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.
As part of a major University-wide investment in science, engineering, and medicine, the Innovation in Data Engineering and Science Initiative aims to help Penn become a leader in developing data-driven approaches that can transform scientific discovery, engineering research, and technological innovation.
From smartphones and fitness trackers to social media posts and COVID-19 cases, the past few years have seen an explosion in the amount and types of data that are generated daily. To help make sense of these large, complex datasets, the field of data science has grown, providing methodologies, tools, and perspectives across a wide range of academic disciplines.
As part of its $750 million investment in science, engineering, and medicine, the University has committed to supporting the future needs of this field. To this end, the Innovation in Data Engineering and Science (IDEAS) initiative will help Penn become a leader in developing data-driven approaches that can transform scientific discovery, engineering research, and technological innovation.
“The IDEAS initiative is game-changing for our University,” says President Amy Gutmann. “This new investment allows us to boost our interdisciplinary efforts across campus, recruit phenomenal additional team members, and generate an even more sound foundation for discovery, experimentation, and design. This initiative is a clear statement that Penn is committed to taking data science head-on.”
“One of the unique things about data science and data engineering is that it’s a very horizontal technology, one that is going to be impacting every department on campus,” says George Pappas, Electrical and Systems Engineering Department chair. “When you have a horizontal technology in a competitive area, we have to figure out specific areas where Penn can become a worldwide leader.”
To do this, IDEAS aims to recruit new faculty across three research areas: artificial intelligence (AI) to transform scientific discovery, trustworthy AI for autonomous systems, and understanding connections between the human brain and AI.
In the area of neuroscience and how the human brain is similar to AI and machine learning approaches, research from PIK Professor Konrad Kording and Dani Bassett’sComplex Systems lab exemplifies the types of cross-disciplinary efforts that are essential for addressing complex questions. By recruiting additional faculty in this area, IDEAS will help Penn make strides in bio-inspired computing and in future life-changing discoveries that could address cognitive disorders and nervous system diseases.
Dani S. Bassett, J. Peter Skirkanich Professor in Bioengineering and in Electrical and Systems Engineering
Bassett runs the Complex Systems lab which tackles problems at the intersection of science, engineering, and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease. They are a pioneer in the emerging field of network science which combines mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits.
Jason Burdick, Ph.D.
Jason A. Burdick, Robert D. Bent Professor in Bioengineering
Burdick runs the Polymeric Biomaterials Laboratory which develops polymer networks for fundamental and applied studies with biomedical applications with a specific emphasis on tissue regeneration and drug delivery. The specific targets of his research include: scaffolding for cartilage regeneration, controlling stem cell differentiation through material signals, electrospinning and 3D printing for scaffold fabrication, and injectable hydrogels for therapies after a heart attack.
César de la Fuente, Ph.D.
César de la Fuente, Presidential Assistant Professor in Bioengineering and Chemical & Biomedical Engineering in Penn Engineering and in Microbiology and Psychiatry in the Perelman School of Medicine
De la Fuente runs the Machine Biology Group which combines the power of machines and biology to prevent, detect, and treat infectious diseases. He pioneered the development of the first antibiotic designed by a computer with efficacy in animals, designed algorithms for antibiotic discovery, and invented rapid low-cost diagnostics for COVID-19 and other infections.
Carl June, M.D.
Carl H. June, Richard W. Vague Professor in Immunotherapy in the Perelman School of Medicine and member of the Bioengineering Graduate Group
June is the Director for the Center for Cellular Immunotherapies and the Parker Institute for Cancer Therapy and runs the June Lab which develops new forms of T cell based therapies. June’s pioneering research in gene therapy led to the FDA approval for CAR T therapy for treating acute lymphoblastic leukemia (ALL), one of the most common childhood cancers.
Vivek Shenoy, Ph.D.
Vivek Shenoy, Eduardo D. Glandt President’s Distinguished Professor in Bioengineering, Mechanical Engineering and Applied Mechanics (MEAM), and in Materials Science and Engineering (MSE)
Shenoy runs the Theoretical Mechanobiology and Materials Lab which develops theoretical concepts and numerical principles for understanding engineering and biological systems. His analytical methods and multiscale modeling techniques gain insight into a myriad of problems in materials science and biomechanics.
The highly anticipated annual list identifies researchers who demonstrated significant influence in their chosen field or fields through the publication of multiple highly cited papers during the last decade. Their names are drawn from the publications that rank in the top 1% by citations for field and publication year in the Web of Science™ citation index.
Bassett and Burdick were both on the Highly Cited Researchers list in 2019 and 2020.
The methodology that determines the “who’s who” of influential researchers draws on the data and analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information™ at Clarivate. It also uses the tallies to identify the countries and research institutions where these scientific elite are based.
David Pendlebury, Senior Citation Analyst at the Institute for Scientific Information at Clarivate, said: “In the race for knowledge, it is human capital that is fundamental and this list identifies and celebrates exceptional individual researchers who are having a great impact on the research community as measured by the rate at which their work is being cited by others.”
The full 2021 Highly Cited Researchers list and executive summary can be found online here.
Our final Penn Bioengineering seminar of the fall semester will take place this Thursday. Keep an eye on the BE events calendar for upcoming spring seminars.
Speaker: Bijan Pesaran, Ph.D.
Professor
Neural Science
New York University
Date: Thursday, December 16, 2021
Time: 3:30-4:30 PM EST
Zoom – check email for link
Room: Moore 216
Abstract: Neural engineering is enjoying an era of transformative growth. Classical methods that dominated the neurosciences for decades are being replaced by powerful new technologies. In this talk, I will discuss how to engineer electrical and optical interfaces to the primate brain. I will first present work on electrode interfaces that stimulate and record at the surface of and within the brain. I will show how simultaneously measuring and manipulating neurons immediately beneath electrode contacts during behavior delivers ground-truth data. The results have implications for electrode interface design and new generations of implantable biomedical devices. I will then turn to optical neural interfaces. Two-photon fluorescence microscopy images the activity of neurons expressing genetically-encoded calcium indicators and is most often performed in small animal models, such as the mouse, worm and fly. I will present a cellular-resolution robotic imaging platform to investigate the non-human primate brain at scale. I will finish by discussing potential applications of this technology to a range of scientific and clinical goals.
Bijan Pesaran Bio: Bijan Pesaran is interested in understanding large-scale circuits in the primate brain and how to engineer novel brain-based therapies. Bijan completed his undergraduate degree in Physics at the University of Cambridge, UK. After a year in the Theoretical Physics department at Bell Labs Murray Hill, he went on to earn his PhD in Physics at the California Institute of Technology. He then made the switch to neuroscience as a postdoctoral fellow in Biology at Caltech. Bijan has been on the faculty at New York University since 2006. He is currently a Professor of Neural Science in the Center for Neural Science. In 2013, he was a CV Starr Visiting Scholar at the Princeton Neuroscience Institute at Princeton University. Among other honors and awards, Bijan has received a Burroughs-Wellcome Career Award in the Biomedical Sciences, a Sloan Research Fellowship, a McKnight Scholar Award, the National Science Foundation CAREER Award and is a member of the Simons Collaboration for the Global Brain.
The impending danger of bacterial resistance to antibiotics is well-documented within the scientific community. Bacteria are the most efficient evolvers, and their ability to develop tolerance to drugs, in addition to antibiotic overuse and misuse, means that researchers have had to get particularly resourceful to ensure the future of modern medicine.
WIRED’s Max G. Levy recently spoke with de la Fuente and postdoctoral researcher and study collaborator Marcelo Torres about the urgency of the team’s work, and why developing these solutions is critical to the survival of civilization as we know it. The team’s algorithm, based on pattern recognition software used to analyze images, makes an otherwise insurmountable feat tangible.
De la Fuente’s lab specializes in using AI to discover and design new drugs. Rather than making some all-new peptide molecules that fit the bill, they hypothesized that an algorithm could use machine learning to winnow down the huge repository of natural peptide sequences in the human proteome into a select few candidates.
“We know those patterns—the multiple patterns—that we’re looking for,” says de la Fuente. “So that allows us to use the algorithm as a search function.”
Speaker: 