Technology-enabled integration of single-cell transcriptomics and microbiome data identifies RNA-targetable host-microbiota networks in colorectal adenoma

Although mechanism-to-intervention processes are becoming possible because to the convergence of single-cell technologies with RNA treatment methods, combined host-microbiome analysis with systematic target identification for colorectal adenoma is still fragmented. Here, we created a repeatable computational pipeline that combines MaAsLin2 for host-microbiome association modeling, QIIME2/DADA2 for microbiome processing, and Seurat/Harmony for single-cell analysis. Under strict statistical control (FDR < 0.05), three single-cell RNA sequencing datasets (GSE117875, GSE178341, and GSE144735; totaling 426,425 cells) were combined with parallel microbiome datasets (PRJNA397906, PRJNA541510, and PRJNA672605; 975 samples). In adenoma-associated microbiomes, we measured a 26.8 % decrease in Shannon diversity (4.21→3.08), with a notable enrichment of Fusobacterium nucleatum and a depletion of Faecalibacterium prausnitzii. Immune activation patterns, goblet cell malfunction (MUC2 2.4-fold drop), and stem cell expansion (LGR5 3.2-fold increase) were all identified by single-cell analysis. 847 significant host-microbiome interactions were found by integration analysis, and F. nucleatum showed a substantial correlation with markers of inflammatory signaling (NFKB1: β=0.64, FDR<0.001) and stem cell proliferation (LGR5: β=0.72, FDR<0.001). 25 RNA-targetable candidates were identified by systematic prioritizing, including mRNA restoration targets (MUC2, FOXP3) and ASO/siRNA suppression targets (NFKB1, IL1B). By converting host-microbiome interaction networks into systematic RNA therapeutic options, this technology framework creates a template for the translation of transcriptomics into therapeutics and offers a repeatable pipeline for the creation of precision interventions in colorectal disease.