Biology subjects

Marrett, K.

Publications and source records attributed to Marrett, K..

4 recordsLinked to original sources

bioRxiv2024

Dendritome Mapping Unveils Spatial Organization of Striatal D1/D2-Neuron Morphology

Morphology is a cardinal feature of a neuron and mediates its functions, but profiling neuronal morphologies at scale remains a formidable challenge. Here we describe a generalizable pipeline for large-scale brainwide profiling of dendritic morphology of genetically-defined single neurons in the mouse brain. We generated a dataset of 3,762 3D-reconstructed and reference-atlas mapped striatal D1- and D2-medium spiny neurons (MSNs). Integrative morphometric analyses reveal distinct impacts of D1/D2 and anatomical locations on MSN morphology. To analyze striatal regional features of MSN dendrites without prior anatomical constraints, we assigned MSNs to a lattice of cubic boxes in the reference brain atlas, and summarized morphometric representation ("eigen-morph") for each box and clustered boxes with shared morphometry. This analysis reveals 6 modules with characteristic dendritic features and spanning contiguous striatal territories, each receiving distinct corticostriatal inputs. Finally, we found aging confers robust and concordant dendritic length and branching defects in D1/D2-MSNs, while Huntingtons disease (HD) mice exhibit MSN-subtype and striatal regional specific pathology. Together, our study demonstrates a systems-biology approach to profile dendritic morphology of genetically-defined single-neurons; and defines novel striatal D1/D2-MSN morphological territories and aging- or HD-associated pathologies.

neuroscience↗

bioRxiv2024

Gossamer: Scaling Image Processing and Reconstruction to Whole Brains

Neuronal reconstruction-a process that transforms image volumes into 3D geometries and skeletons of cells- bottlenecks the study of brain function, connectomics and pathology. Domain scientists need exact and complete segmentations to study subtle topological differences. Existing methods are diskbound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting these vision tasks into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67x the scale with 870x less memory while having 78% higher automated yield than APP2, the previous state of the art in performant reconstruction.

neuroscience↗

bioRxiv2021

Recut: a Concurrent Framework for Sparse Reconstruction of Neuronal Morphology

Advancement in modern neuroscience is bottlenecked by neural reconstruction, a process that extracts 3D neuron morphology (typically in tree structures) from image volumes at the scale of hundreds of GBs. We introduce Recut, an automated and accelerated neural reconstruction pipeline, which provides a unified, and domain specific sparse data representation with 79x reduction in the memory footprint. Recuts reconstruction can process 111 Kneurons/day or 79 TB/day on a 24-core workstation, placing the throughput bottleneck back on microscopic imaging time. Recut allows the full brain of a mouse to be processed in memory on a single server, at 89.5x higher throughput over existing I/O-bounded methods. Recut is also the first fully parallelized end-to-end automated reconstruction pipeline for light microscopy, yielding tree morphologies closer to ground truth than the state-of-the-art while removing involved manual steps and disk I/O overheads. We also optimized pipeline stages to linear algorithmic complexity for scalability in dense settings and allow the most timing-critical stages to optionally run on accelerated hardware.

neuroscience↗

bioRxiv2020

Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems

AbstractMethods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in neurons of the mouse hippocampus, revealing patterns across multiple cell types; layer-specific cell types across mouse visual cortex; and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus ExSeq enables highly multiplexed mapping of RNAs, from nanoscale to system scale. One Sentence SummaryIn situ sequencing of physically expanded specimens enables multiplexed mapping of RNAs at nanoscale, subcellular resolution.

genomics↗