Biology subjects

Martinez-Sanchez, A.

Publications and source records attributed to Martinez-Sanchez, A..

At least 19 recordsLinked to original sources

bioRxiv2026

TomoSegNet: Augmented membrane segmentation for cryo-electron tomography by simulating the cellular context

Membrane segmentation is an essential task in the workflow for processing cryo-electron tomography data. Recently, machine learning algorithms have successfully been adopted to perform membrane segmentation. However, the performance of these approaches is limited by the training dataset, as models are trained from manual annotations, thereby hindering the models generalization and preventing the recovery of membranes that have vanished due to distortions. Here, we address these limitations by generating training data with a simulator. To provide a representative and realistic dataset, we have extended the current state-of-the-art in simulators for cryo-electron tomography by incorporating a biophysical model for membranes. We demonstrate that our machine learning model, trained solely from synthetic data, and thanks to the physical knowledge learned from the simulator, outperforms the current state-of-the-art for membrane segmentation in a diverse set of experimental data. This performance is particularly noteworthy in terms of recovering membranes lost due to imaging distortions.

bioinformatics↗

bioRxiv2026

MTFP1 preserves β-cell cristae structure and bioenergetics to ensure insulin release and glucose homeostasis

Pancreatic {beta}-cells are uniquely dependent on mitochondrial metabolism to couple glucose sensing to insulin secretion, a process impaired in diabetes. Mitochondrial fission process 1 (MTFP1) is an inner mitochondrial membrane protein that plays pleiotropic, tissue-specific roles in mitochondrial function and dynamics. Our previous work has identified Mtfp1 mRNA as a target for miR-125b, a microRNA that negatively regulates insulin secretion from {beta}-cells. Nevertheless, the function of MTFP1 in these cells remained unexplored. Here, we show that MTFP1 is essential for normal glucose-stimulated insulin secretion (GSIS) in mouse and human cell lines and islets, and that mice with {beta}-cell-specific elimination of MTFP1 develop glucose intolerance. Whereas {beta}-cell survival and mitochondrial content were unaffected, oxidative phosphorylation and ATP production were sharply lowered. These changes were accompanied by disruption of mitochondrial cristae structure and a reduced contact surface with the endoplasmic reticulum, providing a mechanistic basis for defective stimulus-secretion coupling. Conversely, MTFP1 overexpression in mouse and human islets sufficed to improve mitochondrial respiration and GSIS. Finally, MTFP1 downregulation blocked the positive effects of miR-125b elimination in GSIS and mitochondrial respiration, unveiling MTFP1 as a downstream effector of miR-125b. Together, our findings identify MTFP1 as a critical regulator of {beta}-cell mitochondrial architecture and function, necessary for efficient insulin secretion and glucose homeostasis, and a potential therapeutic target to enhance {beta}-cell bioenergetic resilience in diabetes.

cell biology↗

bioRxiv2025

Spatial organization of assemblies of protein complexes by colocalization analysis

The precise localization of multiple species of proteins and protein complexes organized in nanodomains and large, non-periodic protein assemblies has been shown to be important for cellular function. Cryo-electron tomography is uniquely suited to visualize such assemblies in situ, with single nanometer precision, together with their cellular environment. Here we present in detail and characterize a parameter-free, second order point pattern analysis method that is applicable to cellular cryo-electron tomograms, and provides quantitative and statistical characterization of the colocalization between two or more distinct groups of complexes, such as those that comprise protein assemblies. By numerical and analytical calculations, we show that this colocalization method can correctly detect and distinguish different forms of point pattern interactions between complexes, and therefore identify specific types of spatial distribution of complexes, which result from multiple biochemical interactions and diffusion in cellular environments. We also present image processing tasks that precede the colocalization to facilitate its applications to cellular junctions and other biological systems.

biophysics↗

bioRxiv2024

DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography

Orientation estimation of macromolecules in cryo-electron tomography (cryo-ET) images is one of the fundamental steps in applying subtomogram averaging. The standard method in particle picking and orientation estimation is template matching (TM), which is computationally very expensive, with its performance depending linearly on the number of template orientations. In addition to conventional image processing methods like TM, the investigation of crowded cell environments using cryo-ET has also been attempted with deep learning (DL) methods. These attempts were restricted to macromolecule localization and identification while orientation estimation was not addressed due to a lack of a large enough dataset of ground truth annotations suitable for DL. To this end, we first generate a large-scale synthetic dataset of 450 tomograms containing almost 200K samples of two macromolecular structures using the PolNet simulator. Utilizing this synthetic dataset, we address the problem of particle orientation estimation as a regression problem by proposing a DL-based model based on multi-layer perceptron networks and a six-degree-of-freedom orientation representation. The iso-surface visualizations of the averaged subtomograms show that the predicted results by the network are overly similar to that of ground truth. Our work shows that orientation estimation of particles using DL methods is in principle possible provided that ground truth data is available. What remains to be solved is the gap between synthetic and experimental data. The source code is available at https://github.com/noushinha/DeepOrientation.

molecular biology↗

bioRxiv2024

Liver kinase B1 (LKB1) regulates the epigenetic landscape of mouse pancreatic beta cells

Liver kinase B1 (LKB1/STK11) is an important regulator of pancreatic {beta}-cell identity and function. Elimination of Lkb1 from the {beta}-cell results in improved glucose-stimulated insulin secretion and is accompanied by profound changes in gene expression, including the upregulation of several neuronal genes. The mechanisms through which LKB1 controls gene expression are, at present, poorly understood. Here, we explore the impact of {beta} cell- selective deletion of Lkb1 on chromatin accessibility in mouse pancreatic islets. To characterize the role of LKB1 in the regulation of gene expression at the transcriptional level, we combine these data with a map of islet active transcription start sites and histone marks. We demonstrate that LKB1 elimination from {beta}-cells results in widespread changes in chromatin accessibility, correlating with changes in transcript levels. Changes occurred in hundreds of promoter and enhancer regions, many of which were close to neuronal genes. We reveal that dysregulated enhancers are enriched in binding motifs for transcription factors important for {beta}-cell identity, such as FOXA, MAFA or RFX6 and we identify microRNAs (miRNAs) that are regulated by LKB1 at the transcriptional level. Overall, our study provides important new insights into the epigenetic mechanisms by which LKB1 regulates {beta}-cell identity and function.

cell biology↗

bioRxiv2024

MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography

Cryo-electron tomography (cryo-ET) provides unique insights into macromolecular complexes in their native environments, yet membrane analysis remains a major bottleneck due to low signal-to-noise ratios, missing wedge artifacts, and the complexity of membrane-associated proteins. Existing tools often require extensive manual annotation, struggle with generalization across datasets, and lack integrated solutions for segmentation, protein localization, and quantitative analysis. We introduce MemBrain v2, a deep learning-enabled framework that unifies these tasks into a streamlined pipeline. MemBrain-seg leverages a diverse, collaboratively generated training dataset and specialized model training strategies to achieve generalizable membrane segmentation across variable tomographic conditions. MemBrain-pick enables data-efficient localization of membrane-bound proteins by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into protein distributions, computing spatial metrics to analyze intra-membrane particle organization. MemBrain v2 integrates seamlessly into cryo-ET workflows, providing an accessible and structured approach to membrane analysis. The full package is available at https://github.com/CellArchLab/MemBrain-v2.

bioinformatics↗

bioRxiv2023

Simulating the cellular context in synthetic datasets for cryo-electron tomography

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

bioinformatics↗

bioRxiv2022

Direct Cryo-ET observation of platelet deformation induced by SARS-CoV-2 Spike protein

SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic. Its high pathogenicity is due to SARS-CoV-2 spike protein (S protein) contacting host-cell receptors. A critical hallmark of COVID-19 is the occurrence of coagulopathies. Here, we report the direct observation of the interactions between S protein and platelets. Live imaging showed that the S protein triggers platelets to deform dynamically, in some cases, leading to their irreversible activation. Strikingly, cellular cryo-electron tomography revealed dense decorations of S protein on the platelet surface, inducing filopodia formation. Hypothesizing that S protein binds to filopodia-inducing integrin receptors, we tested the binding to RGD motif-recognizing platelet integrins and found that S protein recognizes integrin v{beta}3. Our results infer that the stochastic activation of platelets is due to weak interactions of S protein with integrin, which can attribute to the pathogenesis of COVID-19 and the occurrence of rare but severe coagulopathies.

cell biology↗

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

Structural plasticity of Atg18 oligomers: organization of assembled tubes and scaffolds at the isolation membrane

Autophagy-related protein 18 (Atg18) participates in the elongation of early autophagosomal structures in concert with Atg2 and Atg9 complexes. How Atg18 contributes to the structural coordination of Atg2 and Atg9 at the isolation membrane remains to be understood. Here, we determined the cryo-EM structures of Atg18 organized in helical tubes as well as soluble oligomers. The helical assembly is composed of Atg18 tetramers forming a lozenge cylindrical lattice with remarkable structural similarity to the COPII outer coat. When reconstituted with lipid membranes, using subtomogram averaging we determined tilted Atg18 dimer structures bridging two juxtaposed lipid membranes spaced apart by 80 [A]. Together with an AlphaFold Atg18-Atg2 model, we propose that Atg18 oligomers form a structural scaffold coordinating the Atg2 membrane bridge. The observed structural plasticity of Atg18s oligomeric organization and membrane binding provide a molecular framework for the positioning of downstream components of the autophagy machinery.

biophysics↗

bioRxiv2022

MemBrain: A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms

Background and ObjectiveCryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the automated detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. MethodsWe present MemBrain - a new deep learning-based pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. ResultsMemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. ConclusionsMemBrain is a powerful and label-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain

bioinformatics↗

bioRxiv2020

The Structural Basis of Rubisco Phase Separation in the Pyrenoid

Approximately one-third of global CO2 fixation occurs in a phase separated algal organelle called the pyrenoid. Existing data suggest that the pyrenoid forms by the phase-separation of the CO2-fixing enzyme Rubisco with a linker protein; however, the molecular interactions underlying this phase-separation remain unknown. Here we present the structural basis of the interactions between Rubisco and its intrinsically disordered linker protein EPYC1 (Essential Pyrenoid Component 1) in the model alga Chlamydomonas reinhardtii. We find that EPYC1 consists of five evenly-spaced Rubisco-binding regions that share sequence similarity. Single-particle cryo-electron microscopy of one of these regions in complex with Rubisco indicates that each Rubisco holoenzyme has eight binding sites for EPYC1, one on each Rubisco small subunit. Interface mutations disrupt binding, phase separation, and pyrenoid formation. Cryo-electron tomography supports a model where EPYC1 and Rubisco form a co-dependent multivalent network of specific low-affinity bonds, giving the matrix liquid-like properties. Our results advance the structural and functional understanding of the phase separation underlying the pyrenoid, an organelle that plays a fundamental role in the global carbon cycle.

molecular biology↗

bioRxiv2020

In situ architecture of neuronal alpha-Synuclein inclusions

-Synuclein (-Syn) aggregation is a hallmark of devastating neurodegenerative disorders including Parkinsons disease (PD) and multiple systems atrophy (MSA)1,2. -Syn aggregates spread throughout the brain during disease progression2, suggesting mechanisms of intercellular seeding. Formation of -Syn amyloid fibrils is observed in vitro3,4 and fibrillar -Syn has been purified from patient brains5,6, but recent reports questioned whether disease-relevant -Syn aggregates are fibrillar in structure7-9. Here we use cryo-electron tomography (cryo-ET) to image neuronal Lewy body-like -Syn inclusions in situ at molecular resolution. We show that the inclusions consist of -Syn fibrils crisscrossing a variety of cellular organelles such as the endoplasmic reticulum (ER), mitochondria and autophagic structures, without interacting with membranes directly. Neuronal inclusions seeded by recombinant or MSA patient-derived -Syn aggregates have overall similar architecture, although MSA-seeded fibrils show higher structural flexibility. Using gold-labeled seeds we find that aggregate nucleation is predominantly mediated by -Syn oligomers, with fibrils growing unidirectionally from the seed. Our results conclusively demonstrate that neuronal -Syn inclusions contain -Syn fibrils intermixed with cellular membranes, and illuminate the mechanism of aggregate nucleation.

cell biology↗

bioRxiv2020

Trans-synaptic assemblies link synaptic vesicles and neuroreceptors

Synaptic transmission is characterized by fast, tightly coupled processes and complex signaling path-ways that require a distinctly non-random spatial organization of their components. Nanoscale organization of synaptic proteins at glutamatergic synapses was suggested to regulate synaptic plasticity, the process underlying learning and memory. Specifically, direct colocalization of pre- and postsynaptic proteins implicated that the alignment of neurotransmitter release sites with neurotransmitter receptors enables maximal synaptic response. However, direct visualization and the mechanistic understanding of this alignment is lacking. Here we used cryo-electron tomography to visualize synaptic complexes in their native environment with the full complement of their interacting partners, synaptic vesicles and plasma membranes on 2-4 nanometer scale. The application of our recent template-free detection and classification procedure showed that tripartite trans-synaptic assemblies (subcolumns) link synaptic vesicles to postsynaptic receptors, and established that a particular displacement between directly interacting complexes characterizes subcolumns. Furthermore, we obtained de novo average structures of ionotropic glutamate receptors in their physiological composition, embedded in lipid membranes. The data presented support the hypothesis that synaptic function is carried by precisely organized trans-synaptic units. It complements superresolution findings and provides a framework for further exploration of synaptic and other large molecular assemblies that link different cells or cellular regions and may require weak or transient interactions to exert their function.

neuroscience↗

bioRxiv2020

Sexually dimorphic roles for the type 2 diabetes-associated C2cd4b gene in murine glucose homeostasis

Variants close to the VPS13C/C2CD4A/C2CD4B locus are associated with altered risk of type 2 diabetes in genome-wide association studies. Whilst previous functional work has suggested roles for VPS13C and C2CD4A in disease development, none has explored the role of C2CD4B. Here, we show that systemic inactivation of C2cd4b in mice leads to marked, but highly sexually dimorphic, changes in body weight and glucose homeostasis. Female C2cd4b mice display unchanged body weight but abnormal glucose tolerance and defective in vivo, but not in vitro, insulin secretion, associated with a marked decrease in follicle stimulating hormone levels. In sharp contrast, male C2cd4b null mice displayed normal glucose tolerance but an increase in body weight and fasting glycemia after maintenance on high fat diet. No metabolic disturbances were observed after global inactivation of C2cd4a in mice, or in pancreatic {beta} cell function at larval stages in C2cd4ab null zebrafish. These studies suggest that C2cd4b may act centrally to influence sex-dependent circuits which control pancreatic {beta} cell function and glucose tolerance in rodents. However, the absence of sexual dimorphism in the impact of diabetes risk variants argues for additional roles for C2CD4A or VPS13C in the control of glucose homeostasis in man.

genetics↗

bioRxiv2020

Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms

Cryo-electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. While this label-free cryogenic imaging technology produces data containing rich structural information, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present a computational procedure that uses artificial neural networks to simultaneously localize with a multi-class strategy several macromolecular species in cellular cryo-electron tomograms. Once trained, the inference stage of DeepFinder is significantly faster than template matching, and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (~3.2 MDa), Rubisco (~540 kDa soluble complex), and photosystem II (~550 kDa membrane complex) with comparable accuracy to expert-supervised ground truth annotations. Furthermore, we show that DeepFinder is flexible and can be combined with template matching to localize the missing macromolecules not found by one or the other method. The DeepFinder algorithm is therefore very promising for the semi-automated analysis of a wide range of molecular targets in cellular tomograms, including macromolecules with weights of 500-600 kDa and membrane proteins.

bioinformatics↗

bioRxiv2019

The mitochondrial Ca2+ uniporter MCU is required for normal glucose-stimulated insulin secretion in vitro and in vivo

Mitochondrial oxidative metabolism is central to glucose-stimulated insulin secretion (GSIS). Whether Ca2+ uptake into pancreatic {beta}-cell mitochondria potentiates or antagonises this process is still a matter of debate. Although the mitochondrial importer (MCU) complex is thought to represent the main route for Ca2+ transport across the inner mitochondrial membrane, its role in {beta}-cells has not previously been examined in vivo. Here, we inactivated the pore-forming subunit MCUa (MCU) selectively in the {beta}-cell in mice using Ins1Cre-mediated recombination. Glucose-stimulated mitochondrial Ca2+ accumulation, ATP production and insulin secretion were strongly (p<0.05 and p<0.01) inhibited in MCU null animals ({beta}MCU-KO) in vitro. Interestingly, cytosolic Ca2+ concentrations increased (p<0.001) whereas mitochondrial membrane depolarisation improved in {beta}MCU-KO animals. Male {beta}MCU-KO mice displayed impaired in vivo insulin secretion at 5 (p<0.001) but not 15 min. post intraperitoneal (IP) injection of glucose while the opposite phenomenon was observed following an oral gavage at 5 min. Unexpectedly, glucose tolerance was improved (p<0.05) in young {beta}MCU-KO (<12 weeks), but not older animals. We conclude that MCU is crucial for mitochondrial Ca2+ uptake in pancreatic {beta}-cells and is required for normal GSIS. The apparent compensatory mechanisms which maintain glucose tolerance in {beta}MCU-KO mice remain to be established.

physiology↗

bioRxiv2019

Estimation of Membrane Curvature for Cryo-Electron Tomography

Curvature is an important morphological descriptor of cellular membranes. Cryo-electron tomography (cryo-ET) is particularly well-suited to visualize and analyze membrane morphology in a close-to-native state and high resolution. However, current curvature estimation methods cannot be applied directly to membrane segmentations in cryo-ET. Additionally, a reliable estimation requires to cope with quantization noise. Here, we developed and implemented a method for membrane curvature estimation from tomogram segmentations.\n\nFrom a membrane segmentation, a signed surface (triangle mesh) is first extracted. The triangle mesh is then represented by a graph (vertices and edges), which facilitates finding neighboring triangles and the calculation of geodesic distances necessary for local curvature estimation. Here, we present several approaches for accurate curvature estimation based on tensor voting. Beside curvatures, these methods also provide robust estimations of surface normals and principal directions.\n\nWe tested the different methods on benchmark surfaces with known curvature, demonstrating the validity of these methods and their robustness to quantization noise. We also applied two of these approaches to biological cryo-ET data. The results allowed us to determine the best approach to estimate membrane curvature in cellular cryo-ET data.

bioinformatics↗