Biology subjects

Winnubst, J.

Publications and source records attributed to Winnubst, J..

5 recordsLinked to original sources

bioRxiv2025

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context

Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cellfilling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by >750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a [~]10 million {micro}m3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information, we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISMs molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports selfcorrecting neuron reconstruction with molecular context.

neuroscience↗

bioRxiv2023

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

neuroscience↗

bioRxiv2019

Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain

Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.

neuroscience↗

bioRxiv2018

Cell type-specific variation of somatotopic precision across corticostriatal projections

The striatum shows general topographic organization and regional differences in behavioral functions. How corticostriatal topography differs across cortical areas and cell types to support these distinct functions is unclear. This study contrasted corticostriatal projections from two layer 5 cell types, intratelencephalic (IT-type) and pyramidal tract (PT-type) neurons, using viral vectors expressing fluorescent reporters in Cre-driver mice. Long-range corticostriatal projections from sensory and motor cortex are somatotopic, with a decreasing somatotopic specificity as injections move from sensory to motor and frontal areas. Somatotopic organization differs between IT-type and PT-type neurons, including injections in the same site, with IT-type neurons having higher somatotopic stereotypy than PT-type neurons. Furthermore, IT-type projections from interconnected cortical areas have stronger correlations in corticostriatal targeting than PT-type projections do. Thus, as predicted by a long-standing basal ganglia model, corticostriatal projections of interconnected cortical areas form parallel circuits in basal ganglia-thalamus-cortex loops.

neuroscience↗

bioRxiv2017

Distinct descending motor cortex pathways and their roles in movement

Activity in motor cortex predicts specific movements, seconds before they are initiated. This preparatory activity has been observed in L5 descending pyramidal tract (PT) neurons. A key question is how preparatory activity can be maintained without causing movement, and how preparatory activity is eventually converted to a motor command to trigger appropriate movements. We used single cell transcriptional profiling and axonal reconstructions to identify two types of PT neuron. Both types share projections to multiple targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex. In a delayed-response task, these neurons produced early preparatory activity that persisted until the movement. The second type projects to motor centers in the medulla and produced late preparatory activity and motor commands. These results indicate that two motor cortex output neurons are specialized for distinct roles in motor control.

neuroscience↗