# SAM #75N95D27Q00001 Automated High-Throughput Proteomics System and Support Services **Buyer:** NIH Office of Logistics and Acquisition Operations **Posted:** October 28, 2026 **Respond By:** November 06, 2026 **Identifier:** 75N95D27Q00001 **NAICS:** 334516 The National Institutes of Health is seeking an automated proteomics platform to support high-throughput biomarker analysis for Alzheimer’s disease and related dementias. - **Government buyer:** National Institutes of Health (NIH), Office of the Director, Office of Acquisitions; the requirement supports the National Institute on Aging (NIA). - **OEM/vendor:** **Alamar Biosciences** is the named supplier and intended source. - **Product requested:** One high-throughput automated proteomics analysis system, including associated software and accessories. No model or part number is specified. - Processes up to 288 samples per run and supports NULISA assays, with singleplex-to-high-plex analysis capability. - Includes automated qPCR and liquid handling for qPCR and next-generation sequencing (NGS) workflows, onboard computing, integrated analysis software, and cloud-based ARGO Command Center and NULISA Analysis Software. - Required performance includes less than 30 minutes of hands-on time, setup in less than 5 minutes, and results in under 6 hours for singleplex assays or under 15 hours for multiplex assays. - Additional requirements include a cartridge-based reagent system supporting up to three consecutive runs, broad sample compatibility, and an API for custom method development and instrument control. - **Services requested:** Installation and required installation materials; white-glove delivery, handling, and insurance; and on-site instrument and software training with training materials. Quantity: one each. - **Period of performance:** Not stated in the available information. ### Description NIA: Supply: Diagnostic Laboratory Equipment: N/A
To provide ability to identify biomarkers which will provide information about underlying molecular changes associated with Alzheimer¿s Disease and Related Dementias in well characterized samples from patients [View original listing](https://sam.gov/opp/5dacdc7c76c149608b81b3d8da7fd635/view)