Automated label-free holographic screening platform for algae and plant protoplasts
Automated digital inline holography and AI platform for label-free screening of algae and plant protoplast suspensions. It combines automated multiwell sampling, flow-through imaging, and AI analysis to quantify cell concentration, size, morphology, aggregation, and treatment-associated changes—without staining. Volumetric imaging and computational refocusing reduce the need for mechanical focusing, while automated reporting supports consistent comparisons across samples.
Overview
This automated screening platform combines digital inline holographic imaging with AI-driven analysis to analyze algae and plant protoplast cell suspensions without fluorescent labels. It is designed to quantify several endpoints per sample—cell concentration, size, morphology, aggregation, and treatment-associated changes—across multiwell formats, enabling consistent comparisons and reducing manual handling.
The platform’s core value is throughput plus label-free robustness. Automated multiwell sampling combined with flow-through imaging measures many samples under identical conditions, while volumetric imaging with computational refocusing maintains analysis depth without precise mechanical focusing. Automated reporting simplifies comparisons across samples and conditions.
Potential applications include plant biology research, agricultural R&D, and other cell-based screening workflows that need high-content analysis without staining.
Technical specifications
The offering adapts an existing, experimentally demonstrated holographic imaging and AI-classification platform to algae and plant protoplast screens:
- Automated multiwell sampling integrated with flow-through digital inline holography
- AI analysis that quantifies cell concentration, size, morphology, aggregation, and treatment-associated changes
- Label-free readouts remove staining steps and reduce protocol complexity
- Volumetric imaging with computational refocusing minimizes reliance on mechanical focusing and precise cell positioning
- Modular optics, fluidics, and software support adaptation to different cell types and assay protocols
- Automated reporting, sample tracking, and cleaning cycles designed to limit carryover and preserve cell intactness
The underlying imaging and classification system has already been validated for particles and bacteria in liquids.
Technology readiness level
The core holographic imaging and AI classification are experimentally demonstrated, placing the base platform at laboratory scale. Adaptation to algae and plant protoplasts—larger, more fragile cells—is early in development. A planned 6–9 month validation pilot would select one priority assay, integrate automated sampling with flow-through imaging, develop the relevant AI endpoints, and validate against reference measurements describing expected throughput, accuracy, repeatability, samples per hour, operator time, sample consumption, carryover, and cell integrity. This places the overall offering at approximately TRL 3–4, depending on execution of the validated pilot and readiness for broader deployment.
About University of Minnesota
The University of Minnesota is a flagship, comprehensive public research university spanning multiple campuses, with a large research enterprise and clinical integration. Industry engages through co-located labs on the Twin Cities campuses, access to an academic health system for clinical translation, and pilot and field-testing facilities that speed scale-up. A statewide extension network and outreach centers provide real-world sites and data partnerships across Minnesota, while proximity to a dense medtech and Fortune 500 corridor enables frequent collaboration. Research is supported by competitive federal funding, including NIH, NSF, DOE, USDA, and DoD. A dedicated technology transfer office manages IP, licensing, sponsored research agreements, and startup incubation to speed commercialization.
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