LUCERNA INC — Department of Health and Human Services SBIR Phase I: 400

Description

SUMMARYThe goal of this proposal is to improve the sensitivity and specificity of the Spinach based splice sensor platform by developing a novel multiprobeMPassay design and a companion machine learning based classification algorithm called assay classifier engineACEImprovement in sensitivity and specificity of the splice sensor platform enables its application to detect endogenous RNA isoforms with low copy number and distinguish alternative RNA isoforms that share high degree of sequence similaritiesThe aim of any assay development effort is to achieve excellent assay specificity and sensitivityHoweverthis is often a futile endeavor since specificity and sensitivity are two inversely correlated factorsThe underlying reason for poor sensitivity or specificity is due to the off target signals generated by competing molecules present in the sampleIn the field of diagnosticsone of the ways these issues are addressed is to perform multiple single probe testing instead of one single probe testingWhile individual singe probe assays might have poor specificity and sensitivitywhen combinedthese assays synergistically improve the sensitivity and specificity of the ultimate diagnostic determinationIn the field of research and drug discoveryresearchers have employed a multitude of strategiese gsignal amplificationreaction cascadesor sample enrichmentto improve sensitivity and MP design or strand displacement strategies to improve specificitySome of the PCRbased methods have combined both enzyme based signal amplification and MP strategies to improve assay determinationHoweverwhen it comes to detecting targets that are highly similar to their competitorssuch as detecting single nucleotide polymorphismDNA methylationRNA modification and alternative splicingthere is still an unmet need for more sensitive and specific analytical methodsIn the past few yearsLucerna has developed Spinach based sensors to detect intractable metabolites and biomoleculesOne such sensor is the splice sensorwhich is a Spinach based sensor that can generate fluorescence signal based on the alternative RNA isoform of interestOne of the challenges encountered during splice sensor assay development is the lack of sensitivity toward low copy number RNA isoforms and low specificity when distinguishing two splice isoforms that share a high sequence similarityTo overcome this challenge in this proposalwe will develop a MP assay panel comprised of splice sensor variants that recognize the target RNA and the competitor with varying binding affinities and differing signal responsesWe will use data sets generated from the MP assay to train a ML based ACE algorithm to make target determination in test samplesFurtherwe will develop a quantitative MP data set and re train the ACE algorithm to classify the assay signals into various categories based on target concentrations in the test sampleThis new ACE algorithm will then be tested against conventional single probe assays to determine specificity and sensitivity improvement of the MP assay platform PROJECT NARRATIVEImproved specificity and sensitivity are highly sought after features in assays where there are high similarity between the target and its competitors or when the target exists naturally in very low abundanceTo address this unmet needwe will develop a fluorescence sensor based multiprobe assay approach and a companion machine learning based assay classifier engineACEThe ACE algorithm will integrate the multiprobe assay data and classify them based on trained machine learning models to make sample determination with enhanced specificitysensitivityand dynamic range than possible with conventional single probe assays