Researchers at Purdue University have developed a new imaging method that removes the sample-preparation step from multiomics workflows [1].
This innovation addresses a primary bottleneck in biological research by reducing the time and budget required to analyze complex samples. By eliminating manual preparation, the process allows for faster and cheaper spatial analysis of irregular surfaces [1].
The new technique is called Surface Touch Extraction imaging, or STEi [1]. This patent-pending method enables spatial analysis using liquid chromatography-tandem mass spectrometry, known as LC-MS/MS [2]. Traditionally, multiomics workflows required extensive preparation to make samples compatible with analysis tools, a process that often consumed significant resources [3].
By automating the extraction process, the STEi method allows scientists to study irregular surfaces more efficiently [1]. This capability is particularly useful for researchers who need to maintain the spatial orientation of molecules within a sample while analyzing multiple types of biological data simultaneously [2].
The development took place at Purdue University in West Lafayette, Indiana [1]. The research team focused on creating a workflow that could handle the inherent variability of biological samples without the need for the time-intensive steps that typically precede mass spectrometry [3].
This shift toward automation in the multiomics pipeline is intended to lower the barrier for laboratories to conduct high-resolution spatial studies [2]. By streamlining the path from sample collection to data acquisition, the STEi method aims to accelerate the pace of discovery in fields requiring detailed molecular mapping [1].
“The patent-pending STEi imaging technique speeds up spatial analysis and reduces costs.”
The removal of sample preparation in multiomics represents a significant shift toward high-throughput spatial biology. By integrating automated extraction with LC-MS/MS, researchers can analyze the chemical composition of tissues in their original spatial context more rapidly. This could lead to faster diagnostic developments and a deeper understanding of how different molecules interact within complex biological structures.
