The Very Large Scale Microfluidic Integration (VLSMI) platform, a technology developed by the Penn researchers, contains hundreds of mixing channels for mass-producing mRNA-carrying lipid nanoparticles.
Penn Engineering secured a multi-million-dollar contract with Wellcome Leap under the organization’s $60 million RNA Readiness + Response (R3) program, which is jointly funded with the Coalition for Epidemic Preparedness Innovations (CEPI). Penn Engineers aim to create “on-demand” manufacturing technology that can produce a range of RNA-based vaccines.
The Penn Engineering team features Daeyeon Lee, Evan C Thompson Term Chair for Excellence in Teaching and Professor in Chemical and Biomolecular Engineering, Michael Mitchell, Skirkanich Assistant Professor of Innovation in Bioengineering, David Issadore, Associate Professor in Bioengineering and Electrical and Systems Engineering, and Sagar Yadavali, a former postdoctoral researcher in the Issadore and Lee labs and now the CEO of InfiniFluidics, a spinoff company based on their research. Drew Weissman of the Perelman School of Medicine, whose foundational research directly continued to the development of mRNA-based COVID-19 vaccines, is also a part of this interdisciplinary team.
The success of these COVID-19 vaccines has inspired a fresh perspective and wave of research funding for RNA therapeutics across a wide range of difficult diseases and health issues. These therapeutics now need to be equitably and efficiently distributed, something currently limited by the inefficient mRNA vaccine manufacturing processes which would rapidly translate technologies from the lab to the clinic.
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.
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.”
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.
Michael Mitchell, Skirkanich Assistant Professor of Innovation in the Department of Bioengineering, has been awarded the 2022 Society for Biomaterials (SFB) Young Investigator Award for his “outstanding achievements in the field of biomaterials research.”
The Society for Biomaterials is a multidisciplinary society of academic, healthcare, governmental and business professionals dedicated to promoting advancements in all aspects of biomaterial science, education and professional standards to enhance human health and quality of life.
Mitchell, whose research lies at the interface of biomaterials science, drug delivery, and cellular and molecular bioengineering to fundamentally understand and therapeutically target biological barriers, is specifically being recognized for his development of the first nanoparticle RNAi therapy to treat multiple myeloma, an incurable hematologic cancer that colonizes in bone marrow.
“Before this, no one in the drug delivery field has developed an effective gene delivery system to target bone marrow,” said United States National Medal of Science recipient Robert S. Langer in Mitchell’s award citation. “Mike is a standout young investigator and leader that intimately understands the importance of research and collaboration at the interface of nanotechnology and medicine.”
Academic recipients of the SFB Young Investigator Award should not exceed the rank of Assistant Professor and must not be tenured at the time of nomination. The award includes a $1,000 endowment.
The brighter edges of the cells in the middle and upper right panels show the optogenetic proteins collecting at the membrane after light exposure. At higher temperatures, however, the proteins become rapidly inactivated and thus do not stay at the membrane, resulting in the duller edges seen in the bottom right panel.
Most organisms have proteins that react to light. Even creatures that don’t have eyes or other visual organs use these proteins to regulate many cellular processes, such as transcription, translation, cell growth and cell survival.
The field of optogenetics relies on such proteins to better understand and manipulate these processes. Using lasers and genetically engineered versions of these naturally occurring proteins, known as probes, researchers can precisely activate and deactivate a variety of cellular pathways, just like flipping a switch.
Now, Penn Engineering researchers have described a new type of optogenetic protein that can be controlled not only by light, but also by temperature, allowing for a higher degree of control in the manipulation of cellular pathways. The research will open new horizons for both basic science and translational research.
Lukasz Bugaj, Bomyi Lim, and Brian Chow
Lukasz Bugaj, Assistant Professor in Bioengineering (BE), Bomyi Lim, Assistant Professor in Chemical and Biomolecular Engineering, Brian Chow, Associate Professor in BE, and graduate students William Benman in Bugaj’s lab, Hao Deng in Lim’s lab, and Erin Berlew and Ivan Kuznetsov in Chow’s lab, published their study in Nature Chemical Biology. Arndt Siekmann, Associate Professor of Cell and Developmental Biology at the Perelman School of Medicine, and Caitlyn Parker, a research technician in his lab, also contributed to this research.
The team’s original aim was to develop a single-component probe that would be able to manipulate specific cellular pathways more efficiently. The model for their probe was a protein called BcLOV4, and through further investigation of this protein’s function, they made a fortuitous discovery: that the protein is controlled by both light and temperature.
Heatmaps are used by researchers in the lab of Jennifer Phillips-Cremins to visualize which physically distant genes are brought into contact when the genome is in its folded state.
More data is being produced across diverse fields within science, engineering, and medicine than ever before, and our ability to collect, store, and manipulate it grows by the day. With scientists of all stripes reaping the raw materials of the digital age, there is an increasing focus on developing better strategies and techniques for refining this data into knowledge, and that knowledge into action.
Enter data science, where researchers try to sift through and combine this information to understand relevant phenomena, build or augment models, and make predictions.
One powerful technique in data science’s armamentarium is machine learning, a type of artificial intelligence that enables computers to automatically generate insights from data without being explicitly programmed as to which correlations they should attempt to draw.
Advances in computational power, storage, and sharing have enabled machine learning to be more easily and widely applied, but new tools for collecting reams of data from massive, messy, and complex systems—from electron microscopes to smart watches—are what have allowed it to turn entire fields on their heads.
“This is where data science comes in,” says Susan Davidson, Weiss Professor in Computer and Information Science (CIS) at Penn’s School of Engineering and Applied Science. “In contrast to fields where we have well-defined models, like in physics, where we have Newton’s laws and the theory of relativity, the goal of data science is to make predictions where we don’t have good models: a data-first approach using machine learning rather than using simulation.”
Penn Engineering’s formal data science efforts include the establishment of the Warren Center for Network & Data Sciences, which brings together researchers from across Penn with the goal of fostering research and innovation in interconnected social, economic and technological systems. Other research communities, including Penn Research in Machine Learning and the student-run Penn Data Science Group, bridge the gap between schools, as well as between industry and academia. Programmatic opportunities for Penn students include a Data Science minor for undergraduates, and a Master of Science in Engineering in Data Science, which is directed by Davidson and jointly administered by CIS and Electrical and Systems Engineering.
Penn academic programs and researchers on the leading edge of the data science field will soon have a new place to call home: Amy Gutmann Hall. The 116,000-square-foot, six-floor building, located on the northeast corner of 34th and Chestnut Streets near Lauder College House, will centralize resources for researchers and scholars across Penn’s 12 schools and numerous academic centers while making the tools of data analysis more accessible to the entire Penn community.
Faculty from all six departments in Penn Engineering are at the forefront of developing innovative data science solutions, primarily relying on machine learning, to tackle a wide range of challenges. Researchers show how they use data science in their work to answer fundamental questions in topics as diverse as genetics, “information pollution,” medical imaging, nanoscale microscopy, materials design, and the spread of infectious diseases.
Bioengineering: Unraveling the 3D genomic code
Scattered throughout the genomes of healthy people are tens of thousands of repetitive DNA sequences called short tandem repeats (STRs). But the unstable expansion of these repetitions is at the root of dozens of inherited disorders, including Fragile X syndrome, Huntington’s disease, and ALS. Why these STRs are susceptible to this disease-causing expansion, whereas most remain relatively stable, remains a major conundrum.
Complicating this effort is the fact that disease-associated STR tracts exhibit tremendous diversity in sequence, length, and localization in the genome. Moreover, that localization has a three-dimensional element because of how the genome is folded within the nucleus. Mammalian genomes are organized into a hierarchy of structures called topologically associated domains (TADs). Each one spans millions of nucleotides and contains smaller subTADs, which are separated by linker regions called boundaries.
Associate professor and Dean’s Faculty Fellow Jennifer E. Phillips-Cremins.
“The genetic code is made up of three billion base pairs. Stretched out end to end, it is 6 feet 5 inches long, and must be subsequently folded into a nucleus that is roughly the size of a head of a pin,” says Jennifer Phillips-Cremins, associate professor and dean’s faculty fellow in Bioengineering. “Genome folding is an exciting problem for engineers to study because it is a problem of big data. We not only need to look for patterns along the axis of three billion base pairs of letters, but also along the axis of how the letters are folded into higher-order structures.”
To address this challenge, Phillips-Cremins and her team recently developed a new mathematical approach called 3DNetMod to accurately detect these chromatin domains in 3D maps of the genome in collaboration with the lab of Dani Bassett, J. Peter Skirkanich Professor in Bioengineering.
“In our group, we use an integrated, interdisciplinary approach relying on cutting-edge computational and molecular technologies to uncover biologically meaningful patterns in large data sets,” Phillips-Cremins says. “Our approach has enabled us to find patterns in data that classic biology training might overlook.”
In a recent study, Phillips-Cremins and her team used 3DNetMod to identify tens of thousands of subTADs in human brain tissue. They found that nearly all disease-associated STRs are located at boundaries demarcating 3D chromatin domains. Additional analyses of cells and brain tissue from patients with Fragile X syndrome revealed severe boundary disruption at a specific disease-associated STR.
“To our knowledge, these findings represent the first report of a possible link between STR instability and the mammalian genome’s 3D folding patterns,” Phillips-Cremins says. “The knowledge gained may shed new light into how genome structure governs function across development and during the onset and progression of disease. Ultimately, this information could be used to create molecular tools to engineer the 3D genome to control repeat instability.”
Johnson, who has appointments in the Perelman School of Medicine and the School of Engineering and Applied Science, and a secondary appointment in the Annenberg School for Communication, will become the David L. Cohen University Professor.
Penn Integrates Knowledge Professor Kevin Johnson, a pediatrician who has pioneered the use of clinical information systems and artificial intelligence to improve medical research and patient care, has received a named University professorship.
“David Cohen’s extraordinary leadership at the University and Penn Medicine, and longtime dedication to Philadelphia, has without a doubt shaped the booming campus, health system, and city we so much enjoy today,” says Gutmann. “His dedication is mirrored by the extraordinarily influential, innovative, and committed Dr. Kevin Johnson, whose university professorship will now bear Ambassador Cohen’s name.”
Cohen has served for two decades on Penn’s Board of Trustees and recently concluded a 12-year term as chair. He was confirmed by the U.S. Senate last month as United States Ambassador to Canada, bringing to the role decades of experience as a senior executive at Comcast Corp., chair of the Ballard Spahr law firm, chief of staff to Philadelphia Mayor Ed Rendell, trustee chair at Penn, and major player in a number of other business, civic, political, and philanthropic venues.
In addition to serving as a Trustee, Cohen is a Penn alum, having graduated from what is now the University of Pennsylvania Carey School of Law in 1981. His wife and son also attended the Law School. Cohen’s leadership in the University has been credited with helping guide the growth and advancement of both the University and Health System, in close partnership with both President Gutmann and her predecessor, Judith Rodin.
“It’s an honor to hold a professorship named after Mr. Cohen,” Johnson says. “Throughout his career, he has provided inspired leadership across Penn and our city and region. He is a passionate believer in uniting the public, private, and nonprofit sectors to tackle complex challenges and strengthen communities. Those who know me know that I’ve played a similar role as a pediatrician who works with technology, and who uses digital media to communicate to lay audiences about both. His passion for this city and our University’s educational mission are inspiring.”
N.B.: Johnson also holds a secondary appointment in the Department of Bioengineering. Read his full appointment announcement here.
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.
Our body’s natural line of defense against infection and disease, as well as cancer, is our immune system equipped with T cells, a type of white blood cell that determines how we react to foreign substances, or antigens, in the body. While we have an arsenal of T cells to protect us from these various infections, some people lack certain T cells or simply do not have enough to fight off infections, such as the flu or HIV, or defend against the body’s own mutated cancer cells.
Understanding the diversity of T cells and which antigens they target can provide insight into developing personalized immunotherapy to help those patients with weak spots or gaps in their T cell community. Jenny Jiang, Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering, is characterizing this diversity.
Jiang recently received a Cancer Research Institute’s (CRI) Lloyd J. Old STAR grant to support her research on this topic. The CRI STAR grant identifies mid-career “Scientists TAking Risks” in innovative cancer immunotherapy research areas, providing freedom and flexibility to pursue high-risk, high-reward research with financial support of $1.25 Million over the course of five years.
Jiang spoke with CRI science writer Arthur Brodsky about her research and how the STAR grant will support it.
“In our studies of healthy individuals, who have some natural immune protection against commonly encountered viruses like the flu, we noticed that not everyone has T cells that cover all the possible antigens,” says Jiang. “There are differences in the number and types of flu-targeting T cells that each individual has. For some “exotic” antigens, like those of HIV for example, although the general population doesn’t actually have exposure to them, they should still have a very low level of minimum T cells that can offer some protection from possible future infection. So that part of our T cell arsenal acts as a safety net. But some individuals may completely lack those T cells. In those cases, as you can imagine, those people will have a hard time overcoming a future infection.”
Jiang describes how this is similar to how our bodies prevent cancerous tumor growth.
