Biology subjects

Toro-Nahuelpan, M.

Publications and source records attributed to Toro-Nahuelpan, M..

4 recordsLinked to original sources

bioRxiv2023

The distance between the plasma membrane and the actomyosin cortex acts as a nanogate to control cell surface mechanics

Animal cell shape changes are controlled by the actomyosin cortex, a peripheral actin network tethered to the plasma membrane by membrane-to-cortex attachment (MCA) proteins. Previous studies have focused on how myosin motors or actin turnover can generate the local deformations required for morphogenesis. However, how the cell controls local actin nucleation remains poorly understood. By combining molecular engineering with biophysical approaches and in situ characterization of cortical actin network architecture, we show that membrane-to-cortex tethering determines the distance between the plasma membrane and the actomyosin cortex at the nanoscale of single actin nucleators. In turn, the size of this gap dictates actin filament production and the mechanical properties of the cell surface. Specifically, it tunes formin activity, controlling actin bundling and cortical tension. Our study defines the membrane-to-cortex distance as a nanogate that cells can open or close by MCA proteins to control the activity of key molecules at the cell surface.

biophysics↗

bioRxiv2022

Cryo-electron tomography reveals enrichment and identifies microtubule lumenal particles in neuronal differentiation

The functional architecture of the long-lived neuronal microtubule (MT) cytoskeleton is maintained by various MT-associated proteins (MAPs), most of which are known to bind to the MT outer surface. However, electron microscopy (EM) has long ago revealed the presence of particles inside the lumens of neuronal MTs, of yet unknown identity and function. Here, we use cryogenic electron tomography (cryo-ET) to analyze the three-dimensional (3D) structures and organizations of MT lumenal particles in primary hippocampal neurons, human induced pluripotent stem cell-derived neurons and pluripotent P19 cells. We obtain in-cell 3D maps of several lumenal particles from the respective cells and detect structural features that are common to all cell-types, underscoring their potential overarching functions. Mass spectrometry-based proteomics combined with structural modeling suggests a subset of lumenal particles could be tubulin-binding cofactors (TBCs) bound to tubulin monomers. A different subset of smaller particles, which remains unidentified, exhibits densities that bridge across the MT protofilaments. We show that increased lumenal particle concentration within MTs is concomitant with neuronal differentiation and correlates with higher MT curvatures. Enrichment of lumenal particles around MT lattice defects and at freshly polymerized MT open-ends suggest a MT protective role. Together with the identified structural resemblance of a subset of particles to TBCs, these results hint at a role in local tubulin proteostasis for the maintenance of long-lived neuronal MTs.

cell biology↗

bioRxiv2022

Convolutional networks for supervised mining of molecular patterns within cellular context

Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (Deep Picker in Context), an open-source deep-learning framework for supervised structure segmentation and macromolecular complex localization in cellular cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles and cytosol. By comparing our method to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally-distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, by applying pre-trained networks to a HeLa cell dataset, we demonstrate that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate exploitation by the community.

bioinformatics↗

bioRxiv2019

Tailoring cryo-electron microscopy grids by photo-micropatterning for in-cell structural studies

Spatially-controlled cell adhesion on electron microscopy (EM) supports remains a bottleneck in specimen preparation for cellular cryo-electron tomography. Here, we describe contactless and mask-free photo-micropatterning of EM grids for site-specific deposition of extracellular matrix-related proteins. We attained refined cell positioning for micromachining by cryo-focused ion beam milling. Complex patterns generated predictable intracellular organization, allowing direct correlation between cell architecture and in-cell 3D-structural characterization of the underlying molecular machinery.

cell biology↗