# Ultra-sensitive rapid test for detecting bacterial contamination in platelets

> **NIH NIH R43** · PHOTON BIOSCIENCES, LLC · 2020 · $265,092

## Abstract

Project Abstract
Over the past several years, bacterial contamination of platelets has been the greatest
transfusion-transmitted infectious risk in the United States. Bacterial contamination of platelet
components occurs because of its unavoidable storage temperature (22 °C), resulting in
recognized transfusion-transmitted sepsis in at least 1 of 100,000 recipients, and an immediate
fatal outcome in 1 in 500,000 recipients. Currently, the gold-standard tests for bacterial
contamination of platelets rely on limulus amebocyte lysate (LAL), the aqueous extract of the
blood of horseshoe crabs, which forms a clot or gel upon exposure to bacterial endotoxin. Due to
the unsustainability of the LAL method, several new alternative technologies using chromogenic,
chemifluorescent, or chemiluminescent assays have been introduced into the market; however,
they continue to have serious limitations in terms of performance and spectroscopic range (both
in time and wavelength). Photon Biosciences, LLC and S2Media (S2M Enterprises, LLC) have
engineered a new, non-photobleaching luminescent protein named RECAL®, which we intend to
use as the basis for a fully quantitative, highly sensitive, rapid assay kit for checking for bacterial
contamination of platelets.

## Key facts

- **NIH application ID:** 10008218
- **Project number:** 1R43AI149790-01A1
- **Recipient organization:** PHOTON BIOSCIENCES, LLC
- **Principal Investigator:** Kevin Michael Lewis
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $265,092
- **Award type:** 1
- **Project period:** 2020-05-12 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10008218

## Citation

> US National Institutes of Health, RePORTER application 10008218, Ultra-sensitive rapid test for detecting bacterial contamination in platelets (1R43AI149790-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10008218. Licensed CC0.

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