Penn Bioengineer Develops Technology to Keep Track of Living Cells and Tissues

SAFE Bioorthogonal Cycling

Cells in complex organisms undergo frequent changes, and researchers have struggled to monitor these changes and create a comprehensive profile for living cells and tissues. Historically researchers have been limited to only 3-5 markers due to spectral overlaps in fluorescence microscopy, an essential tool required for imaging cells. With only this small handful of markers, it is difficult to monitor protein expressions of live cells and a comprehensive profile of cellular dynamics cannot be created. However, a new study in Nature Biotechnology addresses these limitations by demonstrating a new method for comprehensive profiling of living cells.

Jina Ko, PhD

Jina Ko, Assistant Professor in Bioengineering in the School of Engineering and Applied Science and in Pathology and Laboratory Medicine in the Perelman School of Medicine, served as lead author of this new study. Ko conducted postdoctoral research at Massachusetts General Hospital (MGH) and the Wyss Institute at Harvard University, and the work for this study was done in collaboration with Jonathan Carlson M.D., Ph.D. and Ralph Weissleder M.D., Ph.D. of MGH. Ko’s lab at Penn develops novel technologies using bioengineering, molecular biology, and chemistry to address diagnostic challenges for precision medicine.

To address these limitations in microscopy, Ko’s team developed a new chemistry tool which was highly gentle to cells. This “scission-accelerated fluorophore exchange (or SAFE)” method utilizes “click” chemistry, a type of chemistry that follows examples found in nature to create fast and simple reactions. This new SAFE method enabled Ko to achieve non-toxic conditions to living cells and tissues, whereas previous methods have used harsh chemicals that would strip off fluorophores and consequently would not work with living cells and tissues.

With the development of SAFE, the authors demonstrated that researchers can now effectively perform multiple cycles of cell profiling and can monitor cellular changes over the course of their observations. Instead of the previous limitation of 3-5 markers total, SAFE allows for many more cycles and can keep track of almost as many markers as the researcher wants. One can now stain cells and quench/release fluorophores and repeat the cycle multiple times for multiplexing on living cells. Each cycle can profile 3 markers, and so someone interested in profiling 15 markers could easily perform 5 cycles to achieve this much more comprehensive cell profile. With this breakthrough in more detailed imaging of cells, SAFE demonstrates broad applicability for allowing researchers to better investigate the physiologic dynamics in living systems.

Read the paper, “Spatiotemporal multiplexed immunofluorescence imaging of living cells and tissues with bioorthogonal cycling of fluorescent probes,” in Nature Biotechnology.

This study was supported by the Schmidt Science Fellows in Partnership with the Rhodes Trust and National Institutes of Health, National Cancer Institute (K99CA256353).

Decoding a Material’s ‘Memory’

by Erica K. Brockmeier

A suspension of particles of different sizes during shearing experiments conducted in the lab of Paulo Arratia, with arrows indicating particle “flow” and trajectories. In a new study published in Nature Physics, researchers detail the relationship between a disordered material’s individual particle arrangement and how it reacts to external stressors. The study also found that these materials have “memory” that can be used to predict how and when they will flow. (Image: Arratia lab)

New research published in Nature Physics details the relationship between a disordered material’s individual particle arrangement and how it reacts to external stressors. The study also found that these materials have “memory” that can be used to predict how and when they will flow. The study was led by Larry Galloway, a Ph.D. student in the lab of Paulo Arratia, and Xiaoguang Ma, a former postdoc in the lab of Arjun Yodh, in collaboration with researchers in the labs of Douglas Jerolmack and Celia Reina.

A disordered material is randomly arranged at the particle-scale, e.g. atoms or grains, instead of being systematically distributed—think of a pile of sand instead of a neatly stacked brick wall. Researchers in the Arratia lab are studying this class of materials as part of Penn’s Materials Research Science & Engineering Center, where one of the program’s focuses is on understanding the organization and proliferation of particle-scale rearrangements in disordered, amorphous materials.

The key question in this study was whether one could observe the structure of a disordered material and have some indication as to how stable it is or when it might begin to break apart. This is known as the yield point, or when the material “flows” and begins to move in response to external forces. “For example, if you look at the grains of a sand castle and how they are arranged, can I tell you whether the wind can blow it over or if it has to be hit hard to fall over?” says Arratia. “We want to know, just by looking at the way the particles are arranged, if we can say anything about the way they’re going to flow or if they are going to flow at all.”

While it has been known that individual particle distribution influences yield point, or flow, in disordered materials, it has been challenging to study this phenomenon since the field lacks ways to “quantify” disorder in such materials. To address this challenge, the researchers collaborated with colleagues from across campus to combine expertise across the fields of experimentation, theory, and simulations.

Read the full story in Penn Today.

The authors are Larry Galloway, Erin Teich, Christoph Kammer, Ian Graham, Celia Reina, Douglas Jerolmack, Arjun Yodh, and Paulo Arratia from Penn; Xiaoguang Ma, previously a postdoc at Penn and now at the Southern University of Science and Technology in Shenzhen, China; and Nathan Keim, previously a postdoc at Penn and now at Pennsylvania State University.

Arjun Yodh is the James M. Skinner Professor of Science in the Department of Physics and Astronomy in Penn’s School of Arts & Sciences and a member of the Penn Bioengineering Graduate Group.

Paulo Arratia is a professor in the departments of Mechanical Engineering and Applied Mechanics and Chemical and Biomolecular Engineering in the School of Engineering and Applied Science at the University of Pennsylvania.

Douglas Jerolmack is a professor in the Department of Earth and Environmental Science in Penn’s School of Arts & Sciences and in the Department of Mechanical Engineering and Applied Mechanics at Penn Engineering.

Celia Reina is the William K. Gemmill Term Assistant Professor in the Department of Mechanical Engineering and Applied Mechanics at Penn Engineering.

Understanding Optimal Resource Allocation in the Brain

by Erica K. Brockmeier

A processed image representative of the types of images used in this study. Natural landscapes were transformed into binary images, ones made of black and white pixels, that were decomposed into different textures defined by specific statistics. (Image: Eugenio Piasini)

The human brain uses more energy than any other organ in the body, requiring as much as 20% of the body’s total energy. While this may sound like a lot, the amount of energy would be even higher if the brain were not equipped with an efficient way to represent only the most essential information within the vast, constant stream of stimuli taken in by the five senses. The hypothesis for how this works, known as efficient coding, was first proposed in the 1960s by vision scientist Horace Barlow.

Now, new research from the Scuola Internazionale Superiore di Studi Avanzati (SISSA) and the University of Pennsylvania provides evidence of efficient visual information coding in the rodent brain, adding support to this theory and its role in sensory perception. Published in eLife, these results also pave the way for experiments that can help understand how the brain works and can aid in developing novel artificial intelligence (AI) systems based on similar principles.

According to information theory—the study of how information is quantified, stored, and communicated—an efficient sensory system should only allocate resources to how it represents, or encodes, the features of the environment that are the most informative. For visual information, this means encoding only the most useful features that our eyes detect while surveying the world around us.

Vijay Balasubramanian, a computational neuroscientist at Penn, has been working on this topic for the past decade. “We analyzed thousands of images of natural landscapes by transforming them into binary images, made up of black and white pixels, and decomposing them into different textures defined by specific statistics,” he says. “We noticed that different kinds of textures have different variability in nature, and human subjects are better at recognizing those which vary the most. It is as if our brains assign resources where they are most necessary.”

Read the full story 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.

New Lipid Nanoparticles Improve mRNA Delivery for Engineering CAR T Cells

by Melissa Pappas

The Penn researchers’ latest paper on the design of lipid nanoparticles was featured on the cover of the most recent edition of the journal Nano Letters.

From COVID vaccines to cancer immunotherapies to the potential for correcting developmental disorders in utero, mRNA-based approaches are a promising tool in the fight against a wide range of diseases. These treatments all depend on providing a patient’s cells with genetic instructions for custom proteins and other small molecules, meaning that getting those instructions inside the target cells is of critical importance.

The current delivery method of choice uses lipid nanoparticles (LNPs). Thanks to surfaces customized with binding and signaling molecules, they encapsulate mRNA sequences and smuggle them through the cell membrane. But with a practically unlimited number of variables in the makeup of those surfaces and molecules, figuring out how to design the most effective LNP is a fundamental challenge.

Now, in a study featured on the cover of the journal Nano Letters, researchers from the University of Pennsylvania’s School of Engineering and Applied Science and Perelman School of Medicine have now shown how to computationally optimize the design of these delivery vehicles.

Using an established methodology for comparing a wide range of variables known as “orthogonal design of experiments,” the researchers simultaneously tested 256 candidate LNPs. They found the frontrunner was three times better at delivering mRNA sequences into T cells than the current standard LNP formulation for mRNA delivery.

The study was led by Michael Mitchell, Skirkanich Assistant Professor of Innovation in the Department of Bioengineering in Penn’s School of Engineering and Applied Science, and Margaret Billingsley, a graduate student in his lab.

Read the full story in Penn Engineering Today.

Newly Discovered ‘Encrypted Peptides’ Found in Human Plasma Exhibit Antibiotic Properties

by Melissa Pappas

The antimicrobial peptides the researchers studied are “encrypted” in that they are contained within Apolipoprotein B, a blood plasma protein that is not directly involved in the immune response, but are not normally expressed on their own.

The rise of drug-resistant bacteria infections is one of the world’s most severe global health issues, estimated to cause 10 million deaths annually by the year 2050. Some of the most virulent and antibiotic-resistant bacterial pathogens are the leading cause of life-threatening, hospital-acquired infections, particularly dangerous for immunocompromised and critically ill patients. Traditional and continual synthesis of antibiotics will simply not be able to keep up with bacteria evolution.

To avoid the continual process of synthesizing new antibiotics to target bacteria as they evolve, Penn Engineers have looked at a new, natural resource for antibiotic molecules.

César de la Fuente, Ph.D.

A recent study on the search for encrypted peptides with antimicrobial properties in the human proteome has located naturally occurring antibiotics within our own bodies. By using an algorithm to pinpoint specific sequences in our protein code, a team of Penn researchers along with collaborators, led by César de la Fuente, Presidential Assistant Professor in Psychiatry, Bioengineering, Microbiology, and Chemical and Biomolecular Engineering, and Marcelo Torres, a post doc in de la Fuente’s lab, were able to locate novel peptides, or amino acid chains, that when cleaved, indicated their potential to fend off harmful bacteria.

Now, in a new study published in ACS Nano, the team along with Angela Cesaro, the lead author and post doc in de la Fuente’s lab, have identified three distinct antimicrobial peptides derived from a protein in human plasma and demonstrate their abilities in mouse models. Angela Cesaro performed a great part of the activities during her PhD under the supervision of corresponding author, Professor Angela Arciello, from the University of Naples Federico II. The collaborative study also includes Utrecht University in the Netherlands.

“We identified the cardiovascular system as a hot spot for potential antimicrobials using an algorithmic approach,” says de la Fuente. “Then we looked closer at a specific protein in the plasma.”

Read the full story in Penn Engineering Today.

A New Way to Profile T Cells Can Aid in Personalized Immunotherapy

by Melissa Pappas

A scanning electron micrograph of a healthy human T cell. A better understanding the wide variety of antigen receptors that appear on the surfaces of these critical components of the immune system is necessary for improving a new class of therapies. (Credit: NIAID)

Our bodies are equipped with specialized white blood cells that protect us from foreign invaders, such as viruses and bacteria. These T cells identify threats using antigen receptors, proteins expressed on the surface of individual T cells that recognize specific amino acid sequences found in or on those invaders. Once a T cell’s antigen receptors bind to the corresponding antigen, it can directly kill infected cells or call for backup from the rest of the immune system.

We have hundreds of billions of T cells, each with unique receptors that recognize unique antigens, so profiling this T cell antigen specificity is essential in our understanding of the immune response. It is especially critical in developing targeted immunotherapies, which equip T cells with custom antigen receptors that recognize threats they would otherwise miss, such as the body’s own mutated cancer cells.

Jenny Jiang, Ph.D.

Jenny Jiang, Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering, along with lab members and colleagues at the University of Texas, Austin, recently published a study in Nature Immunology that describes their technology, which simultaneously provides information in four dimensions of T cell profiling. Ke-Yue Ma and Yu-Wan Guo, a former post doc and current graduate student in Jiang’s Penn Engineering lab, respectively, also contributed to this study.

This technology, called TetTCR-SeqHD, is the first to provide such detailed information about single T cells in a high-throughput manner, opening doors for personalized immune diagnostics and immunotherapy development.

There are many pieces of information needed to comprehensively understand the immune response of T cells, and gathering all of these measurements simultaneously has been a challenge in the field. Comprehensive profiling of T cells includes sequencing the antigen receptors, understanding how specific those receptors are in their recognition of invading antigens, and understanding T cell gene and protein expression. Current technologies only screen for one or two of these dimensions due to various constraints.

“Current technologies that measure T cell immune response all have limitations,” says Jiang. “Those that use cultured or engineered T cells cannot tell us about their original phenotype, because once you take a cell out of the body to culture, its gene and protein expression will change. The technologies that address T cell and antigen sequencing with mass spectrometry damage genetic information of the sample. And current technologies that do provide information on antigen specificity use a very expensive binding ligand that can cost more than a thousand dollars per antigen, so it is not feasible if we want to look at hundreds of antigens. There is clearly room for advancement here.”

The TetTCR-SeqHD technology combines Jiang’s previously developed T cell receptor sequencing tool, TetTCR-Seq, described in a Nature Biotechnology paper published in 2018, with the new ability of characterizing both gene and protein expression.

Read the full story in Penn Engineering Today.

A New Model for How the Brain Perceives Unique Odors

by Erica K. Brockmeier

Cathy and Marc Lasry Professor Vijay Balasubramanian at Penn’s BioPond.

A study published in PLOS Computational Biology describes a new model for how the olfactory system discerns unique odors. Researchers from the University of Pennsylvania found that a simplified, statistics-based model can explain how individual odors can be perceived as more or less similar from others depending on the context. This model provides a starting point for generating new hypotheses and conducting experiments that can help researchers better understand the olfactory system, a complex, crucial part of the brain.

The sense of smell, while crucial for things like taste and hazard avoidance, is not as well studied as other senses. Study co-author Vijay Balasubramanian, a theoretical physicist with an interest in how living systems process information, says that olfaction is a prime example of a complex information-processing system found in nature, as there are far more types of volatile molecules—on the scale of tens or hundreds of thousands—than there are receptor types in the nose to detect them, on the scale of tens to hundreds depending on the species.

“Every molecule can bind to many receptors, and every receptor can bind to many molecules, so you get this combinatorial mishmash, with the nose encoding smells in a way that involves many receptor types to collectively tell you what a smell is,” says Balasubramanian. “And because there are many fewer receptor types than molecular species, you basically have to compress a very high dimensional olfactory space into a much lower dimensional space of neural responses.”

Read the full story in Penn Today.

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

This research was supported by the Simons Foundation Mathematical Modeling of Living Systems (Grant 400425) and the Swartz Foundation.

Nerve Repair, With Help From Stem Cells

A cross-disciplinary Penn team is pioneering a new approach to peripheral nerve repair.

In a new publication in the journal npj Regenerative Medicine, a team of Penn researchers from the School of Dental Medicine and the Perelman School of Medicine “coaxed human gingiva-derived mesenchymal stem cells (GMSCs) to grow Schwann-like cells, the pro-regenerative cells of the peripheral nervous system that make myelin and neural growth factors,” addressing the need for regrowing functional nerves involving commercially-available scaffolds to guide nerve growth. The study was led by Anh Le, Chair and Norman Vine Endowed Professor of Oral Rehabilitation in the Department of Oral and Maxillofacial Surgery/Pharmacology at the University of Pennsylvania School of Dental Medicine, and was co-authored by D. Kacy Cullen, Associate Professor in Neurosurgery at the Perelman School of Medicine at Penn and the Philadelphia Veterans Affairs Medical Center and member of the Bioengineering Graduate Group:

D. Kacy Cullen (Image: Eric Sucar)

“To get host Schwann cells all throughout a bioscaffold, you’re basically approximating natural nerve repair,” Cullen says. Indeed, when Le and Cullen’s groups collaborated to implant these grafts into rodents with a facial nerve injury and then tested the results, they saw evidence of a functional repair. The animals had less facial droop than those that received an “empty” graft and nerve conduction was restored. The implanted stem cells also survived in the animals for months following the transplant.

“The animals that received nerve conduits laden with the infused cells had a performance that matched the group that received an autograft for their repair,” he says. “When you’re able to match the performance of the gold-standard procedure without a second surgery to acquire the autograft, that is definitely a technology to pursue further.”

Read the full story and view the full list of collaborators in Penn Today.

Using Big Data to Measure Emotional Well-being in the Wake of George Floyd’s Murder

by Melissa Pappas

George Floyd’s murder had an undeniable emotional impact on people around the world, as evidenced by this memorial mural in Berlin, but quantifying that impact is challenging. Researchers from Penn Engineering and Stanford have used a computational approach on U.S. survey data to break down this emotional toll along racial and geographic lines. Their results show a significantly larger amount of self-reported anger and sadness among Black Americans than their White counterparts. (Photo: Leonhard Lenz)

The murder of George Floyd, an unarmed Black man who was killed by a White police officer, affected the mental well-being of many Americans. The effects were multifaceted as it was an act of police brutality and example of systemic racism that occurred during the uncertainty of a global pandemic, creating an even more complex dynamic and emotional response.

Because poor mental health can lead to a myriad of additional ailments, including poor physical health, inability to hold a job and an overall decrease in quality of life, it is important to understand how certain events affect it. This is especially critical when the emotional burden of these events  falls most on demographics affected by systemic racism. However, unlike physical health, mental health is challenging to characterize and measure, and thus, population-level data on mental health has been limited.

To better understand patterns of mental health on a population scale, Penn Engineers Lyle H. Ungar, Professor of Computer and Information Science (CIS), and Sharath Chandra Guntuku, Research Assistant Professor in CIS, take a computational approach to this challenge. Drawing on large-scale surveys as well as language analysis in social media through their work with the World Well-Being Project, they have developed visualizations of these patterns across the U.S.

Their latest study involves tracking changes in emotional and mental health following George Floyd’s murder. Combining polling data from the U.S. Census and Gallup, Guntuku, Ungar and colleagues have shown that Floyd’s murder spiked a wave of unprecedented sadness and anger across the U.S. population, the largest since relevant data began being recorded in 2009.

Read the full story in Penn Engineering Today.

N.B. Lyle Ungar is also a member of the Penn Bioengineering Graduate Group.

Atomically-thin, Twisted Graphene Has Unique Properties

by Erica K. Brockmeier

New collaborative research describes how electrons move through two different configurations of bilayer graphene, the atomically-thin form of carbon. These results provide insights that researchers could use to design more powerful and secure quantum computing platforms in the future.

New research published in Physical Review Letters describes how electrons move through two different configurations of bilayer graphene, the atomically-thin form of carbon. This study, the result of a collaboration between Brookhaven National Laboratory, the University of Pennsylvania, the University of New Hampshire, Stony Brook University, and Columbia University, provides insights that researchers could use to design more powerful and secure quantum computing platforms in the future.

“Today’s computer chips are based on our knowledge of how electrons move in semiconductors, specifically silicon,” says first and co-corresponding author Zhongwei Dai, a postdoc at Brookhaven. “But the physical properties of silicon are reaching a physical limit in terms of how small transistors can be made and how many can fit on a chip. If we can understand how electrons move at the small scale of a few nanometers in the reduced dimensions of 2-D materials, we may be able to unlock another way to utilize electrons for quantum information science.”

When a material is designed at these small scales, to the size of a few nanometers, it confines the electrons to a space with dimensions that are the same as its own wavelength, causing the material’s overall electronic and optical properties to change in a process called quantum confinement. In this study, the researchers used graphene to study these confinement effects in both electrons and photons, or particles of light.

The work relied upon two advances developed independently at Penn and Brookhaven. Researchers at Penn, including Zhaoli Gao, a former postdoc in the lab of Charlie Johnson who is now at The Chinese University of Hong Kong, used a unique gradient-alloy growth substrate to grow graphene with three different domain structures: single layer, Bernal stacked bilayer, and twisted bilayer. The graphene material was then transferred onto a special substrate developed at Brookhaven that allowed the researchers to probe both electronic and optical resonances of the system.

“This is a very nice piece of collaborative work,” says Johnson. “It brings together exceptional capabilities from Brookhaven and Penn that allow us to make important measurements and discoveries that none of us could do on our own.”

Read the full story in Penn Today.

Charlie Johnson is the Rebecca W. Bushnell Professor of Physics and Astronomy in the Department of Physics and Astronomy in the School of Arts & Sciences at the University of Pennsylvania and a member of the Penn Bioengineering Graduate Group.