Biology subjects

Peet, G. C.

Publications and source records attributed to Peet, G. C..

4 recordsLinked to original sources

bioRxiv2026

Microglia drive demyelination via multiple sclerosis antibodies and BTK signaling

Microglia are the predominant immune cells in multiple sclerosis (MS) demyelinating lesions, where they phagocytose myelin, but whether they destroy myelin or merely scavenge its debris is unknown. Here, we explore whether pathogenic autoantibodies found in MS may induce the phagocytic destruction of myelin by microglia. Applying patient-derived, myelin-targeting antibodies to the mouse cortex, we developed an in vivo model of MS with focal demyelination that depended on epitope specificity and Fc gamma receptor and complement binding. Longitudinal monitoring of microglia-myelin interactions using in vivo two-photon microscopy revealed rapid microglial envelopment of intact myelin driving myelin loss, while single-cell RNA sequencing identified a demyelination-associated microglial signature. Parallel changes were observed in human MS lesions, where microglia enveloped intact myelin and similar genes were upregulated. Inhibition of Brutons tyrosine kinase (BTK) limited microglial transcriptional changes and prevented myelin loss following microglial envelopment. These findings directly implicate microglia in pathological myelin loss and support BTK inhibition as a therapeutic strategy to prevent demyelination by modulating microglia behavior.

neuroscience↗

bioRxiv2026

Language Model Embedding Classifiers Enable Identification of Multiple Sclerosis-Associated BCRs and Repertoires

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease. It affects over 2 million people worldwide but has historically been challenging to diagnose, categorize, and treat. MS has an autoimmune component that involves the production of B-cell receptors (BCRs) and immunoglobulins (Igs) that are associated with disease pathophysiology. We reanalyzed all publicly available RNA sequencing data from MS patients and extracted over 11 million BCR immunoglobulin heavy chain (IGH) sequences obtained from a variety of tissue sources. We developed a new decoder-only BCR DNA embedding model that outperforms state-of-the-art BCR and general-purpose protein language models on sequence embedding tasks. We then trained a language model classifier capable of identifying BCR sequences associated with MS. Using low-dimensional representations of whole repertoire embeddings combined with sequence-disease predictions, we can distinguish MS patient repertoires from healthy, infectious disease, or other autoimmune disease repertoires. Our models also successfully rank known MS-associated myelin-binding IgG sequences relative to controls. These findings provide a methodological foundation for BCR-based MS detection and could facilitate identification and study of disease-associated antibodies from blood. SignificanceAutoimmune diseases are difficult to diagnose and treat. Circulating adaptive immune cells contain accessible information about a patients present and past immune experiences. To better use this information in the context of multiple sclerosis, we extracted a comprehensive dataset of B-cell receptor sequences from archival data, developed a new DNA foundation model to embed these sequences, and applied machine learning methods to classify sequence disease association and patient disease state. These findings will advance our ability to model and predict multiple sclerosis and other diseases.

bioinformatics↗

bioRxiv2025

CaMPARI2 Enables Stimulus-Locked Whole-Brain Activity Mapping at Cellular Resolution in Unrestrained Larval Zebrafish

Visualizing active neurons and circuits in vivo is critical for investigating the neural activity that underlies behavior. While several established methodologies are available to achieve this end in larval zebrafish, they are limited by the scale of tissue visualization, temporal resolution, need to restrain larvae, and/or accessibility of necessary instruments. Here, we establish a pipeline for the visualization and quantification of spatiotemporally precise whole-brain neural activity in larval zebrafish using CaMPARI2, a genetically encoded calcium indicator. Using temporally specific photoconverting UV light exposures, we capture whole-brain "snapshots" of neural activity time-locked to stimuli during unrestrained larval behavior. We optimize experimental conditions for establishing sub-second neuronal activity changes across acoustically-evoked behavioral paradigms spanning minutes to hours. We then leverage this system to pinpoint brain-wide neural activity changes during nonassociative habituation learning, observing distinct activity signatures in the subpallium, preoptic area, and habenulae that are altered through pharmacological disruption of habituation learning. This approach effectively complements the temporal precision achievable through post hoc activity detection methods and expands the accessibility of large-scale behavioral circuit dissection beyond highly specialized real-time volumetric imaging equipment.

neuroscience↗

bioRxiv2021

Miniature Structured Illumination Microscope for in vivo 3D Imaging of Brain Structures with Optical Sectioning

We present a high-resolution miniature, light-weight fluorescence microscope with electrowetting lens and onboard CMOS for high resolution volumetric imaging and structured illumination for rejection of out-of-focus and scattered light. The miniature microscope (SIMscope3D) delivers structured light using a coherent fiber bundle to obtain optical sectioning with an axial resolution of 18 m. Volumetric imaging of eGFP labeled cells in fixed mouse brain tissue at depths up to 220 m is demonstrated. The functionality of SIMscope3D to provide background free 3D imaging is shown by recording time series of microglia dynamics in awake mice at depths up to 120 m in the brain.

bioengineering↗