Nanotechnology at the interface of cell biology, materials science and medicine

The atomic force microscope (AFM) and related scanning probe microscopes have become resourceful tools to study cells, supramolecular assemblies and single biomolecules, because they allow investigations of such structures in native environments. Quantitative information has been gathered about the surface structure of membrane proteins to lateral and vertical resolutions of 0.5 nm and 0.1 nm, respectively, about the forces that keep protein–protein and protein–nucleic acid assemblies together as well as single proteins in their native conformation, and about the nanomechanical properties of cells in health and disease. Such progress has been achieved mainly because of constant development of AFM instrumentation and sample preparation methods.

This special issue of Nanotechnology presents papers from leading laboratories in the field of nanobiology, covering a wide range of topics in the form of original and novel scientific contributions. It addresses achievements in instrumentation, sample preparation, automation and in biological applications. These papers document the creativity and persistence of researchers pursuing the goal to unravel the structure and dynamics of cells, supramolecuar structures and single biomolecules at work. Improved cantilever sensors, novel optical probes, and quantitative data on supports for electrochemical experiments open new avenues for characterizing biological nanomachines down to the single molecule. Comparative measurements of healthy and metastatic cells promise new methods for early detection of tumors, and possible assessments of drug efficacy. High-speed AFMs document possibilities to monitor crystal growth and to observe large structures at video rate. A wealth of information on amyloid-type fibers as well as on membrane proteins has been gathered by single molecule force spectroscopy—a technology now being automated for large-scale data collection.

With the progress of basic research and a strong industry supporting instrumentation development by improving robustness and reliability and making new instruments available to the community, nanobiology has the potential to develop into a field with great impact on our understanding of the complexity of life, and to provide a major contribution to human health.

This special issue of Nanotechnology on nanobiology would not have been possible without the highly professional support from Nina Couzin, Amy Harvey and the Nanotechnology team at IOP Publishing. We are thankful for their most constructive and effective help in pushing the project forward. We are also thankful to all the authors who have contributed with excellent original articles, as well as to the referees who have helped to make this special issue such an insightful document of a rapidly moving field.


Andreas Engel1 and Mervyn Miles2
1 University of Basel, Switzerland
2 University of Bristol, UK

MIT work could help develop better computer vision systems

Watch and learn: Time teaches us how to recognize visual objects
In work that could aid efforts to develop more brain-like computer vision systems, MIT neuroscientists have tricked the visual brain into confusing one object with another, thereby demonstrating that time teaches us how to recognize objects.

As you scan this visual scene (indicated with green circle), you spot a beaver out of the corner of your eye. As you glance towards it, the image is swapped for a monkey. Using analogous stimuli to produce swaps at specific locations in the visual field, MIT graduate student Nuo Li and professor James DiCarlo show that the brain starts to confuse different objects after a few hours exposure to this altered visual world. The confusion is exactly that which is expected if the brain uses temporal contiguity to teach it how to recognize objects. Movie courtesy of Nuo Li, McGovern Institute for Brain Research at MIT

It may sound strange, but human eyes never see the same image twice. An object such as a cat can produce innumerable impressions on the retina, depending on the direction of gaze, angle of view, distance and so forth. Every time our eyes move, the pattern of neural activity changes, yet our perception of the cat remains stable.

"This stability, which is called 'invariance,' is fundamental to our ability to recognize objects -- it feels effortless, but it is a central challenge for computational neuroscience," explained James DiCarlo of the McGovern Institute for Brain Research at MIT, the senior author of the new study appearing in the Sept. 12 issue of Science. "We want to understand how our brains acquire invariance and how we might incorporate it into computer vision systems."

A possible explanation is suggested by the fact that our eyes tend to move rapidly (about three times per second), whereas physical objects usually change more slowly. Therefore, differing patterns of activity in rapid succession often reflect different images of the same object. Could the brain take advantage of this simple rule of thumb to learn object invariance?

In previous work, DiCarlo and colleagues tested this "temporal contiguity" idea in humans by creating an altered visual world in which the normal rule did not apply. An object would appear in peripheral vision, but as the eyes moved to examine it, the object would be swapped for a different object. Although the subjects did not perceive the change, they soon began to confuse the two objects, consistent with the temporal contiguity hypothesis.

In the new study, DiCarlo, who is also an associate professor in the MIT Department of Brain and Cognitive Sciences, and graduate student Nuo Li sought to understand the brain mechanisms behind this effect. They had monkeys watch a similarly altered world while recording from neurons in the inferior temporal (IT) cortex -- a high-level visual brain area where object invariance is thought to arise. IT neurons "prefer" certain objects and respond to them regardless of where they appear within the visual field.

"We first identified an object that an IT neuron preferred, such as a sailboat, and another, less preferred object, maybe a teacup," Li said. "When we presented objects at different locations in the monkey's peripheral vision, they would naturally move their eyes there. One location was a swap location. If a sailboat appeared there, it suddenly became a teacup by the time the eyes moved there. But a sailboat appearing in other locations remained unchanged."

After the monkeys spent time in this altered world, their IT neurons became confused, just like the previous human subjects. The sailboat neuron, for example, still preferred sailboats at all locations -- except at the swap location, where it learned to prefer teacups. The longer the manipulation, the greater the confusion, exactly as predicted by the temporal contiguity hypothesis.

Importantly, just as human infants can learn to see without adult supervision, the monkeys received no feedback from the researchers. Instead, the changes in their brain occurred spontaneously as the monkeys looked freely around the computer screen.

"We were surprised by the strength of this neuronal learning, especially after only one or two hours of exposure," DiCarlo said. "Even in adulthood, it seems that the object-recognition system is constantly being retrained by natural experience. Considering that a person makes about 100 million eye movements per year, this mechanism could be fundamental to how we recognize objects so easily."

The team is now testing this idea further using computer vision systems viewing real-world videos.

This work was funded by the NIH, the McKnight Endowment Fund for Neuroscience and a gift from Marjorie and Gerald Burnett.