The technology describes a method for characterizing known or unknown materials using libraries of probes that bind to them. These probes, which can be oligonucleotides or peptides linked to polynucleotide barcodes, are analyzed via sequencing to identify material-specific patterns, enabling the detection and identification of known and unknown materials.
Characterizing unknown materials remains a significant challenge, due to the reliance on reagents that are specific to known targets. This becomes problematic in scenarios such as exploring unknown regions in remote environments on Earth, such as mines or wellbores, or in extraterrestrial settings. For new diseases, existing methods may lack the sensitivity and specificity required to accurately identify new disease markers, leading to potential delays in diagnosis and treatment development. Consequently, there is a need for innovative approaches that can unbiasedly identify and characterize both known and unknown materials. Existing techniques often lack the versatility and sensitivity required for such tasks, necessitating the development of new technology that can provide comprehensive and accurate material characterization without prior knowledge of the target.
This patent-pending technology from UT Austin uses Proximity Ligation Assay (PLA) technique as an advanced method for characterizing materials using probe libraries. It utilizes libraries of probes, which can be oligonucleotide probes or peptides conjugated to polynucleotide barcodes, which contain random sequences enabling unbiased sensing of materials within a sample. When these probe libraries bind to a material, unique patterns of the bound probes are identified. These patterns, which indicate the substrates bound by the probes, are analyzed by sequencing the bound probes and interpreting the sequence data to determine the identity of the material. The technology also includes optimized PLA ligation and PCR amplification parameters, as well as developed algorithms and tools for analyzing the resulting data.
This technology is differentiated by its ability to perform unbiased sensing using random sequence probe libraries, which identifies both known and unknown materials. Unlike traditional methods that rely on reagents specific to known targets, this approach explores unknown materials in diverse environments, such as mines, wellbores, or other planets, and in evaluating new disease markers. The co-refinement of PLA parameters and analysis methodologies ensures high specificity and sensitivity, making it a powerful tool for material characterization. The integration of advanced sequencing and data analysis tools further enhances its capability to generate detailed and accurate material fingerprints, setting it apart from conventional detection strategies.