Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD (above), in the Lehigh University news release. “If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins.” This method differs greatly from traditional clinical laboratory methods for identifying disease biomarkers. (Photo copyright: Memorial Sloan-Kettering Cancer Center.)
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
CRISPR-Act 3.0 could significantly increase crop yields and plant diversity worldwide and help fight against global hunger and climate change
Clinical laboratory professionals and pathologists who read Dark Daily are highly aware of CRISPR gene editing technology. We’ve covered the topic in multiple ebriefings over many years. But how many know there’s a version of CRISPR specifically designed for editing and activating plant genes?
Scientists at the University of Maryland (UMD) developed a new version of CRISPRa (CRISPR Activation) for plants which they claim has four to six times the activation capacity of currently available CRISPRa systems and can activate up to seven genes at once. They call their new and improved CRISPRa technology “CRISPR-Act 3.0.”
According to a paper published in the journal Nature Plants, titled, “CRISPR-Act3.0 for Highly Efficient Multiplexed Gene Activation in Plants,” the UMD researchers developed “a highly robust CRISPRa system working in rice, Arabidopsis (rockcress), and tomato, CRISPR-Act 3.0, through systematically exploring different effector recruitment strategies and various transcription activators based on deactivated Streptococcus pyogenes Cas9 (dSpCas9).
“While my lab has produced systems for simultaneous gene editing [multiplexed editing] before, editing is mostly about generating loss of function to improve the crop,” said Yiping Qi, PhD (above), one of the authors of the UMD study, in a new release. “But if you think about it,” he added, “that strategy is finite, because there aren’t endless genes that you can turn off and actually still gain something valuable. Logically, it is a very limited way to engineer and breed better traits, whereas the plant may have already evolved to have different pathways, defense mechanisms, and traits that just need a boost.” (Photo copyright: University of Maryland.)
CRISPR-Act 3.0 Increases Function of Multiple Genes Simultaneously
The UMD researchers successfully applied CRISPR-Act 3.0 technology to activate many types of genes in plants, including the ability to expedite the breeding process via faster flowering. They hope that activating genes in plants to improve functionality will result in better plants and crops.
“Through activation, you can really uplift pathways or enhance existing capacity, even achieve a novel function. Instead of shutting things down, you can take advantage of the functionality already there in the genome and enhance what you know is useful,” said Yiping Qi, PhD, associate professor, Department of Plant Science and Landscape Architecture at the University of Maryland, in a UMD new release.
The scientists also noted that there may be other advantages to this type of multiplexed activation of genes.
“Having a much more streamlined process for multiplexed activation can provide significant breakthroughs. For example, we look forward to using this technology to screen the genome more effectively and efficiently for genes that can help in the fight against climate change and global hunger,” Qi added. “We can design, tailor, and track gene activation with this new system on a larger scale to screen for genes of importance, and that will be very enabling for discovery and translational science in plants.”
The researchers hope this technology can have a major impact on the efficiency of crop and food production.
“This type of technology helps increase crop yield and sustainably feed a growing population in a changing world,” Qi said. “I am very pleased to continue to expand the impacts of CRISPR technologies.”
Feeding the World’s Hungry with CRISPR
CRISPR is a robust tool used for editing genomes that typically operates as “molecular scissors” to cut DNA. CRISPR-Act 3.0, however, uses deactivated CRISPR-Cas9 which can only bind and not cut. This allows the system to work on the activation of proteins for designated genes of interest by binding to certain segments of DNA. The UMD researchers believe there is significant potential for expanding the multiplexed activation further, which could alter and improve genome engineering.
“People always talk about how individuals have potential if you can nurture and promote their natural talents,” Qi said in the UMD news release. “This technology is exciting to me because we are promoting the same thing in plants—how can you promote their potential to help plants do more with their natural capabilities? That is what multiplexed gene activation can do, and it gives us so many new opportunities for crop breeding and enhancement.”
CRISPR is being developed and enhanced in many research settings, and knowledge of how to best use the gene editing technology is rapidly advancing. Though more research on CRISPR-Act 3.0 is needed to ensure its reliability, it’s exciting to consider the potential of gene activation for massively increasing crop yield worldwide.
Not to mention how new CRISPR technologies continue to drive innovations in clinical laboratory diagnostics and precision medicine treatments.
The St. Louis-based in vitro diagnostics (IVD) developer is making PrecivityAD available to physicians while awaiting FDA clearance for the non-invasive test
Clinical laboratories have long awaited a test for Alzheimer’s disease and the wait may soon be over. The first blood test to aid physicians and clinical laboratories in the diagnosis of patients with memory and cognitive issues has been released by C₂N Diagnostics of St. Louis. The test measures biomarkers associated with amyloid plaques in the brain—the pathological hallmark of Alzheimer’s.
In a news release, PrecivityAD describes the laboratory-developed test (LDT) as “a highly sensitive blood test using mass spectrometry and is performed in C₂N’s CLIA-certified laboratory. While the test by itself cannot diagnose Alzheimer’s disease … the test is an important new tool for physicians to aid in the evaluation process.”
PrecivityAD provides physicians with an Amyloid Probability Score (APS) for each patient. For example:
A low APS (0-36) is consistent with a negative amyloid PET scan result and, thus, has a low likelihood of amyloid plaques, an indication other causes of cognitive symptoms should be investigated.
An intermediate APS (37-57) does not distinguish between the presence or absence of amyloid plaques and indicates further diagnostic evaluation may be needed to assess the underlying cause(s) for the patient’s cognitive symptoms.
A high APS (58-100) is consistent with a positive amyloid positron-emission tomography (PET) scan result and, thus, a high likelihood of amyloid plaques. Presence of amyloid plaques is consistent with an Alzheimer’s disease diagnosis in someone who has cognitive decline, but alone is insufficient for a final diagnosis.
The $1,250 test is not currently covered by health insurance or Medicare. However, C₂N Diagnostics has pledged to offer discounts to patients based on income levels.
Jeff Cummings, MD, ScD (above) Research Professor, Department of Brain Health, University of Nevada, Las Vegas, said in a C₂N Diagnostics press release, “A blood test for Alzheimer’s is a game changer.” While there is no cure for Alzheimer’s, a non-invasive blood test can help providers diagnose patients when their symptoms are mild and often misdiagnosed. “Advances in Alzheimer’s diagnostics are key to more effective identification, diagnosis, and clinical trial recruitment,” he added. Currently, brain changes caused by the disease are most commonly identified through PET scans. (Photo copyright: University of Nevada Las Vegas.)
Additional Research Requested
While C₂N’s PrecivityAD is the first test of its kind to reach the commercial market, it has not received US Food and Drug Administration (FDA) clearance, nor has the company published detailed data on the test’s accuracy. However, the PrecivityAD website says the laboratory-developed test “correctly identified brain amyloid plaque status (as determined by quantitative PET scans) in 86%” of 686 patients, all of whom were older than 60 years of age with subjective cognitive impairment or dementia.
But some Alzheimer’s advocacy groups are tempering their enthusiasm about the breakthrough. Eliezer Masliah, MD, Director of the Division of Neuroscience, National Institute on Aging, told the Associated Press (AP), “I would be cautious about interpreting any of these things,” he said of the company’s claims. “We’re encouraged, we’re interested, we’re funding this work, but we want to see results.”
Heather Snyder, PhD, Vice President, Medical and Scientific Relations at the Alzheimer’s Association told the AP her organization will not endorse a test without FDA clearance. The Alzheimer’s Association also would like to see the test studied in larger and diverse populations. “It’s not quite clear how accurate or generalizable the results are,” she said.
Braunstein defended the decision to make the test for Alzheimer’s immediately available to physicians, asking in the AP article, “Should we be holding that technology back when it could have a big impact on patient care?”
Howard Fillit, MD, Founding Executive Director and Chief Science Officer of the Alzheimer’s Drug Discovery Foundation (ADDF), maintains the first-of-its-kind blood test is an important milestone in Alzheimer’s research. ADDF invested in C₂N’s development of the test.
“Investing in biomarker research has been a core goal for the ADDF because having reliable, accessible, and affordable biomarkers for Alzheimer’s diagnosis is step one in finding drugs to prevent, slow, and even cure the disease,” Fillit said in an ADDF news release.
C₂N is also developing a Brain Health Panel to detect multiple blood-based markers for Alzheimer’s disease that will aid in better disease staging, treatment monitoring, and differential diagnosis.
Second Alzheimer’s Test in Development
Soon medical laboratories may have two different in vitro diagnostic tests for Alzheimer’s disease. On December 2, Fujirebio Diagnostics filed for FDA 510(k) premarket clearance for its Lumipulse G β-Amyloid Ratio (1-42/1-40) test, which looks for biomarkers found in cerebral spinal fluid.
“Accurate and earlier intervention will also facilitate the development of new drug therapies, which are urgently needed as the prevalence of Alzheimer’s disease increases with a rapidly aging population globally,” Fujirebio Diagnostics President and CEO Monte Wiltse said in a news release.
The Lumipulse G β-Amyloid test, which is intended for use in patients aged 50 and over presenting with cognitive impairment, has received CE-marking for use in the European Union.
Clinical laboratory managers will want to keep a close eye on rapidly evolving developments in testing for Alzheimer’s disease. It is the sixth leading cause of death in the United States and any clinical laboratory test that could produce an early and accurate diagnosis of Alzheimer’s Disease would become a valuable tool for physicians who treat patients with the symptoms of Alzheimer’s.
The AI protein-structure-prediction system may ‘revolutionize life sciences by enabling researchers to better understand disease,’ researchers say
Genomics leaders watched with enthusiasm as artificial intelligence (AI) accelerated discoveries that led to new clinical laboratory diagnostic tests and advanced the evolution of personalized medicine. Now Google’s London-based DeepMind has taken that a quantum step further by demonstrating its AI can predict the shape of proteins to within the width of one atom and model three-dimensional (3D) structures of proteins that scientist have been trying to map accurately for 50 years.
Pathologists and clinical laboratory professionals know that it is estimated that there are around 30,000 human genes. But the human proteome has a much larger number of unique proteins. The total number is still uncertain because scientists continue to identify new human proteins. For this reason, more knowledge of the human protein is expected to trigger an expanding number of new assays that can be used by medical laboratories for diagnostic, therapeutic, and patient-monitoring purposes.
DeepMind’s AI tool is called AlphaFold and the protein-structure-prediction system will enable scientists to quickly move from knowing a protein’s DNA sequence to determining its 3D shape without time-consuming experimentation. It “is expected to accelerate research into a host of illnesses, including COVID-19,” BBC News reported.
This protein-folding breakthrough not only answers one of biology’s biggest mysteries, but also has the potential to revolutionize life sciences by enabling researchers to better understand disease processes and design personalized therapies that target specific proteins.
In November, DeepMind’s AlphaFold won the 14th Community Wide Experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP14), a biennial competition in which entrants receive amino acid sequences for about 100 proteins whose 3D structures are unknown. By comparing the computational predictions with the lab results, each CASP14 competitor received a global distance test (GDT) score. Scores above 90 out of 100 are considered equal to experimental methods. AlphaFold produced models for about two-thirds of the CASP14 target proteins with GDT scores above 90, a CASP14 press release states.
According to MIT Technology Review, DeepMind’s discovery is significant. That’s because its speed at predicting the structure of proteins is unprecedented and it matched the accuracy of several techniques used in clinical laboratories, including:
Unlike the laboratory techniques, which, MIT noted, are “expensive and slow” and “can take hundreds of thousands of dollars and years of trial and error for each protein,” AlphaFold can predict a protein’s shape in a few days.
“AlphaFold is a once in a generation advance, predicting protein structures with incredible speed and precision,” Arthur D. Levinson, PhD, Founder and CEO of Calico Life Sciences, said in a DeepMind blogpost. “This leap forward demonstrates how computational methods are poised to transform research in biology and hold much promise for accelerating the drug discovery process.”
Science reported that AlphaFold, which scored a median of 87—25 points above the next best predictions—did so well that CASP14 organizers worried DeepMind may have been somehow cheated. To validate the results, they asked AlphaFold to complete a “special challenge”—modeling a membrane protein from an ancient species of microbes called archaea, which they had been unable to model satisfactorily using X-ray crystallography. AlphaFold returned a detailed image of a three-part protein with two long helical arms in the middle. “It’s almost perfect,” Andrei Lupas, PhD, Director at the Max Planck Institute for Developmental Biology, told Science. “They could not possibly have cheated on this. I don’t know how they do it.” (Graphic copyright: DeepMind/Nature.)
“Even tiny rearrangements of these vital molecules can have catastrophic effects on our health, so one of the most efficient ways to understand disease and find new treatments is to study the proteins involved,” Moult said in the CASP14 press release. “There are tens of thousands of human proteins and many billions in other species, including bacteria and viruses, but working out the shape of just one requires expensive equipment and can take years.”
Science reported that the 3D structures of only 170,000 proteins have been solved, leaving roughly 200 million proteins that have yet to be modeled. Therefore, AlphaFold will help researchers in the fields of genomics, microbiomics, proteomics, and other omics understand the structure of protein complexes.
“Being able to investigate the shape of proteins quickly and accurately has the potential to revolutionize life sciences,” Andriy Kryshtafovych, PhD, Project Scientist at University of California, Davis, Genome Center, said in the press release. “Now that the problem has been largely solved for single proteins, the way is open for development of new methods for determining the shape of protein complexes—collections of proteins that work together to form much of the machinery of life, and for other applications.”
Clinical laboratories play a major role in the study of human biology. This breakthrough in genomics research and new insights into proteomics may provide opportunities for medical labs to develop new diagnostic tools and assays that better identify proteins of interest for diagnostic and therapeutic purposes.
Expected wave of retirements from clinical laboratories might be deferred for several more years
Popular wisdom has been that, as they hit retirement age, baby boomers will leave the workforce in large numbers. Now a news report says that many baby boomers may defer retirement because of poor finances and too much debt. If true, that may be good news for clinical laboratories and pathology groups across the United States.
After all, baby boomers make up a considerable proportion of the laboratory workforce. Often with decades of experience at a single medical laboratory, they are highly-skilled and have extensive experience in laboratory operations and lab test interpretation. Thus, were the large majority of baby boomers to decide to retire as they reach 65 years of age, it could leave big gaps in staffing at many medical laboratory organizations throughout the country. (more…)
