Low-cost platform maps gene activity and metabolites in the same tissue sample

Spatial transcriptomics and spatial metabolomics are two techniques used to study the activity of genes and metabolites within tissue samples. However, existing methods for these techniques are often expensive and difficult to use, making it challenging for many laboratories to perform them. To address these challenges, researchers have developed OpenFISH, an open and low-cost imaging-based platform that integrates spatial transcriptomics with spatial metabolomics on the same slide.

This new platform significantly reduces the cost of spatial transcriptomics by about 95% compared to leading commercial platforms.

OpenFISH utilizes a modular probe design to cut probe synthesis costs and a simple coding system for genes, eliminating the need for a microfluidic system. The experimental procedure has also been optimized, reducing wet-lab time to no more than 13 hours. With just a standard 20× widefield fluorescence microscope, clear signals can be captured in situ.

The researchers applied OpenFISH to two neuroscience applications: examining cell-type-associated transposable element elevation during inflammation and neuronal cell lamination distortion after Reln gene knockout. They observed reproducible upregulation of transposable elements, implicating them as active drivers or modulators of neuroinflammatory pathways. They also detected a decrease in D1-type inhibitory striatal neurons.

The integration of OpenFISH with MALDI-MSI, a state-of-the-art untargeted spatial metabolomics method, allows for the detection of cell-type-associated metabolites in mouse brain cells. This combined pipeline reveals important information about the molecular interactions within biological systems and improves anatomical depiction.

In mouse brains, the combined maps linked metabolites to cell types and enhanced the anatomical detail. In an Alzheimer's disease model, microglia showed the strongest changes, and multiple metabolites associated with specific cell types were elevated in these mice compared to healthy controls. This cost-effective spatial transcriptomics tool, paired with spatial metabolomics, promises to better untangle complex molecular interactions in biological systems.