Biology subjects

Matelsky, J. K.

Publications and source records attributed to Matelsky, J. K..

12 recordsLinked to original sources

bioRxiv2026

Physical contact reveals a hidden layer of cortical architecture

Neurons interact at synapses, but they also communicate through physical contact and proximity, including diffusion, glia-mediated interactions, and ephaptic coupling. Standard connectomes map synapses, but cannot capture the full set of cell-cell contacts that can support these pathways. Here we extract contactomes from two large mouse visual cortex volumes at nanoscale resolution and quantify every cell-cell contact, the shared surface area of each contact, and the relationship between contact and synaptic connectivity. We find that contactomes are 5 - 10x denser than synaptic graphs, revealing that neurons physically contact a much larger set of potential neighbors than they synaptically connect to. We further find that most nearby potential neighbors are already in physical contact, indicating that local structural change would add few new candidate synaptic partners. Finally, we find that astrocytes form a single large syncytium-like network that spans the tissue and directly contacts nearly all neurons, and that glial processes lie within a micron or two of almost every synapse, indicating that synapses reside within a pervasive glia-shaped microenvironment. Together, these results show that physical contact forms a distinct layer of brain architecture that extends far beyond the synaptic connectome.

neuroscience↗

bioRxiv2025

Comparative Connectomics Highlights Conserved Architectural Synaptic Motifs in the Drosophila Mushroom Body

While the influence of synaptic plasticity on learning and memory has been extensively studied, the detailed patterns of synaptic connectivity remain incompletely mapped. Convergent synaptic motifs -- a tight grouping of at least two axons whose active zones are within 300nm and which are presynaptic to the same target -- are a common feature of neural circuits in the insect brain and are believed to serve as an important computational primitive in many brain areas. The Mushroom Body (MB) of Drosophila, for instance, is the center of associative learning and memory, where sensory information is conducted by Kenyon cells (KCs), the intrinsic neurons of the MB, and integrated by MB output neurons (MBONs). Indeed, the majority of KC-to-MBON synapses occur in a convergent motif. Nonetheless, the functional role of this convergent motif is not well studied. To gain insight into their potential role in the MB, we combine big-data network neuroscience tools with existing electron microscopy connectome datasets to detect and map the distribution of convergent synaptic motifs. We find that convergent motifs consistently occur across the MB in different individuals, including the -lobe where they were first quantified, and we report on both the variance and consistency in the formation of these motifs across different MB regions and individuals. Our discovery of multiply-convergent motifs -- where two KCs target multiple postsynaptic targets simultaneously -- reveals a previously unrecognized synaptic economy that may optimize information transfer while conserving neural resources. These stereotyped arrangements likely represent fundamental organizational principles underlying associative learning across species. Lastly, to our knowledge, this study offers the first and most extensive comparative analysis of synaptic motifs across Drosophila connectomes, establishing a framework for enabling systematic motif analysis of synapses across species.

neuroscience↗

bioRxiv2025

Local graph-motif features improve gene interaction network prediction

Gene interaction networks specify how genes interact to produce an organisms phenotype. These networks are often incomplete due to absent or unobserved information. Predicting these missing links is critical for many applications, including genome-wide association studies and phenotype prediction. Efforts have previously applied graph neural networks (GNNs) to this missing-link prediction problem, but these techniques too have limitations when the sparsity of the networks is very high. Here, we apply a novel feature engineering technique that uses local graph motif incidence to enhance the feature set for variational graph autoencoders (VGAE). We compare the performance of our technique against state-of-the-art approaches, and then progressively hide more and more of the original graph edges. Our results show that VGAEs with our local-area motif prevalence (LAMP) features outperform state-of-the-art node embeddings for a wide range of missing edges on both a benchmark and a biological dataset. We also observe that this combined VGAE and LAMP technique has the potential to facilitate the search for novel genetic interactions in an experimental adaptive sampling context with far fewer samples. Improvements to gene interaction imputation can lower the barrier to new pharmaceutical and epidemiological discoveries by revealing hidden gene interactions that steer the development of potential drug targets.

bioinformatics↗

bioRxiv2024

Automated segmentation of synchrotron-scanned fossils

Computed tomography has revolutionised the study of the internal three-dimensional structure of fossils. Historically, fossils typically spent years in preparation to be freed from the enclosing rock. Now, X-ray and synchrotron tomography reveal structure that is otherwise invisible and data acquisition can be fast. However, manual segmentation of these 3D volumes can still take months to years. This is especially challenging for resource-poor teams, as scanning may be free, but the computing power and (AI-assisted) segmentation software required to handle the resulting large data sets are complex to use and expensive. Here we present a free, browser-based segmentation tool that reduces computational overhead by splitting volumes into small chunks, allowing processing on low-memory, inexpensive hardware. Our tool also speeds up collaborative ground-truth generation and 3D visualization, all in-browser. We developed and evaluated our pipeline on various open-data scans of differing contrast, resolution, textural complexity, and size. Our tool successfully isolated the Thrinaxodon and Broomistega pair from an Early Triassic burrow. It isolated cranial bones from the Cretaceous acipenseriform Parapsephurus willybemisi on both 45.53 {micro}m and 13.67 {micro}m resolution scanning data. We also isolated bones of the Middle Triassic sauropterygian Nothosaurus and a challenging scan of a squamate embryo inside an egg dating back to the Early Cretaceous. Our tool reliably reproduces expert-supervised segmentation at a fraction of the time and cost, offering greater accessibility than existing tools. Beyond the online tool, all our code is open source, enabling contributions from the palaeontology community to further this emerging machine learning ecosystem.

paleontology↗

bioRxiv2023

Data-driven motif discovery in biological neural networks

AO_SCPLOWBSTRACTC_SCPLOWData from a variety of domains are represented as graphs, including social networks, transportation networks, computer networks, and biological networks. A key question spans these domains: are there meaningful repeated subgraphs, or motifs, within the structure of these larger networks? This is a particularly relevant problem when searching for repeated neural circuits in networks of biological neurons, as the field now regularly produces large brain connectivity maps of neurons and synapses, or connectomes. Given acquisition costs, however, these neuron-synapse connectivity maps are mostly one-of-a-kind. With current graph analysis techniques, it is very challenging to discover new "interesting" subgraphs a priori given small sample sizes of host graphs. Another challenge is that for even relatively modest graph sizes, an exhaustive search of all possible subgraphs is computationally intractable. For these reasons, motif discovery in biological graphs remains an unsolved challenge in the field. In this work, we present a motif discovery approach that can derive a list of undirected or directed motifs, with occurrence counts which are statistically significant compared to randomized graphs, from a single graph example. We first address common pitfalls in the current most common approaches when testing for motif statistical significance, and outline a strategy to ameliorate this problem with improved graph randomization techniques. We then propose a progressive-refinement approach for motif discovery, which addresses issues of computational cost. We demonstrate that our sampling correction technique allows for significance testing of target motifs while highlighting misleading conclusions from standard random graph models. Finally, we share our reference implementation, which is available as an open-source Python package, and demonstrate real-world preliminary results on the C. elegans connectome and the ellipsoid body of the Drosophila melanogaster fruit fly connectome.

neuroscience↗

bioRxiv2022

Cell lineage predicts neural connectivity beyond cell type

During nervous system development, repeated cell divisions of the zygote give rise to a "family tree" of neurons related by mitotic lineage. The developmental process also gives rise to neuronal connections, and neurons phenotypically converge to different cell types. Neural connections are steered by cell type, but they may be driven in part by lineage: We do not know if lineage matters for the developmental neurogenesis process. We thus asked if mitotic lineage predicts neural connections beyond cell type alone. Using three C. elegans datasets, we fit models for edge prediction tasks: predicting synaptic targets, and predicting synaptic sources. Adding the mitotic lineage improved these predictions. Our results suggest that the family tree matters for the connections made by neurons, and that developmental lineage is a variable that should be considered more deeply in models of connectome assembly.

neuroscience↗

bioRxiv2022

In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue

The healthy human brain has long been considered a sterile environment, with the blood brain barrier preventing the formation of a bacterial brain microbiome. Recent electron microscopy (EM) imaging of brain tissue has, however, provided the first preliminary evidence of bacteria in otherwise healthy brain slices. Whether due to contamination, disease, or a previously unknown relationship of bacteria to healthy brain tissue, novel tools are needed to detect and search for bacteria in nanoscale, volumetric EM images. While computer vision tools are widely used in cell segmentation and object detection problems in EM imaging, no bacteria detection tool or dataset exists. Overcoming the rarity of training data, this work presents the first pipeline for training a bacteria detection network for EM images, leveraging existing deep networks for object detection. A deployment and proofreading pipeline is presented, along with characterization of deployment to public EM image datasets. While bacteria in healthy brain tissue were not discovered in this work, this tool presents an opportunity for large scale bacteria search in EM imaging for both scientific discovery and experimental quality control, and serves more generally as a framework for sparse object detection in large imagery datasets.

neuroscience↗

bioRxiv2022

Scalable graph analysis tools for the connectomics community

Neuroscientists now have the opportunity to analyze synaptic resolution connectomes that are larger than the memory on single consumer workstations. As dataset size and tissue diversity have grown, there is increasing interest in conducting comparative connectomics research, including rapidly querying and searching for recurring patterns of connectivity across brain regions and species. There is also a demand for algorithm reuse -- applying methods developed for one dataset to another volume. A key technological hurdle is enabling researchers to efficiently and effectively query these diverse datasets, especially as the raw image volumes grow beyond terabyte sizes. Existing community tools can perform such queries and analysis on smaller scale datasets, which can fit locally in memory, but the path to scaling remains unclear. Existing solutions such as neuPrint or FlyBrainLab enable these queries for specific datasets, but there remains a need to generalize algorithms and standards across datasets. To overcome this challenge, we present a software framework for comparative connectomics and graph discovery to make connectomes easy to analyze, even when larger-than-RAM, and even when stored in disparate datastores. This software suite includes visualization tools, a web portal, a connectivity and annotation query engine, and the ability to interface with a variety of data sources and community tools from the neuroscience community. These tools include MossDB (an immutable datastore for metadata and rich annotations); Grand (for prototyping larger-than-RAM graphs); GrandIso-Cloud (for querying existing graphs that exceed the capabilities of a single work-station); and Motif Studio (for enabling the public to query across connectomes). These tools interface with existing frameworks such as neuPrint, graph databases such as Neo4j, and standard data analysis tools such as Pandas or NetworkX. Together, these tools enable tool and algorithm reuse, standardization, and neuroscience discovery.

neuroscience↗

bioRxiv2021

Circuit motifs and graph properties of connectome development in C. elegans

Network science is a powerful tool that can be used to better explore the complex structure of brain networks. Leveraging graph and motif analysis tools, we interrogate C. elegans connectomes across multiple developmental time points and compare the resulting graph characteristics and substructures over time. We show the evolution of the networks and highlight stable invariants and patterns as well as those that grow or decay unexpectedly, providing a substrate for additional analysis.

bioinformatics↗

bioRxiv2020

Connectome subgraph isomorphisms and graph queries with DotMotif

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

neuroscience↗

bioRxiv2020

intern: Integrated Toolkit for Extensible and Reproducible Neuroscience

As neuroscience datasets continue to grow in size, the complexity of data analyses can require a detailed understanding and implementation of systems computer science for storage, access, processing, and sharing. Currently, several general data standards (e.g., Zarr, HDF5, precompute, tensorstore) and purpose-built ecosystems (e.g., BossDB, CloudVolume, DVID, and Knossos) exist. Each of these systems has advantages and limitations and is most appropriate for different use cases. Using datasets that dont fit into RAM in this heterogeneous environment is challenging, and significant barriers exist to leverage underlying research investments. In this manuscript, we outline our perspective for how to approach this challenge through the use of community provided, standardized interfaces that unify various computational backends and abstract computer science challenges from the scientist. We introduce desirable design patterns and our reference implementation called intern.

neuroscience↗

bioRxiv2017

A substrate for modular, extensible data-visualization

As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborators and with the public can be especially onerous, as it is challenging to reconcile software dependencies, data formats, and specific user needs in an easily accessible package. We present substrate, a data-visualization framework designed to simplify communication and code reuse across diverse research teams. Our platform provides a simple, powerful, browser-based interface for scientists to rapidly build effective three-dimensional scenes and visualizations. We aim to reduce the gap of existing systems, which commonly prescribe a limited set of high-level components, that are rarely optimized for arbitrarily large data visualization or for custom data types. To further engage the broader scientific community and enable seamless integration with existing scientific workflows, we also present pytri, a Python library that bridges the use of substrate with the ubiquitous scientific computing platform, Jupyter. Our intention is to reduce the activation energy required to transition between exploratory data analysis, data visualization, and publication-quality interactive scenes.

bioinformatics↗