Machine-learning guided discovery of emergent antimicrobial activity from dynamic covalent assemblies（Mus musculus， scRNA seq）

Single-cell RNA sequencing (scRNA-seq) was performed on skin wound tissues collected from a murine subcutaneous Staphylococcus aureus infection model. Mice received either A5B5 assembly treatment or PBS control treatment. Treatment group (A5B5): 2 biological replicates, sample IDs A1 and A2; Control group (PBS): 2 biological replicates, sample IDs P1 and P2. At the designated endpoint after treatment, wound tissues were harvested and processed into single-cell suspensions. scRNA-seq libraries were prepared using the 10x Genomics Chromium Single Cell 3′ Reagent Kits v3.1, targeting approximately 10,000 cells per sample, and sequenced on an Illumina NovaSeq 6000 platform at an average depth of approximately 50,000 reads per cell. Raw sequencing data were processed with Cell Ranger, followed by downstream analysis in Seurat, including quality control, normalization, batch correction, clustering, and cell-type annotation. Monocyte/macrophage populations were further analyzed for treatment-associated cellular and transcriptional changes.