Grau Roma, L. | bioRxiv Science

bioRxiv2025

Tattoo ink induces inflammation in the draining lymph node and impairs the immune response against a COVID-19 vaccine

Despite safety concerns regarding the toxicity of tattoo ink, no studies have reported the consequences of tattooing on the immune response. In this work, we have characterized the transport and accumulation of different tattoo inks in the lymphatic system using a murine model. Upon quick lymphatic drainage, we observed that macrophages mainly capture the ink in the lymph node (LN). An initial inflammatory reaction at local and systemic levels follows ink capture. Notably, the inflammatory process is maintained over time as we observed clear signs of inflammation in the draining LN two months following tattooing. In addition, the capture of ink by macrophages was associated with the induction of apoptosis in both human and murine models. Furthermore, the ink accumulated in the LN altered the immune response against a COVID-19 vaccine. We observed a reduced antibody response following vaccination with a mRNA-based SARS-CoV-2 vaccine, which was associated with a decreased expression of the Spike protein in macrophages in the draining LN. Considering the unstoppable trend of tattooing in the population, our results are crucial in informing the toxicology programs, policymakers, and the general public regarding the potential risk of the tattooing practice associated with an altered immune response.

immunology↗

bioRxiv2024

Deep Learning-based Modeling for Preclinical Drug Safety Assessment

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a timeintensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on Rattus norvegicus. We show that TRACE can perform various downstream toxicology tasks spanning histopathological response assessment, lesion severity scoring, morphological retrieval, and automatic dose-response characterization. In an independent reader study, TRACE was evaluated alongside ten board-certified veterinary pathologists and achieved higher concordance with the consensus opinion than the average of the pathologists. Our study represents a substantial leap over existing computational models in toxicology by offering the first framework for accelerating and automating toxicological pathology assessment, promoting significant progress with faster, more consistent, and reliable diagnostic processes. Live Demo: https://mahmoodlab.github.io/tox-foundation-ui/

bioinformatics↗