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1.
Cell ; 172(4): 696-705.e12, 2018 02 08.
Artigo em Inglês | MEDLINE | ID: mdl-29398115

RESUMO

Protein aggregation and dysfunction of the ubiquitin-proteasome system are hallmarks of many neurodegenerative diseases. Here, we address the elusive link between these phenomena by employing cryo-electron tomography to dissect the molecular architecture of protein aggregates within intact neurons at high resolution. We focus on the poly-Gly-Ala (poly-GA) aggregates resulting from aberrant translation of an expanded GGGGCC repeat in C9orf72, the most common genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. We find that poly-GA aggregates consist of densely packed twisted ribbons that recruit numerous 26S proteasome complexes, while other macromolecules are largely excluded. Proximity to poly-GA ribbons stabilizes a transient substrate-processing conformation of the 26S proteasome, suggesting stalled degradation. Thus, poly-GA aggregates may compromise neuronal proteostasis by driving the accumulation and functional impairment of a large fraction of cellular proteasomes.


Assuntos
Alanina/análogos & derivados , Proteína C9orf72 , Neurônios , Ácido Poliglutâmico , Complexo de Endopeptidases do Proteassoma , Agregados Proteicos , Alanina/genética , Alanina/metabolismo , Esclerose Lateral Amiotrófica/genética , Esclerose Lateral Amiotrófica/metabolismo , Esclerose Lateral Amiotrófica/patologia , Animais , Proteína C9orf72/genética , Proteína C9orf72/metabolismo , Demência Frontotemporal/genética , Demência Frontotemporal/metabolismo , Demência Frontotemporal/patologia , Células HEK293 , Humanos , Neurônios/metabolismo , Neurônios/patologia , Ácido Poliglutâmico/genética , Ácido Poliglutâmico/metabolismo , Complexo de Endopeptidases do Proteassoma/genética , Complexo de Endopeptidases do Proteassoma/metabolismo , Biossíntese de Proteínas , Estabilidade Proteica , Estrutura Quaternária de Proteína , Ratos , Ratos Sprague-Dawley
2.
Cell ; 171(1): 179-187.e10, 2017 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-28890085

RESUMO

Expression of many disease-related aggregation-prone proteins results in cytotoxicity and the formation of large intracellular inclusion bodies. To gain insight into the role of inclusions in pathology and the in situ structure of protein aggregates inside cells, we employ advanced cryo-electron tomography methods to analyze the structure of inclusions formed by polyglutamine (polyQ)-expanded huntingtin exon 1 within their intact cellular context. In primary mouse neurons and immortalized human cells, polyQ inclusions consist of amyloid-like fibrils that interact with cellular endomembranes, particularly of the endoplasmic reticulum (ER). Interactions with these fibrils lead to membrane deformation, the local impairment of ER organization, and profound alterations in ER membrane dynamics at the inclusion periphery. These results suggest that aberrant interactions between fibrils and endomembranes contribute to the deleterious cellular effects of protein aggregation. VIDEO ABSTRACT.


Assuntos
Doença de Huntington/patologia , Corpos de Inclusão/patologia , Neurônios/patologia , Neurônios/ultraestrutura , Peptídeos/metabolismo , Amiloide/química , Animais , Microscopia Crioeletrônica , Retículo Endoplasmático/metabolismo , Retículo Endoplasmático/patologia , Feminino , Células HeLa , Humanos , Proteína Huntingtina/genética , Proteína Huntingtina/metabolismo , Corpos de Inclusão/química , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Microscopia Eletrônica de Transmissão , Mutação , Agregação Patológica de Proteínas , Tomografia/métodos
3.
Cell ; 171(1): 148-162.e19, 2017 Sep 21.
Artigo em Inglês | MEDLINE | ID: mdl-28938114

RESUMO

Approximately 30%-40% of global CO2 fixation occurs inside a non-membrane-bound organelle called the pyrenoid, which is found within the chloroplasts of most eukaryotic algae. The pyrenoid matrix is densely packed with the CO2-fixing enzyme Rubisco and is thought to be a crystalline or amorphous solid. Here, we show that the pyrenoid matrix of the unicellular alga Chlamydomonas reinhardtii is not crystalline but behaves as a liquid that dissolves and condenses during cell division. Furthermore, we show that new pyrenoids are formed both by fission and de novo assembly. Our modeling predicts the existence of a "magic number" effect associated with special, highly stable heterocomplexes that influences phase separation in liquid-like organelles. This view of the pyrenoid matrix as a phase-separated compartment provides a paradigm for understanding its structure, biogenesis, and regulation. More broadly, our findings expand our understanding of the principles that govern the architecture and inheritance of liquid-like organelles.


Assuntos
Chlamydomonas reinhardtii/citologia , Cloroplastos/ultraestrutura , Proteínas de Algas/metabolismo , Dióxido de Carbono/metabolismo , Chlamydomonas reinhardtii/química , Chlamydomonas reinhardtii/metabolismo , Cloroplastos/química , Cloroplastos/metabolismo , Microscopia Crioeletrônica , Biogênese de Organelas , Ribulose-Bifosfato Carboxilase/metabolismo
4.
Nat Methods ; 18(11): 1386-1394, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34675434

RESUMO

Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, 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 DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is 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 (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase-oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms.


Assuntos
Algoritmos , Microscopia Crioeletrônica/métodos , Aprendizado Profundo , Tomografia com Microscopia Eletrônica/métodos , Processamento de Imagem Assistida por Computador/métodos , Substâncias Macromoleculares/química , Redes Neurais de Computação , Chlamydomonas reinhardtii/metabolismo , Complexo de Proteína do Fotossistema II/química , Ribossomos/química , Ribulose-Bifosfato Carboxilase/química
5.
Nat Methods ; 17(2): 240, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31988520

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

6.
Nat Methods ; 17(2): 240, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31969729

RESUMO

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

7.
Nat Methods ; 17(2): 209-216, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31907446

RESUMO

With faithful sample preservation and direct imaging of fully hydrated biological material, cryo-electron tomography provides an accurate representation of molecular architecture of cells. However, detection and precise localization of macromolecular complexes within cellular environments is aggravated by the presence of many molecular species and molecular crowding. We developed a template-free image processing procedure for accurate tracing of complex networks of densities in cryo-electron tomograms, a comprehensive and automated detection of heterogeneous membrane-bound complexes and an unsupervised classification (PySeg). Applications to intact cells and isolated endoplasmic reticulum (ER) allowed us to detect and classify small protein complexes. This classification provided sufficiently homogeneous particle sets and initial references to allow subsequent de novo subtomogram averaging. Spatial distribution analysis showed that ER complexes have different localization patterns forming nanodomains. Therefore, this procedure allows a comprehensive detection and structural analysis of complexes in situ.


Assuntos
Microscopia Crioeletrônica/métodos , Animais , Análise por Conglomerados , Masculino , Camundongos , Ratos , Ratos Wistar , Reprodutibilidade dos Testes
8.
PLoS Comput Biol ; 16(8): e1007962, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32776920

RESUMO

Curvature is a fundamental 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 molecular resolution. However, current curvature estimation methods cannot be applied directly to membrane segmentations in cryo-ET, as these methods cannot cope with some of the artifacts introduced during image acquisition and membrane segmentation, such as quantization noise and open borders. Here, we developed and implemented a Python package for membrane curvature estimation from tomogram segmentations, which we named PyCurv. From a membrane segmentation, a signed surface (triangle mesh) is first extracted. The triangle mesh is then represented by a graph, which facilitates finding neighboring triangles and the calculation of geodesic distances necessary for local curvature estimation. PyCurv estimates curvature based on tensor voting. Beside curvatures, this algorithm also provides robust estimations of surface normals and principal directions. We tested PyCurv and three well-established methods on benchmark surfaces and biological data. This revealed the superior performance of PyCurv not only for cryo-ET, but also for data generated by other techniques such as light microscopy and magnetic resonance imaging. Altogether, PyCurv is a versatile open-source software to reliably estimate curvature of membranes and other surfaces in a wide variety of applications.


Assuntos
Membrana Celular/fisiologia , Microscopia Crioeletrônica/métodos , Imageamento Tridimensional/métodos , Software , Algoritmos , Animais , Células HeLa , Humanos , Camundongos , Saccharomyces cerevisiae
10.
J Struct Biol ; 186(1): 49-61, 2014 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-24625523

RESUMO

Electron tomography enables three-dimensional (3D) visualization and analysis of the subcellular architecture at a resolution of a few nanometers. Segmentation of structural components present in 3D images (tomograms) is often necessary for their interpretation. However, it is severely hampered by a number of factors that are inherent to electron tomography (e.g. noise, low contrast, distortion). Thus, there is a need for new and improved computational methods to facilitate this challenging task. In this work, we present a new method for membrane segmentation that is based on anisotropic propagation of the local structural information using the tensor voting algorithm. The local structure at each voxel is then refined according to the information received from other voxels. Because voxels belonging to the same membrane have coherent structural information, the underlying global structure is strengthened. In this way, local information is easily integrated at a global scale to yield segmented structures. This method performs well under low signal-to-noise ratio typically found in tomograms of vitrified samples under cryo-tomography conditions and can bridge gaps present on membranes. The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure.


Assuntos
Membrana Celular/ultraestrutura , Tomografia com Microscopia Eletrônica/métodos , Algoritmos , Caulobacter crescentus/ultraestrutura , Microscopia Crioeletrônica , HIV-1/ultraestrutura , Imageamento Tridimensional , Razão Sinal-Ruído , Sinapses/ultraestrutura , Vírion/ultraestrutura
11.
IEEE Trans Med Imaging ; PP2024 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-38717878

RESUMO

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.

12.
J Struct Biol ; 181(1): 61-70, 2013 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-23085430

RESUMO

Three-dimensional (3D) electron microscopy (EM) has become a major player in structural cell biology as it enables the analysis of subcellular architecture at an unprecedented level of detail. Interpretation of the resulting 3D volumes strongly depends on segmentation, which consists in decomposing the volume into their structural components. The computational approaches proposed so far have not turned out to be of general applicability. Thus, manual segmentation still remains a prevalent method. Here, a new computational framework for segmentation of 3D EM datasets is introduced. It relies on detection and characterization of ridges (i.e. local maxima). The detected ridges are modelled as asymmetric Gaussian functions whose parameters constitute ridge descriptors. This local information is then used to cluster the ridges, which leads to the ultimate segmentation. In this work we focus on membranes and locally planar structures in general. The performance of the framework is illustrated with its application to a number of complex 3D datasets and a quantitative analysis.


Assuntos
Algoritmos , Tomografia com Microscopia Eletrônica , Imageamento Tridimensional/métodos , Animais , Axônios/ultraestrutura , Cerebelo/ultraestrutura , Camundongos , Mitocôndrias/ultraestrutura , Miocárdio/ultraestrutura , Distribuição Normal , Ratos , Retina/ultraestrutura , Células de Schwann/ultraestrutura , Sinapses/ultraestrutura , Vaccinia virus/ultraestrutura
13.
Nat Commun ; 14(1): 8086, 2023 Dec 06.
Artigo em Inglês | MEDLINE | ID: mdl-38057304

RESUMO

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, Atg18 oligomers in solution as well as on lipid membrane scaffolds. 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 Å. Moreover, lipid reconstitution experiments further delineate the contributions of Atg18's FRRG motif and the amphipathic helical extension in membrane interaction. The observed structural plasticity of Atg18's oligomeric organization and membrane binding properties provide a molecular framework for the positioning of downstream components of the autophagy machinery.


Assuntos
Proteínas de Saccharomyces cerevisiae , Proteínas de Saccharomyces cerevisiae/metabolismo , Proteínas de Membrana/metabolismo , Membranas/metabolismo , Proteínas Relacionadas à Autofagia/metabolismo , Autofagia , Lipídeos
14.
Nat Commun ; 14(1): 620, 2023 02 04.
Artigo em Inglês | MEDLINE | ID: mdl-36739444

RESUMO

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 shows that the S protein triggers platelets to deform dynamically, in some cases, leading to their irreversible activation. Cellular cryo-electron tomography reveals 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 find that S protein recognizes integrin αvß3. 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.


Assuntos
COVID-19 , Humanos , SARS-CoV-2/metabolismo , Glicoproteína da Espícula de Coronavírus/metabolismo , Plaquetas/metabolismo , Pandemias
15.
Comput Methods Programs Biomed ; 218: 106693, 2022 May.
Artigo em Inglês | MEDLINE | ID: mdl-35240361

RESUMO

Cryo-electron tomography (cryo-ET) is uniquely suited to precisely localize macromolecular complexes in situ, that is in a close-to-native state within their cellular compartments, in three-dimensions at high resolution. Point pattern analysis (PPA) allows quantitative characterization of the spatial organization of particles. However, current implementations of PPA functions are not suitable for applications to cryo-ET data because they do not consider the real, typically irregular 3D shape of cellular compartments and molecular complexes. Here, we designed and implemented first and the second-order, uni- and bivariate PPA functions in a Python package for statistical spatial analysis of particles located in three dimensional regions of arbitrary shape, such as those encountered in cellular cryo-ET imaging (PyOrg). To validate the implemented functions, we applied them to specially designed synthetic datasets. This allowed us to find the algorithmic solutions that provide the best accuracy and computational performance, and to evaluate the precision of the implemented functions. Applications to experimental data showed that despite the higher computational demand, the use of the second-order functions is advantageous to the first-order ones, because they allow characterization of the particle organization and statistical inference over a range of distance scales, as well as the comparative analysis between experimental groups comprising multiple tomograms. Altogether, PyOrg is a versatile, precise, and efficient open-source software for reliable quantitative characterization of macromolecular organization within cellular compartments imaged in situ by cryo-ET, as well as to other 3D imaging systems where real-size particles are located within regions possessing complex geometry.


Assuntos
Tomografia com Microscopia Eletrônica , Processamento de Imagem Assistida por Computador , Microscopia Crioeletrônica/métodos , Tomografia com Microscopia Eletrônica/métodos , Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Substâncias Macromoleculares , Análise Espacial
16.
Comput Methods Programs Biomed ; 224: 106990, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-35858496

RESUMO

BACKGROUND AND OBJECTIVE: Cryo-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 efficient detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. METHODS: We present MemBrain - a new deep learning-aided 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. RESULTS: MemBrain 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. CONCLUSIONS: MemBrain is a powerful and annotation-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.


Assuntos
Aprendizado Profundo , Microscopia Crioeletrônica/métodos , Tomografia com Microscopia Eletrônica/métodos , Elétrons , Processamento de Imagem Assistida por Computador/métodos , Proteínas de Membrana
17.
bioRxiv ; 2022 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-36451880

RESUMO

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 α v ß 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.

18.
J Struct Biol ; 175(3): 372-83, 2011 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-21616152

RESUMO

Electron tomography allows three-dimensional visualization of cellular landscapes in molecular detail. Segmentation is a paramount stage for the interpretation of the reconstructed tomograms. Although several computational approaches have been proposed, none has prevailed as a generic method and thus segmentation through manual annotation is still a common choice. In this work we introduce a segmentation method targeted at membranes, which define the natural limits of compartments within biological specimens. Our method is based on local differential structure and on a Gaussian-like membrane model. First, it isolates information through scale-space and finds potential membrane-like points at a local scale. Then, the structural information is integrated at a global scale to yield the definite segmentation. We show and validate the performance of the algorithm on a number of tomograms under different experimental conditions.


Assuntos
Tomografia com Microscopia Eletrônica/métodos , Algoritmos , Dictyostelium/ultraestrutura , Complexo de Golgi/ultraestrutura , HIV/ultraestrutura , Mitocôndrias/ultraestrutura , Vaccinia virus/ultraestrutura
19.
Sensors (Basel) ; 11(9): 8412-29, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-22164083

RESUMO

Images from high dynamic range (HDR) scenes must be obtained with minimum loss of information. For this purpose it is necessary to take full advantage of the quantification levels provided by the CCD/CMOS image sensor. LinLog CMOS sensors satisfy the above demand by offering an adjustable response curve that combines linear and logarithmic responses. This paper presents a novel method to quickly adjust the parameters that control the response curve of a LinLog CMOS image sensor. We propose to use an Adaptive Proportional-Integral-Derivative controller to adjust the exposure time of the sensor, together with control algorithms based on the saturation level and the entropy of the images. With this method the sensor's maximum dynamic range (120 dB) can be used to acquire good quality images from HDR scenes with fast, automatic adaptation to scene conditions. Adaptation to a new scene is rapid, with a sensor response adjustment of less than eight frames when working in real time video mode. At least 67% of the scene entropy can be retained with this method.


Assuntos
Algoritmos , Computadores
20.
Sci Adv ; 7(10)2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33674312

RESUMO

Synaptic transmission is characterized by fast, tightly coupled processes and complex signaling pathways that require a precise protein organization, such as the previously reported nanodomain colocalization of pre- and postsynaptic proteins. Here, we used cryo-electron tomography to visualize synaptic complexes together with their native environment comprising interacting proteins and lipids on a 2- to 4-nm scale. Using template-free detection and classification, we 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 plasma membrane. These data support the hypothesis that synaptic function is carried by precisely organized trans-synaptic units. It 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.

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