Tumor cells express and maintain HMGB1 in the reduced isoform to enhance CXCR4-mediated migration.

During inflammation and tissue regeneration, the alarmin High Mobility Group Box 1 (HMGB1), in its reduced isoform, enhances the activity of the chemokine CXCL12, forming a heterocomplex that acts via the chemokine receptor CXCR4. Despite the established roles of both HMGB1 and CXCL12 in tumor progression and metastatic spread to distal sites, the role of the CXCL12/HMGB1 heterocomplex in cancer has never been investigated. By employing a newly established mass spectrometry protocol that allows an unambiguous distinction between reduced (red-HMGB1) and oxidized (ox-HMGB1) HMGB1 isoforms in cell lysates, we demonstrate that human epithelial cells derived from breast (MCF-7 and MDA-MB-231) and prostate (PC-3) cancer predominantly express red-HMGB1, while primary CD3+ T lymphocytes from peripheral blood express both HMGB1 isoforms. All these cancer cells release HMGB1 in the extracellular microenvironment together with varying concentrations of thioredoxin and thioredoxin reductase. The CXCL12/HMGB1 heterocomplex enhances, via CXCR4, the directional migration and invasiveness of cancer cells characterized by high metastatic potential that possess a fully active thioredoxin system, contributing to maintain red-HMGB1. On the contrary, cancer cells with low metastatic potential, lack thioredoxin reductase, promptly uptake CXCL12 and fail to respond to the heterocomplex. Our study demonstrates that the responsiveness of cancer cells to the CXCL12/HMGB1 heterocomplex, resulting in enhanced cell migration and invasiveness, depends on the maintenance of HMGB1 in its reduced isoform, and suggests disruption of the heterocomplex as a potential therapeutic target to inhibit invasion and metastatic spread in cancer therapies.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.