In situ macrophage CAR programming via ROS-responsive microneedles enhances efferocytosis and promotes mucosal regeneration
Source abstract
Abstract
Radiotherapy for head and neck cancer frequently induces severe radiation-induced oral mucositis (RIOM), in which excessive reactive oxygen species (ROS), defective macrophage efferocytosis, and persistent inflammation form a self-amplifying pathological loop that delays mucosal repair. Current symptomatic treatments are insufficient to simultaneously suppress oxidative stress and restore immune-mediated clearance of damaged cells. Here, we developed a detachable microneedle patch with ROS-responsive tips (PTC MN) for localized in situ macrophage programming within RIOM lesions. The PTC MN was fabricated from a PPBA-TA-PVA hydrogel matrix, which combines intrinsic ROS-scavenging capacity with oxidative stress triggered degradation, and loaded with engineered hybrid nanovesicles (HLENs-CAR) carrying a plasmid encoding CAR-ectoCRT-IL-4. This design enabled local delivery of gene-loaded nanovesicles, macrophage-targeted genetic programming, and sustained retention of the therapeutic payload in the injured mucosa. In a murine RIOM model, PTC MN accelerated mucosal epithelial regeneration, reduced oxidative stress and inflammatory infiltration, enhanced reparative macrophage responses, and attenuated fibrosis-associated tissue remodeling. Functionally, the platform enhanced macrophage efferocytosis, promoted IL-4-associated reparative polarization, and shifted the lesion microenvironment toward inflammation resolution and tissue regeneration. Collectively, this study establishes a smart in situ immunomodulation strategy that integrates ROS-responsive microneedle delivery with CAR-macrophage programming, providing a potential therapeutic paradigm for refractory mucosal injury.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Phagocytosis and Immune Regulation · Wound Healing and Treatments · RNA Interference and Gene Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
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