Opportunity
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/ATo 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