MAC-Seq: Coupling Low-Cost, High-Throughput RNA-Seq with Image-Based Phenotypic Screening in 2D and 3D Cell Models.

Area of Science:

• Genomics
• Molecular Biology
• Cell Biology

Background:

• Transcriptomic profiling is crucial for understanding cancer but standard methods are low-throughput and costly.
• Complex in vitro cell models require resource-efficient, high-throughput techniques for detailed analysis.
• Existing methods struggle with limited sample availability and large-scale phenotype-genotype correlation.

Purpose of the Study:

• To introduce and detail the Multiplexed Analysis of Cells (MAC-seq) workflow.
• To demonstrate MAC-seq's applicability in a 384-well format for 2D and 3D cell cultures.
• To highlight MAC-seq's compatibility with high-throughput imaging and screening.

Main Methods:

• Developed and optimized a low-cost, ultrahigh-throughput RNA-sequencing workflow (MAC-seq) in a plate format.
• Applied MAC-seq to 2D and 3D cell cultures in a 384-well format.
• Sequenced samples to a depth of one million reads per well.

Main Results:

• Identified over ten thousand expressed genes per well on average under experimental conditions.
• Demonstrated the feasibility of MAC-seq for measuring cell perturbations.
• Showcased compatibility with high-content cell imaging for phenotyping.

Conclusions:

• MAC-seq provides a resource-efficient, high-throughput solution for transcriptomic analysis.
• The protocol facilitates detailed time-course analytics and phenotype-genotype correlation at scale.
• MAC-seq has significant potential for integration with high-throughput screening applications in cell biology and drug discovery.