Biology subjects

McLaughlin, B.

Publications and source records attributed to McLaughlin, B..

4 recordsLinked to original sources

bioRxiv2025

Bispecific CD20xCD40 Antibodies Achieve Multi-Lineage Modulation of Humoral and Cellular Immunity

Monoclonal antibodies targeting CD20 and CD40 modulate humoral and cellular immunity and have shown therapeutic benefit in B cell malignancies and autoimmune diseases. Here, we describe the generation and preclinical evaluation of two novel bispecific antibodies (DFI201 and DFI205) that combine CD20-mediated B cell depletion inhibition of CD40-mediated influencing populations of B cells, myeloid cells, and T cells. Both bispecifics retained potent B cell cytotoxicity and uniquely extended depletion to non-classical monocytes. A study in cynomolgus macaques showed that administration of bispecific at 5 and 50 mg/kg, or combination therapy with ofatumumab and the anti-CD40 mAb DFI105, achieved rapid and sustained depletion of CD19+CD20+ B cells in blood and lymphoid tissues, with superior memory B cell clearance compared to monospecific controls Both bispecifics transiently reduced non-classical monocytes and potently depleted dendritic cell subsets (DC1 and DC2). The bispecifics expand regulatory T cells and induce T cell exhaustion without activating T Cells. No cytokine release syndrome or adverse safety signals were observed. Functional immunization with keyhole limpet hemocyanin demonstrated that CD40 engagement was essential to suppress KLH-specific IgG formation. These results establish CD20xCD40 bispecific antibodies as a safe and effective approach to achieve deep, multi-lineage immunomodulation with potential for autoimmune indications.

immunology↗

bioRxiv2024

Foundry-fabricated dual-color nanophotonic neural probes for photostimulation and electrophysiological recording

SignificanceCompact tools capable of delivering multicolor optogenetic stimulation to deep tissue targets with sufficient span, spatiotemporal resolution, and optical power remain challenging to realize. Here, we demonstrate foundry-fabricated nanophotonic neural probes for blue and red photostimulation and electrophysiological recording, which use a combination of spatial multiplexing and on-shank wavelength-demultiplexing to increase the number of on-shank emitters. AimWe demonstrate Si photonic neural probes with 26 photonic channels and 26 recording sites, which were fabricated on 200-mm diameter wafers at a commercial Si photonics foundry. Each photonic channel consists of an on-shank demultiplexer and separate grating coupler emitters for blue and red light, for a total of 52 emitters. ApproachWe evaluate neural probe functionality through bench measurements and in vivo experiments by photostimulating through 16 of the available 26 emitter pairs. ResultsWe report neural probe electrode impedances, optical transmission, and beam profiles. We validated a packaged neural probe in optogenetic experiments with mice sensitive to blue or red photostimulation. ConclusionsOur foundry-fabricated nanophotonic neural probe demonstrates dense dual-color emitter integration on a single shank for targeted photostimulation. Given its two emission wavelengths, high emitter density, and long site span, this probe will facilitate experiments involving bidirectional circuit manipulations across both shallow and deep structures simultaneously.

neuroscience↗

bioRxiv2021

Selective stimulation of the ferret abdominal vagus nerve with multi-contact nerve cuff electrodes

Dysfunction and diseases of the gastrointestinal (GI) tract are a major driver of medical care. The vagus nerve innervates and controls multiple organs of the GI tract and vagus nerve stimulation (VNS) could provide a means for affecting GI function and treating disease. However, the vagus nerve also innervates many other organs throughout the body, and off-target effects of VNS could cause major side effects such as changes in blood pressure. In this study, we aimed to achieve selective stimulation of populations of vagal afferents using a multi-contact cuff electrode wrapped around the abdominal trunks of the vagus nerve. Four-contact nerve cuff electrodes were implanted around the dorsal (N=3) or ventral (N=3) abdominal vagus nerve in six ferrets, and the response to stimulation was measured via a 32-channel microelectrode array (MEA) inserted into the nodose ganglion. Selectivity was characterized by the ability to evoke responses in MEA channels through one bipolar pair of cuff contacts but not through the other bipolar pair. We demonstrated that is was possible to selectively activate subpopulations of vagal afferents using abdominal VNS. Additionally, we quantified the conduction velocity of evoked responses to determine what types of nerve fibers (i.e. A{delta} vs. C) responded to stimulation. We also quantified the spatial organization of evoked responses in the nodose MEA to determine if there is somatotopic organization of the neurons in that ganglion. Finally, we demonstrated in a separate set of three ferrets that stimulation of the abdominal vagus via a four-contact cuff could selectively alter gastric myoelectric activity, suggesting that abdominal VNS can potentially be used to control GI function.

bioengineering↗

bioRxiv2019

Machine learning prediction of emesis and gastrointestinal state in ferrets

Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus to duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.

neuroscience↗