Drug-induced differential gene expression analysis (DGEA) is an essential tool for uncovering the molecular basis of phenotypic changes in cells upon drug treatment, and ultimately for understanding the mechanisms of individual tumor responses to anticancer drugs. Performing high-throughput DGEA after drug treatment is challenging due to the very high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells. We introduce a novel, miniaturized method operating at the nanoliter scale for drug-induced DGEA. This innovative approach facilitates high-throughput and parallel analysis of the drug response of cells derived from patients, effectively circumventing issues related to limited samples and the laborious nature of traditional protocols. The method is based on the Droplet Microarray (DMA) platform, a microscope glass slide with a pattern of hydrophilic spots separated by a superhydrophobic background, which enables the formation of droplets suitable for testing a minute number of cells with compounds. DMA allows for phenotypic analysis using microscopy, followed by obtaining cDNA from the treated cells and DGEA. The procedure involves cell lysis for mRNA isolation and cDNA conversion on DMA, followed by pooling of the droplets and subjecting them to qPCR analysis. In this work, we demonstrate the protocol for drug-induced DGEA on the DMA platform using cell lines and primary patient-derived chronic lymphocytic leukemia (CLL) cells. The methodology established here is critical for performing DGEA on a limited number of cells, with potential applications in functional precision oncology. In this way, this method helps to gain insights from the molecular profiling of unique patient-derived samples after drug treatment in vitro, which is essential for understanding individual tumor response to anticancer drugs.

The provided data are technical repeats from the research experimental details. including qPCR data and image analysis for cellular viability.