Source: RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE submitted to
OPTICAL CRISPR NANO-DIAGNOSTICS AGAINST COMMON SALMONELLA SEROTYPES USING MOBILE-APP DRIVEN IMAGE ANALYSIS AND DATA TRANSMISSION
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
ACTIVE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1031549
Grant No.
2024-67022-41531
Cumulative Award Amt.
$611,000.00
Proposal No.
2022-08596
Multistate No.
(N/A)
Project Start Date
Nov 15, 2023
Project End Date
Nov 14, 2027
Grant Year
2024
Program Code
[A1511]- Agriculture Systems and Technology: Nanotechnology for Agricultural and Food Systems
Project Director
Yigit, M.
Recipient Organization
RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK, THE
1400 WASHINGTON AVE
ALBANY,NY 12222
Performing Department
(N/A)
Non Technical Summary
Foodborne pathogens are the main source of foodborne illnesses and cause significantthreat to public health and global economy.Without timely intervention, these pathogens can completely disrupt the supply chain for critical foodstuffs and consequently elevate economic stress and public anxiety. Among all the leading foodborne pathogens,Salmonellahas proven to be the costliest ($3.7 billion/year)to the US economy.In this proposal, our goal is to develop a detection kit againstthe most prevalent serotypes and account for half of all human infections in the US.The proposed approach offers the detection ofSalmonellaunder 6-hr; surpassingthe current methods which can takeup to 5 days. This is important for rapid and routine sampling to avoid?the spread ofSalmonella. We also propose to develop machine learning algorithms for data analysis and mobile app for data transmission. The mobile app facilitated data transmission is important for taking immediate actions and the containment of disease transmission, thus preventing further health risks and economic loss. The proposed detection kit and technologies offer a universal diagnostic assay which can be employed beyondSalmonelladetection. Therefore, there is potential for broader applications.
Animal Health Component
70%
Research Effort Categories
Basic
20%
Applied
70%
Developmental
10%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
5035010200050%
7235010202050%
Knowledge Area
503 - Quality Maintenance in Storing and Marketing Food Products; 723 - Hazards to Human Health and Safety;

Subject Of Investigation
5010 - Food;

Field Of Science
2020 - Engineering; 2000 - Chemistry;
Goals / Objectives
Foodborne pathogens are the main source of foodborne illnesses and cause significant threat to public health and global economy. Without timely intervention, these pathogens can completely disrupt the supply chain for critical foodstuffs and consequently elevate economic stress and public anxiety. Among all the leading foodborne pathogens, Salmonella has proven to be the costliest ($3.7 billion/year) to the US economy. In this proposal, our goal is to develop an innovative nano-diagnostic approach for Salmonella Enteritidis and Salmonella Typhimurium, which are the most prevalent serotypes and account for half of all human infections in the US.The objectives of this proposal are: (1) Ultra-sensitive fluorometric detection of Salmonella contamination in food products using a benchtop spectrofluorometer. (2) Translation of the fluorescence detection into instrument-free visual Salmonella diagnostics using nanoparticles, and (3) Machine-learning assisted optical image analysis/classification and data transmission through lab-built mobile-app.The proposed approach offers the detection of Salmonella under 6-hr; surpassing the current microbial culturing method which can take up to 5 days. This is important for rapid and routine sampling to avoid potential cross-contamination or the spread of Salmonella. The data analysis through image-classification and mobile-app facilitated data transmission is important for taking immediate actions and the containment of disease transmission, thus preventing further health risks and economic loss. The proposed simple-to-use optical nano-diagnostic setup, as proposed here, offers a universal diagnostic template which can be employed beyond Salmonella detection. Therefore, there is potential for broader applications once the proposed diagnostic is realized.
Project Methods
Technical Objective 1. Ultra-sensitive fluorometric detection ofSalmonellacontamination in food products using a benchtop spectrofluorometer.RPA primers will be designed to amplify conserved sequences of DNA that are specific toS. Enteritidis andS. Typhimurium serotypes ofSalmonella. The serovars will be purchased from ATCC and cultured to extract their genomes for the amplification reaction. Specificity of the designed primers will be confirmed using non-Salmonellastrains as controls. The background noise, cross-reactivity, false negative and false positive controls will be performed.RACECARwill be performed for the quantitative fluorometric detection of the pathogen using a benchtop spectrophotometer. The results obtained from the assay will be compared to those obtained by the standard bacterial culturing assay. When comparable to the standard, our methodology will be employed for real sample analysis. First, artificially contaminated specimens will undergo theRACECARprotocol for pathogen identification. Once our entire detection process has been rigorously authenticated, we will source 200 food samples from local farms, markets, and backyard poultry to identifySalmonella-positive samples. As an orthogonal method for reference, all positive and 5% of the negative results(since we expect the majority of the samples to beSalmonella-negative)will be checked through conventional culturing and polymerase chain reaction (PCR) through double-blinded testing.Technical Objective 2. Translation of the fluorescence detection into instrument-free visualSalmonelladiagnostics using nanoparticles.The molecular diagnostic steps (CRISPR-Cas12a, HCR and RPA), used in technical objective 1 for fluorescence detection will be translated into visual diagnostics using gold nanoparticles. The colorimetric change of gold nanoparticles, due to the shift in surface plasmon, will be a visual indication ofSalmonellacontamination. Biochemical experiments in objective 1 will be re-performed for this aim, except the results will be quantified using absorbance spectrometry. Poultry products and fresh produce confirmed to beSalmonella-free will be tested before and afterSalmonellaspiking to evaluate the potential of this approach before proceeding to real sample analysis. The food samples listed in objective 1 will be re-evaluated with this visual method. Similarly, samples with positive hits and5% of the negative hits will bereconfirmed throughconventional culturing and PCR methodsthrough double-blinded testing.Technical Objective 3. Machine-learning assisted optical image analysis/classification and data transmission through lab-built mobile-app.Following the development of the quantitative assays in objectives 1 and 2, a semi-quantitative image analysis and data transmission protocol will be developed in objective 3. A fluorescence gel-doc imager will be used to capture images of experimental results (microplates and/or test tubes) from objective 1, whereas a mobile phone camera will be used to capture visual gold nanoparticle assay results (microplates and/or test tubes) from objective 2. Standard tubes with different fluorescence content or colored samples will be used as references. A ML algorithm will be developed/programmed to analyze the fluorescence and colorimetric images from objectives 1 and 2. More specifically, using freely available convolutional neural network (CNN) architectures, i.e., AlexNet, Inception ImageNet, and ResNet50, and fluorescence/colorimetric images of experimental results; a transfer learning model will be programmed through the Python library TensorFlow 2.To communicate the results with stakeholders, a mobile-phone application will be developed through the Python library Kivy that will be linked to a cloud database. The setup will allow for detection ofSalmonellathrough automated CNN predictions on optical images. The app will have a feature to transmit the results for review by CDC, FDA, USDA's Animal and Plant Health Inspection Service, USDA Agricultural Research Service, USDA Agricultural Marketing Service, and other relevant State and local departments. *Such streamlined capabilities, from the detection ofSalmonellato the communication with affiliated groups of interest, will further prevent the risk ofSalmonellaexposure to the public and consequently curb its impact on the US economy and public health.

Progress 11/15/23 to 11/14/24

Outputs
Target Audience:In the first year, we conducted several poster presentations and talks, including those by undergraduate and graduate students. The results of our Salmonella detection assays were showcased during these events, which included retreats, presentations in different universities,Gordon research conference, and regionalsymposia. Our findings received significant attention, with several news articles acknowledging the support of this grant. Through press releases, both the general public and the academic community became aware of our work. Notably, a recent paper funded by this USDA grant was featured on the front page of theTimes Union, the largest local newspaper. Additionally, we published a high-impact paper that was selected for the cover page of a prestigious journal. The first year of this grant has been remarkably productive. The products section of this report provides a comprehensive list of all the presentations and publications used to effectively disseminate our research findings and engage our target audience. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?This grant has had a significant impact on undergraduate and graduate training. Our first paper was co-authored by three undergraduate and three graduate students. One of the senior undergraduate authors secured a position at Regeneron Pharmaceuticals, while another junior studentwas accepted as an intern at the same company. Two graduate students graduated during this time--one now works as a Senior Consultant and Staff Scientist at Booz Allen, specializing in writing solicitations for grant agencies, while the lead author of the paper secured a position at a biotech company in Boston. This grant has played a central role in creating numerous job opportunities for our undergraduate andgraduatestudents. Additionally, the funding from this grant has supported research that has led to results for at least two publications ( one published, one in review). One of these new datasets enabled a third-year graduate student to develop a USDA fellowship application proposing the extension of our technology to detect other foodborne pathogens. Since last year, I have recruited two more undergraduate students and two more graduate students. One of the undergraduate students, highly motivated and well-trained, has already taken the lead author role on a recently submitted manuscript. Undergraduate students in my groupwish to stay on campus over the summer to continue working in the lab and collecting excitingdata.We have fostered a highly dynamic and collaborative lab environment where everyone works in synergy toward shared goals thanks to this grant. Finally, the findings obtained through this grant have also allowed us to submit additional research proposals, further expanding the scope and impact of our work. Undergraduate Student Awards: 2024 RNA Summer in-person ResearchandOutstanding Poster Award (Emmett Hanson) 2024 Shelton Bank Award for Excellence (Emmett Hanson) 2024 Presidential Award for Undergraduate Research (Hilal Dagci) 2024 Outstanding Senior Award, a President's Award for Leadership (Hilal Dagci) How have the results been disseminated to communities of interest?The results have been disseminated by multiple presentations and press releases which are listed below. 2024, (Mehmet Yigit, PI) Talk at Stevens Institute of Technology,March 1st, 2024. "Machine Learning-Supported Nanotechnology and Synthetic Biology for Biosensing and Precision Therapeutics" 2024, (Mehmet Yigit, PI) Talk at the 2024 RNA Institute Retreat at Lake George, June 18th, 2024 :"Machine Learning-Supported Nanotechnology and Synthetic Biology for Biosensing" 2024, (Mehmet Yigit, PI), Poster at Nanoscale Science and Engineering for Agriculture and Food SystemsGordonResearch Conference. Dates: 06/23/2024 - 06/28/2024, Location: Southern New Hampshire University in Manchester, New Hampshire, United States Presentingpostertitled: "2022-08596 Optical CRISPR Nano-diagnostics Against Common Salmonella Serotypes Using Mobile-App Driven Image Analysis and Data Transmission" 2024, (Emmett Hanson, Undergraduate Student), Poster. Title: RNA nano-switch and synthetic biology for paper-based Salmonella detection. UAlbany Showcase, April 30, 2024 2024, (Emmett Hanson, Undergraduate Student), Poster. at Life Sciences Research Symposium "Silver nanoclusters for machine-learning assisted detection of whole pathogen genomes on paper substrates"/ 2024, Nabeel Kalla (graduate student). Talk, Title: Expanding our Diagnostic Toolkit using Nanotechnology Combined with Synthetic Biology. Department of Chemistry, Albany, April, 29, 2024 Press Releases: 1. News at State University of New York system (May 13, 2024): Title: UAlbany Scientists Receive USDA Funding to Develop Color-Changing Salmonella Detection Kit https://www.albany.edu/news-center/news/2024-ualbany-scientists-receive-usda-funding-develop-color-changing-salmonella 2. News at State University of New York system (Dec. 5, 2024): Title: UAlbany Chemists Develop Color-Changing Test for Rapid Salmonella Detection https://www.albany.edu/news-center/news/2024-ualbany-chemists-develop-color-changing-test-rapid-salmonella-detection 3. News at Times Union which is a daily newspaper, serving the Capital Region of New York (Dec. 5, 2024): Title: UAlbany team working on rapid test for salmonella https://www.timesunion.com/food/article/salmonella-university-albany-test-strip-19986193.php What do you plan to do during the next reporting period to accomplish the goals?We are currently working on several manuscripts forenhancing the sensitivity of our detection assay and improving its portability for point-of-care applications. Our next goal is to expand into detecting the pathogens directly. Additionally, we have developed machine learning algorithms that we plan to integrate into our upcoming detection assays.

Impacts
What was accomplished under these goals? Our initial findings, supported by this grant, were published inAdvanced Healthcare Materialsand featured on the cover. In this study, we developed a paper-based sensor for the visual detection of theSalmonellagenome. By integrating a cell-free protein synthesis reaction with CRISPR-Cas12a, the sensor can detect as few as 100 copies of theSalmonellagenome on a paper disc, indicated by a color change from yellow to red. The citation for the paper is:Kachwala, M.J., Hamdard, F., Cicek, D., Dagci, H., Smith, C.W., Kalla, N. and Yigit, M.V. (2024), Universal CRISPR-Cas12a and Toehold RNA Cascade Reaction on Paper Substrate for Visual Salmonella Genome Detection (Adv. Healthcare Mater. 22/2024). Adv. Healthcare Mater., 13: 2470137. https://doi.org/10.1002/adhm.202470137 Recently, we submitted another article showcasing a method for detectingSalmonellausing fluorescent nanoparticles.

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Kachwala, M.J., Hamdard, F., Cicek, D., Dagci, H., Smith, C.W., Kalla, N. and Yigit, M.V. (2024), Universal CRISPR-Cas12a and Toehold RNA Cascade Reaction on Paper Substrate for Visual Salmonella Genome Detection (Adv. Healthcare Mater. 22/2024). Adv. Healthcare Mater., 13: 2470137. https://doi.org/10.1002/adhm.202470137