Single-cell analysis is a critical approach for extracting nuanced biological insights. Combinatorial barcoding is a highly efficient method for scRNA-seq at scale, offering both sensitivity in gene detection and flexibility in sample input. However, this approach becomes challenging when working with sample inputs of fewer than 100,000 cells. To address this limitation, we have developed a modified fixation and cell capture method optimized for low-input samples while maintaining compatibility with combinatorial barcoding. This advancement allows researchers to fix, store, and barcode cells efficiently without requiring costly or specialized instruments.

The Low Input Fixation method enables the fixation and profiling of up to 384 samples in parallel, significantly reducing workflow duration while ensuring consistent handling. This minimizes both experimental variability and the burden of scaling. Fixed samples remain stable at -80℃, and capture efficiency remains consistently high for inputs as low as 10,000 cells. The workflow is designed to maximize sample recovery while providing accurate and comprehensive transcriptional profiling. To validate this approach, we applied it to a cancer drug screening panel, utilizing cell input amounts ranging from 10,000 to 20,000 cells per treatment.

Robust cell capture minimizes cell loss during drug perturbations, leading to consistent gene detection. Whole transcriptome analysis using Evercode WTK identifies gene expression changes associated with each drug class and cell type. The results validate putative drug targets within the panel and demonstrate that panel-based perturbation allows for the scalable detection of off-target effects. The stability and efficiency of this method make it a powerful tool for studying transcriptional responses in low-input single-cell experiments.

This work demonstrates that combining low-input fixation with combinatorial barcoding enables high-throughput cancer drug and compound screening. By facilitating the efficient processing of low-input samples, this method expands the applicability of single-cell transcriptomics to a broader range of biological and clinical studies.