An open source toolkit for repurposing Illumina sequencing systems as versatile fluidics and imaging platforms

Area of Science:

• Life Sciences
• Microscopy
• Bio-imaging

Background:

• Advanced omics methods rely on fluorescence microscopy for high-resolution imaging.
• These methods require precise control over temperature, reagents, and imaging parameters over extended periods.
• Automated systems are crucial for reproducible data generation, but specialized solutions are scarce.

Purpose of the Study:

• To present PySeq2500, an open-source software and hardware solution.
• To enable the repurposing of Illumina HiSeq 2500 instruments for automated fluidics-coupled fluorescence imaging.
• To provide a non-specialist accessible platform for complex, multi-day imaging experiments.

Main Methods:

• Developed PySeq2500, an open-source Python codebase and flow cell design.
• Modified the Illumina HiSeq 2500 (epifluorescence microscope with integrated fluidics) into a programmable imaging platform.
• Implemented customizable protocols for 4-channel imaging, temperature control, reagent exchange, and extended sample stability.

Main Results:

• Demonstrated unattended execution of iterative indirect immunofluorescence imaging (4i) over multiple days.
• Generated highly multiplexed cell type and pathological feature maps in mouse and human spinal cord sections.
• Validated the system's capability for robust, reproducible data generation in complex workflows.

Conclusions:

• PySeq2500 transforms a widely available instrument into an open platform for advanced imaging.
• Enables non-specialists to implement state-of-the-art fluidics-coupled imaging methods.
• Facilitates accessible automation for complex, multi-day biological research.