Center for Computational and Theoretical Biology
Refine
Has Fulltext
- yes (47)
Is part of the Bibliography
- yes (47)
Year of publication
Document Type
- Journal article (40)
- Doctoral Thesis (6)
- Preprint (1)
Keywords
- machine learning (5)
- active zone (4)
- Caenorhabditis elegans (2)
- bee decline (2)
- dSTORM (2)
- deep learning (2)
- evolution (2)
- foraging (2)
- genetic architecture (2)
- juvenile hormone (2)
Institute
- Center for Computational and Theoretical Biology (47)
- Theodor-Boveri-Institut für Biowissenschaften (20)
- Pathologisches Institut (4)
- Physiologisches Institut (4)
- Julius-von-Sachs-Institut für Biowissenschaften (3)
- Klinik und Poliklinik für Unfall-, Hand-, Plastische und Wiederherstellungschirurgie (Chirurgische Klinik II) (3)
- Neurochirurgische Klinik und Poliklinik (3)
- Urologische Klinik und Poliklinik (3)
- Comprehensive Cancer Center Mainfranken (2)
- Klinik und Poliklinik für Dermatologie, Venerologie und Allergologie (2)
EU-Project number / Contract (GA) number
- 2020010013 (1)
- 250194-Carnivorom (1)
- 835102) (1)
Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical “clear core” vesicles (CCV) and the typically larger “dense core” vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3].
To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.
The abundance of high-quality genotype and phenotype data for the model organism Arabidopsis thaliana enables scientists to study the genetic architecture of many complex traits at an unprecedented level of detail using genome-wide association studies (GWAS). GWAS have been a great success in A. thaliana and many SNP-trait associations have been published. With the AraGWAS Catalog (https://aragwas.1001genomes.org) we provide a publicly available, manually curated and standardized GWAS catalog for all publicly available phenotypes from the central A. thaliana phenotype repository, AraPheno. All GWAS have been recomputed on the latest imputed genotype release of the 1001 Genomes Consortium using a standardized GWAS pipeline to ensure comparability between results. The catalog includes currently 167 phenotypes and more than 222 000 SNP-trait associations with P < 10\(^{-4}\), of which 3887 are significantly associated using permutation-based thresholds. The AraGWAS Catalog can be accessed via a modern web-interface and provides various features to easily access, download and visualize the results and summary statistics across GWAS.