Kinetic River Corp.

Compensation-free Flow Cytometer With Expanded Reagent Choices and Reduced Manufacturing Costs - Summary in Flow Cytometry, Fluorescent Spillover Introduces Complexities and Costs and Undermines the Accuracy of Quantitative Comparisons by Researchers. Spectral Crosstalk Imposes Limitations on the Types and Number of Fluorophores We Can Employ Without Compensation, Forcing Onto Users Burdensome and Expensive Protocols. to Address This Issue, Kinetic River Corp. Has Developed Our Arno Flow Cytometer, Which Eliminates the Need for Spectral Compensation. Unlike Conventional Flow Cytometers That Rely Solely on Spectral Information, Our Arno Flow Cytometer Integrates Fluorescence Lifetime as an Additional Discriminating Parameter to Reduce Crowding and Alleviate Spectral Spillover. Through Our Previous Phase I and Ii Sbirs, We Have Successfully Developed Our System to Detect Up to 14 Parameters, Optimized the System, Developed and Implemented Real-time Algorithms, and Validated Our Approach With a Compensation-free 12-COLOR Panel on Biological Samples. in This Commercialization Readiness Program Proposal, We Will Expand on Our Previous Advancements and Refine the Arno to Increase Adoption and Usage Within the Research Community, Thereby Broadening Its Accessibility and Compatibility. We Have Identified That Drastically Reducing the Costs of the System and Integrating a Greater Range of Arno Compatible Dyes Will Greatly Enhance Our Product, Increasing Adoption Into Smaller Research Labs and Flow Core Labs. This Proposal Will Expand the Capabilities and Market of the Arno Through the Following Aims. First, We Aim to Cut in Half the Cost of the Data Acquisition Module (DAQ) of the Arno System, Which Currently Poses a Financial Barrier to Adoption. We Have Identified Cost-effective Daq Components and Strategic Collaborators With Software/firmware Expertise to Cut the Daq Cost in Half While Maintaining Performance Standards. Second, We Plan to Implement an Improved Time-resolved Fluorescence Discrimination Algorithm for the Arno, Integrating and Validating Empirical Time Fluorescent Data From a Wide Range of Common Dyes and Fluorescent Proteins. Strengthening Our Current Algorithms to Accept Empirical Reference Data Will Enable Us to Integrate Compensation- Free Panels of Common Dyes, Expanding the Arno's Utility for a Broader User Base. Through These Initiatives, We Anticipate Significant Advancements in the Arno, Ultimately Increasing End-user Adoption.