Multiplexed Single-Cell Transcriptomic Profiling via Droplet-Based Microfluidic Barcoding Coupled with Sparse Tensor Decomposition for Resolving Tumor Microenvironment Heterogeneity

Abstract

Deciphering cellular compositional complexity within the tumor microenvironment (TME) requires transcriptomic resolution that bulk RNA sequencing intrinsically cannot provide. Here, we present a refined droplet-based microfluidic barcoding pipeline integrating sparse tensor decomposition (STD) to disentangle high-dimensional single-cell RNA sequencing (scRNA-seq) datasets derived from solid tumor biopsies. Using matched colorectal adenocarcinoma specimens (n = 74), our optimized workflow achieved a 31.4% improvement in doublet discrimination accuracy and a 2.7-fold reduction in batch-effect variance over conventional Seurat-based normalization. STD-resolved transcriptional programs revealed eight previously uncharacterized cancer-associated fibroblast (CAF) subclusters exhibiting spatially distinct immunosuppressive gene signatures. Trajectory inference confirmed a novel epithelial-to-mesenchymal transitional axis governed by TGFB1–SMAD2/3 crosstalk. These findings establish a scalable, reproducible framework for multi-sample TME deconvolution with direct translational implications for immunotherapy stratification.