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bioRxiv2026

FLASH radiotherapy drives intestinal regeneration via macrophage-derived WNT4 signaling and attenuated T-cell inflammation

Ultra-high dose rate radiotherapy (FLASH-RT) reduces normal-tissue toxicity, yet how elevated dose rate improves tissue repair remains unclear. Here we combined whole-abdominal FLASH-RT and conventional radiotherapy (CONV-RT) in mice with single-cell transcriptomics, trajectory analysis, cell-cell communication inference, and in vivo perturbation experiments to define the intestinal repair ecosystem. Compared with CONV-RT, FLASH preserved Lgr5+ intestinal stem cells and Fabp1+ absorptive epithelial cells. Trajectory analysis showed that FLASH maintained stem-cell differentiation toward mature epithelial states enriched for fatty-acid metabolism. During a day-3 repair window, FLASH was accompanied by enrichment of Mrc1 macrophages, increased WNT4-associated signaling involving Fzd5Lrp5/6-expressing stem cells, and early transient {beta}-catenin activation. In vivo augmentation of Wnt4 signaling promoted crypt regeneration after CONV-RT. FLASH also reduced Tcf7 and Tbx21 CD4 T-cell populations and TNF- levels, consistent with attenuation of the pro-inflammatory CD4 T-cell response during intestinal recovery. Together, these findings indicate that FLASH-associated tissue sparing involves coordinated remodeling of regenerative and inflammatory immune states, suggesting that radiation dose rate influences not only initial tissue injury but also the immune microenvironment of subsequent recovery. Targeting this immune-stem cell crosstalk may provide a strategy to mimic FLASH-like radioprotection.

biophysics↗

bioRxiv2022

INSIDER: Interpretable Sparse Matrix Decomposition for Bulk RNA Expression Data Analysis

RNA-Seq is widely used to capture transcriptome dynamics across tissues from different biological entities even across biological conditions, with the aim of understanding the contribution of gene activities to phenotypes of biosamples. However, due to variation from tissues and biological entities (or other biological conditions), joint analysis of bulk RNA expression profiles across multiple tissues from a number of biological entities to achieve the aim is hindered. Moreover, it is crucial to consider interactions between biological variables. For example, different brain disorders may affect brain regions heterogeneously. Thus, modeling the disorder-region interaction can shed light on the heterogeneity. To address these key challenges, we propose a general and flexible statistical framework based on matrix factorization, named INSIDER (https://github.com/kai0511/insider). INSIDER decomposes variation from different biological variables into a shared low-rank latent space. In particular, it considers interactions between biological variables and introduces the elastic net penalty to induce sparsity, thus facilitating interpretation. In the framework, the biological variables and interaction terms can be defined based on the research questions and study design. Besides, it enables us to compute the adjusted expression profiles for biological variables that control variation from other biological variables. Lastly, it allows various downstream analyses, such as clustering donors with donor representations, revealing development trajectory in its application to the BrainSpan data, and uncovering mechanisms underlying variables like phenotype and interactions between biological variables (e.g., phenotypes and tissues).

bioinformatics↗