Biology subjects

de Folter, J.

Publications and source records attributed to de Folter, J..

4 recordsLinked to original sources

bioRxiv2022

OptoPi: An open source flexible platform for the analysis of small animal behaviour

Behaviour is the ultimate output of neural circuit computations, and therefore its analysis is a cornerstone of neuroscience research. However, every animal and experimental paradigm requires different illumination conditions to capture and, in some cases, manipulate specific behavioural features. This means that researchers often develop, from scratch, their own solutions and experimental set-ups. Here, we present OptoPi, an open source, affordable ([~] {pound}600), behavioural arena with accompanying multi-animal tracking software. The system features highly customisable and reproducible visible and infrared illumination and allows for temporally precise optogenetic stimulation. OptoPi acquires images using a Raspberry Pi camera, features motorised LED-based illumination, Arduino control, as well as spectrum and irradiance monitoring to fine-tune illumination conditions with real time feedback. Our open-source software (BIO) can be used to simultaneously track multiple animals while accurately keeping individual animals identity both in on-line and off-line modes. We demonstrate the functionality of OptoPi by recording and tracking under different illumination conditions the spontaneous behaviour of larval zebrafish as well as adult Drosophila flies and their first instar larvae, an experimental animal that due to its small size and transparency has classically been hard to track. Further, we showcase OptoPis optogenetic capabilities through a series of experiments using transgenic Drosophila larvae.

neuroscience↗

bioRxiv2022

OxoScan-MS: Oxonium ion scanning mass spectrometry facilitates plasma glycoproteomics in large scale

Protein glycosylation is a complex and heterogeneous post-translational modification. Specifically, the human plasma proteome is rich in glycoproteins, and as protein glycosylation is frequently dysregulated in disease, glycoproteomics is considered an underexplored resource for biomarker discovery. Here, we present OxoScan-MS, a data-independent mass spectrometric acquisition technology and data analysis software that facilitates sensitive, fast, and cost-effective glycoproteome profiling of plasma and serum samples in large cohort studies. OxoScan-MS quantifies glycosylated peptide features by exploiting a scanning quadrupole to assign precursors to oxonium ions, glycopeptide-specific fragments. OxoScan-MS reaches a high level of sensitivity and selectivity in untargeted glycopeptide profiling, such that it can be efficiently used with fast microflow chromatography without a need for experimental enrichment of glycopeptides from neat plasma. We apply OxoScan-MS to profile the plasma glycoproteomic in an inpatient cohort hospitalised due to severe COVID-19, and obtain precise quantities for 1,002 glycopeptide features. We reveal that severe COVID-19 induces differential glycosylation in disease-relevant plasma glycoproteins, including IgA, fibrinogen and alpha-1-antitrypsin. Thus, with OxoScan-MS we present a strategy for quantitatively mapping glycoproteomes that scales to hundreds and thousands of samples, and report glycoproteomic changes in severe COVID-19.

systems biology↗

bioRxiv2021

Automatic instance segmentation of mitochondria in electron microscopy data

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

bioinformatics↗

bioRxiv2020

Citizen science, cells and CNNs - deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

cell biology↗