Tag: NEMO Prize

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Single-cell Cancer Detection Project Wins 2021 NEMO Prize

This scProteome-seq array shows separated protein biomarkers (green and magenta spots) from thousands of single cells.

Penn Health-Tech’s Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize awards $80,000 to support early-stage ideas joining engineering and medicine. The goal of the prize is to encourage collaboration between the University of Pennsylvania’s Perelman School of Medicine and the School of Engineering and Applied Science by supporting innovative ideas that might not receive funding from traditional sources.

This year, the NEMO Prize has been awarded to a team of researchers from Penn Engineering’s Department of Bioengineering. Their project aims to develop a technology that can detect multiple cancer biomarkers in single cells from tumor biopsy samples.

As cancer cells grow in the body, one of the characteristics that influences tumor growth and response to treatment is cancer cell state heterogeneity, or differences in cell states. Methods that rapidly catalogue cell heterogeneity may be able to detect rare cells responsible for tumor growth and drug resistance.

Single-cell transcriptomics (scRNA-seq) is the standard method for studying cell states; by amplifying and analyzing the cell’s complement of RNA sequences at a given time, researchers can get a snapshot of what proteins the cell is in the process of making. However, this method does not fully capture the function of the cell. The field of proteomics, which captures the actual protein content of cells along with post-translational modifications, provides a better picture of the cell’s function, but single-cell proteomic methods with the same sensitivity as scRNA-seq do not currently exist.

Alex Hughes, Lukasz Bugaj and Andrew Tsourkas

This collaborative project, which joins Assistant Professors Alex Hughes and Lukasz Bugaj, as well as Professor Andrew Tsourkas, aims to change that by developing multiplexed, sensitive and highly specific single-cell proteomics technologies to advance our understanding of cancer, its detection and its treatment.

This new technology, called scProteome-seq, builds from Hughes’s previous work.

“My specific expertise here is as an inventor of single-cell western blotting, which is the core technology that our team is building on,” says Hughes. “Single-cell proteomics technologies of this type have a track-record of commercial translation for applications in basic science and clinical automation, so our approach has a high potential for real-world impact.”

The current technology from Hughes’ lab separates proteins in cells by their molecular weight and “blots” them on a piece of paper. Improvements to this technology included in this project will remove the limitation of using light-emitting dyes to detect different proteins and instead use DNA barcodes to differentiate them.

Read the full story in Penn Engineering Today.

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Penn Health-Tech Q&A with César de la Fuente

Created in the lab of César de la Fuente, this miniaturized, portable version of rapid COVID-19 test, which is compatible with smart devices, can detect SARS-CoV-2 within four minutes with nearly 100% accuracy. (Image: Courtesy of César de la Fuente)

César de la Fuente, Presidential Assistant Professor in Bioengineering, Chemical and Biomolecular Engineering, Microbiology, and Psychiatry, was the inaugural recipient of the Nemirovsky Engineering and Medicine Opportunity (NEMO) Prize from Penn Health-Tech in 2020 for his low-cost, rapid COVID test. Now with promising results recently published in the journal Matter (showing 90 percent accuracy in as little as four minutes), Penn Health-Tech caught up with de la Fuente to discuss his experience over the past year:

“How did [your project] evolve in the past year?

‘We started with one prototype and now have three entirely different prototypes for the test. Two use electrochemistry, and we are now working on a new technology that uses calorimetry. With calorimetry, when the cotton swabs are exposed to the virus, they change color. This means users are able to see if they’re affected by a virus through a simple color change, making it more of a visual detection method.'”

Read the full Q&A in the Penn Health-Tech blog.

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César de la Fuente Wins Inaugural NEMO Prize, Will Develop Rapid COVID Virus Breath Tests

The paper-based tests could be integrated directly into facemasks and provide instant results at testing sites.

Cesar de la Fuente-Nunez, PhD

When Penn Health-Tech announced its Nemirovsky Engineering and Medicine Opportunity, or NEMO Prize, in February, the center’s researchers could only begin to imagine the impact the looming COVID-19 pandemic was about to unleash. But with the promise of $80,000 to support early-stage ideas at the intersection of engineering and medicine, the contest quickly sparked a winning innovation aimed at combating the crisis.

Judges from the University of Pennsylvania’s School of Engineering and Applied Sciences and Perelman School of Medicine awarded its first NEMO Prize to César de la Fuente, PhD, who proposed a paper-based COVID diagnostic system that could capture viral particles on a person’s breath, then give a result in a matter of seconds when taken to a testing site.

Similar tests for bacteria cost less than a dollar each to make. De la Fuente, a Presidential Assistant Professor in the departments of Psychiatry, Microbiology, and Bioengineering, is aiming to make COVID tests at a similar price point and with a smaller footprint so that they could be directly integrated into facemasks, providing further incentive for their regular use.

“Wearing a facemask is vital to containing the spread of COVID because, before you know you’re sick, they block your virus-carrying droplets so those droplets can’t infect others,” de la Fuente says. “What we’re proposing could eventually lead to a mask that can be infected by the virus and let you know that you’re infected, too.”

De la Fuente’s lab has conducted molecular dynamic simulations of the regions of the SARS-COV-2 spike protein (blue) that bind to the human ACE2 receptor (red and yellow).

De la Fuente’s expertise is in synthetic biology and molecular-scale simulations of disease-causing viruses and bacteria. Having such fine-grained computational models of these microbes’ binding sites allow de la Fuente to test them against massive libraries of proteins, seeing which bind best. Other machine learning techniques can then further narrow down the minimum molecular structures responsible for binding, resulting in functional protein fragments that are easier to synthesize and manipulate.

The spike-shaped proteins that give coronaviruses their crown-like appearance and name bind to a human receptor known as ACE2. De la Fuente and his colleagues are now aiming to characterize the molecular elements and environmental factors that would allow for the most precise, reliable detection of the virus.

Read the full story on the Penn Engineering blog.