An MRI shows stem cells labeled with iron oxide nanoparticles being injected into an animal’s brain. Click to view video. (Credit: Piotr Walczak/Johns Hopkins Medicine)
Working with animals, a team of scientists reports it has delivered stem cells to the brain with unprecedented precision by threading a catheter through an artery and infusing the cells under real-time MRI guidance.
In a description of the work, published online Sept. 12 in the Journal of Cerebral Blood Flow and Metabolism, they express hope that the tests in anesthetized dogs and pigs are a step toward human trials of a technique to treat Parkinson’s disease, stroke, and other brain damaging disorders.
“Although stem cell-based therapies seem very promising, we’ve seen many clinical trials fail. In our view, what’s needed are tools to precisely target and deliver stem cells to larger areas of the brain,” says Piotr Walczak, M.D., Ph.D., associate professor of radiology at the Johns Hopkins University School of Medicine’s Institute for Cell Engineering. The therapeutic promise of human stem cells is derived from their ability to develop into any kind of cell and, in theory, regenerate injured or diseased tissues ranging from the insulin-making islet cells of the pancreas that are lost in type 1 diabetes to the dopamine-producing brain cells that die off in Parkinson’s disease.
Ten years ago, Shinya Yamanaka’s research group in Japan raised hopes further when it developed a technique for “resetting” mature cells, such as skin cells, to become so-called induced pluripotent stem cells. That gave researchers an alternative to embryonic stem cells that could allow the creation of therapeutic stem cells that matched the genetic makeup of each patient, greatly reducing the chances of cell rejection after they were infused or transplanted. But while induced pluripotent stem cells have enabled great strides forward in research, Walczak says they are not yet approved for any treatment, and barriers to success remain.
In a bid to address once such barrier – how to get the cells exactly where needed and no place else – Walczak and his colleague Miroslaw Janowski, M.D., Ph.D., assistant professor of radiology, sought a way around strategies that require physicians to puncture patients’ skulls or inject them intravenously. The former, Walczak says, is not only unpleasant, but also only allows delivery of stem cells to one limited place in the brain. In contrast, injecting cells intravenously scatters the cells throughout the body, with few likely to land where they’re most needed, says Walczak.
“Our idea was to do something in between,” says Janowski, using intra-arterial injection, which involves threading a catheter, or hollow tube, into a blood vessel, usually in a leg, and guiding it to a vessel in a hard-to-reach spot like the brain. The technique currently is used mainly to repair large vessels in the brain, says Janowski, but the research team hoped it might also be used to get stem cells to the exact place where they were needed. To do that, they would need a way of monitoring the catheter placement and movement of implanted cells in real time.
Walczak and Janowski teamed with colleagues including Monica Pearl, M.D., an associate professor of radiology practicing in the Division of Interventional Neuroradiology, who specializes in intra-arterial procedures. Usually the procedure is performed using an X-ray image as a guide, but that approach ruled out watching injected stem cells’ movements and making adjustments in real time.
In their experiments, after placing the catheter under X-ray guidance, they transferred anesthetized dog and pig subjects to an MRI machine, where images were taken every few seconds throughout the procedure. Once the catheter was in the brain, Pearl pre-injected small amounts of a harmless contrast agent that included iron oxide and could be detected on the MRI. “By using MRI to see in real time where the contrast agent went, we could predict where injected stem cells would go and make adjustments to the catheter placement, if needed,” says Janowski. Adds Jeff Bulte, Ph.D., a professor of radiology who participated in the study, “It’s like having GPS guidance in your car to help you stay on the right route, instead of only finding out you’re lost when you arrive at the wrong place.”
The team then injected both small stem cells (glial progenitor cells from the brain) and large mesenchymal stem cells from bone marrow into the animals under MRI, and found that in both cases, the pre-injected contrast agent and MRI allowed them to accurately predict where the cells would end up. They could also tell whether clumps of cells were forming in arteries and, if so, possibly intervene to avoid letting the clumps grow large enough to cut off blood flow and pose a danger. “If further research confirms our progress, we think this procedure could be a big step forward in precision medicine, allowing doctors to deliver stem cells or medications exactly where they’re needed for each patient,” says Walczak. The research team is planning to test the procedure in animals as a treatment for stroke and cancer, delivering both medications and stem cells while the catheter is in place.
Other authors on the paper are Joanna Wojtkiewicz, Aleksandra Habich, Piotr Holak, Zbigniew Adamiak and Wojciech Maksymowicz of the University of Warmia and Mazury in Poland; Adam Nowakowski and Barbara Lukomska of the Mossakowski Medical Research Center in Poland; Jiadi Xu of the Kennedy Krieger Institute; and Moussa Chehade and Philippe Gailloud of the Johns Hopkins University.
The study was funded by the National Institute of Neurological Disorders and Stroke (grant numbers NS076573, NS045062, NS081544), the Maryland Stem Cell Research Fund, the Department of Defense (grant number PT120368), the Polish National Science Centre (grant number NCN 2012/07/B/NZ4/01427), the National Centre for Research and Development, and a Mobility Plus Fellowship from the Polish Ministry of Science and Higher Education.
Authors: Marco De Nadai, Radu L. Vieriu, Gloria Zen, Stefan Dragicevic, Nikhil Naik, Michele Caraviello, Cesar A. Hidalgo, Nicu Sebe, Bruno Lepri
To appear in the Proceedings of ACM Multimedia Conference (MM), 2016. October 15 - 19, 2016, Amsterdam, Netherlands
Policy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of neighborhoods independent characteristics? Or are they just two sides of the same coin? In a world where people avoid unsafe looking places, neighborhoods that look unsafe will be less lively, and will fail to harness the natural surveillance of human activity. But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent. In this paper we explore the connection between the levels of activity and the perception of safety of neighborhoods in two major Italian cities by combining mobile phone data (as a proxy for activity or liveliness) with scores of perceived safety estimated using a Convolutional Neural Network trained on a dataset of Google Street View images scored using a crowdsourced visual perception survey. We find that: (i) safer looking neighborhoods are more active than what is expected from their population density, employee density, and distance to the city centre; and (ii) that the correlation between appearance of safety and activity is positive, strong, and significant, for females and people over 50, but negative for people under 30, suggesting that the behavioral impact of perception depends on the demographic of the population. Finally, we use occlusion techniques to identify the urban features that contribute to the appearance of safety, finding that greenery and street facing windows contribute to a positive appearance of safety (in agreement with Oscar Newman's defensible space theory). These results suggest that urban appearance modulates levels of human activity and, consequently, a neighborhood's rate of natural surveillance
Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.
Mol Syst Biol. (2016) 12: 878
Everyone agrees that reproducibility and replicability are fundamental characteristics of scientific studies. These topics are attracting increasing attention, scrutiny, and debate both in the popular press and the scientific literature. But there are no formal statistical definitions for these concepts, which leads to confusion since the same words are used for different concepts by different people in different fields. We provide formal and informal definitions of scientific studies, reproducibility, and replicability that can be used to clarify discussions around these concepts in the scientific and popular press.
Network Neuroscience features innovative scientific work that significantly advances our understanding of network organization and function in the brain across all scales, from molecules and neurons to circuits and systems.
Positioned at the intersection of brain and network sciences, the journal covers empirical and computational studies that record, analyze or model relational data among elements of neurobiological systems, including neuronal signaling and information flow in circuits, patterns of functional connectivity recorded with electrophysiological or imaging methodology, studies of anatomical connections among neurons and brain regions, and interactions among biomolecules or genes.