We are searching data for your request:
Upon completion, a link will appear to access the found materials.
Say there was a nanotechnology allowing to "grow" and wire up electronic components/sensors measured only a few nanometers in cross-section, though spanning in length across microns. Say this was possible inside living cells (for the purpose of monitoring or even controlling living tissues at individual cell level).
Would such a technology be doomed in the bud simply because cells will immediately deploy antibodies to fight the foreign objects off, and either succeed in it (rendering the growing of said components impossible) or die if they can't succeed in it? Or are there chances to get the cells ignore the electronic components and live with them happily?
What are any other potential obstacles to this sort of technology from biological perspective?
VIII Molecular Nanoelectronics
The first discussion of molecular nanoelectronics was the proposal by Aviram and Ratner in 1974 to produce a rectifier from organic molecules. The first example of a single molecular electronic device did not appear until 1990, the major problem relating to the difficulty of making individual electrical contacts to molecules which may only be a few nm in size. The development of the scanning tunneling microscope (STM) basically enabled the first measurements in this field to be started and has remained one of the major tools in electrically characterising single molecules.
Some of the first demonstrations of electronic properties of single molecules by Purdue University included Coulomb blockade and Coulomb staircase when a STM tip measured the conduction through gold nanoparticles self-assembled with SAMs. A second set of experiments at IBM, Zurich, demonstrated a STM tip deforming a C60 bucky ball. The resulting mechanical deformation modifies the resonance tunneling bands of the molecule and produces electromechanical amplification. Hitachi demonstrated a molecular abacus where a STM tip was used to move 0.25 nm high C60 molecules along monoatomic steps and then counted by imaging with the STM tip. While all these devices demonstrate functionality that may be used in circuits, the scaling to the level of present CMOS circuits would be impossible.
Another approach to molecular electronics is the use of organic molecules. A collaboration between Yale and South Carolina Universities demonstrated the conduction through a benzene molecule attached to two gold electrodes using thiol groups to bind the molecule to the gold. Benezene rings have delocalized Π-electrons out of the plane of the molecule through which electrons can be transported when an appropriate bias is applied across the molecule. Carbon–carbon double and triple bonds provide similar orbitals out of the plane and therefore combinations of these polyphenylene molecules create conducting wires now known as Tour wires. Molecules could then be designed with conducting sections along with barriers created by methyl groups that do not have delocalised electron orbitals. Therefore organic chemists can design molecules where the high occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) through which electronic transport is controlled can be manipulated in a similar fashion to the band structure engineering in semiconductors ( Fig. 14 ). Molecular RTDs operating at room temperature have been demonstrated. The low temperature properties of some of the RTD designs demonstrate PVCRs of over 1000 at low temperatures while the room temperature properties require improvement before they can be used in circuits.
FIGURE 14 . (a) The chemical structure of an organic RTD. (b) The HOMO and LUMO levels responsible for the electronic transport through the molecule. (c) The equivalent band structure for a semiconductor RTD.
Work at the Mitre Corporation has investigated possible architectures using organic molecules. A number of designs have been proposed based on diode logic using the Tour wires and diodes. AND, OR, and XOR gate designs are given along with an adder. The major problem with such organic systems is that the conductivity is relatively poor through the interconnecting Tour wires. The RC time constants of most of the devices is likely to be relatively large and some rough “back-of-the-envelope” calculations suggest that typical speeds will be up to tens of megahertz rather than gigahertz as presently available with CMOS. The major limitation is the conductivity, and unless better conductors or architectures for which the performance does not depend on resistance can be found, the organic systems will always be much slower than silicon.
Carbon nanotubes have also been used to conduct electrons and demonstrate reasonable conductivities for their size. A large number of groups have published results in the field. Both single and multiple walled tubes can be created that may potentially reduce resistance. Conductivities as high as 2000 Sm −1 that corresponds to a resistance per millimeter of 200 Ω have been measured, substantially better than organic polyphenylene molecules. A transistor was demonstrated, although the gate was a silicon substrate and the carbon nanotube had been placed across two metal electrodes fabricated on top of the thermally oxidized silicon substrate. Metal-nanotube rectifiers have also been demonstrated. To date, no switch or three-terminal device has been produced that is a basic requirement for most logical architectures.
Numerous groups have demonstrated measurements on nanocrystals many have used cadmium selenide. CdSe nanocrystals can be prepared with sizes down to about 2 nm and with attached linker molecules that may bind to numerous surfaces including gold. A single electron transistor has been demonstrated where in a similar experiment to the carbon nanotube transistor, an oxidized silicon substrate is used as a gate to a CdSe nanocrystal placed between two gold electrodes on the SiO2 surface.
DNA self-assembly was discussed in the previous section. There have also been a substantial number of papers measuring the conductivity of different types of DNA. Results range from highly insulating behavior to semiconducting and even metallic behavior. The results come from many different structures and types of DNA, and as yet no systematic investigation of varying specific parameters and the resulting changes in conductivity has been present. It would appear that the only consistent results are from DNA with self-assembled metallic nanoparticles. The best results to date correspond to Pd nanoparticles attached to a DNA strand, which has demonstrated 100 Sm −1 conductivity.
There are a large number of additional ideas presently around in the literature. To date, however, there has not been any demonstration of an all molecular transistor or any structure with gain. As will be discussed in the next section, gain is crucial to the distribution of information over large circuits. For a switch or transistor, a voltage corresponding to the difference in HOMO and LUMO energies will be required to switch conduction on or off. In most of the proposed devices this corresponds to about 1 eV, and hence 1 V must be applied to switch a device on or off. If the resistance of the molecule is about 200 Ω and there is 10 10 molecules, then the circuit will consume $10 8 W! Molecular nanoelectronics is at a very early stage and potentially offers very cheap self-assembly fabrication routes, but a substantial break through is required if the technology is ever to come to fruition.
First Living Robots Created by Assembling Living Cells From Frogs Into Entirely New Life-Forms
A book is made of wood. But it is not a tree. The dead cells have been repurposed to serve another need.
Now a team of scientists has repurposed living cells—scraped from frog embryos—and assembled them into entirely new life-forms. These millimeter-wide “xenobots” can move toward a target, perhaps pick up a payload (like a medicine that needs to be carried to a specific place inside a patient)—and heal themselves after being cut.
“These are novel living machines,” says Joshua Bongard, a computer scientist and robotics expert at the University of Vermont who co-led the new research. “They’re neither a traditional robot nor a known species of animal. It’s a new class of artifact: a living, programmable organism.”
The new creatures were designed on a supercomputer at UVM—and then assembled and tested by biologists at Tufts University. “We can imagine many useful applications of these living robots that other machines can’t do,” says co-leader Michael Levin who directs the Center for Regenerative and Developmental Biology at Tufts, “like searching out nasty compounds or radioactive contamination, gathering microplastic in the oceans, traveling in arteries to scrape out plaque.”
The results of the new research were published on January 13, 2020, in the Proceedings of the National Academy of Sciences.
A team of scientists at the University of Vermont and Tufts University designed living robots on a UVM supercomputer. Then, at Tufts, they re-purposed living frog cells — and assembled them into entirely new life-forms. These tiny ‘xenobots’ can move on their own, circle a target and heal themselves after being cut. These novel living machines are neither a traditional robot nor a known species of animal. They’re a new class of artifact: a living, programmable organism. They could, one day, be used for tasks as varied as searching out radioactive contamination, gathering microplastic in the oceans, or traveling in human arteries to scrape out plaque.
Bespoke Living Systems
People have been manipulating organisms for human benefit since at least the dawn of agriculture, genetic editing is becoming widespread, and a few artificial organisms have been manually assembled in the past few years—copying the body forms of known animals.
But this research, for the first time ever, “designs completely biological machines from the ground up,” the team writes in their new study.
With months of processing time on the Deep Green supercomputer cluster at UVM’s Vermont Advanced Computing Core, the team—including lead author and doctoral student Sam Kriegman—used an evolutionary algorithm to create thousands of candidate designs for the new life-forms. Attempting to achieve a task assigned by the scientists—like locomotion in one direction—the computer would, over and over, reassemble a few hundred simulated cells into myriad forms and body shapes. As the programs ran—driven by basic rules about the biophysics of what single frog skin and cardiac cells can do—the more successful simulated organisms were kept and refined, while failed designs were tossed out. After a hundred independent runs of the algorithm, the most promising designs were selected for testing.
Then the team at Tufts, led by Levin and with key work by microsurgeon Douglas Blackiston—transferred the in silico designs into life. First they gathered stem cells, harvested from the embryos of African frogs, the species Xenopus laevis. (Hence the name “xenobots.”) These were separated into single cells and left to incubate. Then, using tiny forceps and an even tinier electrode, the cells were cut and joined under a microscope into a close approximation of the designs specified by the computer.
A time-lapse recording of cells being manipulated and assembled, using in silico designs to create in vivo living machines, called xenobots. These novel living robots were created by a team from Tufts University and the University of Vermont.
Assembled into body forms never seen in nature, the cells began to work together. The skin cells formed a more passive architecture, while the once-random contractions of heart muscle cells were put to work creating ordered forward motion as guided by the computer’s design, and aided by spontaneous self-organizing patterns—allowing the robots to move on their own.
These reconfigurable organisms were shown to be able move in a coherent fashion—and explore their watery environment for days or weeks, powered by embryonic energy stores. Turned over, however, they failed, like beetles flipped on their backs.
Later tests showed that groups of xenobots would move around in circles, pushing pellets into a central location—spontaneously and collectively. Others were built with a hole through the center to reduce drag. In simulated versions of these, the scientists were able to repurpose this hole as a pouch to successfully carry an object. “It’s a step toward using computer-designed organisms for intelligent drug delivery,” says Bongard, a professor in UVM’s Department of Computer Science and Complex Systems Center.
Many technologies are made of steel, concrete or plastic. That can make them strong or flexible. But they also can create ecological and human health problems, like the growing scourge of plastic pollution in the oceans and the toxicity of many synthetic materials and electronics. “The downside of living tissue is that it’s weak and it degrades,” say Bongard. “That’s why we use steel. But organisms have 4.5 billion years of practice at regenerating themselves and going on for decades.” And when they stop working—death—they usually fall apart harmlessly. “These xenobots are fully biodegradable,” say Bongard, “when they’re done with their job after seven days, they’re just dead skin cells.”
Robotics expert Joshua Bongard, a computer scientist at the University of Vermont, co-led new research that led to the creation of a new class of artifact: a living, programmable organism a called xenobot. Credit: Joshua Brown, UVM
Your laptop is a powerful technology. But try cutting it in half. Doesn’t work so well. In the new experiments, the scientists cut the xenobots and watched what happened. “We sliced the robot almost in half and it stitches itself back up and keeps going,” says Bongard. “And this is something you can’t do with typical machines.”
Cracking the Code
Both Levin and Bongard say the potential of what they’ve been learning about how cells communicate and connect extends deep into both computational science and our understanding of life. “The big question in biology is to understand the algorithms that determine form and function,” says Levin. “The genome encodes proteins, but transformative applications await our discovery of how that hardware enables cells to cooperate toward making functional anatomies under very different conditions.”
To make an organism develop and function, there is a lot of information sharing and cooperation—organic computation—going on in and between cells all the time, not just within neurons. These emergent and geometric properties are shaped by bioelectric, biochemical, and biomechanical processes, “that run on DNA-specified hardware,” Levin says, “and these processes are reconfigurable, enabling novel living forms.”
The scientists see the work presented in their new PNAS study—”A scalable pipeline for designing reconfigurable organisms,”—as one step in applying insights about this bioelectric code to both biology and computer science. “What actually determines the anatomy towards which cells cooperate?” Levin asks. “You look at the cells we’ve been building our xenobots with, and, genomically, they’re frogs. It’s 100% frog DNA—but these are not frogs. Then you ask, well, what else are these cells capable of building?”
“As we’ve shown, these frog cells can be coaxed to make interesting living forms that are completely different from what their default anatomy would be,” says Levin. He and the other scientists in the UVM and Tufts team—with support from DARPA’s Lifelong Learning Machines program and the National Science Foundation—believe that building the xenobots is a small step toward cracking what he calls the “morphogenetic code,” providing a deeper view of the overall way organisms are organized—and how they compute and store information based on their histories and environment.
Many people worry about the implications of rapid technological change and complex biological manipulations. “That fear is not unreasonable,” Levin says. “When we start to mess around with complex systems that we don’t understand, we’re going to get unintended consequences.” A lot of complex systems, like an ant colony, begin with a simple unit—an ant—from which it would be impossible to predict the shape of their colony or how they can build bridges over water with their interlinked bodies.
“If humanity is going to survive into the future, we need to better understand how complex properties, somehow, emerge from simple rules,” says Levin. Much of science is focused on “controlling the low-level rules. We also need to understand the high-level rules,” he says. “If you wanted an anthill with two chimneys instead of one, how do you modify the ants? We’d have no idea.”
“I think it’s an absolute necessity for society going forward to get a better handle on systems where the outcome is very complex,” Levin says. “A first step towards doing that is to explore: how do living systems decide what an overall behavior should be and how do we manipulate the pieces to get the behaviors we want?”
In other words, “this study is a direct contribution to getting a handle on what people are afraid of, which is unintended consequences,” Levin says—whether in the rapid arrival of self-driving cars, changing gene drives to wipe out whole lineages of viruses, or the many other complex and autonomous systems that will increasingly shape the human experience.
“There’s all of this innate creativity in life,” says UVM’s Josh Bongard. “We want to understand that more deeply—and how we can direct and push it toward new forms.”
Reference: “A scalable pipeline for designing reconfigurable organisms” by Sam Kriegman, Douglas Blackiston, Michael Levin and Josh Bongard, 13 January 2020, Proceedings of the National Academy of Sciences.
Nanotechnology In Medicine: Huge Potential, But What Are The Risks?
Nanotechnology, the manipulation of matter at the atomic and molecular scale to create materials with remarkably varied and new properties, is a rapidly expanding area of research with huge potential in many sectors, ranging from healthcare to construction and electronics. In medicine, it promises to revolutionize drug delivery, gene therapy, diagnostics, and many areas of research, development and clinical application.
This article does not attempt to cover the whole field, but offers, by means of some examples, a few insights into how nanotechnology has the potential to change medicine, both in the research lab and clinically, while touching on some of the challenges and concerns that it raises.
The prefix “nano” stems from the ancient Greek for “dwarf”. In science it means one billionth (10 to the minus 9) of something, thus a nanometer (nm) is is one billionth of a meter, or 0.000000001 meters. A nanometer is about three to five atoms wide, or some 40,000 times smaller than the thickness of human hair. A virus is typically 100 nm in size.
The ability to manipulate structures and properties at the nanoscale in medicine is like having a sub-microscopic lab bench on which you can handle cell components, viruses or pieces of DNA, using a range of tiny tools, robots and tubes.
Therapies that involve the manipulation of individual genes, or the molecular pathways that influence their expression, are increasingly being investigated as an option for treating diseases. One highly sought goal in this field is the ability to tailor treatments according to the genetic make-up of individual patients.
This creates a need for tools that help scientists experiment and develop such treatments.
Imagine, for example, being able to stretch out a section of DNA like a strand of spaghetti, so you can examine or operate on it, or building nanorobots that can “walk” and carry out repairs inside cell components. Nanotechnology is bringing that scientific dream closer to reality.
For instance, scientists at the Australian National University have managed to attach coated latex beads to the ends of modified DNA, and then using an “optical trap” comprising a focused beam of light to hold the beads in place, they have stretched out the DNA strand in order to study the interactions of specific binding proteins.
Meanwhile chemists at New York University (NYU) have created a nanoscale robot from DNA fragments that walks on two legs just 10 nm long. In a 2004 paper published in the journal Nano Letters, they describe how their “nanowalker”, with the help of psoralen molecules attached to the ends of its feet, takes its first baby steps: two forward and two back.
One of the researchers, Ned Seeman, said he envisages it will be possible to create a molecule-scale production line, where you move a molecule along till the right location is reached, and a nanobot does a bit chemisty on it, rather like “spot-welding” on a car assembly line. Seeman’s lab at NYU is also looking to use DNA nanotechnology to make a biochip computer, and to find out how biological molecules crystallize, an area that is currently fraught with challenges.
The work that Seeman and colleagues are doing is a good example of “biomimetics”, where with nanotechnology they can imitate some of the biological processes in nature, such as the behavior of DNA, to engineer new methods and perhaps even improve them.
DNA-based nanobots are also being created to target cancer cells. For instance, researchers at Harvard Medical School in the US reported recently in Science how they made an “origami nanorobot” out of DNA to transport a molecular payload. The barrel-shaped nanobot can carry molecules containing instructions that make cells behave in a particular way. In their study, the team successfully demonstrates how it delivered molecules that trigger cell suicide in leukemia and lymphoma cells.
Nanobots made from other materials are also in development. For instance, gold is the material scientists at Northwestern University use to make “nanostars”, simple, specialized, star-shaped nanoparticles that can href=”http://www.medicalnewstoday.com/articles/243856.php”>deliver drugs directly to the nuclei of cancer cells. In a recent paper in the journal ACS Nano, they describe how drug-loaded nanostars behave like tiny hitchhikers, that after being attracted to an over-expressed protein on the surface of human cervical and ovarian cancer cells, deposit their payload right into the nuclei of those cells.
The researchers found giving their nanobot the shape of a star helped to overcome one of the challenges of using nanoparticles to deliver drugs: how to release the drugs precisely. They say the shape helps to concentrate the light pulses used to release the drugs precisely at the points of the star.
Scientists are discovering that protein-based drugs are very useful because they can be programmed to deliver specific signals to cells. But the problem with conventional delivery of such drugs is that the body breaks most of them down before they reach their destination.
But what if it were possible to produce such drugs in situ, right at the target site? Well, in a recent issue of Nano Letters, researchers at Massachusetts Institute of Technology (MIT) in the US show how it may be possible to do just that. In their proof of principle study, they demonstrate the feasibility of self-assembling “nanofactories” that make protein compounds, on demand, at target sites. So far they have tested the idea in mice, by creating nanoparticles programmed to produce either green fluorescent protein (GFP) or luciferase exposed to UV light.
The MIT team came up with the idea while trying to find a way to attack metastatic tumors, those that grow from cancer cells that have migrated from the original site to other parts of the body. Over 90% of cancer deaths are due to metastatic cancer. They are now working on nanoparticles that can synthesize potential cancer drugs, and also on other ways to switch them on.
Nanofibers are fibers with diameters of less than 1,000 nm. Medical applications include special materials for wound dressings and surgical textiles, materials used in implants, tissue engineering and artificial organ components.
Nanofibers made of carbon also hold promise for medical imaging and precise scientific measurement tools. But there are huge challenges to overcome, one of the main ones being how to make them consistently of the correct size. Historically, this has been costly and time-consuming.
But last year, researchers from North Carolina State University, revealed how they had developed a new method for making carbon nanofibers of specific sizes. Writing in ACS Applied Materials & Interfaces in March 2011, they describe how they managed to grow carbon nanofibers uniform in diameter, by using nickel nanoparticles coated with a shell made of ligands, small organic molecules with functional parts that bond directly to metals.
Nickel nanoparticles are particularly interesting because at high temperatures they help grow carbon nanofibers. The researchers also found there was another benefit in using these nanoparticles, they could define where the nanofibers grew and by correct placement of the nanoparticles they could grow the nanofibers in a desired specific pattern: an important feature for useful nanoscale materials.
Lead is another substance that is finding use as a nanofiber, so much so that neurosurgeon-to-be Matthew MacEwan, who is studying at Washington University School of Medicine in St. Louis, started his own nanomedicine company aimed at revolutionizing the surgical mesh that is used in operating theatres worldwide.
The lead product is a synthetic polymer comprising individual strands of nanofibers, and was developed to repair brain and spinal cord injuries, but MacEwan thinks it could also be used to mend hernias, fistulas and other injuries.
Currently, the surgical meshes used to repair the protective membrane that covers the brain and spinal cord are made of thick and stiff material, which is difficult to work with. The lead nanofiber mesh is thinner, more flexible and more likely to integrate with the body’s own tissues, says MacEwan. Every thread of the nanofiber mesh is thousands of times smaller than the diameter of a single cell. The idea is to use the nanofiber material not only to make operations easier for surgeons to carry out, but also so there are fewer post-op complications for patients, because it breaks down naturally over time.
Researchers at the Polytechnic Institute of New York University (NYU-Poly) have recently demonstrated a new way to make nanofibers out of proteins. Writing recently in the journal Advanced Functional Materials, the researchers say they came across their finding almost by chance: they were studying certain cylinder-shaped proteins derived from cartilage, when they noticed that in high concentrations, some of the proteins spontaneously came together and self-assembled into nanofibers.
They carried out further experiments, such as adding metal-recognizing amino acids and different metals, and found they could control fiber formation, alter its shape, and how it bound to small molecules. For instance, adding nickel transformed the fibers into clumped mats, which could be used to trigger the release of an attached drug molecule.
The researchers hope this new method will greatly improve the delivery of drugs to treat cancer, heart disorders and Alzheimer’s disease. They can also see applications in regeneration of human tissue, bone and cartilage, and even as a way to develop tinier and more powerful microprocessors for use in computers and consumer electronics.
A schematic illustration showing how nanoparticles or other cancer drugs might be used to treat cancer. This illustration was made for the Opensource Handbook of Nanoscience and Nanotechnology
Recent years have seen an explosion in the number of studies showing the variety of medical applications of nanotechnology and nanomaterials. In this article we have glimpsed just a small cross-section of this vast field. However, across the range, there exist considerable challenges, the greatest of which appear to be how to scale up production of materials and tools, and how to bring down costs and timescales.
But another challenge is how to quickly secure public confidence that this rapidly expanding technology is safe. And so far, it is not clear whether that is being done.
There are those who suggest concerns about nanotechnology may be over-exaggerated. They point to the fact that just because a material is nanosized, it does not mean it is dangerous, indeed nanoparticles have been around since the Earth was born, occurring naturally in volcanic ash and sea-spray, for example. As byproducts of human activity, they have been present since the Stone Age, in smoke and soot.
Of attempts to investigate the safety of nanomaterials, the National Cancer Institute in the US says there are so many nanoparticles naturally present in the environment that they are “often at order-of-magnitude higher levels than the engineered particles being evaluated”. In many respects, they point out, “most engineered nanoparticles are far less toxic than household cleaning products, insecticides used on family pets, and over-the-counter dandruff remedies,” and that for instance, in their use as carriers of chemotherapeutics in cancer treatment, they are much less toxic than the drugs they carry.
It is perhaps more in the food sector that we have seen some of the greatest expansion of nanomaterials on a commercial level. Although the number of foods that contain nanomaterials is still small, it appears set to change over the next few years as the technology develops. Nanomaterials are already used to lower levels of fat and sugar without altering taste, or to improve packaging to keep food fresher for longer, or to tell consumers if the food is spoiled. They are also being used to increase the bioavailablity of nutrients (for instance in food supplements).
But, there are also concerned parties, who highlight that while the pace of research quickens, and the market for nanomaterials expands, it appears not enough is being done to discover their toxicological consequences.
This was the view of a science and technology committee of the House of Lords of the British Parliament, who in a recent report on nanotechnology and food, raise several concerns about nanomaterials and human health, particularly the risk posed by ingested nanomaterials.
For instance, one area that concerns the committee is the size and exceptional mobility of nanoparticles: they are small enough, if ingested, to penetrate cell membranes of the lining of the gut, with the potential to access the brain and other parts of the body, and even inside the nuclei of cells.
Another is the solubility and persistence of nanomaterials. What happens, for instance, to insoluble nanoparticles? If they can’t be broken down and digested or degraded, is there a danger they will accumulate and damage organs? Nanomaterials comprising inorganic metal oxides and metals are thought to be the ones most likely to pose a risk in this area.
Also, because of their high surface area to mass ratio, nanoparticles are highly reactive, and may for instance, trigger as yet unknown chemical reactions, or by bonding with toxins, allow them to enter cells that they would otherwise have no access to.
For instance, with their large surface area, reactivity and electrical charge, nanomaterials create the conditions for what is described as “particle aggregation” due to physical forces and “particle agglomoration” due to chemical forces, so that individual nanoparticles come together to form larger ones. This may lead not only to dramatically larger particles, for instance in the gut and inside cells, but could also result in disaggregation of clumps of nanoparticles, which could radically alter their physicochemical properties and chemical reactivity.
“Such reversible phenomena add to the difficulty in understanding the behaviour and toxicology of nanomaterials,” says the committee, whose overall conclusion is that neither Government nor the Research Councils are giving enough priority to researching the safety of nanotechnology, especially “considering the timescale within which products containing nanomaterials may be developed”.
They recommend much more research is needed to “ensure that regulatory agencies can effectively assess the safety of products before they are allowed onto the market”.
It would appear, therefore, whether actual or perceived, the potential risk that nanotechnology poses to human health must be investigated, and be seen to be investigated. Most nanomaterials, as the NCI suggests, will likely prove to be harmless.
But when a technology advances rapidly, knowledge and communication about its safety needs to keep pace in order for it to benefit, especially if it is also to secure public confidence. We only have to look at what happened, and to some extent is still happening, with genetically modified food to see how that can go badly wrong.
Yuste R: Fluorescence microscopy today. Nat Methods. 2005, 2: 902-904. 10.1038/nmeth1205-902.
Megason SG, Fraser SE: Imaging in systems biology. Cell. 2007, 130: 784-795. 10.1016/j.cell.2007.08.031.
Conchello J, Lichtman JW: Optical sectioning microscopy. Nat Methods. 2005, 2: 920-931. 10.1038/nmeth815.
Sung M-H, McNally JG: Live cell imaging and systems biology. Wiley Interdiscip Rev Syst Biol Med. 2010, 3: 167-182.
Planchon TA, Gao L, Milkie DE, Davidson MW, Galbraith JA, Galbraith CG, Betzig E: Rapid three-dimensional isotropic imaging of living cells using Bessel beam plane illumination. Nat Methods. 2011, 8: 417-423. 10.1038/nmeth.1586.
Weber M, Huisken J: Light sheet microscopy for real-time developmental biology. Curr Opin Genet Dev. 2011, 21: 566-572. 10.1016/j.gde.2011.09.009.
Gao L, Shao L, Higgins CD, Poulton JS, Peifer M, Davidson MW, Wu X, Goldstein B, Betzig E: Noninvasive Imaging beyond the Diffraction Limit of 3D Dynamics in Thickly Fluorescent Specimens. Cell. 2012, 151: 1370-1385. 10.1016/j.cell.2012.10.008.
Wicker K, Heintzmann R: Interferometric resolution improvement for confocal microscopes. Opt Express. 2007, 15: 12206-12216. 10.1364/OE.15.012206.
Abrahamsson S, Chen J, Hajj B, Stallinga S, Katsov AY, Wisniewski J, Mizuguchi G, Soule P, Mueller F, Darzacq CD, Darzacq X, Wu C, Bargmann CI, Agard DA, Dahan M, Gustafsson MGL: Fast multicolor 3D imaging using aberration-corrected multifocus microscopy. Nat Methods. 2013, 10: 60-63.
Wiedenmann J, Oswald F, Nienhaus GU: Fluorescent proteins for live cell imaging: opportunities, limitations, and challenges. IUBMB Life. 2009, 61: 1029-1042. 10.1002/iub.256.
Day RN, Davidson MW: The fluorescent protein palette: tools for cellular imaging. Chem Soc Rev. 2009, 38: 2887-2921. 10.1039/b901966a.
Shcherbo D, Shemiakina II, Ryabova AV, Luker KE, Schmidt BT, Souslova EA, Gorodnicheva TV, Strukova L, Shidlovskiy KM, Britanova OV, Zaraisky AG, Lukyanov KA, Loschenov VB, Luker GD, Chudakov DM: Near-infrared fluorescent proteins. Nat Methods. 2010, 7: 827-829. 10.1038/nmeth.1501.
Jaiswal JK, Goldman ER, Mattoussi H, Simon SM: Use of quantum dots for live cell imaging. Nat Methods. 2004, 1: 73-78. 10.1038/nmeth1004-73.
Thompson MA, Lew MD, Moerner WE: Extending microscopic resolution with single-molecule imaging and active control. Annu Rev Biophys. 2012, 41: 321-342. 10.1146/annurev-biophys-050511-102250.
Wysocki LM, Lavis LD: Advances in the chemistry of small molecule fluorescent probes. Curr Opin Chem Biol. 2011, 15: 752-759. 10.1016/j.cbpa.2011.10.013.
Lukinavičius G, Johnsson K: Switchable fluorophores for protein labeling in living cells. Curr Opin Chem Biol. 2011, 15: 768-774. 10.1016/j.cbpa.2011.10.015.
Du W, Wang Y, Luo Q, Liu B-F: Optical molecular imaging for systems biology: from molecule to organism. Anal Bioanal Chem. 2006, 386: 444-457. 10.1007/s00216-006-0541-z.
Yasuda R, Noji H, Kinosita K, Yoshida M: F1-ATPase is a highly efficient molecular motor that rotates with discrete 120 degree steps. Cell. 1998, 93: 1117-1124. 10.1016/S0092-8674(00)81456-7.
Ueno H, Nishikawa S, Iino R, Tabata KV, Sakakihara S, Yanagida T, Noji H: Simple dark-field microscopy with nanometer spatial precision and microsecond temporal resolution. Biophys J. 2010, 98: 2014-2023. 10.1016/j.bpj.2010.01.011.
Okuno D, Iino R, Noji H: Rotation and structure of FoF1-ATP synthase. J Biochem. 2011, 149: 655-664. 10.1093/jb/mvr049.
Sengupta P, Van Engelenburg S, Lippincott-Schwartz J: Visualizing cell structure and function with point-localization superresolution imaging. Dev Cell. 2012, 23: 1092-1102. 10.1016/j.devcel.2012.09.022.
Lubeck E, Cai L: Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat Methods. 2012, 9: 743-748. 10.1038/nmeth.2069.
Antony PMA, Balling R, Vlassis N: From systems biology to systems biomedicine. Curr Opin Biotechnol. 2012, 23: 604-608. 10.1016/j.copbio.2011.11.009.
Cirillo C, Tack J, Vanden Berghe P: Nerve activity recordings in routine human intestinal biopsies. Gut. 2012, 65: 227-235.
Berning S, Willig KI, Steffens H, Dibaj P, Hell SW: Nanoscopy in a living mouse brain. Science. 2012, 335: 551-10.1126/science.1215369.
Perron A, Akemann W, Mutoh H, Knöpfel T: Genetically encoded probes for optical imaging of brain electrical activity. Prog Brain Res. 2012, 196: 63-77.
Akemann W, Mutoh H, Perron A, Rossier J, Knöpfel T: Imaging brain electric signals with genetically targeted voltage-sensitive fluorescent proteins. Nat Methods. 2010, 7: 643-649. 10.1038/nmeth.1479.
Buchser W: Assay development guidelines for image-based high content screening, high content analysis and high content imaging. Assay Guidance Manual. 2012, Bethesda, MD: National Center for Advancing Translational Sciences (NCATS), 1-69.
Conrad C, Gerlich DW: Automated microscopy for high-content RNAi screening. J Cell Biol. 2010, 188: 453-461. 10.1083/jcb.200910105.
Shen F, Hodgson L, Hahn K: Digital autofocus methods for automated microscopy. Methods Enzymol. 2006, 414: 620-632.
Ankers JM, Spiller DG, White MR, Harper CV: Spatio-temporal protein dynamics in single living cells. Curr Opin Biotechnol. 2008, 19: 375-380. 10.1016/j.copbio.2008.07.001.
Fuchs F, Pau G, Kranz D, Sklyar O, Budjan C, Steinbrink S, Horn T, Pedal A, Huber W, Boutros M: Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol Syst Biol. 2010, 6: 370-
Held M, Schmitz MHA, Fischer B, Walter T, Neumann B, Olma MH, Peter M, Ellenberg J, Gerlich DW: Cell Cognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat Methods. 2010, 7: 747-754. 10.1038/nmeth.1486.
Wählby C, Kamentsky L, Liu ZH, Riklin-Raviv T, Conery AL, O’Rourke EJ, Sokolnicki KL, Visvikis O, Ljosa V, Irazoqui JE, Golland P, Ruvkun G, Ausubel FM, Carpenter AE: An image analysis toolbox for high-throughput C. elegans assays. Nat methods. 2012, 9: 714-716. 10.1038/nmeth.1984.
Long F, Peng H, Liu X, Kim SK, Myers E: A 3D digital atlas of C. elegans and its application to single-cell analyses. Nat Methods. 2009, 6: 667-672. 10.1038/nmeth.1366.
Ronneberger O, Liu K, Rath M, Rueβ D, Mueller T, Skibbe H, Drayer B, Schmidt T, Filippi A, Nitschke R, Brox T, Burkhardt H, Driever W: ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains. Nat Methods. 2012, 9: 735-742. 10.1038/nmeth.2076.
Ghosh KK, Burns LD, Cocker ED, Nimmerjahn A, Ziv Y, Gamal AE, Schnitzer MJ: Miniaturized integration of a fluorescence microscope. Nat Methods. 2011, 8: 871-878. 10.1038/nmeth.1694.
Savidge TC, Newman P, Pothoulakis C, Ruhl A, Neunlist M, Bourreille A, Hurst R, Sofroniew MV: Enteric glia regulate intestinal barrier function and inflammation via release of S-nitrosoglutathione. Gastroenterology. 2007, 132: 1344-1358. 10.1053/j.gastro.2007.01.051.
Chao JA, Yoon YJ, Singer RH: Imaging Translation in Single Cells Using Fluorescent Microscopy. Cold Spring Harb Perspect Biol. 2012, 4: 1-2.
Loo L-H, Lin H-J, Singh DK, Lyons KM, Altschuler SJ, Wu LF: Heterogeneity in the physiological states and pharmacological responses of differentiating 3 T3-L1 preadipocytes. J Cell Biol. 2009, 187: 375-384. 10.1083/jcb.200904140.
Zaretsky I, Polonsky M, Shifrut E, Reich-Zeliger S, Antebi Y, Aidelberg G, Waysbort N, Friedman N: Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays. Lab Chip. 2012, 12: 5007-5015. 10.1039/c2lc40808b.
Watmuff B, Pouton CW, Haynes JM: In vitro maturation of dopaminergic neurons derived from mouse embryonic stem cells: implications for transplantation. PLoS One. 2012, 7: e31999-10.1371/journal.pone.0031999.
Tay S, Hughey JJ, Lee TK, Lipniacki T, Quake SR, Covert MW: Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature. 2010, 466: 267-271. 10.1038/nature09145.
Lee TK, Covert MW: High-throughput, single-cell NF-κB dynamics. Curr Opin Genet Dev. 2010, 20: 677-683. 10.1016/j.gde.2010.08.005.
Padilla-Parra S, Tramier M: FRET microscopy in the living cell: different approaches, strengths and weaknesses. Bioessays. 2012, 34: 369-376. 10.1002/bies.201100086.
Gilbert PM, Havenstrite KL, Magnusson KEG, Sacco A, Leonardi NA, Kraft P, Nguyen NK, Thrun S, Lutolf MP, Blau HM: Substrate elasticity regulates skeletal muscle stem cell self-renewal in culture. Science. 2010, 329: 1078-1081. 10.1126/science.1191035.
Magnusson K, Jaldén J: A batch algorithm using iterative application of the Viterbi algorithm to track cells and construct cell lineages. Biomed Imaging (ISBI). 2012, 2012: 382-385.
Walter T, Held M, Neumann B, Hériché J-K, Conrad C, Pepperkok R, Ellenberg J: Automatic identification and clustering of chromosome phenotypes in a genome wide RNAi screen by time-lapse imaging. J Struct Biol. 2010, 170: 1-9. 10.1016/j.jsb.2009.10.004.
Neumann B, Walter T, Hériché J, Bulkescher J, Erfle H, Conrad C, Rogers P, Poser I, Held M, Liebel U, Cetin C, Sieckmann F, Pau G, Kabbe R, Wünsche A, Satagopam V, Schmitz MHA, Chapuis C, Gerlich DW, Schneider R, Eils R, Huber W, Peters J, Hyman AA, Durbin R, Pepperkok R, Ellenberg J: Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature. 2010, 464: 721-727. 10.1038/nature08869.
Conrad C, Wünsche A, Tan TH, Bulkescher J, Sieckmann F, Verissimo F, Edelstein A, Walter T, Liebel U, Pepperkok R, Ellenberg J: Micropilot: automation of fluorescence microscopy-based imaging for systems biology. Nat Methods. 2011, 8: 246-249. 10.1038/nmeth.1558.
Giaever G, Chu AM, Ni L, Connelly C, Riles L, Véronneau S, Dow S, Lucau-Danila A, Anderson K, André B, Arkin AP, Astromoff A, El-Bakkoury M, Bangham R, Benito R, Brachat S, Campanaro S, Curtiss M, Davis K, Deutschbauer A, Entian K-D, Flaherty P, Foury F, Garfinkel DJ, Gerstein M, Gotte D, Güldener U, Hegemann JH, Hempel S, Herman Z: Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002, 418: 387-391. 10.1038/nature00935.
Ni L, Snyder M: A genomic study of the bipolar bud site selection pattern in Saccharomyces cerevisiae. Mol Biol Cell. 2001, 12: 2147-2170.
Yang Yu B, Elbuken C, Ren CL, Huissoon JP: Image processing and classification algorithm for yeast cell morphology in a microfluidic chip. J Biomedical Optics. 2011, 16: 1-9.
Mortimer RK, JOHNSTON JR: Life span of individual yeast cells. Nature. 1959, 183: 1751-1752. 10.1038/1831751a0.
Steffen KK, Kennedy BK, Kaeberlein M: Measuring replicative life span in the budding yeast. J Vis Exp. 2009, 1-5.
Lee SS, Avalos Vizcarra I, Huberts DHEW, Lee LP, Heinemann M: Whole lifespan microscopic observation of budding yeast aging through a microfluidic dissection platform. Proc Natl Acad Sci U S A. 2012, 109: 4916-4920. 10.1073/pnas.1113505109.
Gordon A, Colman-Lerner A, Chin TE, Benjamin KR, Yu RC, Brent R: Single-cell quantification of molecules and rates using open-source microscope-based cytometry. Nat Methods. 2007, 4: 175-181. 10.1038/nmeth1008.
Ohya Y, Sese J, Yukawa M, Sano F, Nakatani Y, Saito TL, Saka A, Fukuda T, Ishihara S, Oka S, Suzuki G, Watanabe M, Hirata A, Ohtani M, Sawai H, Fraysse N, Latgé J-P, François JM, Aebi M, Tanaka S, Muramatsu S, Araki H, Sonoike K, Nogami S, Morishita S: High-dimensional and large-scale phenotyping of yeast mutants. Proc Natl Acad Sci U S A. 2005, 102: 19015-19020. 10.1073/pnas.0509436102.
Ohtani M, Saka A, Sano F, Ohya Y, Morishita S: Development of image processing program for yeast cell morphology. J Bioinform Comput Biol. 2004, 1: 695-709. 10.1142/S0219720004000363.
Saito TL, Ohtani M, Sawai H, Sano F, Saka A, Watanabe D, Yukawa M, Ohya Y, Morishita S: SCMD: Saccharomyces cerevisiae Morphological Database. Nucleic Acids Res. 2004, 32: D319-D322. 10.1093/nar/gkh113.
Ohnuki S, Oka S, Nogami S, Ohya Y: High-content, image-based screening for drug targets in yeast. PLoS One. 2010, 5: e10177-10.1371/journal.pone.0010177.
Artal-Sanz M, De Jong L, Tavernarakis N: Caenorhabditis elegans: a versatile platform for drug discovery. Biotechnol J. 2006, 1: 1405-1418. 10.1002/biot.200600176.
Silverman GA, Luke CJ, Bhatia SR, Long OS, Vetica AC, Perlmutter DH, Pak SC: Modeling molecular and cellular aspects of human disease using the nematode Caenorhabditis elegans. Pediatr Res. 2009, 65: 10-18. 10.1203/PDR.0b013e31819009b0.
Rankin CH: From gene to identified neuron to behaviour in Caenorhabditis elegans. Nat Rev Genet. 2002, 3: 622-630.
Jorgensen EM, Mango SE: The art and design of genetic screens: caenorhabditis elegans. Nat Rev Genet. 2002, 3: 356-369. 10.1038/nrg794.
Gosai SJ, Kwak JH, Luke CJ, Long OS, King DE, Kovatch KJ, Johnston PA, Shun TY, Lazo JS, Perlmutter DH, Silverman GA, Pak SC: Automated high-content live animal drug screening using C. elegans expressing the aggregation prone serpin α1-antitrypsin Z. PloS one. 2010, 5: e15460-10.1371/journal.pone.0015460.
Brignull HR, Morley JF, Morimoto RI: The stress of misfolded proteins: C. elegans models for neurodegenerative disease and aging. Adv Exp Med Biol. 2007, 594: 167-189. 10.1007/978-0-387-39975-1_15.
Crane MM, Stirman JN, Ou C-Y, Kurshan PT, Rehg JM, Shen K, Lu H: Autonomous screening of C. elegans identifies genes implicated in synaptogenesis. Nat Methods. 2012, 9: 977-980. 10.1038/nmeth.2141.
Baek J-H, Cosman P, Feng Z, Silver J, Schafer WR: Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively. J Neurosci Methods. 2002, 118: 9-21. 10.1016/S0165-0270(02)00117-6.
Cronin CJ, Mendel JE, Mukhtar S, Kim Y-M, Stirbl RC, Bruck J, Sternberg PW: An automated system for measuring parameters of nematode sinusoidal movement. BMC Genet. 2005, 6: 5-
Feng Z, Cronin CJ, Wittig JH, Sternberg PW, Schafer WR: An imaging system for standardized quantitative analysis of C. elegans behavior. BMC bioinformatics. 2004, 5: 115-10.1186/1471-2105-5-115.
Geng W, Cosman P, Berry CC, Feng Z, Schafer WR: Automatic tracking, feature extraction and classification of C elegans phenotypes. IEEE Trans Biomed Eng. 2004, 51: 1811-1820. 10.1109/TBME.2004.831532.
Huang K-M, Cosman P, Schafer WR: Machine vision based detection of omega bends and reversals in C. elegans. J Neurosci Methods. 2006, 158: 323-336. 10.1016/j.jneumeth.2006.06.007.
Huang K-M, Cosman P, Schafer WR: Automated detection and analysis of foraging behavior in Caenorhabditis elegans. J Neurosci Methods. 2008, 171: 153-164. 10.1016/j.jneumeth.2008.01.027.
Ramot D, Johnson BE, Berry TL, Carnell L, Goodman MB: The Parallel Worm Tracker: a platform for measuring average speed and drug-induced paralysis in nematodes. PLoS One. 2008, 3: e2208-10.1371/journal.pone.0002208.
Buckingham SD, Sattelle DB: Strategies for automated analysis of C. elegans locomotion. Invert Neurosci. 2008, 8: 121-131. 10.1007/s10158-008-0077-3.
Hoshi K, Shingai R: Computer-driven automatic identification of locomotion states in Caenorhabditis elegans. J Neurosci Methods. 2006, 157: 355-363. 10.1016/j.jneumeth.2006.05.002.
Tsibidis GD, Tavernarakis N: Nemo: a computational tool for analyzing nematode locomotion. BMC Neurosci. 2007, 8: 86-10.1186/1471-2202-8-86.
Buckingham SD, Sattelle DB: Fast, automated measurement of nematode swimming (thrashing) without morphometry. BMC Neurosci. 2009, 10: 84-10.1186/1471-2202-10-84.
Tsechpenakis G, Bianchi L, Metaxas D, Driscoll M: A novel computational approach for simultaneous tracking and feature extraction of C. elegans populations in fluid environments. IEEE Trans Biomed Eng. 2008, 55: 1539-1549.
Sznitman R, Gupta M, Hager GD, Arratia PE, Sznitman J: Multi-environment model estimation for motility analysis of Caenorhabditis elegans. PLoS One. 2010, 5: e11631-10.1371/journal.pone.0011631.
Green RA, Kao H-L, Audhya A, Arur S, Mayers JR, Fridolfsson HN, Schulman M, Schloissnig S, Niessen S, Laband K, Wang S, Starr DA, Hyman AA, Schedl T, Desai A, Piano F, Gunsalus KC, Oegema K: A high-resolution C. elegans essential gene network based on phenotypic profiling of a complex tissue. Cell. 2011, 145: 470-482. 10.1016/j.cell.2011.03.037.
Peng H, Long F, Liu X, Kim SK, Myers EW: Straightening Caenorhabditis elegans images. Bioinformatics. 2008, 24: 234-242. 10.1093/bioinformatics/btm569.
Olarte OE, Licea-Rodriguez J, Palero JA, Gualda EJ, Artigas D, Mayer J, Swoger J, Sharpe J, Rocha-Mendoza I, Rangel-Rojo R, Loza-Alvarez P: Image formation by linear and nonlinear digital scanned light-sheet fluorescence microscopy with Gaussian and Bessel beam profiles. Biomed Opt express. 2012, 3: 1492-1505. 10.1364/BOE.3.001492.
Patton EE, Zon LI: The art and design of genetic screens: zebrafish. Nat Rev Genet. 2001, 2: 956-966. 10.1038/35103567.
Zon LI, Peterson RT: In vivo drug discovery in the zebrafish. Nat Rev Drug Discov. 2005, 4: 35-44. 10.1038/nrd1606.
Pardo-Martin C, Chang T-Y, Koo BK, Gilleland CL, Wasserman SC, Yanik MF: High-throughput in vivo vertebrate screening. Nat Methods. 2010, 7: 634-636. 10.1038/nmeth.1481.
Taylor KL, Grant NJ, Temperley ND, Patton EE: Small molecule screening in zebrafish: an in vivo approach to identifying new chemical tools and drug leads. Cell Commun signal. 2010, 8: 11-10.1186/1478-811X-8-11.
Kimmel CB, Ballard WW, Kimmel SR, Ullmann B, Schilling TF: Stages of embryonic development of the zebrafish. Dev Dyn. 1995, 203: 253-310. 10.1002/aja.1002030302.
Peravali R, Gehrig J, Giselbrecht S, Lütjohann DS, Hadzhiev Y, Müller F, Liebel U: Automated feature detection and imaging for high-resolution screening of zebrafish embryos. Biotechniques. 2011, 50: 319-324.
Eguíluz C, Viguera E, Millán L, Pérez J: Multitissue array review: a chronological description of tissue array techniques, applications and procedures. Pathol Res Pract. 2006, 202: 561-568. 10.1016/j.prp.2006.04.003.
Wang C-W, Fennell D, Paul I, Savage K, Hamilton P: Robust automated tumour segmentation on histological and immunohistochemical tissue images. PLoS One. 2011, 6: e15818-10.1371/journal.pone.0015818.
Sommer C, Straehle C, Kothe U, Hamprecht FA: Ilastik: Interactive learning and segmentation toolkit. IEEE Int Symp Biomed Imaging. 2011, 2011: 230-233.
Kovar JL, Simpson MA, Schutz-Geschwender A, Olive DM: A systematic approach to the development of fluorescent contrast agents for optical imaging of mouse cancer models. Anal Biochem. 2007, 367: 1-12. 10.1016/j.ab.2007.04.011.
Baker M: Animal models: inside the minds of mice and men. Nature. 2011, 475: 123-128. 10.1038/475123a.
De Chaumont F, Coura RD-S, Serreau P, Cressant A, Chabout J, Granon S, Olivo-Marin J-C: Computerized video analysis of social interactions in mice. Nat Methods. 2012, 9: 410-417. 10.1038/nmeth.1924.
Nägerl UV, Willig KI, Hein B, Hell SW, Bonhoeffer T: Live-cell imaging of dendritic spines by STED microscopy. Proc Natl Acad Sci U S A. 2008, 105: 18982-18987. 10.1073/pnas.0810028105.
Urban NT, Willig KI, Hell SW, Nägerl UV: STED nanoscopy of actin dynamics in synapses deep inside living brain slices. Biophys J. 2011, 101: 1277-1284. 10.1016/j.bpj.2011.07.027.
Masedunskas A, Milberg O, Porat-Shliom N, Sramkova M, Wigand T, Amornphimoltham P, Weigert R: Intravital microscopy: A practical guide on imaging intracellular structures in live animals. Bioarchitecture. 2012, 2: 143-157. 10.4161/bioa.21758.
Gavins FN: Intravital microscopy: new insights into cellular interactions. Curr Opin Pharmacol. 2012, 12: 601-607. 10.1016/j.coph.2012.08.006.
Farrar MJ, Bernstein IM, Schlafer DH, Cleland TA, Fetcho JR, Schaffer CB: Chronic in vivo imaging in the mouse spinal cord using an implanted chamber. Nat Methods. 2012, 9: 297-302. 10.1038/nmeth.1856.
Barretto RPJ, Schnitzer MJ: In vivo microendoscopy of the hippocampus. Cold Spring Harb Protoc. 2012, 2012: 1092-1099.
Barretto RPJ, Schnitzer MJ: In Vivo Optical Microendoscopy for Imaging Cells Lying Deep within Live Tissue. Cold Spring Harb Protoc. 2012, 2012: 1029-1034.
Lecoq J, Schnitzer MJ: An infrared fluorescent protein for deeper imaging. Nat Biotechnol. 2011, 29: 715-716. 10.1038/nbt.1941.
Barretto RPJ, Ko TH, Jung JC, Wang TJ, Capps G, Waters AC, Ziv Y, Attardo A, Recht L, Schnitzer MJ: Time-lapse imaging of disease progression in deep brain areas using fluorescence microendoscopy. Nat Med. 2011, 17: 223-228. 10.1038/nm.2292.
Piyawattanametha W, Cocker ED, Burns LD, Barretto RP, Jung JC, Ra H, Solgaard O, Schnitzer MJ: In vivo brain imaging using a portable 2.9 g two-photon microscope based on a microelectromechanical systems scanning mirror. Opt Lett. 2009, 34: 2309-2311. 10.1364/OL.34.002309.
Barretto RPJ, Messerschmidt B, Schnitzer MJ: In vivo fluorescence imaging with high-resolution microlenses. Nat Methods. 2009, 6: 511-512. 10.1038/nmeth.1339.
Flusberg BA, Nimmerjahn A, Cocker ED, Mukamel EA, Barretto RPJ, Ko TH, Burns LD, Jung JC, Schnitzer MJ: High-speed, miniaturized fluorescence microscopy in freely moving mice. Nat Methods. 2008, 5: 935-938. 10.1038/nmeth.1256.
Myers G: Why bioimage informatics matters. Nat Methods. 2012, 9: 659-660. 10.1038/nmeth.2024.
Swedlow JR, Eliceiri KW: Open source bioimage informatics for cell biology. Trends Cell Biol. 2009, 19: 656-660. 10.1016/j.tcb.2009.08.007.
Peng H: Bioimage informatics: a new area of engineering biology. Bioinformatics. 2008, 24: 1827-1836. 10.1093/bioinformatics/btn346.
Eliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath BS, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B, Stuurmann N, Swedlow JR, Tomancak P, Carpenter AE: Biological imaging software tools. Nat Methods. 2012, 9: 697-710. 10.1038/nmeth.2084.
Davies ER: Computer and Machine Vision: Theory, Algorithms, Practicalities. 2012, New York, NY: Academic Press, 1-912.
Walter T, Shattuck DW, Baldock R, Bastin ME, Carpenter AE, Duce S, Ellenberg J, Fraser A, Hamilton N, Pieper S, Ragan MA, Schneider JE, Tomancak P, Hériché J-K: Visualization of image data from cells to organisms. Nat methods. 2010, 7: S26-S41. 10.1038/nmeth.1431.
Zwolinski L, Kozak M, Kozak K: 1Click1View: Interactive Visualization Methodology for RNAi Cell-Based Microscopic Screening. BioMed Res Int. 2013, 2013: 1-11.
Schmid B, Schindelin J, Cardona A, Longair M, Heisenberg M: A high-level 3D visualization API for Java and ImageJ. BMC Bioinformatics. 2010, 11: 274-10.1186/1471-2105-11-274.
Mosaliganti KR, Noche RR, Xiong F, Swinburne IA, Megason SG: ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes. PLoS Comput Biol. 2012, 8: e1002780-10.1371/journal.pcbi.1002780.
Horvath P, Wild T, Kutay U, Csucs G: Machine Learning Improves the Precision and Robustness of High-Content Screens: Using Nonlinear Multiparametric Methods to Analyze Screening Results. J Biomol Screen. 2011, 16: 1059-1067. 10.1177/1087057111414878.
Kvilekval K, Fedorov D, Obara B, Singh A, Manjunath BS: B: A platform for bioimage analysis and management. Bioinformatics (Oxford, England). 2010, 26: 544-552. 10.1093/bioinformatics/btp699.
Goff SA, Vaughn M, McKay S, Lyons E, Stapleton AE, Gessler D, Matasci N, Wang L, Hanlon M, Lenards A, Muir A, Merchant N, Lowry S, Mock S, Helmke M, Kubach A, Narro M, Hopkins N, Micklos D, Hilgert U, Gonzales M, Jordan C, Skidmore E, Dooley R, Cazes J, McLay R, Lu Z, Pasternak S, Koesterke L, Piel WH: The iPlant Collaborative: Cyberinfrastructure for Plant Biology. Front plant sci. 2011, 2: 34-
Linkert M, Rueden CT, Allan C, Burel J-M, Moore W, Patterson A, Loranger B, Moore J, Neves C, Macdonald D, Tarkowska A, Sticco C, Hill E, Rossner M, Eliceiri KW, Swedlow JR: Metadata matters: access to image data in the real world. J Cell Biol. 2010, 189: 777-782. 10.1083/jcb.201004104.
Kankaanpää P, Paavolainen L, Tiitta S, Karjalainen M, Päivärinne J, Nieminen J, Marjomäki V, Heino J, White DJ: BioImageXD: an open, general-purpose and high-throughput image-processing platform. Nat Methods. 2012, 9: 683-689. 10.1038/nmeth.2047.
Rämö P, Sacher R, Snijder B, Begemann B, Pelkmans L: CellClassifier: supervised learning of cellular phenotypes. Bioinformatics. 2009, 25: 3028-3030. 10.1093/bioinformatics/btp524.
Boutros M, Brás LP, Huber W: Analysis of cell-based RNAi screens. Genome Biol. 2006, 7: R66-10.1186/gb-2006-7-7-r66.
Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome biology. 2006, 7: R100-10.1186/gb-2006-7-10-r100.
Kamentsky L, Jones TR, Fraser A, Bray M-A, Logan DJ, Madden KL, Ljosa V, Rueden C, Eliceiri KW, Carpenter AE: Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics . 2011, 27: 1179-1180. 10.1093/bioinformatics/btr095.
Jones TR, Carpenter AE, Lamprecht MR, Moffat J, Silver SJ, Grenier JK, Castoreno AB, Eggert US, Root DE, Golland P, Sabatini DM: Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci U S A. 2009, 106: 1826-1831. 10.1073/pnas.0808843106.
Jones TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, Golland P, Carpenter AE: CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC bioinformatics. 2008, 9: 482-10.1186/1471-2105-9-482.
Pau G, Fuchs F, Sklyar O, Boutros M, Huber W: EBImage--an R package for image processing with applications to cellular phenotypes. Bioinformatics. 2010, 26: 979-981. 10.1093/bioinformatics/btq046.
Bjornsson CS, Lin G, Al-Kofahi Y, Narayanaswamy A, Smith KL, Shain W, Roysam B: Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue. J Neurosci Methods. 2008, 170: 165-178. 10.1016/j.jneumeth.2007.12.024.
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J-Y, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A: Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012, 9: 676-682. 10.1038/nmeth.2019.
Hamilton NA, Teasdale RD: Visualizing and clustering high throughput sub-cellular localization imaging. BMC Bioinformatics. 2008, 9: 81-10.1186/1471-2105-9-81.
Hamilton NA, Wang JTH, Kerr MC, Teasdale RD: Statistical and visual differentiation of subcellular imaging. BMC Bioinformatics. 2009, 10: 94-10.1186/1471-2105-10-94.
De Chaumont F, Dallongeville S, Olivo-Marin J-C ICY: A new open-source community image processing software. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2011, Chicago, IL: IEEE, 234-237.
De Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, Meas-Yedid V, Pankajakshan P, Lecomte T, Le Montagner Y, Lagache T, Dufour A, Olivo-Marin J-C: Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods. 2012, 9: 690-696. 10.1038/nmeth.2075.
Kreshuk A, Straehle CN, Sommer C, Koethe U, Cantoni M, Knott G, Hamprecht FA: Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS One. 2011, 6: e24899-10.1371/journal.pone.0024899.
Abràmoff MD, Hospitals I, Magalhães PJ, Abràmoff M: Image Processing with ImageJ. Biophotonics Int. 2004, 11: 36-42.
Schneider CA, Rasband WS, Eliceiri KW: NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012, 9: 671-675. 10.1038/nmeth.2089.
Collins T: ImageJ for microscopy. BioTechniques. 2007, 43: S25-S30.
Pietzsch T, Preibisch S, Tomancák P, Saalfeld S: ImgLib2--generic image processing in Java. Bioinformatics. 2012, 28: 3009-3011. 10.1093/bioinformatics/bts543.
Schroeder W: The ITK Software Guide Second Edition Updated for ITK version 2. 4. 2005
Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Thiel K, Wiswedel B: KNIME - the Konstanz information miner. ACM SIGKDD Explorations Newsletter. 2009, 11: 26-10.1145/1656274.1656280.
Peng H, Long F, Ding C: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005, 27: 1226-1238.
Goldberg IG, Allan C, Burel J-M, Creager D, Falconi A, Hochheiser H, Johnston J, Mellen J, Sorger PK, Swedlow JR: The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 2005, 6: R47-10.1186/gb-2005-6-5-r47.
Swedlow JR, Goldberg IG, Eliceiri KW: Bioimage informatics for experimental biology. Annu Rev Biophys. 2009, 38: 327-346. 10.1146/annurev.biophys.050708.133641.
Allan C, Burel J, Moore J, Blackburn C, Linkert M, Loynton S, Macdonald D, Moore WJ, Neves C, Patterson A, Porter M, Tarkowska A, Loranger B, Avondo J, Lagerstedt I, Lianas L, Leo S, Hands K, Hay RT, Patwardhan A, Best C, Kleywegt GJ, Zanetti G, Swedlow JR: OMERO: flexible, model-driven data management for experimental biology. Nat Methods. 2012, 9: 245-253. 10.1038/nmeth.1896.
Moore J, Allan C, Burel J-M, Loranger B, MacDonald D, Monk J, Swedlow JR: Open tools for storage and management of quantitative image data. Methods Cell Biol. 2008, 85: 555-570.
Cho BH, Cao-Berg I, Bakal JA, Murphy RF: OMERO.searcher: content-based image search for microscope images. Nat Methods. 2012, 9: 633-634. 10.1038/nmeth.2086.
Bauch A, Adamczyk I, Buczek P, Elmer F-J, Enimanev K, Glyzewski P, Kohler M, Pylak T, Quandt A, Ramakrishnan C, Beisel C, Malmström L, Aebersold R, Rinn B: openBIS: a flexible framework for managing and analyzing complex data in biology research. BMC Bioinformatics. 2011, 12: 468-10.1186/1471-2105-12-468.
Vaccarella A, Enquobahrie A, Ferrigno G, De ME: Modular multiple sensors information management for computer-integrated surgery. Int J Med Robot. 2012, 8: 253-260. 10.1002/rcs.1412.
Zhao T, Velliste M, Boland MV, Murphy RF: Object type recognition for automated analysis of protein subcellular location. IEEE Trans Image Process. 2005, 14: 1351-1359.
Peng T, Bonamy GMC, Glory-Afshar E, Rines DR, Chanda SK, Murphy RF: Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns. Proc Natl Acad Sci U S A. 2010, 107: 2944-2949. 10.1073/pnas.0912090107.
Rajaram S, Pavie B, Wu LF, Altschuler SJ: PhenoRipper: software for rapidly profiling microscopy images. Nat Methods. 2012, 9: 635-637. 10.1038/nmeth.2097.
Peng H, Ruan Z, Long F, Simpson JH, Myers EW: V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat Biotechnol. 2010, 28: 348-353. 10.1038/nbt.1612.
Peng H, Long F, Myers EW: VANO: a volume-object image annotation system. Bioinformatics. 2009, 25: 695-697. 10.1093/bioinformatics/btp046.
Rueden C, Eliceiri KW, White JG: VisBio: a computational tool for visualization of multidimensional biological image data. Traffic. 2004, 5: 411-417. 10.1111/j.1600-0854.2004.00189.x.
Schroeder W, Martin KW, LORENSEN B: The Visualization Toolkit An Object-Oriented Approach to 3-D Graphics. 1996, N.J.: Prentice Hall PTR - Upper Saddle River, 1-826.
Engel K, Bauer M, Greiner G, Ertl T, Group CG, Group IS: Interactive Volume Rendering on Standard PC Graphics Hardware Using Multi-Textures and Multi-Stage Rasterization. ACM SIGGRAPH/Eurographics Workshop on Graphics Hardware. 2001, 2000: 109-118.
Orlov N, Shamir L, Macura T, Johnston J, Eckley DM, Goldberg IG: WND-CHARM: Multi-purpose image classification using compound image transforms. Pattern Recognit Lett. 2008, 29: 1684-1693. 10.1016/j.patrec.2008.04.013.
Gustafsson MGL: Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution. Proc Natl Acad Sci U S A. 2005, 102: 13081-13086. 10.1073/pnas.0406877102.
Huang B, Wang W, Bates M, Zhuang X: Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science. 2008, 319: 810-813. 10.1126/science.1153529.
Hess ST, Girirajan TPK, Mason MD: Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys J. 2006, 91: 4258-4272. 10.1529/biophysj.106.091116.
Jones SA, Shim S-H, He J, Zhuang X: Fast, three-dimensional super-resolution imaging of live cells. Nat Methods. 2011, 8: 499-508. 10.1038/nmeth.1605.
Keller PJ, Schmidt AD, Wittbrodt J, Stelzer EHK: Digital scanned laser light-sheet fluorescence microscopy (DSLM) of zebrafish and Drosophila embryonic development. Cold Spring Harb Protoc. 2011, 2011: 1235-1243.
Bria A, Iannello G: TeraStitcher - A Tool for Fast Automatic 3D-Stitching of Teravoxel-Sized Microscopy Images. BMC Bioinformatics. 2012, 13: 316-10.1186/1471-2105-13-316.
Cardona A, Tomancak P: Current challenges in open-source bioimage informatics. Nat Methods. 2012, 9: 661-665. 10.1038/nmeth.2082.
Carpenter AE, Kamentsky L, Eliceiri KW: A call for bioimaging software usability. Nat Methods. 2012, 9: 666-670. 10.1038/nmeth.2073.
Edelstein A, Amodaj N, Hoover K, Vale R, Stuurman N: Computer control of microscopes using μManager. Current protocols in molecular biology. Edited by: Ausubel FM. 2010, Chapter 14:Unit14.20
Shamir L, Delaney JD, Orlov N, Eckley DM, Goldberg IG: Pattern recognition software and techniques for biological image analysis. PLoS Comput Biol. 2010, 6: e1000974-10.1371/journal.pcbi.1000974.
Loo L-H, Wu LF, Altschuler SJ: Image-based multivariate profiling of drug responses from single cells. Nat Methods. 2007, 4: 445-453.
Johnston J, Iser WB, Chow DK, Goldberg IG, Wolkow CA: Quantitative image analysis reveals distinct structural transitions during aging in Caenorhabditis elegans tissues. PLoS One. 2008, 3: e2821-10.1371/journal.pone.0002821.
Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ: Multidimensional drug profiling by automated microscopy. Science. 2004, 306: 1194-1198. 10.1126/science.1100709.
Cornelissen F, Cik M, Gustin E: Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry. J Biomol Screen. 2012, 17: 496-506. 10.1177/1087057111432885.
Uni-potent stem cells
Despite the increasing interest in totipotent and pluripotent stem cells, unipotent stem cells have not received the most attention in research. A unipotent stem cell is a cell that can create cells with only one lineage differentiation. Muscle stem cells are one of the example of this type of cell (15). The word ‘uni’ is derivative from the Latin word ‘unus’ meaning one. In adult tissues in comparison with other types of stem cells, these cells have the lowest differentiation potential. The unipotent stem cells could create one cell type, in the other word, these cells do not have the self-renewal property. Furthermore, despite their limited differentiation potential, these cells are still candidates for treatment of various diseases (16).
ESCs are self-renewing cells that derived from the inner cell mass of a blastocyst and give rise to all cells during human development. It is mentioned that these cells, including human embryonic cells, could be used as suitable, promising source for cell transplantation and regenerative medicine because of their unique ability to give rise to all somatic cell lineages (17). In the other words, ESCs, pluripotent cells that can differentiate to form the specialized of the various cell types of the body (18). Also, ESCs capture the imagination because they are immortal and have an almost unlimited developmental potential. Due to the ethical limitation on embryo sampling and culture, these cells are used less in research (19).
HSCs are multipotent cells that give rise to blood cells through the process of hematopoiesis (20). These cells reside in the bone marrow and replenish all adult hematopoietic lineages throughout the lifetime of the human and animal (21). Also, these cells can replenish missing or damaged components of the hematopoietic and immunologic system and can withstand freezing for many years (22).The mammalian hematopoietic system containing more than ten different mature cell types that HSCs are one of the most important members of this. The ability to self-renew and multi-potency is another specific feature of these cells (23).
Noise permits stochastic and non-deterministic computations
Combinatorial logic circuits, FSMs and TMs model deterministic computations, which are essentially step-wise descriptions of mathematical functions that map inputs to outputs, where each step follows in a predetermined way from the previous step. Deterministic computation can be generalised to include stochastic and non-deterministic computation. Stochastic (or probabilistic, or randomised) computing refers to algorithms which can make random choices during their execution. Specifically, a given input may generate a number of different computational paths, each with some probability, and the cumulative probability of all such paths is equal to one. Probabilistic algorithms are a cornerstone of computer science 63 , and are used to solve approximately, but efficiently, hard optimisation problems for which deterministic algorithms would be intractable. One of the most important algorithms in systems biology, the Gillespie algorithm, is stochastic. Gillespie showed from basic quantum mechanics that a set of biochemical reactions must be understood as a stochastic process 64 , and offered an efficient way for simulating (sampling) its dynamics 65 . Therefore, cells may already be seen as “stochastic processors” that could provide a substrate for implementing probabilistic algorithms. Indeed, the utility of stochastic processors has already been recognised in the silicon world 50,66 .
In non-deterministic models of computation, as in stochastic ones, there may be many computational paths from input to output, but crucially each of these computational paths is explored simultaneously by the algorithm, with a result analogous to parallel execution of multiple distinct deterministic algorithms. While non-determinism has considerable impact on theoretical computer science, practical implementations of non-deterministic computation remain elusive. Nevertheless, algorithmic solutions to some computational tasks can be significantly easier to express as non-deterministic, and biological systems extensively exploit non-determinism, both at the population level 67,68 and at the scale of evolution 69 . Perhaps more speculatively, we might also consider the role of quantum effects in biology 70 , and whether or not these may be harnessed for the purposes of non-deterministic algorithms in biological systems.
Conditional Reprogramming: An Interview with Dr. Richard Schlegel on Growing Cancer Cells
Chromosomes from glioblastoma cells grown from a human tumor sample using the conditional reprogramming technique.
In January, researchers published the complete protocol for a new method of growing normal and tumor cells from patient samples in the laboratory. Known as conditional reprogramming, the technique was first developed in 2012 as a new way to establish cultures of cells and keep them alive indefinitely.
Richard Schlegel, M.D., Ph.D., who directs the Center for Cell Reprogramming at Georgetown University Medical Center, discusses the method, which he co-developed, and how it is being used in cancer research.
What is conditional reprogramming?
Conditional reprogramming (CR) is a cell culture technique that can be used to rapidly and efficiently establish patient-derived cell cultures from both normal and diseased cells, including tumor cells. With this technique, we can grow a million new cells in a week and keep them alive for as long as they are needed. The method can also be applied to animal models, including mouse, rat, ferret, dog, horse, and even fish.
Why is it important to be able to culture cells derived from patients?
Most of the cancer cell lines currently available do not mimic the precise genetic, molecular, and phenotypic changes observed in tumor cells derived from specific patients. Our method is the perfect definition of precision medicine. It allows the expansion of a patient's tumor, thereby making it possible to identify the specific mutations in these cells and to screen the cells for sensitivity to drugs.
How does the method work in practice?
The technique is relatively simple. It involves culturing the cells from patient samples with irradiated mouse cells and a compound known as a Rho kinase (ROCK) inhibitor. Although the CR methodology is relatively straightforward, laboratories that use this technique need to understand how to culture irradiated mouse cells together with the patient’s tumor cells. We have trained nearly 50 laboratories on how to use CR.
Why is the technique called conditional reprogramming?
We called the method conditional reprogramming for two reasons. First, when normal epithelial cells are transferred from standard cell culture medium conditions to CR cell conditions, the cells are rapidly reprogrammed so that they take on the characteristics of adult stem cells, as we reported in 2012. During this process, the cells become less differentiated and begin to divide rapidly .
Second, when cells in CR cultures—which are undifferentiated and stem-like—are transferred to conditions that mimic in vivo environments, they revert to their normal differentiated state and organize into structures similar to the tissue from which they were derived. Therefore, the reprogramming is reversible, or conditional.
What does your new publication include?
The summary we just published in Nature Protocols provides detailed and specific instructions for performing the technique. This updated methodology describes several steps that are critical for achieving successful results, as well as some minor modifications that allow for the growth of non-epithelial cells, such as those of neural, neuroendocrine, endocrine, and mesenchymal origin.
What are some advantages of using conditional reprogramming in cancer research?
The greatest advantage of CR is its rapid and efficient expansion of cell cultures from patient tissue samples. This allows researchers to screen tumors for sensitivity to anticancer drugs or immunotherapies quickly enough for the information to be of clinical use. For example, we used reprogrammed cells to expand a biopsy from a patient’s lung tumor and, within a week, used the cultured cells to identify a successful treatment for the patient.
The technique can also generate large numbers of cells for use in other patient-derived cancer models, such as xenograft models and organoid cultures. Finally, the CR method can be used to expand benign or potentially precancerous lesions from patients, which cannot be achieved with the patient-derived xenograft model. This could help researchers investigate the earliest changes that occur in cancer.
How have cancer researchers used conditional reprogramming to date?
Cancer researchers have primarily focused on two aspects of the method. The first is to grow tumor cells that can be screened for responses to anticancer drugs. The second is for basic science studies to define the genetic, epigenetic, biochemical, and metabolic alterations in tumor cells from cancer patients and animal models.
Are there applications for this approach in the emerging field of precision medicine?
There are definitely exciting possibilities for CR in precision medicine. The method was cited in the Precision Medicine Initiative® in Oncology as a way to develop new laboratory models for research. In addition, CR has been used to identify potential new drug combinations for lung cancer and to identify molecular pathways of resistance to therapies. Currently, researchers are using CR in a clinical trial that is testing individualized therapies for patients with pancreatic cancer.
Are certain types of cancer or normal cells easier to grow than others?
Actually, most tumor cell types can be grown with CR. And with the adaptations we describe in our new paper, tumors of non-epithelial origin—such as neural, neuroendocrine, and stromal—can now be propagated. We have not yet been able to propagate hematologic malignancies, however.
Richard Schlegel, M.D., Ph.D., Georgetown University Medical Center
Could the technique be adopted widely right now?
Yes. With this goal in mind, we recently participated in a Cold Spring Harbor Laboratory course that provided students with both didactic and wet lab training in the method. In addition, a number of facilities have been set up recently to use CR to generate new tumor cell cultures, including at the Broad Institute and Duke University.
Finally, NCI recently announced a funding opportunity designed to support the biological comparisons of patient-derived models of cancer. This will fund comprehensive comparisons of next-generation cancer models, including CR cells.
Does conditional reprogramming capture the genetic diversity, or heterogeneity, of tumors?
While the CR technique retains the heterogeneity of cell types from normal tissues, such as the lungs, for example, whether it does so in tumors is not yet known. We do know, however, that the tumor cell cultures retain the specific genetic alterations found in the primary tumor. We expect that future studies will be able to use CR to help identify the spectrum of genetic changes present in isolated tumor cells.
Bioprinting: Living cells in a 3D printer
Tissue growth and the behavior of cells can be controlled and investigated particularly well by embedding the cells in a delicate 3D framework. This is achieved using additive 3D printing methods -- so called "bioprinting" techniques. However, this involves a number of challenges: Some methods are very imprecise or only allow a very short time window in which the cells can be processed without being damaged. In addition, the materials used must be cell-friendly during and after the 3D biopriting process. This restricts the variety of possible materials.
A high-resolution bioprinting process with completely new materials has now been developed at TU Wien (Vienna): Thanks to a special "bio ink" for the 3D printer, cells can now be embedded in a 3D matrix printed with micrometer precision -- at a printing speed of one meter per second, orders of magnitude faster than previously possible.
The environment matters
"The behavior of a cell behaves depends crucially on the mechanical, chemical and geometric properties of its environment," says Prof. Aleksandr Ovsianikov, head of the 3D Printing and Biofabrication research group at the Institute of Materials Science and Technology (TU Wien). "The structures in which the cells are embedded must be permeable to nutrients so that the cells can survive and multiply. But it is also important whether the structures are stiff or flexible, whether they are stable or degrade over time."
It is possible to first produce suitable structures and then colonise them with living cells -- but this approach can make it difficult to place the cells deep inside the scaffold, and it is hardly possible to achieve a homogeneous cell distribution that way. The much better option is to embed the living cells directly into the 3D structure during the production of the structure -- this technique is known as "bioprinting."
Printing microscopically fine 3D objects is no longer a problem today. However, the use of living cells presents science with completely new challenges: "Until now, there has simply been a lack of suitable chemical substances," says Aleksandr Ovsianikov. "You need liquids or gels that solidify precisely where you illuminate them with a focused laser beam. However, these materials must not be harmful to the cells, and the whole process has to happen extremely quickly."
Two photons at once
In order to achieve an extremely high resolution, two-photon polymerization methods have been used at TU Wien for years. This method uses a chemical reaction that is only initiated when a molecule of the material simultaneously absorbs two photons of the laser beam. This is only possible where the laser beam has a particularly high intensity. At these points the substance hardens, while it remains liquid everywhere else. Therefore, this two-photon method is best suited to produce extremely fine structures with high precision.
However, these high resolution techniques usually have the disadvantage of being very slow -- often in the range of micrometers or a few millimeters per second. At TU Wien, however, cell-friendly materials can be processed at a speed of more than one meter per second -- a decisive step forward. Only if the entire process can be completed within a few hours is there a good chance of the cells surviving and developing further.
Numerous new options
"Our method provides many possibilities to adapt the environment of the cells," says Aleksandr Ovsianikov. Depending on how the structure is built, it can be made stiffer or softer. Even fine, continuous gradients are possible. In this way, it is possible to define exactly how the structure should look in order to allow the desired kind of cell growth and cell migration. The laser intensity can also be used to determine how easily the structure will be degraded over time.
Ovsianikov is convinced that this is an important step forward for cell research: "Using these 3D scaffolds, it is possible to investigate the behavior of cells with previously unattainable accuracy. It is possible to study the spread of diseases, and if stem cells are used, it is even possible to produce tailor-made tissue in this way."
The research project is an international and interdisciplinary cooperation in which three different institutes of the TU Vienna were involved: Ovsianikov's research group was responsible for the printing technology itself, the Institute of Applied Synthesic Chemistry developed fast and cell friendly photoinitiators (the substances that initiate the hardening process when illuminated) and the Institute of Lightweight Structures and Structural Biomechanics analyzed the mechanical properties of the printed structures.
Bioprinting: Ethical and societal implications
In a recent ASCB Post article in the “What’s it all about?” series, Amanda Haage explains developments in the recent field of 3D printing with biological materials (i.e., bioprinting). Although these methods are still being fine-tuned, the field holds tremendous promise in such areas as biology, pharmacology, and medicine. For example, one day scientists may be able to use bioprinting to manufacture artificial organs for patients who need life-saving organ transplants, and bioprinting may speed up the process of testing the safety of new drugs with little need for animal testing. However, as this field is still new and rapidly growing, it’s important for us as a society to have conversations now about how this technology will challenge our ethical and cultural ideals. As noted above by Haage and discussed at length in a recent review on social and ethical implication of bioprinting, the broader societal impacts of this field need to be better addressed now before the technology becomes more widespread. Although the ideas discussed below are far from comprehensive, here are three areas in the field of bioprinting where we will need to bridge the gap between science and humanity.
Reducing the demand for animal testing with “organs-on-a-chip”This lung-on-a-chip serves as an accurate model of human lungs to test for drug safety and efficacy. Credit: Wyss Institute for Biologically Inspired Engineering, Harvard University
Although the ability to produce an entire organ by bioprinting is far off in the future, scientists are already producing smaller organoids and tissues in the lab, sometimes called “organs-on-a-chip.” These lab-produced organs-on-a-chip are already being used by several pharmaceutical and cosmetic companies (including L’Oréal, AstraZeneca, Sanofi, and Roche) to test the safety of new drugs and products on certain types of tissues (such as skin, nerves, and liver). These organs-on-a-chip allow researchers to quickly and reproducibly test many drugs or products at once, as well as reduce the need for animals to test the safety and toxicity of products. Although animal models are still necessary and invaluable for certain kinds of biomedical research, such as testing how diseases like cancer or dementia progress through the entire body, reducing the number of animals needed for earlier steps in the research process will be a win for the ethical use of animals in research. At the same time, scientists will need to carefully assess if “organs-on-a-chip” are as effective as animal models at predicting drug toxicities, since ensuring patient safety during new clinical trials should always be a top priority.
In December at the ASCB/EMBO Meeting, ASCB will release a white paper on organoids that will discuss the challenges and opportunities in this area of research.
Bioprinting organ transplants: Democratizing life-saving treatment or widening the gap of income inequality in medicine?
Although currently only a hypothetical scenario, bioprinting organs may revolutionize the field of organ transplants by significantly reducing huge costs and wait times. According to the National Foundation for Transplants, the current cost of transplanting an organ in the United States can easily surpass $500,000-$1,000,000, and certain insurance companies make patients prove they can pay 20% of the upfront costs before the transplant can occur. This total does not include post-transplant medications and medical care to prevent organ rejection, which costs tens of thousands of additional dollars per year. In addition, the average wait time for a suitable organ donor for most organ transplants ranges from six months to two years. As of now, the ability to print an entire functional organ is still many years or even decades away. However, as happens with all sectors of technology, it’s predicted that bioprinting will become cheaper, faster, and more widespread as time goes on. A printed organ that costs tens of thousands of dollars and could be produced in a few weeks would still be a huge leap for the field compared with the current costs of organ donation and would be a boon for the hundreds of thousands of patients in dire need of a transplant.
Pediatric patients, in particular, have the potential to hugely benefit from bioprinting technology. Children provide a unique challenge for transplant and biomedical device technologies because kids’ bodies are still growing and changing. For example, if a child receives an artificial heart valve, they may need multiple surgeries in the future to upgrade to a larger valve as they continue to grow. Bioprinting new tissues or organs for pediatric patients may allow for the new devices to grow with the child, reducing the need for multiple surgeries.
That being said, expensive personalized therapies such as bioprinting also pose the risk of widening the ever-growing socioeconomic gap in medical treatment. Widespread affordable accessibility has been a challenge with other cutting-edge and pricey therapies, such as gene therapy, cancer immunotherapy, and genomics-driven personalized medicine. 3D bioprinting runs the same risk of becoming accessible only to the very rich (or very well-insured) if we as a society don’t make a way for it to become widely available to anyone who needs it, not just for anyone who can afford it.
Intellectual property: Who owns and profits from a bioprinted product?http://www.yole.fr/iso_album/organs-on-chips_investments_yole_april2017_433x280.jpg
The process of producing a 3D bioprinted organ or tissue is incredibly complex and uses methods developed by many different people to turn an idea into a living, functional product. Although the field of 3D printing as a whole relies heavily on open-access data and designs, the question of who owns the results has legal and monetary implications in regards to regulation and patents even before bioprinted products are sold to patients. To strike a balance between full open-access for patients to promote accessibility and restricted use with strong legal protections for companies to promote innovation, a recent law review proposed that we allow patents on the bioprinting process but not on the actual final products. “Organ-on-a-chip” technologies alone are projected to be worth about $60 million by 2022, and the field has the potential to become a multi-billion dollar industry, so deciding how to properly patent this technology could greatly influence how the field develops and how much access patients and consumers would have.
In addition, medical devices such as printed organs don’t fit easily into our existing system of clinical trials, so scientists may need to develop a new system of preclinical and clinical trials to test the safety in humans of bioprinting and other “open-access” devices. As technology improves, it’s likely that scientists will also develop more ways to customize bioprinted organs (such as using a patient’s own induced pluripotent stem cells to grow new tissue types), leading to even more challenges to the idea of who legally “owns” and profits from something that is printed with living material. Furthermore, if cells are taken from a donor instead of directly from the patient, protections will be needed to keep identifying genetic information private and to ensure proper informed consent from donors (i.e., that donors know exactly what their donated cells will be used for). Legal and economic questions like these won’t be answered by scientists alone, so collaborations between scientists, policymakers, lawyers, and more will be needed to fully address these issues.
Science is messy and complicated, not only because life itself is so incredibly complex, but also because science is inseparable from how we interact with it as a society. Bioprinting is a prime example of technology affecting humanity and vice versa. To fully harness the benefits of bioprinting, we need to have conversations now about when it is ethical and beneficial to use the technology and who really gains from it, both medically and economically.
The views and opinions expressed in this blog are the views of the author(s) and do not represent the official policy or position of ASCB.