Category: issues

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Leveraging the Body’s Postal System to Understand and Treat Disease

by Nathi Magubane

Microwell device with a solution in the reservoir (Image: Courtesy of David E. Reynolds)

Akin to the packages sent from one person to another via an elaborate postal system, cells send tiny parcels that bear contents and packaging material that serve key purposes: To protect the contents from the outside world and to make sure it gets to the right place via a label with an address. 

These packages are known as extracellular vesicles (EVs)—lipid-bound molecules that serve a variety of regulatory and maintenance functions throughout the body. They assist in the removal of unwanted materials within the cell, and they transport proteins, aid in DNA and RNA transfer, and promote tumorigeneses in cancerous cells. 

Given their myriad roles, EVs have taken center stage for many researchers in the biomedical space as they have the potential to improve current methods of disease detection and treatment. The main challenge, however, is accurately identifying the molecular contents of EVs while also characterizing the EVs, which, unlike other cellular components that are more homogenous, have more heterogeneity.

Now, a team of researchers at the University of Pennsylvania has developed a novel platform, droplet-free double digital assay, for not only profiling individual EVs but also accurately discerning their molecular contents. The researchers took the digital assay, which quantifies the contents of a molecule via binary metric—a 1 corresponds to the presence of a molecule and a zero to the lack thereof—and applies it to the EV. The work is published in Advanced Science.

The team was led by Jina Ko, an assistant professor with appointments in the School of Engineering and Applied Science and Perelman School of Medicine. “Our method allows for highly accurate quantification of the individual molecules inside an EV,” Ko says . “This opens up many doors in the realm of early disease detection and treatment.”

The researchers first compartmentalized individual EVs utilizing a microwell approach to isolate the EVs. Next, they captured individual molecules within the EVs and amplified the signal for clarity. The team then was able to determine the expression levels of pivotal EV biomarkers with remarkable precision via fluorescence.

Read the full story in Penn Today.

Jina Ko is an assistant professor in the Department of Pathology and Laboratory Medicine in the Perelman School of Medicine and an assistant professor in the Department of Bioengineering in the School of Engineering and Applied Science at the University of Pennsylvania.

David Reynolds is a Ph.D. candidate in the Department of Bioengineering in Penn Engineering.

Other authors include, Menghan Pan, George Galanis, Yoon Ho Roh, Renee-Tyler T. Morales, Shailesh Senthil Kumar, and Su-Jin Heo of the Department of Bioengineering at Penn Engineering; Jingbo Yang and Xiaowei Xu of the Department of Pathology and Laboratory Medicine at Penn Medicine; and Wei Guo of the Department of Biology in the School of Arts & Sciences at Penn.

The research was supported by the National Institutes of Health: grants R00CA256353, R35 GM141832, and CA174523 (SPORE).

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Innovation and Impact: “RNA: Past, Present and Future”

by Melissa Pappas

(Left to right): Mike Mitchell, Noor Momin, and David Meaney recording the Innovation & Impact podcast.

In the most recent episode of the Penn Engineering podcast Innovation & Impact, titled “RNA: Past, Present and Future,” David F. Meaney, Senior Associate Dean of Penn Engineering and Solomon R. Pollack Professor in Bioengineering, is joined by Mike Mitchell, Associate Professor in Bioengineering, and Noor Momin, who will be joining Penn Engineering as an Assistant Professor in Bioengineering early next year, to discuss the impact that RNA has had on health care and biomedical engineering technologies.

Mitchell outlines his lab’s research that spans drug delivery, new technology in protecting RNA and its applications in treating cancer. Momin details her research, which is focused on optimizing the immune system to protect against illnesses such as cardiovascular diseases and cancer. With Meaney driving the discussion around larger questions, including the possibility of a cancer vaccine, the three discuss what they are excited about now and where the field is going in the future with these emerging, targeted treatments.

Read the full story in Penn Engineering Today.

Subscribe to the Innovation & Impact podcast on Apple Music, Spotify or your favorite listening platforms or find all the episodes on the Penn Engineering YouTube channel.

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How the Hippocampus Distinguishes True and False Memories

by Erica Moser

Image: iStock/metamorworks

Let’s say you typically eat eggs for breakfast but were running late and ate cereal. As you crunched on a spoonful of Raisin Bran, other contextual similarities remained: You ate at the same table, at the same time, preparing to go to the same job. When someone asks later what you had for breakfast, you incorrectly remember eating eggs.

This would be a real-world example of a false memory. But what happens in your brain before recalling eggs, compared to what would happen if you correctly recalled cereal?

In a paper published in Proceedings of the National Academy of Sciences, University of Pennsylvania neuroscientists show for the first time that electrical signals in the human hippocampus differ immediately before recollection of true and false memories. They also found that low-frequency activity in the hippocampus decreases as a function of contextual similarity between a falsely recalled word and the target word.

“Whereas prior studies established the role of the hippocampus in event memory, we did not know that electrical signals generated in this region would distinguish the imminent recall of true from false memories,” says psychology professor Michael Jacob Kahana, director of the Computational Memory Lab and the study’s senior author. He says this shows that the hippocampus stores information about an item with the context in which it was presented.

Researchers also found that, relative to correct recalls, the brain exhibited lower theta and high-frequency oscillations and higher alpha/beta oscillations ahead of false memories. The findings came from recording neural activity in epilepsy patients who were already undergoing invasive monitoring to pinpoint the source of their seizures.

Noa Herz, lead author and a postdoctoral fellow in Kahana’s lab at the time of the research, explains that the monitoring was done through intracranial electrodes, the methodology researchers wanted to use for this study. She says that, compared to scalp electrodes, this method “allowed us to more precisely, and directly, measure the neural signals that were generated in deep brain structures, so the activity we are getting is much more localized.”

Read the full story in Penn Today.

Michael Kahana is the Edmund J. and Louise W. Kahn Term Professor of Psychology in the School of Arts & Sciences and director of the Computational Memory Lab at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.

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Harnessing Artificial Intelligence for Real Biological Advances—Meet César de la Fuente

by Eric Horvath

In an era peppered by breathless discussions about artificial intelligence—pro and con—it makes sense to feel uncertain, or at least want to slow down and get a better grasp of where this is all headed. Trusting machines to do things typically reserved for humans is a little fantastical, historically reserved for science fiction rather than science. 

Not so much for César de la Fuente, PhD, the Presidential Assistant Professor in Psychiatry, Microbiology, Chemical and Biomolecular Engineering, and Bioengineering in Penn’s Perelman School of Medicine and School of Engineering and Applied Science. Driven by his transdisciplinary background, de la Fuente leads the Machine Biology Group at Penn: aimed at harnessing machines to drive biological and medical advances. 

A newly minted National Academy of Medicine Emerging Leaders in Health and Medicine (ELHM) Scholar, among earning a host of other awards and honors (over 60), de la Fuente can sound almost diplomatic when describing the intersection of humanity, machines and medicine where he has made his way—ensuring multiple functions work together in harmony. 

“Biology is complexity, right? You need chemistry, you need mathematics, physics and computer science, and principles and concepts from all these different areas, to try to begin to understand the complexity of biology,” he said. “That’s how I became a scientist.”

Read the full story in Penn Medicine News.

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SCALAR: A Microchip Designed to Transform the Production of mRNA Therapeutics and Vaccines

Led by Michael Mitchell and David Issadore of the School of Engineering and Applied Science, a team of researchers has developed a platform that could rapidly accelerate the development of mRNA-based lipid nanoparticle vaccines and therapeutics at both the small and large scale, SCALAR. (Image: iStock / Anatoly Morozov)

Following the global COVID-19 pandemic, the development and rapid deployment of mRNA vaccines highlighted the critical role of lipid nanoparticles (LNPs) in the context of pharmaceuticals. Used as the essential delivery vehicles for fragile RNA-based therapies and vaccines, LNPs protect the RNA from degradation and ensure effective delivery within the body.

Despite their critical importance, the large-scale manufacturing of these LNPs saw numerous bottlenecks during the pandemic, underscoring the need for scalable production techniques that could keep pace with global demand.

Now, in a paper published in the Proceedings of the National Academy of the Sciences, researchers at the University of Pennsylvania describe how the Silicon Scalable Lipid Nanoparticle Generation platform (SCALAR), a reusable silicon- and glass-based platform designed to transform the production landscape of LNPs for RNA therapeutics and vaccines, offers a scalable and efficient solution to the challenges exposed during the COVID-19 crisis.

“We’re excited to create a piece of technology platform that bridges the gap between small-scale discovery and large-scale manufacturing in the realm of RNA lipid nanoparticle vaccines and therapeutics,” says co-author Michael Mitchell, associate professor of bioengineering in the School of Engineering and Applied Science at Penn. “By doing so, we’ve effectively leapfrogged the clunky, time-consuming, and costly barriers that slow down the production ramp-up of promising new RNA medicines and vaccines.”

The intricacies of RNA-based therapies require the RNA to be encased in a delivery system capable of navigating the body’s biological obstacles. LNPs fulfill this role, allowing the RNA to reach the intended cells for maximum therapeutic impact. SCALAR aims to take this a step further, allowing for an unprecedented three orders of magnitude scalability in LNP production rates, addressing the speed and consistency bottlenecks that hinder existing methods.

Sarah Shepherd, the first author of the paper and a recent Ph.D. graduate who worked in the Mitchell Lab, says, “With SCALAR, we’re not just reacting to today’s challenges but proactively preparing for tomorrow’s opportunities and crises. This technology is flexible, uses mixing architectures well-documented in microfluidics, and is scalable enough to meet future demands in real time. That’s an enormous leap forward for the field.”

Shepherd says that SCALAR builds on prior work from the Mitchell lab and is based on a microfluidic chip platform. Akin to a computer chip, wherein a computer’s electrically integrated circuit has numerous little transistors transporting signals as ones or zeroes to produce an output, the SCALAR microchip precisely controls their two key reagents, lipids and RNA, to generate LNPs.

Read the full story in Penn Today.

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Penn Engineers Create Low-Cost, Eco-Friendly COVID Test

by Kat Sas

Fabrication steps of the biodegradable BC substrate and the electrochemical devices. (1) Incubation of the bacterium Gluconacetobacter hansenii. (2) BC substrate collected and treated, resulting in a clear sheet. (3) The biodegradable BC sheet is screen-printed, (4) resulting in a device with 3 electrodes, (4) which are cut out using a scissor, (5) resulting in a portable, biodegradable, and inexpensive electrochemical sensor.

The availability of rapid, accessible testing was integral to overcoming the worst surges of the COVID-19 pandemic, and will be necessary to keep up with emerging variants. However, these tests come with unfortunate costs.

Polymerase chain reaction (PCR) tests, the “gold standard” for diagnostic testing, are hampered by waste. They require significant time (results can take up to a day or more) as well as specialized equipment and labor, all of which increase costs. The sophistication of PCR tests makes them harder to tweak, and therefore slower to respond to new variants. They also carry environmental impacts. For example, most biosensor tests developed to date use printed circuit boards, or PCBs, the same materials used in computers. PCBs are difficult to recycle and slow to biodegrade, using large amounts of metal, plastic and non-eco-friendly materials.

In addition, most PCR tests end up in landfills, resulting in material waste and secondary contamination. An analysis by the World Health Organization (WHO) estimated that, as of February 2022, “over 140 million test kits, with a potential to generate 2,600 tonnes of non-infectious waste (mainly plastic) and 731,000 litres of chemical waste (equivalent to one-third of an Olympic-size swimming pool) have been shipped.”

In order to balance the need for fast, affordable and accurate testing while addressing these environmental concerns, César de la Fuente, Presidential Assistant Professor in Bioengineering and Chemical and Biomolecular Engineering in the School of Engineering and Applied Science, with additional primary appointments in Psychiatry and Microbiology within the Perelman School of Medicine, has turned his attention to the urgent need for “green” testing materials.

The de la Fuente lab has been working on creative ways to create faster and cheaper testing for COVID-19 since the outbreak of the pandemic. Utilizing his lab’s focus on machine biology and the treatment of infectious disease, they created RAPID, an aptly named test that generates results in minutes with a high degree of accuracy. An even more cost-effective version, called LEAD, was created using electrodes made from graphite. A third test, called COLOR, was a low-cost optodiagnostic test printed on cotton swabs.

The team’s latest innovation incorporates the speed and cost-effectiveness of previous tests with eco-friendly materials. In a paper published in Cell Reports Physical Science, the group introduces a new test made from Bacterial Cellulose (BC), an organic compound synthesized from several strains of bacteria, as a substitute for PCBs.

Read the full story in Penn Engineering Today.

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Penn and CHOP Researchers Show Gene Editing Tools Can be Delivered to Perinatal Brain

Genetic diseases that involve the central nervous system (CNS) often impact children before birth, meaning that once a child is born, irreversible damage has already been done. Given that many of these conditions result from a mutation in a single gene, there has been growing interest in using gene editing tools to correct these mutations before birth.

However, identifying the appropriate vehicle to deliver these gene editing tools to the CNS and brain has been a challenge. Viral vectors used to deliver gene therapies have some potential drawbacks, including pre-existing viral immunity and vector-related adverse events, and other options like lipid nanoparticles (LNPs) have not been investigated extensively in the perinatal brain.

Now, researchers in the Center for Fetal Research at Children’s Hospital of Philadelphia (CHOP) and Penn Engineering have identified an ionizable LNP that can deliver mRNA base editing tools to the brain and have shown it can mitigate CNS disease in perinatal mouse models. The findings, published in ACS Nano, open the door to mRNA therapies that could be delivered pre- or postnatally to treat genetic CNS diseases.

The research team began by screening a library of ionizable LNPs – microscopic fat bubbles that have a positive charge at low pH but neutral charge at physiological conditions in the body. After identifying which LNPs were best able to penetrate the blood-brain barrier in fetal and newborn mice, they optimized their top-performing LNP to be able to deliver base editing tools. The LNPs were then used to deliver mRNA for an adenine base editor, which would correct a disease-causing mutation in the lysosomal storage disease, MPSI, by changing the errant adenine to guanine.

The researchers showed that their LNP was able to improve the symptoms of the lysosomal storage disease in the neonatal mouse brain, as well as deliver mRNA base editing tools to the brain of other animal models. They also showed the LNP was stable in human cerebrospinal fluid and could deliver mRNA base editing tools to patient-derived brain tissue.

“This proof-of-concept study – co-led by Rohan Palanki, an MD/PhD student in my lab, and Michael Mitchell’s lab at Penn Bioengineering – supports the safety and efficacy of LNPs for the delivery of mRNA-based therapies to the central nervous system,” said co-senior author William H. Peranteau, MD, an attending surgeon in the Division of General, Thoracic and Fetal Surgery at CHOP and the Adzick-McCausland Distinguished Chair in Fetal and Pediatric Surgery. “Taken together, these experiments provide the foundation for additional translational studies and demonstrate base editing facilitated by a nonviral delivery carrier in the NHP fetal brain and primary human brain tissue.”

This story was written by Dana Bate. It originally appeared on CHOP’s website.

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Could Psychedelics Simultaneously Treat Chronic Pain and Depression?

Ahmad Hammo

Ongoing clinical trials have demonstrated that psychedelics like psilocybin and LSD can have rapid and long-lived antidepressant and anti-anxiety effects. A related clinical problem is chronic pain, which is notoriously difficult to treat and often associated with depression and anxiety.

This summer, Ahmad Hammo, a rising third-year student in bioengineering in the School of Engineering and Applied Science, is conducting a pilot study to explore psilocybin’s potential as a therapy for chronic pain and the depression that often accompanies it.

“There’s a strong correlation between chronic pain and depression, so I’m looking at how a psychedelic might be used for treating both of these things simultaneously,” says Hammo, who is originally from Amman, Jordan.

Hammo is working under the guidance of neuroanesthesiologist and neuroscientist Joseph Cichon, an assistant professor in the Perelman School of Medicine. The effort is supported by the Penn Undergraduate Research Mentoring (PURM) program, administered by the Center for Undergraduate Research and Fellowships, which awards undergraduate students $5,000 to spend 10 weeks conducting research alongside Penn faculty.

Hammo’s project focuses on neuropathic pain, pain associated with nerve damage. Like other forms of chronic pain, most experts believe that chronic neuropathic pain is stored in the brain.

“Neuropathic pain can lead to a centralized pain syndrome where the pain is still being processed in the brain,” Cichon says. “It’s as if there’s a loop that keeps playing over and over again, and this chronic form is completely divorced from that initial injury.”

Read the full story in Penn Today.

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Could the Age of the Universe Be Twice as Old as Current Estimates Suggest?

by Nathi Magubane

NASA’s James Webb Space Telescope has produced the deepest and sharpest infrared image of the distant universe to date. Known as Webb’s First Deep Field, this image of galaxy cluster SMACS 0723 is rich with detail. Thousands of galaxies—including the faintest objects ever observed in the infrared—have appeared in Webb’s view for the first time. The image shows the galaxy cluster SMACS 0723 as it appeared 4.6 billion years ago. The combined mass of this galaxy cluster acts as a gravitational lens, magnifying much more distant galaxies behind it. Webb’s Near-Infra Red Cam has brought those distant galaxies into sharp focus—they have tiny, faint structures that have never been seen before, including star clusters and diffuse features. (Image: NASA, ESA, CSA, and STScI)

Could the universe be twice as old as current estimates put forward? Rajendra Gupta of the University of Ottawa recently published a paper suggesting just that. Gupta claims the universe may be around 26.7 billion years rather than the commonly accepted 13.8 billion. The news has generated many headlines as well as criticism from astronomers and the larger scientific community.

Penn Today met with professors Vijay Balasubramanian and Mark Devlin to discuss Gupta’s findings and better understand the rationale of these claims and how they fit in the broader context of problems astronomers are attempting to solve.

How do we know how old the universe actually is?

Balasubramanian: The universe is often reported to be 13.8 billion years old, but, truth be told, this is an amalgamation of various measurements that factor in different kinds of data involving the apparent ages of ‘stuff’ in the universe.

This stuff includes observable or ordinary matter like you, me, galaxies far and near, stars, radiation, and the planets, then dark matter—the sort of matter that doesn’t interact with light and which makes up about 27% of the universe—and finally, dark energy, which makes up a massive chunk of the universe, around 68%, and is what we believe is causing the universe to expand.

And so, we take as much information as we can about the stuff and build what we call a consensus model of the universe, essentially a line of best fit. We call the model the Lambda Cold Dark Matter (ΛCDM).

Lambda represents the cosmological constant, which is linked to dark energy, namely how it drives the expansion of the universe according to Einstein’s theory of general relativity. In this framework, how matter and energy behave in the universe determines the geometry of spacetime, which in turn influences how matter and energy move throughout the cosmos. Including this cosmological constant, Lambda, allows for an explanation of a universe that expands at an accelerating rate, which is consistent with our observations.

Now, the Cold Dark Matter part represents a hypothetical form of dark matter. ‘Dark’ here means that it neither interacts with nor emits light, so it’s very hard to detect. ‘Cold’ refers to the fact that its particles move slowly because when things cool down their components move less, whereas when they heat up the components get excited and move around more relative to the speed of light.

So, when you consider the early formation of the universe, this ‘slowness’ influences the formation of structures in the universe like galaxies and clusters of galaxies, in that smaller structures like the galaxies form before the larger ones, the clusters.

Devlin: And then taking a step back, the way cosmology works and pieces how old things are is that we look at the way the universe looks today, how all the structures are arranged within it, and we compare it to how it used to be with a set of cosmological parameters like Cosmic Microwave Background (CMB) radiation, the afterglow of the Big Bang, and the oldest known source of electromagnetic radiation, or light. We also refer to it as the baby picture of the universe because it offers us a glimpse of what it looked like at 380,000 years old, long before stars and galaxies were formed.

And what we know about the physical nature of the universe from the CMB is that it was something really smooth, dense, and hot. And as it continued to expand and cool, the density started to vary, and these variations became the seeds for the formation of cosmic structures.
The denser regions of the universe began to collapse under their own gravity, forming the first stars, galaxies, and clusters of galaxies. So, this is why, when we look at the universe today, we see this massive cosmic web of galaxies and clusters separated by vast voids. This process of structure formation is still ongoing.

And, so, the ΛCDM model suggests that the primary driver of this structure formation was dark matter, which exerts gravity and which began to clump together soon after the Big Bang. These clumps of dark matter attracted the ordinary matter, forming the seeds of galaxies and larger cosmic structures.

So, with models like the ΛCDM and the knowledge of how fast light travels, we can add bits of information, or parameters, and we have from things like the CMB and other sources of light in our universe, like the ones we get from other distant galaxies, and we see this roadmap for the universe that gives us it’s likely age. Which we think is somewhere in the ballpark of 13.8 billion years.

Read the full Q&A in Penn Today.

Vijay Balasubramanian is the Cathy and Marc Lasry Professor in the Department of Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania. He is a member of the Penn Bioengineering Graduate Group.

Mark Devlin is the Reese W. Flower Professor of Astronomy and Astrophysics in the Department of Physics and Astronomy in the School of Arts & Sciences at Penn.

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Riccardo Gottardi Recognized for Airway Research

Matthew Aronson (left), Ph.D. student in Bioengineering, and Riccardo Gottardi, Assistant Proessor in Bioengineering and Pediatrics.

Riccardo Gottardi, Assistant Professor in Pediatrics in the Perelman School of Medicine and in Bioengineering in the School of Engineering and Applied Science, has been named a “Young Innovator of Cellular and Molecular Bioengineering” by Cellular and Molecular Bioengineering, the official journal of the Biomedical Engineering Society (BMES). Gottardi is Chief Scientist in the Pediatric Airway Frontier Program at the Children’s Hospital of Philadelphia (CHOP). He leads the Bioengineering and Biomaterials (Bio2) Lab, and was recognized here for his research to prevent subglottic stenosis in children.

Gottardi’s work in subglottic stensosis, a severe narrowing of the airway in response to intubation, was recently profiled in CHOP’s Cornerstone Blog. CHOP’s award press release describes Gottardi’s innovative treatment:

“Prior studies by Dr. Gottardi’s lab used in vitro models to demonstrate that incorporating AMPs into polymer-coated tubes can inhibit bacterial growth and modulate the upper-airway microbiome. In a recent study in Cellular and Molecular Engineering, led by [Bioengineering] PhD student Matthew Aronson of the Gottardi Lab, the researchers went a step further and used both ex vivo and in vivo models to show how their patent-pending antimicrobial peptide-eluting endotracheal tube (AMP-ET) effectively targeted the local airway microbiota, reducing inflammation and resolving stenosis.

‘I am honored to be recognized by Cellular and Molecular Engineering for this exciting and notable award,” Dr. Gottardi said. “We are hopeful that our airway innovation will show similar success in human trials, so that we can improve outcomes for intubated pediatric patients.’”

Read CHOP’s full announcement of the award here.