Spatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result. We propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analyzing ST data sets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots' stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbors when perturbations are applied. Stardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.

The enhancer of zeste homolog 2 (EZH2) histone methyltransferase inhibitors tazemetostat and valemetostat recently have received approval for clinical use in follicular lymphoma and adult T-cell leukemia/lymphoma, respectively. In follicular lymphoma, the expression of activation-induced cytidine deaminase (AID) is responsible for increased mutational signatures and genomic instability. Because EZH2 inhibitors induce epigenetic and transcriptional changes in normal and lymphoma B cells, we studied whether these inhibitors could alter the pattern of AID-mediated chromosomal translocations. In this study, we showed that treatment with EZH2 inhibitors did not significantly change AID expression or AID-dependent chromosomal translocation frequency when used as monotherapy in either CH12F3 mouse B cells or MEC-1 human B cells. In contrast, when combined with phosphoinositide 3-kinase δ (PI3Kδ) inhibition, which enhances AID expression, EZH2 inhibition significantly increased the frequency of chromosomal translocations when compared with either EZH2 or PI3Kδ inhibition alone both in mouse CH12F3 cells and human MEC-1 cells. EZH2 inhibition also further enhanced translocation formation in mouse B cells that were DNA ligase IV (Ligase4) deficient. Mechanistically, EZH2 inhibition in B cells depletes the repressive histone modification H3 trimethylation at lysine 27 (H3K27me3) while concurrently enhancing the active histone modification H3 acetylation at lysine 27 (H3K27ac), thereby selectively increasing transcriptional activity and facilitating chromosomal translocation formation in the presence of high AID activity or Ligase4 deficiency. These findings highlight the impact of drugs that induce epigenetic changes to influence chromosomal translocations, and demonstrate the genetic safety of EZH2 inhibitors as monotherapy while highlighting the increased risk of genomic instability when used in cells prone to translocations, such as B cells with high AID levels or DNA repair deficiency.

Motivation: Large language models (LLMs) are rapidly becoming indispensable across the life‑sciences spectrum, from literature mining through clinical decision support to experimental design. Yet, in single‑cell RNA‑sequencing (scRNA‑seq) analysis, most LLM‑enabled tools remain opaque: they output a single label per cluster without disclosing the chain‑of‑ thought that led to that decision. This opaqueness undermines reproducibility, complicates peer‑review, and ultimately slows the adoption of otherwise powerful methods. Results: We developed GPTBioInsightor, an LLM‑powered assistant that not only annotates cell types, cell states, and pathway activities but also narrates how it arrived at each conclusion, step-by-step. Across benchmark datasets—including peripheral blood mononuclear cells (PBMC3K) and pancreatic ductal adenocarcinoma—GPTBioInsightor achieved at least parity with expert manual curation while delivering transparent reasoning, confidence scores, and literature‑based evidence. By closing the “interpretability gap,” GPTBioInsightor equips wet‑lab biologists, computational scientists, and reviewers with an audit‑ready trail, thereby accelerating discovery and fostering trust in AI‑assisted bioinformatics. Availability and implementation: GPTBioInsightor is freely available on GitHub under a BSD-3-Clause license (https://github.com/huang-sh/GPTBioInsightor).