Biology subjects

Chater, T. E.

Publications and source records attributed to Chater, T. E..

3 recordsLinked to original sources

bioRxiv2026

Push-and-pull protein dynamics leads to log-normal synaptic sizes and probabilistic multi-spine plasticity

A typical neuron receives thousands of inputs and is able to adapt the strength of its synapses to store new information and meet ongoing computational demands. The synaptic response to plasticity induction is stochastic and spatially structured but is traditionally described by deterministic models representing the "average" dynamics. Growing experimental evidence indicates that not only the stimulation protocol determines the plasticity outcome but that the initial synaptic sizes, their fluctuations, and the spatial competition for the plasticity-relevant proteins play a decisive role. This probabilistic perspective makes it hard to predict the fate of a given synapse and requires a conceptual shift from a single synapse view to a probabilistic multi-spine competitive process where the plasticity needs and the available resources are considered together. Here, we propose a data-driven modeling framework able to predict collective plasticity outcomes along a dendrite based on the initial size, the number, and the spatial distance between simultaneously stimulated synapses. Our data analysis reveals a log-normal distribution of protein numbers for many plasticity-mediating proteins and shows that this log-normal protein allocation constrains and controls the collective plasticity outcome across multiple stimulated and non-stimulated synapses while preserving a global size distribution. Our findings highlight how local stochastic processes and global protein allocation rules give rise to synaptic plasticity outcomes, offering a new framework to understand and predict dendritic computation.

neuroscience↗

bioRxiv2024

SpyDen: Automating molecular and structural analysis across spines and dendrites

Investigating the molecular composition across neural compartments such as axons, dendrites, or synapses is critical for our understanding of learning and memory. State-of-the-art microscopy techniques can now resolve individual molecules and pinpoint their position with micrometre or even nanometre resolution across tens or hundreds of micrometres, allowing the labelling of multiple structures of interest simultaneously. Algorithmically, tracking individual molecules across hundreds of micrometres and determining whether they are inside any cellular compartment of interest can be challenging. Historically, microscopy images are annotated manually, often using multiple software packages to detect fluorescence puncta (e.g. labelled mRNAs) and then trace and quantify cellular compartments of interest. Advanced ANN-based automated tools, while powerful, are often able to help only with selected parts of the data analysis pipeline, may be optimised for specific spatial resolutions or cell preparations or may not be fully open source and open access to be sufficiently customisable. To address these challenges, we developed SpyDen. SpyDen is a Python package based upon three principles: i) ease of use for multi-task scenarios, ii) open-source accessibility and data export to a common, open data format, iii) the ability to edit any software-generated annotation and generalise across spatial resolutions. Equipped with a graphical user interface and accompanied by video tutorials, SpyDen provides a collection of powerful algorithms that can be used for neurite and synapse detection as well as fluorescent puncta and intensity analysis. We validated SpyDen using expert annotation across numerous use cases to prove a powerful, integrated platform for efficient and reproducible molecular imaging analysis.

neuroscience↗

bioRxiv2019

Decoding neuronal diversity by single-cell Convert-seq

The conversion of cell fates is controlled by hierarchical gene regulatory networks (GRNs) that induce remarkable alterations in cellular and transcriptome states. The identification of key regulators within these networks from myriad of candidate genes, however, poses a major research challenge. Here we present Convert-seq, combining single-cell RNA sequencing (scRNA-seq) and pooled ectopic gene expression with a new strategy to discriminate sequencing reads derived from exogenous and endogenous transcripts. We demonstrate Convert-seq by associating hundreds of single cells during reprogramming of human fibroblasts to induced neurons (iN) with exogenous and endogenous transcriptional signatures. Convert-seq identified GRNs modulating the emergence of developmental trajectories and predicted combinatorial activation of exogenous transcription factors controlling iN subtype specification. Functional validation of iN subtypes generated by novel combinations of exogenous transcription factors establish Convert-seq as a broadly applicable workflow to rapidly identify key transcription factors and GRNs orchestrating the reprogramming of virtually any cell type.

neuroscience↗