2-CAR occurs preferentially within protrusions rather than flat membrane regions and is accompanied by enhanced recruitment of the kinase ZAP-70 and LAT. While the localisation of the kinase Lck remained unchanged, protrusion-cell contacts trigger a pronounced exclusion of the phosphatase CD45, an effect observed both with and without the cytosolic signalling domain of the CAR. Overall, the signalling machinery rearranged more rapidly and efficiently at contacts mediated by protrusive structures compared to non-protrusive regions. Together, our data provide a quantitative framework illustrating how signalling proteins are dynamically reorganised to facilitate CAR-mediated activation within these specialised structures.

cell biology↗

bioRxiv2024

Circadian period is compensated for repressor protein turnover rates in single cells

Most mammalian cells possess molecular circadian clocks generating widespread rhythms, e.g. in transcript and protein abundance. While circadian clocks are robust to fluctuations in the cellular environment, little is known about how circadian period is compensated for fluctuating metabolic states. Here, we exploit the heterogeneity of single cells both in circadian period and metabolic state, governing protein stability, to study their interdependence without the need for genetic manipulation. We generated cells expressing key circadian proteins (CRY1/2 and PER1/2) as endogenous fusions with fluorescent proteins and simultaneously monitored circadian rhythms and degradation in thousands of single cells. We found that the circadian period is compensated for fluctuations in the turnover rates of circadian repressor proteins and uncovered possible mechanisms using a mathematical model. In addition, the stabilities of the repressor proteins are circadian phase-dependent and correlate with the circadian period in a phase-dependent manner, in contrast to the prevailing model.

cell biology↗

bioRxiv2022

Automated Analysis of Neuronal Morphology through an Unsupervised Classification Model of Neurites

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these billions of often far-reaching connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering often mm distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and error prone. Especially fully automated segmentation and classification of all neurites remain unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in fluorescence micrographs with minimal requirements for user interaction. Neurons are segmented using a Hessian-based algorithm to detect thin neurite structures combined with intensity- and shape-based detection of the cell body. To measure the number of branches in a neuron accurately, rather than just determining branch points, neurites are classified into axon, dendrites and their branches of increasing order by their length using a geodesic distance transform of the cell skeleton. The software was benchmarked against a large, published dataset and reproduced the phenotype observed after manual annotation before. Our tool promises greatly accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.

bioinformatics↗

bioRxiv2021

Precise measurement of nanoscopic septin ring structures in deep learning-assisted quantitative superresolution microscopy

The combination of image analysis and fluorescence superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning-based object recognition enables the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, deep learning methods are prone to different forms of bias. A biased recognition of structures may prohibit the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. What is required to overcome this problem is a detailed investigation of potential sources of bias and the rigorous testing of trained models using real or simulated data covering a wide dynamic range of possible results. Here we combine single molecule localization-based superresolution microscopy of septin ring structures with the training of several different deep learning models for a quantitative investigation of bias resulting from different training approaches and finally quantitative changes in septin ring structures. We find that trade-off exists between measurement accuracy and the dynamic range of recognized phenotypes. Using our trained models, we furthermore find that septin ring size can be explained by the number of subunits they are assembled from alone. Our work provides a new experimental system for the investigation of septin polymerization.

cell biology↗

bioRxiv2020

Live-cell imaging of circadian clock protein dynamics in CRISPR-generated knock-in cells

The current model of the mammalian circadian oscillator is predominantly based on data from genetics and biochemistry experiments, while the cell biology of circadian clocks is still in its infancy. Here, we describe a new strategy for the efficient generation of knock-in reporter cell lines using CRISPR technology that is particularly useful for lowly or transiently expressed genes, such as those coding for circadian clock proteins. We generated single and double knock-in cells with endogenously expressed PER2 and CRY1 fused to fluorescent proteins, which allowed to simultaneously monitor the dynamics of CRY1 and PER2 proteins in live single cells. Both proteins are highly rhythmic in the nucleus of human cells with PER2 showing a much higher amplitude than CRY1. Surprisingly, CRY1 protein is nuclear at all circadian times indicating the absence of circadian gating of nuclear import. Furthermore, in the nucleus of individual cells CRY1 abundance rhythms are phase-delayed ([~]5 hours), and CRY1 levels are much higher (>6 times) compared to PER2 questioning the current model of the circadian oscillator. Our knock-in strategy will allow the generation of additional single, double or triple knock-in cells for circadian clock proteins, which should greatly advance our understanding about the cell biology of circadian clocks.

cell biology↗

bioRxiv2018

Expansion stimulated emission depletion microscopy (ExSTED)

Stimulated emission depletion (STED) microscopy is routinely used to resolve the ultra-structure of cells with a [~]10-fold higher resolution compared to diffraction limited imaging. While STED microscopy is based on preparing the excited state of fluorescent probes with light, the recently developed expansion microscopy (ExM) provides sub-diffraction resolution by physically enlarging the sample before microscopy. Expansion of fixed cells by crosslinking and swelling of hydrogels easily enlarges the sample [~]4-fold and hence increases the effective optical resolution by this factor. To overcome the current limits of these complimentary approaches, we here combined ExM with STED (ExSTED) and demonstrate an increase in resolution of up to 30-fold compared to conventional microscopy (<10 nm lateral and [~]50 nm isotropic). While the increase in resolution is straight forward, we found that high fidelity labelling via multi-epitopes is required to obtain emitter densities that allow to resolve ultra-structural details with ExSTED. Our work provides a robust template for super resolution microscopy of entire cells in the ten nanometer range.

biophysics↗