A5079933409- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
- Neural dynamics and brain function
- Neuroinflammation and Neurodegeneration Mechanisms
- Gene expression and cancer classification
- Neuroscience and Neuropharmacology Research
- Receptor Mechanisms and Signaling
- Advanced Fluorescence Microscopy Techniques
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Retinal Development and Disorders
- Medical Image Segmentation Techniques
- Olfactory and Sensory Function Studies
- Visual Attention and Saliency Detection
- Memory and Neural Mechanisms
- Gene Regulatory Network Analysis
- Advanced Image and Video Retrieval Techniques
- Mathematical Analysis and Transform Methods
- Advanced MRI Techniques and Applications
- Cellular Mechanics and Interactions
- Extracellular vesicles in disease
- Neuropeptides and Animal Physiology
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
- Bioinformatics and Genomic Networks
Allen Institute for Brain Science
2018-2025
Allen Institute
2018-2025
University of Washington
2023-2024
Columbia University
2014-2020
Massachusetts Institute of Technology
2014
Harvard University
2014
Stanford University
2006-2009
Bilkent University
2003
Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens molecularly distinct neuropeptidergic modulatory networks that directly interconnect all This begins with discovery transcripts one or more neuropeptide precursor (NPP) neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly...
A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an priori selection genes, often covering less than 1% the genome, and key question how to optimally determine small gene panel. We address this challenge by introducing flexible deep learning framework, PERSIST, identify informative targets for studies leveraging reference scRNA-seq data. Using datasets spanning...
Abstract Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, genetic attributes. To better constrain definition neuronal types, we characterized transcriptomes and intrinsic physiological properties over 3,700 GABAergic mouse visual cortical neurons reconstructed local morphologies 350 those neurons. We found that most transcriptomic (t-types) occupy specific laminar positions within cortex, many t-types exhibit consistent...
Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce computational optimization workflow to generate 9,200 single-neuron with active conductances. These are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from mouse visual cortex. We...
Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors a challenging instance segmentation problem. More broadly, curvilinear, filamentous structures continues to pose significant challenges. To this problem, we extend notion simple points from digital topology connected sets...
Stress response is essential for adapting to an ever-changing environment. However, the mechanisms that render some individuals susceptible stress are poorly understood. While chronic known induce dendritic atrophy and spine loss in medial prefrontal cortex (mPFC), its impact on synapses made by long-range projections terminating mPFC remains unknown. Here, we labeled male mouse dendrites formed ventral hippocampus (VH), basolateral amygdala (BLA) tegmental area (VTA) afferents using...
Reconstructing the intricate local morphology of neurons and their long-range projecting axons can address many connectivity related questions in neuroscience. The main bottleneck connectomics pipelines is correcting topological errors, as multiple entangled neuronal arbors a challenging instance segmentation problem. More broadly, curvilinear, filamentous structures continues to pose significant challenges. To this problem, we extend notion simple points from digital topology connected sets...
Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small SPC, stellate SSC) are based primarily on local dendrosomatic axonal structures, especially apical dendrites. This study bridges these perspectives...
A practical acceleration algorithm for real-time magnetic resonance imaging (MRI) is presented. Neither separate training scans nor embedded samples are used. The Kalman filter based provides a fast and causal reconstruction of dynamic MRI acquisitions with arbitrary readout trajectories. tested against abrupt changes in the conditions offline reconstructions vivo cardiac experiments
Time series of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> xmlns:xlink="http://www.w3.org/1999/xlink">vivo</i> magnetic resonance images exhibit high levels temporal correlation. Higher resolution reconstructions are obtained by acquiring data at a fraction the Nyquist rate and resolving resulting aliasing using correlation information. The dynamic imaging experiment is modeled as linear dynamical system. A Kalman filter based...
Significance Synaptic connectivity provides the foundation for our present understanding of neuronal network function, but static cannot explain learning and memory. We propose a computational role diversity cortical types their associated cell-type–specific neuromodulators in improving efficiency synaptic weight adjustments task networks.
Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations cells can obscure such insights. Here, we report large-scale automation neuronal morphology reconstruction analysis on a dataset 813 inhibitory neurons characterized using Patch-seq method, which enables measurement multiple properties from individual neurons, including local transcriptional signature. We demonstrate that these automated...
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole taxonomy transcriptomically-defined cell types, yet type definitions include multiple cellular properties can offer additional insights into neuron's role in circuits. While the Patch-seq method investigate how transcriptomic relate local morphological and electrophysiological linking identities long-range projections major unresolved...
Abstract Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates generation of taxonomies based on electrophysiological, morphological molecular single properties. Establishing correspondence across data modalities understanding underlying principles has proven challenging. Bio-realistic computational models offer ability to probe cause-and-effect have historically been used explore phenomena at single-neuron level. Here we introduce...
Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression colors individual cells promising tool, although its use the nervous system has been limited due to various sources variability expression. Moreover, intermingled anatomy neuronal trees challenging for existing segmentation algorithms. Here, we propose method automate neurons such (potentially pseudo-colored) images. The uses spatio-color relations between voxels, generates supervoxels reduce size...
Reconstructing multiple molecularly defined neurons from individual brains and across brain regions can reveal organizational principles of the nervous system. However, high resolution imaging whole is a technically challenging slow process. Recently, oblique light sheet microscopy has emerged as rapid method that provide fluorescence at voxel size 0.4 × 2.5 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . On other hand, complex...
Abstract Progress in histological methods and microscope technology has enabled dense staining imaging of axons over large brain volumes, but tracing such volumes requires new computational tools for 3D reconstruction data acquired from serial sections. We have developed a pipeline automated volume assembly densely stained imaged sections, which leverages machine learning-based segmentation to enable stitching alignment with the axon traces themselves. validated this segmentation-driven...