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Quantifying NETosis via Automated High Content Imaging Convolutional Neural Networks

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41AI131840-01A1
Agency Tracking Number: R41AI131840
Amount: $299,751.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NIAID
Solicitation Number: PA16-303
Timeline
Solicitation Year: 2016
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-02-09
Award End Date (Contract End Date): 2020-01-31
Small Business Information
120 MASON FARM RD GENETIC MED BLDG 3RD FLOOR
Chapel Hill, NC 27514-4617
United States
DUNS: 078882699
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 LAILA ELSHERIF
 (919) 966-7409
 laila_elsherif@med.unc.edu
Business Contact
 JAMES BONE
Phone: (434) 996-8107
Email: jbone@epicypher.com
Research Institution
 UNIV OF NORTH CAROLINA CHAPEL HILL
 
104 Airport Drive, Suite 2200
CHAPEL HILL, NC 27599-0001
United States

 Nonprofit college or university
Abstract

PROJECT SUMMARY
NETosis was identified as a distinct mode of cell death in neutrophils more than a decade agoDysregulation
of NETosis has been implicated in the etiology of human pathologies such as preeclampsiasickle cell
diseasesystemic lupus erythematosusmultiple sclerosisrheumatoid arthritissepsiscystic fibrosislupus
nephritisand coagulopathies that include cancer associated thrombosisThe literature consistently cites the
lack of a standardized methodology for quantitation of NETosis as an impediment to basic and translational
researchThusthe premise is that there is a compellingunmet need for a standardizedquantitative and
automated method for the measurement of NETosis to accelerate neutrophil and inflammation based research
and facilitate the discovery and development of therapeutic compoundsThe scope of this STTR project is to develop a high throughput image analysis and quantitation method by
using high content imaging and the revolutionary technology of convolutional neural networksCNNfor the
identification and quantitation of NETosis in human neutrophilsThe target readout is based on the primary
morphological difference between NETotic and non NETotic nucleithe decondensation of chromatinThis
image based quantitative method will be observer independent and will enable robust and rapid evaluation of a
large number of samples that would exceed any attempts at manual assessmentIn Phase I we will complete the following Specific AimsAimOptimize and standardize the highthroughput platform for quantitation of NETosis in adherent human neutrophilsThis includes standard
assay optimization procedurestraining the CNN to identify and quantitate NETotic neutrophiland
demonstrating that the CNN reliably distinguishes between necrosis and NETosiswhose phenotypes appear
similar to the human eyeAimValidate the NETosis assay biochemically and clinicallyThis includes
concentration response assays with NETosis agonistsassessment of NETosis inhibitorsand evaluation of
the NETotic status of Sickle Cell Disease patient samplesa disease in which aberrant NETosis has been
implicatedThe expected outcome of this Phase I effort is to demonstrate proof of concept for this automated highthroughput NETosis assayFurtherwe expect to provide insight into the utility of the assay for assessment of
inhibitors of NETosis as therapeutic agentsUpon completion of our Phase I aimsour Phase II program will
focus on further optimizing and validating this NETosis assay and preparing it for commercializationPROJECT NARRATIVE
Aberrant NETosis has been implicated in the etiology of several inflammatory and autoimmune diseasesThe
lack of a standardizedquantitative and automated method for the measurement of NETosis is impeding basic
and translational researchWe have developed a high throughput assay using convolutional neural networks
to quantify NETosis in human neutrophilsThis assay will accelerate neutrophil and inflammation based
research and facilitate the discovery and development of compounds with therapeutic potential

* Information listed above is at the time of submission. *

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