Biology subjects

Zhang, G.-W.

Publications and source records attributed to Zhang, G.-W..

7 recordsLinked to original sources

bioRxiv2026

Multi-scale spatial transcriptomic atlas of the whole mouse spinal cord

The spinal cord coordinates sensory, motor and autonomic functions through diverse neuronal and non-neuronal populations, yet how molecularly defined cell types are organized across its full extent remains incompletely understood. Here we use Stereo-seq to generate a genome-wide, single-cell-resolution spatial atlas of the whole adult mouse spinal cord. Profiling 322,827 cells across 81 anatomically registered transverse sections spanning cervical, thoracic, lumbar and sacral levels, we resolve 12 major cell classes and 94 transcriptomic subclusters. Major cell classes occupy characteristic anatomical territories conserved along the rostrocaudal axis, whereas individual subclusters exhibit reproducible laminar, mediolateral and segment-associated distributions. Transcriptome-wide analysis identifies coordinated spatial gene-expression modules that delineate anatomical domains independently of predefined cell-type labels. Notably, oligodendrocyte subtypes exhibit a distinct and reproducible laminar organization within spinal cord white matter across spinal levels, revealing finer structural subdivisions beyond conventional anatomical descriptions. Integrating cell-type identities with local spatial organization further defines hierarchical domains that recapitulate canonical anatomy while resolving finer subdivisions within established regions. Together, these findings define a multi-scale cellular architecture linking transcriptomic diversity, spatial gene-expression programs and anatomical organization throughout the spinal cord.

neuroscience↗

bioRxiv2026

Glutamatergic Neurons in caudal Pontine Reticular Nucleus (PRNc) Mediate Backward Locomotion in mice

Locomotion is essential for animals to adaptively navigate their environment. However, despite extensive investigation into neural mechanisms underlying forward movement, the brain circuits controlling backward locomotion, a critical motor behavior for navigating confined spaces and avoiding threats, remain poorly understood. Here, using a specially designed behavioral paradigm for backward locomotion in mice, we find that glutamatergic neurons in the caudal pontine reticular nucleus (PRNc) are selectively activated during backward, but not forward, locomotion. Activity increase of these neurons is temporally aligned with backward movement, with its amplitude correlated with speed. Optogenetic manipulations further demonstrate that these neurons are both sufficient and necessary for initiating and sustaining backward walking. Moreover, anatomical tracing reveals that the PRNc broadly integrates bilateral cortical and subcortical inputs and projects extensively to brain-wide and spinal cord networks. These findings suggest PRNc as a previously unrecognized brainstem center in direction-specific locomotor control circuits. Key findings of this work and our initial identification of this brainstem locomotor node were presented at the Society for Neuroscience Annual Meeting in November, 15th, 2025 (SfN Neuroscience 2025, San Diego, CA; Program No. PSTR027.02).

neuroscience↗

bioRxiv2026

EthoTrace: from behaviour to neural dynamics in a unified analysis framework

Understanding how neural populations support natural behaviour requires knowing what a population does while an animal performs a specific, self-initiated action. For responses evoked by an experimenter-delivered stimulus, the signal can be aligned by the trigger itself, and no behavioural scoring is needed to define the event. Complex behaviours such as parental care and social interaction provide no such trigger. They are defined from the video record, usually offline and post hoc, and are conventionally scored in one program and exported to another for analysis. In this study we developed EthoTrace, a single self-contained HTML file that runs in a desktop browser without installation, server or network access and carries a session from behaviour and calcium signal through to the statistical comparison. Behaviour is annotated on a timeline shared with the {Delta}F/F trace, the signal is corrected and scaled under a stated baseline convention, single bouts stay inspectable in their behavioural context, and repeated bouts are aligned without filling absent data. We demonstrate the complete workflow on a parenting behaviour experiment, in which behaviour was scored and the photometry signal analysed in a single session folder. We expect EthoTrace to facilitate integrative analysis in neuroscience, where behavioural observation and fiber photometry signals are interpreted together.

animal behavior and cognition↗

bioRxiv2026

NeuroFlow: An Integrated, Cross-Platform Workflow for Mouse Brain Atlas Registration and Quantification

Registration of histological sections to a reference atlas is essential for anatomical localization and region-based quantitative analysis. Although established workflows are powerful, image preparation, registration, quantification, and visualization often rely on multiple software packages, some of which require platform-specific installation or locally configured programming environments. Here, we present NeuroFlow, a browser-based workflow for quantitative analysis of mouse brain histology. NeuroFlow integrates image registration, signal detection, quantification, and visualization within a single interface and operates across major operating systems without additional software installation. It supports affine and nonlinear alignment, as well as real-time oblique reslicing of the reference atlas. All processing is performed locally in a desktop browser, without requiring a local Python environment, MATLAB installation, or associated packages and toolboxes, and without uploading images to a remote server. This design preserves user control over data and keeps intermediate results accessible for inspection and review. NeuroFlow is available at https://guangweizhang.com/tool-neuroflow.html.

neuroscience↗

bioRxiv2026

TRACE: End-to-end temporal inference and annotation of animal behaviors from video

Quantitative analysis of animal behavior is fundamental to neuroscience and ethology but remains constrained by the scalability, subjectivity, and limited reproducibility of manual annotation. Many automated approaches infer behavior through intermediate representations such as pose trajectories, whereas direct video modeling retains motion, appearance, and context, but its accuracy and efficiency remain incompletely established. Here we introduce V-TRACE (Video-based Temporal Recognition and Annotation of Continuous Ethograms of Animal Behavior), an end-to-end platform with a graphical user interface for behavioral detection and annotation. V-TRACE combines transformer-based video encoders with a multi-scale temporal module to model behavioral dynamics across a broad range of durations. Its modular design supports interchangeable encoder architectures within a common detection pipeline to produce frame-resolved behavioral identities and derive temporal boundaries from continuous recordings. Across datasets spanning species and experimental contexts, V-TRACE demonstrates high accuracy and high-throughput inference, enabling scalable, efficient, and context-aware animal behavior analysis directly from video.

animal behavior and cognition↗

bioRxiv2023

Integrative mapping of spatial transcriptomic and amyloid pathology in Alzheimer's disease at single-cell resolution

Alzheimers disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Despite decades of research, the underlying molecular and cellular changes of AD remain unresolved, especially in terms of the spatial structure of gene expression changes that correlates with pathology, e.g. amyloid beta (A-beta) plaques. Recent advances in imaging-or sequencing-based single-cell spatial transcriptomics have allowed a systematic dissection of molecular and cell architectures in the brain and other tissues. In this study, we employed the recently developed Stereo-seq technology to spatially profile the whole-genome transcriptomics in the 5xFAD mouse model and established the methodology to analyze the specific neuronal transcriptomic changes spatially correlated with amyloid pathology at single cell resolution. More specifically, we developed a pipeline for integrative image- and non-image-based cell segmentation, VoxelMorph-based non-linear alignment, and Unet-based object detection to achieve reliable transcriptomics analysis at the single-cell resolution, and investigated the spatial relationship between diverse neuronal clusters and A-beta depositions. This work has demonstrated the potential of using the Stereo-seq technology as a powerful tool to investigate AD and other complex neurological disorders.

neuroscience↗

bioRxiv2019

Track-Control, an automatic video-based real-time closed-loop behavioral control toolbox

Approaches of optogenetic manipulation of neuronal activity have boosted our understanding of the functional architecture of brain circuits underlying various behaviors. In the meantime, rapid development in computer vision greatly accelerates the automation of behavioral analysis. Real-time and event-triggered interference is often necessary for establishing a tight correlation between neuronal activity and behavioral outcome. However, it is time consuming and easily causes variations when performed manually by experimenters. Here, we describe our Track-Control toolbox, a fully automated system with real-time object detection and low latency closed-loop hardware feedback. We demonstrate that the toolbox can be applied in a broad spectrum of behavioral assays commonly used in the neuroscience field, including open field, plus maze, Morris water maze, real-time place preference, social interaction, and sensory-induced defensive behavior tests. The Track-Control toolbox has proved an efficient and easy-to-use method with excellent flexibility for functional extension. Moreover, the toolbox is free, open source, graphic processing unit (GPU)-independent, and compatible across operating system (OS) platforms. Each lab can easily integrate Track-Control into their existing systems to achieve automation.

animal behavior and cognition↗