Much of the world, including research at Penn Medicine, has focused its attention on how T cells–which play a central role in immune response—might shape the trajectory of COVID-19 infection, and how immunotherapy can shed light on treatment of the disease.
Already a leader in immunotherapy research and treatment, Penn Medicine pioneered the groundbreaking development of CAR T cell cancer therapy. Avery Posey, an assistant professor of systems pharmacology and translational therapeutics, trained as a postdoctoral fellow in the lab of Carl June, who pioneered CAR T cell immunotherapy to treat cancer. Now as a faculty member at Penn, Posey has maintained a focus on T cell therapeutics, mostly for the treatment of cancer.
“This research combines two of my biggest interests—the use of gene therapy to treat disease and the investigation of little known biology, such as the roles of glycans in cell behavior. The pursuit of new knowledge, the roads less traveled—those are my inspirations,” Posey says.
To combat the COVID-19 pandemic caused by the SARS-CoV2 virus, Dr. Andrew Tsourkas’s Targeted Imaging Therapeutics and Nanomedicine (Titan) Lab in Penn Bioengineering, in collaboration with the Penn-based startup, AlphaThera, was recently awarded a $667,000 SBIR Phase II Grant Extension to support its efforts in commercializing COVID-19 detection technology. The grant supports work to address the growing need for anti-viral antibody testing. Specifically, the Tsourkas Lab and AlphaThera hope to leverage their expertise with antibody conjugation technologies to reduce the steps and complexity of existing detection assays to enable greater production and higher sensitivity tests. AlphaThera was founded in 2016 by Andrew Tsourkas, PhD, Professor of Bioengineering and James Hui, MD, PhD, a graduate of the Perelman School of Medicine and Penn Bioengineering’s doctoral program.
During this pandemic it is crucial to characterize disease prevalence among populations, understand immunity, test vaccine efficacy and monitor disease resurgence. Projections have indicated that millions of daily tests will be needed to effectively control the virus spread. One important testing method is the serological assay: These tests detect the presence of SARS-CoV2 antibodies in a person’s blood produced by the body’s immune system responding to infection. Serological tests not only diagnose active infections, but also establish prior infection in an individual, which can greatly aid in forecasting disease spread and contact tracing. To perform the serological assays for antibody detection, well-established immunoassay methods are used such as ELISA.
A variety of issues have slowed the distribution of these serological assays for antibody testing. The surge in demand for testing has caused shortages in materials and reagents that are crucial for the assays. Furthermore, complexity in some of the assay formats can slow both production and affect the sensitivity of test results. Recognizing these problems, AlphaThera is leveraging its novel conjugation technology to greatly improve upon traditional assay formats.
With AlphaThera’s conjugation technology, the orientation of antibodies can be precisely controlled so that they are aligned and uniformly immobilized on assay detection plates. This is crucial as traditional serological assays often bind antibodies to plates in a non-uniform manner, which increases variability of results and reduces sensitivity. See Fig 1 below. With AlphaThera’s uniform antibody immobilization, assay specificity could increase by as much as 1000- fold for detection of a patient’s SaRS-CoV2 antibodies.
Furthermore, AlphaThera is addressing the shortage of assay reagents, specifically secondary antibody reagents, by removing certain steps from traditional serological assays. Rather than relying on secondary antibodies for detection of the patient antibodies, AlphaThera’s technology can label the patient SaRS-CoV2 primary antibodies directly in serum with a detection reagent. This eliminates several processing steps, reducing the time of the assay by as much as 50%, as well as the costs.
The Tsourkas Lab and AlphaThera have initiated their COVID-19 project, expanding into the Pennovation Center and onboarding new lab staff. Other antibody labeling products have also become available and are currently being prepared for commercialization. Check out the AlphaThera website to learn more about their technology at https://www.alphathera.com.
NIH SBIR Phase II Grant Extension— 5-R44-EB023750-03 (PI: Yu) — 10/07/2020 – 10/07/2021
In a ‘Wired’ feature, Bassett helps explain the growing field of network neuroscience and how the form and function of the brain are connected.
Early attempts to understand how the brain works included the pseudoscience of phrenology, which theorized that various mental functions could be determined through the shape of the skull. While those theories have long been debunked, modern neuroscience has shown a kernel of truth to them: those functions are highly localized to different regions of the brain.
Now, Danielle Bassett, Professor of J. Peter Skirkanich Professor of Bioengineering and Electrical and Systems Engineering, is pioneering a new subfield that goes even deeper into the connection between the brain’s form and function: network neuroscience.
In a recent feature article in Wired, Bassett explains the concepts behind this new subfield. While prior understanding has long relied on the idea that certain areas of the brain control certain functions, Bassett and other network neuroscientists are using advances in imaging and machine learning to reveal the role the connections between those areas play.
For Bassett, one of the first indicators that these connections mattered more than previously realized was the shape of the neurons themselves.
Speaking with Wired’s Grace Huckins, Bassett says:
“Neurons are not spherical — neurons have a cell body, and then they have this long tail that allows them to connect to many other cells. You can even look at the morphology of the neuron and say, ‘Oh, well, connectivity has to matter. Otherwise, it wouldn’t look like this.’”
Read more about Bassett and the field of network neuroscience in Wired.
Danielle Bassett, J. Peter Skirkanich Professor in the departments of Bioengineering and Electrical and Systems Engineering, has been called the “doyenne of network neuroscience.” The burgeoning field applies insights from the field of network science, which studies how the structure of networks relate to their performance, to the billions of neuronal connections that make up the brain.
Much of Basset’s research draws on mathematical and engineering principles to better understand how mental traits arise, but also applies them more broadly to other challenges in neuroscience.
The researchers used machine learning techniques to create a new classification system for neurodegenerative diseases, one which may redraw the boundaries between them and help explain clinical differences in patients who received the same diagnoses.
BioWorld’s Anette Breindl spoke with Bassett about the team’s findings.
Now, investigators have developed a new approach to classifying neurodegenerative disorders that used the overall patterns of protein aggregation, rather than specific proteins, to define six clusters of patients that crossed traditional diagnostic categories.
“We find that perhaps the way that clinicians have been diagnosing these disorders… is not necessarily the way these disorders work,” Danielle Bassett told BioWorld. “The way we’ve been trying to carve nature at joints is not the way that nature has joints. The joints are elsewhere.”
Continue reading Breindl’s article, “For neurodegeneration, a different way to slice the pie,” at BioWorld.
The paper-based tests could be integrated directly into facemasks and provide instant results at testing sites.
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 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.
An interdisciplinary research team has found statistical evidence of women being under-cited in academic literature. They are now studying similar effects along racial lines.
By Izzy Lopez
Scientific papers are the backbone of a research community and the citation of those papers sparks conversation in a given field. This cycle of publication and citation leads to new knowledge, but what happens when implicit discrimination in a field leads to papers by minority scholars being cited less often than their counterparts? A new team of researchers has come together to ask this question and dig into the numbers of gender and racial bias in neuroscience.
The team members include physicist and neuroscientist Danielle Bassett, J. Peter Skirkanich Professor of Bioengineering at the University of Pennsylvania, with secondary appointments in the Departments of Neurology and Psychiatry in Penn’s Perelman School of Medicine, statistician Jordan Dworkin, then a graduate student in Penn Medicine’s Department of Biostatistics, Epidemiology and Bioinformatics, and ethicist Perry Zurn, an Assistant Professor of Philosophy at American University.
Their study on gender bias, which recently appeared in Nature Neuroscience, reports on the extent and drivers of gender imbalance in neuroscience reference lists. The team has also published a perspective paper in Neuron that makes practical recommendations for improving awareness of this issue and correcting for biases.
They are now working on a second study, led by Maxwell Bertolero, a postdoctoral researcher in Bassett’s lab, that considers the extent and drivers of racial imbalances in neuroscience reference lists.
Together, Bassett, Dworkin and Zurn are using their combined research strengths to uncover the under-citation of women or otherwise minority-led papers in neuroscience and to assess its significance. This research is fundamental in highlighting a true gap in representation in research paper citations, which can have detrimental effects for women and other minorities leading science. In addition, they provide actionable steps to address the problem and build a more equitable future.
Your research team is a distinctive one. How did you come together for a study about gender discrimination in neuroscience citations?
Jordan Dworkin: It was a fortunate coincidence. In the run-up to a big neuroscience conference, I started seeing discussions on Twitter about gender-based discrimination in neuroscience. There were stories being shared of women’s papers being overlooked and reviewers seeing reference lists that were almost entirely made up of men. It was illuminating, especially because some people in the discussion were hesitant to take those experiences at face value. This skepticism, and occasional combativeness, seemed to stem from the view that citations are an untouchable, scientific bastion where researchers’ decisions are fully objective. The tension between that view and scholars’ lived experiences encouraged me to explore the existing literature on this issue.
As it turns out, there is really strong literature on issues of diversity and citation in science. Some disciplines have done field-specific investigations, such as the foundational studies in political science, international relations, and economics, but there wasn’t yet any research in neuroscience. Since biomedical sciences often have different approaches to citation, it seemed that it would be worth doing a deeper neuroscience-specific investigation to give quantitative backing to the issue of gender bias in neuroscience research.
Danielle Bassett: When Jordan and I started working together on this project, I knew it was important. To do it right, we needed to present the information in a way that made it actionable, with clear recommendations about how each of us as scientists can help address the issue. We also needed to add someone to the team with expertise in gender theory and research ethics. We especially wanted to make sure we were discussing gender bias in a way that was informed by recent advances in gender studies. That’s when we brought Perry in.
Perry Zurn: I’m a philosopher by training, with a focus on ethics and politics. Citations are both an ethical and a political issue. Citations reflect whose questions and whose contributions are recognized as important in the scholarly conversation. As such, citations can either bring in marginalized voices, voices that have been historically excluded from a conversation, or they can simply replicate that exclusion. My own field of philosophy has just as much of a problem with gender and racial diversity as STEM fields, something Dani and I have been talking about for a long time. This work seemed like a natural point of collaboration.
Describe this study and what it means for promoting gender diversity in neuroscience.
Bassett: For years now, various scholars and activists in science have drawn attention to issues of gender and racial inequities in the field. Most of these conversations, however, have placed responsibility for change in the hands of people in power, such as journal editors, grant reviewers, department chairs, presidents of scientific societies, etc. But many of the imbalances people notice, whether in conversation with peers or through studies like ours, are perpetuated by researchers at all levels. Given that every research project is built on prior research, and therefore every paper has a reference list of citations, every researcher can make a difference. Who we choose to cite matters.
Dworkin: To understand the role of gender in citation practices, we looked at the authors and reference lists of articles published in five top neuroscience journals since 1995. We accounted for self-citations, and various potentially relevant characteristics of papers, and we found that women-led papers are under-cited relative to what would be expected if gender was not a consideration in citation behavior. Importantly, we also found that the under-citation of women-led papers is driven largely by the citation behavior of men-led teams. We also found that this trend is getting worse over time, because the field is getting more diverse while citation rates are generally staying the same.
For a very simple example, if there were 10 women and 90 men neuroscientists in 1980, then citing 10% women would be roughly proportional. But with a diversifying field, say there are now 200 women and 200 men neuroscientists and citations are still 10% women. Sure, the percentage of women cited didn’t go down, but that percentage is now vastly lower than the true percentage in the field. That’s a dramatic example, but it shows you that if we’re going to call for equality in scientific citation, the number of women-led papers on a given reference list should reflect, or even exceed, the number of available and relevant women-led papers in a field, and our work found that it does not.
Bassett: This under-citation of women scientists is a key issue because the gaps in the amount of engagement that women’s work receives could have detrimental downstream effects on conference invitations, grant and fellowship awards, tenure and promotion, inclusion in syllabi, and even student evaluations. As a result, understanding and eliminating gender bias in citation practices is vital for addressing gender imbalances in a field.
Why are citations important to gender representation in neuroscience?
Dworkin: Unlike hiring and grant funding, citations are something every researcher participates in. For example, as a graduate student I did not have any role on a faculty search committee, or any power in an academic society to decide on conference speakers, but I still have reference lists in all my papers. Citations are a unique area where all researchers play a direct role, where each person has a chance to reflect on their own practices and use those practices to create change in their field. Their ubiquity means that citations function as a conversation within a field, and their presence or absence can signal whose work is valued and whose is not. On a more concrete level, citations are often used as metrics for a variety of important, potentially career-defining, decisions.
Bassett: There are a lot of underrepresented scholars who have fantastic ideas and write really interesting papers but they’re not being acknowledged — and cited — in the way they deserve. And there are great role models for all the young women who are thinking about going into science, but unless the older women scientists are being cited, the younger ones will never see them. Without serious changes in the field, and a deep commitment to gender and racial diversity, many young women and minority scientists won’t stick with it, they won’t be hired, they won’t be promoted, and they won’t be put in the textbooks.
Zurn: Exactly. I think it’s important not only to think about who we’re citing as leading scientists, but also what sorts of people we’re representing as scientists at all. If you are looking at neuroscience as a field and you see predominantly white cisgender men in the research labs and the reference lists, then you begin to think that is what a neuroscientist looks like. But this homogeneity is neither representative of an increasingly diverse field like neuroscience, nor supportive of continuing efforts to diversify STEM in general. We need to expand what a scientist looks like and citations are one way to do that.
Danielle Bassett also has appointments in Penn Engineering’s Department of Electrical and Systems Engineering and Penn Arts & Sciences Department of Physics and Astronomy.
Jordan Dworkin is now an Assistant Professor of Clinical Biostatistics in the Department of Psychiatry at Columbia University.
Kristin Linn, Assistant Professor of Biostatics, Russell Shinohara, Associate Professor of Biostatistics, and Erin Teich, a postdoctoral researcher in Bassett’s lab, also contributed to the study published in Nature Neuroscience. It was supported by the National Institute of Neurological Disorders and Stroke through grants R01 NS085211 and R01 NS060910, the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation through CAREER Award PHY-1554488.
In May, Lyle Ungar, Professor of Computer and Information Science and Angela Duckworth, Rosa Lee and Egbert Chang Professor in Penn Arts & Sciences and the Wharton School, contributed to a New York Times op-ed on how to slow the COVID-19 pandemic through a culture of mask-wearing.
As infections continue to rise, Ungar and Duckworth are following up with another op-ed. Writing in the Philadelphia Inquirer, they outline the need to rapidly ramp-up the city and state’s contact tracing capacity:
New research finds that works of literature, musical pieces, and social networks have a similar underlying structure that allows them to share large amounts of information efficiently.
By Erica K. Brockmeier
To an English scholar or avid reader, the Shakespeare Canon represents some of the greatest literary works of the English language. To a network scientist, Shakespeare’s 37 plays and the 884,421 words they contain also represent a massively complex communication network. Network scientists, who employ math, physics, and computer science to study vast and interconnected systems, are tasked with using statistically rigorous approaches to understand how complex networks, like all of Shakespeare, convey information to the human brain.
New research published in Nature Physics uses tools from network science to explain how complex communication networks can efficiently convey large amounts of information to the human brain. Conducted by postdoc Christopher Lynn, graduate students Ari Kahn and Lia Papadopoulos, and professor Danielle S. Bassett, the study found that different types of networks, including those found in works of literature, musical pieces, and social connections, have a similar underlying structure that allows them to share information rapidly and efficiently.
Technically speaking, a network is simply a statistical and graphical representation of connections, known as edges, between different endpoints, called nodes. In pieces of literature, for example, a node can be a word, and an edge can connect words when they appear next to each other (“my” — “kingdom” — “for” — “a” — “horse”) or when they convey similar ideas or concepts (“yellow” — “orange” — “red”).
The advantage of using network science to study things like languages, says Lynn, is that once relationships are defined on a small scale, researchers can use those connections to make inferences about a network’s structure on a much larger scale. “Once you define the nodes and edges, you can zoom out and start to ask about what the structure of this whole object looks like and why it has that specific structure,” says Lynn.
Building on the group’s recent study that models how the brain processes complex information, the researchers developed a new analytical framework for determining how much information a network conveys and how efficient it is in conveying that information. “In order to calculate the efficiency of the communication, you need a model of how humans receive the information,” he says.
The Chan Zuckerberg Initiative (CZI) has announced $14 million in funding to support 29 interdisciplinary teams who are investigating the role of inflammation in disease. Among these recipients is Dan Huh, Associate Professor in Bioengineering, whose placenta-on-a-chip research will “explore how maternal and fetal cells respond to specific inflammatory signals and analyze the network of placental cells and immune cells that impact pregnancy outcomes in chronic inflammatory diseases.”
Kellie Ann Jurado, Presidential Assistant Professor in the Perelman School of Medicine’s Department of Microbiology, will lead the research team. She and Huh will collaborate with Monica Mainigi, William Shippen, Jr. Assistant Professor of Human Reproduction in Penn Medicine.
Huh’s placenta-on-a-chip consists of a small block of silicone containing microfluidic channels separated by a membrane of human cells. Variations in designs and cell types allow researchers to study how different molecules cross that barrier, allowing for experiments that would be otherwise impossible or unethical. For example, Huh and his group previously used a placenta-on-a-chip designed to model the placental barrier to research the effect of maternally-administered medications on the fetal bloodstream.
In this new study, Huh, Jurando and Mainigi were motivated by even more fundamental questions of pregnancy.
“It has been known for quite some time that women with chronic inflammatory diseases are at increased risk of developing various complications during pregnancy,” Huh says. “Despite accumulating clinical evidence, we understand little about how inflammation contributes to adverse pregnancy outcomes.”
Clear-fronted face masks, better and more frequent interpreters, and amped up involvement from local organizations have made a big difference during the COVID-19 pandemic.
By Michele Berger
Because COVID-19 spreads via respiratory droplets that disperse through sneezes and coughs, shielding the mouth and nose is an important weapon against the virus. But it can also hinder conversations for people who rely on reading lips. “Communication barriers are already difficult sometimes, and this makes it more difficult,” says linguist Jami Fisher, director of Penn’s American Sign Language (ASL)/Deaf Studies program.
It’s one of the trickiest aspects of this pandemic for those in the Deaf and hard-of-hearing communities, Fisher says. The challenge doesn’t stem just from misunderstandings due to wearing masks. It’s also about the dissemination of accurate and timely information, knowing who to rely on and how to assess what’s being said.
Trusted sources like the Swarthmore, Pennsylvania–based nonprofit Deaf-Hearing Communication Centre (DHCC), a Penn community partner, have filled that gap, frequently updating information on its social media channels and websites. Governors and mayors are more frequently using Certified Deaf Interpreters (CDI) during press briefings, and Penn alum Kate Panzer, who graduated in 2018, started a project with DHCC to sew masks with clear fronts to offer both lip-reading access and protection.
Like much of the country, Panzer has stayed inside for the past several months. When the pandemic started to worsen, she temporarily left a research position in Michigan and returned to her childhood home in Media, Pennsylvania. And like many people, she wanted to give back.
At Penn, she’d taken several American Sign Language classes through the program Fisher runs, so when she read an article about a student in Kentucky making clear-fronted masks, it piqued her interest. She reached out to Fisher, who connected her with Kyle Rosenberg, DHCC’s community development and outreach coordinator.
As a volunteer, she shared her mask idea with Rosenberg. “Even in normal times, the Deaf community really struggles with clear communication,” says Rosenberg, who is himself deaf. “ASL is very visual. It relies on body language. Covering up the mouth with a mask makes communication 10 times harder.”
Rosenberg helped Panzer tweak a design and create a process to reach the community, and they took their first order on April 23. Since then, they’ve shipped about 450 masks, with a backlog of requests for hundreds more.
Though the response has been overwhelmingly positive, when constructive feedback comes in, they do take it to heart, Panzer says. For example, when mask-wearers told them that the elastic bands they’d been using rubbed uncomfortably against hearing aids, they switched to fabric ties that go around the back of the head. The masks are not medical grade, so they can’t be used in a hospital setting, but Panzer says her goal was to improve everyday interactions.
“When you can only see the eyes, it takes a lot out of expressive communication for Deaf people,” says Fisher, whose parents and one brother are deaf. “It’s really important that they be able to more fully convey facial expressions and mouth movements that influence meaning.” Masks with clear fronts help.
NB: Kate has done prior work with ASL during her time at Penn Bioengineering. Kate’s 2018 Senior Design team created a two-way interface to help communication between deaf patients and hearing medical professionals called MEDISIGN. Fellow team members included fellow BE alumni Jackie Valeri, Nick Stiansen, and Karol Szymula. Watch their presentation on the Penn Engineering youtube channel.