Machine-Learning and Mass Spectrometer at the Heart of UC Davis COVID-19 Rapid Test

UC Davis Health, in partnership with SpectraPass, is evaluating a new type of rapid COVID-19 test. The research will involve about 2,000 people in Sacramento and Las Vegas.

The idea behind the new platform is a scalable system that can quickly and accurately perform on-site tests for hundreds or potentially thousands of people.

Nam Tran is a professor of clinical pathology in the UC Davis School of Medicine and a co-developer of the novel testing platform with SpectraPass, a Las Vegas-based startup.

Tran explained that the system doesn’t look for the SARS-CoV-2 virus like a PCR test does. Instead, it detects an infection by analyzing the body’s response to it. When ill, the body produces differing protein profiles in response to infection. These profiles may indicate different types of infection, which can be detected by machine learning.

“The goal of this study is to have enough COVID-19 positive and negative individuals to train our machine learning algorithm to identify patients infected by SARS-CoV-2,” said Tran.

A study published by Tran and his colleagues earlier this year in Nature Scientific Reports found the novel method to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests. 

In addition to identifying positive cases of COVID-19, the platform also uses next-generation sequencing to confirm multiple respiratory pathogens like the flu and the common cold.

The sequencing panel at UC Davis Health can detect over 280 respiratory pathogens, including SARS-CoV-2 and related variants — allowing the study to train the machine-learning algorithms to differentiate COVID-19 from other respiratory diseases.

So far, the study has not seen any participants with the new omicron variant.

“Our team has tested the system with samples from patients infected with delta and other variants of the SARS-CoV-2 virus. We are fairly certain that omicron will be detected as well, but we won’t know for sure until we encounter a study participant with the variant,” Tran said.

The Emergency Department (ED) at the UC Davis Medical Center is conducting the testing in Sacramento. Collection for testing in Las Vegas is conducted at multiple businesses and locations.

The team expects the study will continue until the end of winter. The results from the new study will be used to seek emergency use authorization (EUA) from the Food and Drug Administration.

Testing system builds on MILO

The novel testing system uses an analytical instrument known as a mass spectrometer. It’s paired with machine learning algorithms produced by software called the Machine Intelligence Learning Optimizer or MILO. MILO was developed by Tran, Hooman Rashidi, a professor in the Department of Pathology and Laboratory Medicine, and Samer Albahra, assistant professor and medical director of pathology artificial intelligence in the Department of Pathology and Laboratory Medicine.

As with many other COVID-19 tests, a nasal swab is used to collect a sample. Proteins from the nasal sample are ionized with the mass spectrometer’s laser, then measured and analyzed by the MILO machine learning algorithms to generate a positive or negative result.

In addition to conducting the mass spectrometry testing, UC Davis serves as a reference site for the study, performing droplet digital PCR (ddPCR) tests, the “gold standard” for COVID-19 testing, to assess the accuracy of the mass spectrometry tests.

University-industry partnership formed in 2020

The project originated with Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company and founder of SpectraPass. Gallagher is also a UC Davis alumnus and a longtime supporter of innovation and entrepreneurship at UC Davis.  

In 2020, when the COVID-19 pandemic brought the travel and hospitality industries almost to a standstill, Gallagher began conceptualizing approaches to allow people to gather again safely. He teamed with researchers at UC Davis Health to develop the new platform and launched SpectraPass.

In addition to the novel testing solution, SpectraPass is also developing digital systems to accompany the testing technology. Those include tools to authenticate and track verified test results from the system so an individual can access and use them. The goal is to facilitate accurate, large-scale rapid testing that will help keep businesses and the economy open through the current and any future pandemics.

“The official start of our multi-center study across multiple locations marks an important milestone in our journey at SpectraPass. We are excited to test and generate data on a broader scale. Our goal is to move the platform from a promising new technology to a proven solution that can ultimately benefit the broader population,” said Greg Ourednik, president of SpectraPass.

Source: UC Davis Newsroom 

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SMART Researchers Develop Method for Early Detection of Bacterial Infection in Crops

Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) Interdisciplinary Research Group (IRG) of Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, and their local collaborators from Temasek Life Sciences Laboratory (TLL), have developed a rapid Raman spectroscopy-based method for detecting and quantifying early bacterial infection in crops. The Raman spectral biomarkers and diagnostic algorithm enable the noninvasive and early diagnosis of bacterial infections in crop plants, which can be critical for the progress of plant disease management and agricultural productivity.

Due to the increasing demand for global food supply and security, there is a growing need to improve agricultural production systems and increase crop productivity. Globally, bacterial pathogen infection in crop plants is one of the major contributors to agricultural yield losses. Climate change also adds to the problem by accelerating the spread of plant diseases. Hence, developing methods for rapid and early detection of pathogen-infected crops is important to improve plant disease management and reduce crop loss.

The breakthrough by SMART and TLL researchers offers a faster and more accurate method to detect bacterial infection in crop plants at an earlier stage, as compared to existing techniques. The new results appear in a paper titled “Rapid detection and quantification of plant innate immunity response using Raman spectroscopy” published in the journal Frontiers in Plant Science.

“The early detection of pathogen-infected crop plants is a significant step to improve plant disease management,” says Chua Nam Hai, DiSTAP co-lead principal investigator, professor, TLL deputy chair, and co-corresponding author. “It will allow the fast and selective removal of pathogen load and curb the further spread of disease to other neighboring crops.”

Traditionally, plant disease diagnosis involves a simple visual inspection of plants for disease symptoms and severity. “Visual inspection methods are often ineffective, as disease symptoms usually manifest only at relatively later stages of infection, when the pathogen load is already high and reparative measures are limited. Hence, new methods are required for rapid and early detection of bacterial infection. The idea would be akin to having medical tests to identify human diseases at an early stage, instead of waiting for visual symptoms to show, so that early intervention or treatment can be applied,” says MIT Professor Rajeev Ram, who is a DiSTAP principal investigator and co-corresponding author on the paper.

While existing techniques, such as current molecular detection methods, can detect bacterial infection in plants, they are often limited in their use. Molecular detection methods largely depend on the availability of pathogen-specific gene sequences or antibodies to identify bacterial infection in crops; the implementation is also time-consuming and nonadaptable for on-site field application due to the high cost and bulky equipment required, making it impractical for use in agricultural farms.

“At DiSTAP, we have developed a quantitative Raman spectroscopy-based algorithm that can help farmers to identify bacterial infection rapidly. The developed diagnostic algorithm makes use of Raman spectral biomarkers and can be easily implemented in cloud-based computing and prediction platforms. It is more effective than existing techniques as it enables accurate identification and early detection of bacterial infection, both of which are crucial to saving crop plants that would otherwise be destroyed,” explains Gajendra Pratap Singh, scientific director and principal investigator at DiSTAP and co-lead author.

A portable Raman system can be used on farms and provides farmers with an accurate and simple yes-or-no response when used to test for the presence of bacterial infections in crops. The development of this rapid and noninvasive method could improve plant disease management and have a transformative impact on agricultural farms by efficiently reducing agricultural yield loss and increasing productivity.

“Using the diagnostic algorithm method, we experimented on several edible plants such as choy sum,” says DiSTAP and TLL principal investigator and co-corresponding author Rajani Sarojam. “The results showed that the Raman spectroscopy-based method can swiftly detect and quantify innate immunity response in plants infected with bacterial pathogens. We believe that this technology will be beneficial for agricultural farms to increase their productivity by reducing their yield loss due to plant diseases.”

The researchers are currently working on the development of high-throughput, custom-made portable or hand-held Raman spectrometers that will allow Raman spectral analysis to be quickly and easily performed on field-grown crops.

SMART and TLL developed and discovered the diagnostic algorithm and Raman spectral biomarkers. TLL also confirmed and validated the detection method through mutant plants. The research is carried out by SMART and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise (CREATE) program.

SMART was established by MIT and the NRF in 2007. The first entity in CREATE developed by NRF, SMART serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both Singapore and MIT. SMART currently comprises an Innovation Center and five IRGs: Antimicrobial Resistance, Critical Analytics for Manufacturing Personalized-Medicine, DiSTAP, Future Urban Mobility, and Low Energy Electronic Systems. SMART research is funded by the NRF under the CREATE program.

Led by Professor Michael Strano of MIT and Professor Chua Nam Hai of Temasek Lifesciences Laboratory, the DiSTAP program addresses deep problems in food production in Singapore and the world by developing a suite of impactful and novel analytical, genetic, and biomaterial technologies. The goal is to fundamentally change how plant biosynthetic pathways are discovered, monitored, engineered, and ultimately translated to meet the global demand for food and nutrients. Scientists from MIT, TTL, Nanyang Technological University, and National University of Singapore are collaboratively developing new tools for the continuous measurement of important plant metabolites and hormones for novel discovery, deeper understanding and control of plant biosynthetic pathways in ways not yet possible, especially in the context of green leafy vegetables; leveraging these new techniques to engineer plants with highly desirable properties for global food security, including high-yield density production, and drought and pathogen resistance; and applying these technologies to improve urban farming.

Source: MIT News 

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