Week in BioE (July 12, 2019)

by Sophie Burkholder

DNA Microscopy Gives a Better Look at Cell and Tissue Organization

A new technique that researchers from the Broad Institute of MIT and Harvard University are calling DNA microscopy could help map cells for better understanding of genetic and molecular complexities. Joshua Weinstein, Ph.D., a postdoctoral associate at the Broad Institute, who is also an alumnus of Penn’s Physics and Biophysics department and former student in Penn Bioengineering Professor Ravi Radhakrishnan’s lab, is the first author of this paper on optics-free imaging published in Cell.

The primary goal of the study was to find a way of improving analysis of the spatial organization of cells and tissues in terms of their molecules like DNA and RNA. The DNA microscopy method that Weinstein and his team designed involves first tagging DNA, and allowing the DNA to replicate with those tags, which eventually creates a cloud of sorts that diffuses throughout the cell. The DNA tags subsequent interactions with molecules throughout the cell allowed Weinstein and his team to calculate the locations of those molecules within the cell using basic lab equipment. While the researchers on this project focused their application of DNA microscopy on tracking human cancer cells through RNA tags, this new method opens the door to future study of any condition in which the organization of cells is important.

Read more on Weinstein’s research in a recent New York Times profile piece.

Penn Engineers Demonstrate Superstrong, Reversible Adhesive that Works like Snail Slime

A snail’s epiphragm. (Photo: Beocheck)

If you’ve ever pressed a picture-hanging strip onto the wall only to realize it’s slightly off-center, you know the disappointment behind adhesion as we typically experience it: it may be strong, but it’s mostly irreversible. While you can un-stick the used strip from the wall, you can’t turn its stickiness back on to adjust its placement; you have to start over with a new strip or tolerate your mistake. Beyond its relevance to interior decorating, durable, reversible adhesion could allow for reusable envelopes, gravity-defying boots, and more heavy-duty industrial applications like car assembly.

Such adhesion has eluded scientists for years but is naturally found in snail slime. A snail’s epiphragm — a slimy layer of moisture that can harden to protect its body from dryness — allows the snail to cement itself in place for long periods of time, making it the ultimate model in adhesion that can be switched on and off as needed. In a new study, Penn Engineers demonstrate a strong, reversible adhesive that uses the same mechanisms that snails do.

This study is a collaboration between Penn Engineering, Lehigh University’s Department of Bioengineering, and the Korea Institute of Science and Technology.

Read the full story on Penn Engineering’s Medium blog. 

Low-Dose Radiation CT Scans Could Be Improved by Machine Learning

Machine learning is a type of artificial intelligence growing more and more popular for applications in bioengineering and therapeutics. Based on learning from patterns in a way similar to the way we do as humans, machine learning is the study of statistical models that can perform specific tasks without explicit instructions. Now, researchers at Rensselaer Polytechnic Institute (RPI) want to use these kinds of models in computerized tomography (CT) scanning by lowering radiation dosage and improving imaging techniques.

A recent paper published in Nature Machine Intelligence details the use of modularized neural networks in low-dose CT scans by RPI bioengineering faculty member Ge Wang, Ph.D., and his lab. Since decreasing the amount of radiation used in a scan will also decrease the quality of the final image, Wang and his team focused on a more optimized approach of image reconstruction with machine learning, so that as little data as possible would be altered or lost in the reconstruction. When tested on CT scans from Massachusetts General Hospital and compared to current image reconstruction methods for the scans, Wang and his team’s method performed just as well if not better than scans performed without the use of machine learning, giving promise to future improvements in low-dose CT scans.

A Mind-Controlled Robotic Arm That Requires No Implants

A new mind-controlled robotic arm designed by researchers at Carnegie Mellon University is the first successful noninvasive brain-computer interface (BCI) of its kind. While BCIs have been around for a while now, this new design from the lab of Bin He, Ph.D.,  a Trustee Professor and the Department Head of Biomedical Engineering at CMU, hopes to eliminate the brain implant that most interfaces currently use. The key to doing this isn’t in trying to replace the implants with noninvasive sensors, but in improving noisy EEG signals through machine learning, neural decoding, and neural imaging. Paired with increased user engagement and training for the new device, He and his team demonstrated that their design enhanced continuous tracking of a target on a computer screen by 500% when compared to typical noninvasive BCIs. He and his team hope that their innovation will help make BCIs more accessible to the patients that need them by reducing the cost and risk of a surgical implant while also improving interface performance.

People and Places

Daeyeon Lee, professor in the Department of Chemical and Biomolecular Engineering and member of the Bioengineering Graduate Group Faculty here at Penn, has been selected by the U.S. Chapter of the Korean Institute of Chemical Engineers (KIChE) as the recipient of the 2019 James M. Lee Memorial Award.

KIChE is an organization that aims “to promote constructive and mutually beneficial interactions among Korean Chemical Engineers in the U.S. and facilitate international collaboration between engineers in U.S. and Korea.”

Read the full story on Penn Engineering’s Medium blog.

We would also like to congratulate Natalia Trayanova, Ph.D., of the Department of Biomedical Engineering at Johns Hopkins University on being inducted into the Women in Tech International (WITI) Hall of Fame. Beginning in 1996, the Hall of Fame recognizes significant contributions to science and technology from women. Trayanova’s research specializes in computational cardiology with a focus on virtual heart models for the study of individualized heart irregularities in patients. Her research helps to improve treatment plans for patients with cardiac problems by creating virtual simulations that help reduce uncertainty in either diagnosis or courses of therapy.

Finally, we would like to congratulate Andre Churchwell, M.D., on being named Vanderbilt University’s Chief Diversity Officer and Interim Vice Chancellor for Equity, Diversity, and Inclusion. Churchwell is also a professor of medicine, biomedical engineering, and radiology and radiological sciences at Vanderbilt, with a long career focused in cardiology.

Foundational Engineering Theory in Design and Translation

Ramakrishnan
What a nanoparticle remembers during its journey is pictorially represented in the above figure, and this “memory” is crucial in predicting its approach to the blood vessel wall and its subsequent capture by cell surface receptors, collectively determining the efficacy of therapeutic drug delivery. Reprinted from: Ramakrishnan et al, “Motion of a nano-spheroid in a cylindrical vessel flow: Brownian and hydrodynamic interactions,” J Fluid Mech. 2017;821:117-152, with permission of Cambridge UP, owner of copyright.

A recent article coauthored by Ramakrishnan Natesan, a postdoctoral fellow in the Department of Bioengineering who works in the lab of Dr. Ravi Radhakrishnan, and published in the Journal of Fluid Mechanics provides an elegant and rigorous approach to integrate the memory, errant motion, and adhesion effects in the dynamics of colloidal nanoparticles of different sizes and shapes. The method described in the article computationally analyzes how the hydrodynamic forces are influenced by size, shape, and nature of confining boundary amidst blood flow.

In traditional modes of therapeutic treatment, such as a direct intravenous (IV) injection, only a small fraction of injected drug accesses the diseased tissue. Suboptimal therapeutic delivery represents an acute challenge by limiting the efficacy of biotherapeutics. Strategies to address and overcome this challenge may be based on theoretical and computational approaches to in order to help design innovative, quantitative, experimental methods. Targeted therapeutic delivery using nanoparticles coated with specific targeting molecules is such an approach in therapeutic and diagnostic applications.

Targeted delivery is inherently a multiscale problem: a broad range of length and time scales govern the hydrodynamic, microscopic, and molecular interactions mediating nanoparticle motion in blood flow and capture due to cell binding. The events following upon the injection of a targeted therapeutic nanoparticle bearing a drug (nanocarrier) include flow through blood vessels and maneuvering around much larger entities in the blood, such as the red blood cells. Nanoparticles eventually break free to approach the wall of the blood vessel — a phenomenon collectively known as margination.

After margination, the nanoparticle is relatively free from the influences of the blood cells but starts to “feel” the approach to the wall. It needs to get excruciatingly close to the wall to stick — a phenomenon known as adhesion or capture. In the backdrop of this arduous journey is the inescapable randomness of its motion caused by Brownian forces, an erratic form of motion that only impacts nanoscale objects. The interplay among fluid forces, Brownian fluctuations, and wall interactions shape the detailed itinerary of the nanoparticle.  How it moves at a given location and given time is intricately coupled with the motion of the surrounding fluid, namely the blood plasma, which is mostly water. Together, they decide to pave the path forward in time described by a “memory function.”

“The optimization of future drug delivery agents, such as targeted therapeutic nanocarriers, could be based on our computations,” Dr. Ramakrishnan says. “This will, in effect, establish a rational computational platform for fast tracking the clinical translation from carrier design to clinical practice.”