Bench Press

Influenza viruses are negative-stranded, enveloped orthomyxoviruses that contain eight gene segments encoding various viral proteins. During the viral life cycle point mutations drive genetic drift responsible for seasonal influenza epidemics. Novel influenza viruses can also be produced through genetic reassortment between viruses. These viruses can result in devastating pandemics as they expose their host populations to novel antigenicity. These alterations in the influenza viruses’ genomes require that vaccine strains be updated annually to account for changes in virus populations.

Currently two types of vaccines are utilized to combat seasonal flu, each with their own limitations:

1. Chemically inactivated virus delivered via injection – mainly acts by inducing antibody response rather than cellular immunity and has suboptimal efficacy in elderly patients.
2. Live attenuated influenza virus vaccine of cold-adapted virus delivered via nasal spray – induces both humoral and cellular immunity, but is restricted to healthy children, adolescents, and adults, performing better in immunologically naive young children than adults.

So despite the relative effectiveness of the current offerings there is definitely room for improvement. That’s where research being done by Dr. Steffen Mueller and colleagues at Stony Brook University comes in. Building on prior research on producing synthetically attenuated polio viruses, Dr. Mueller’s group utilized a technique dubbed Synthetic Attenuated Virus Engineering (SAVE) to generate synthetic influenza virus vaccine candidates. They detail their work in a paper in Nature Biotechnology titled Live attenuated influenza virus vaccines by computer-aided rational design.

What’s truly unique about SAVE is that it only uses silent mutations within the viral genome to produce live attenuated virus. As described in the paper:

The central idea of SAVE is to recode and synthesize a viral genome in a way that perfectly preserves the WT amino acid sequence, while rearranging existing synonymous codons to create a suboptimal arrangement of pairs of codons. For reasons that are not understood, some pairs of codons occur more frequently, and others less frequently, than expected. … Although the mechanism of attenuation is unclear, preliminary evidence suggests that translation is affected. Attenuation can be ‘titrated’ by adjusting the extent of codon-pair deoptimization. Because codon-pair deoptimization results from miniscule effects at each of hundreds or thousands of nucleotide mutations (without changing amino acid sequences), reversion to virulence is extremely unlikely. Aided by computer algorithms, codon pair–deoptimized viral genomes can be rapidly designed and synthesized, and live virus can be generated by reverse genetics.

Figure 1

Utilizing SAVE Dr. Meuller’s group generated influenza strains with the following proteins synthetically deoptimized, polymerase subunit B1 (PB1), nucleoprotein (NP), and hemagglutinin (HA). They tested strains with a single deoptimized gene as well as one strain with all three altered genes. First Dr. Meuller’s group analyzed each of their strains in vitro to assess their growth characteristics. They found that all the mutant viruses produced plaques similar to wild type and produced reasonable titers although slightly lower (about tenfold lower). What was most interesting about their in vitro characterization however was their western blot analysis of the synthetically designed proteins. In each mutant it is clear that the gene that underwent SAVE produces significantly less protein in comparison to wild type (PR8) (Figure 1). The effect is specific and lends support to the idea that codon-pair deoptimization results in an effect on translation.

After checking their in vitro characteristics, Dr. Meuller’s group tested for attenuation by infected mice. They found that despite the reasonably robust viral growth each mutant strain demonstrated an attenuation effect. A strain combining all three modified genes (PR8^3F) resulted in an increase in median LD₅₀ of about 13,000 fold. Further tests on PR8^3F showed effective attenuation of symptoms and viral load in comparison to wild type (Figure 2).

Figure 2

In addition to assessing attenuation of the virus, Dr. Meuller’s group assessed the immune response and protective immunity in detail by immunizing mice and then challenging them 28 days after inoculation. As seen in Figure 3 below (a, b) strain PR8^3F had a much larger range of safe doses in comparison to wild type. Viral load is effectively limited (c) in PR8^3F inoculated animals and antibody response is robust (d) even in very low doses in comparison to LD₅₀ of PR8^3F.

Figure 3

Ultimately, this paper illustrates a really interesting new technology that seems capable of revolutionizing vaccine development. It provides a novel vaccine design strategy that appears to produce robust immune protection. It remains to be seen if SAVE vaccine candidates can address the limitations of current flu vaccines as well as other issues, but this paper is a strong first step for this technology.

(Source – Nature Biotechnology)

I have always been impressed with the work of astronomers. Unlike biologists and chemists who can, for a wide array of topics, actually touch and feel what they are studying, astronomers have to make conclusions with only careful observations conducted with powerful telescopes and computers informed by understanding the laws of physics (quantum and relativity included) and backed up by complex computational models.

One phenomena which astronomers can use to better explore deep space is an effect predicted by Einstein’s theory of general relativity called gravitational lensing. General relativity predicts that the path of light can be bent by gravitational fields; the most dramatic example of this would be a black hole, where gravity is so strong that light “falls” back into it. The same effect, on a less dramatic scale, could result in the path of light being bent on its way to Earth by the gravity of another object. The term “gravitational lens” refers to the fact that this bending is similar to the bending of light by a telescope lens.

Now, for the layperson, the fact that light can bend is probably just a cool effect which has no practical importance. But, to a well-trained astronomer, the knowledge of how gravity works lets them use the phenomena of gravitational lensing to understand both the objects that are emitting light (because the lensing effect allows us to see objects which are so far away that they are blocked by another object) and the “lens” itself (understanding the mass, structure, and position of what is bending the light).

Take a look at the pictures (HT: Wikipedia) below of Einstein rings. These occur when the line of sight to a bright, faraway object is being blocked by another object. However, because of the gravitational lens effect, the light from that faraway object can bend around the closer object, resulting in a ring which gives scientists a chance to study not only the faraway object but also understand the structure of the intervening space.

There are countless other examples of the application of gravitational lensing in the study of astronomy, but one of the most clever that I heard about recently was the study of dark matter. The theory in a nutshell: the universe is believed to be mostly dark matter – matter which does not reflect or emit any light whatsoever. Because it doesn’t seem to emit or reflect electromagnetic radiation, there has been no direct observational way to study it. However, dark matter does have mass. This means it has gravity and can thus bend light as a gravitational lens!

Researchers were able to took astronomical survey data from around the world and, using sophisticated computer algorithms and programs, compile a picture of gravitational lensing due to dark matter. From that, they were then able to digitally put together a picture of the structure of the dark matter in (at least part of) the universe and get a sense for how it’s evolved over time (the further from Earth you look, the further back in time):

And with this they made a striking conclusion – we all have dark matter to thank for the existence of the stars and the galaxies:

Our results are consistent with predictions of gravitationally induced structure formation, in which the initial, smooth distribution of dark matter collapses into filaments then into clusters, forming a gravitational scaffold into which gas can accumulate, and stars can be built.

Awesome.

(Image credit) (Image credit)

What would you do you if you were curious about the relative importance of selective pressures on a population of lizards on Caribbean islands? Since you’re reading Bench Press you might be inclined to turn to the power of computer modeling which can provide numerous advantages particularly when traditional experiments can’t be conducted. We’ve seen examples of computer models analyzing near earth asteroids, potential epidemics, and classic math and physics problems. However we’ve also seen that at times purely mathematical approaches can result in errors as well. Our inability to accurately describe problems with numbers 100% of the time makes it imperative that we continue to think creatively about ways to design experiments to test hypotheses.

That’s why I was particularly impressed by a paper in Nature by Ryan Calsbeek and Robert M. Cox, who wanted to explore the importance of selective pressures on anole lizards in the Caribbean. Field experiments to measure the effect of selective pressures are rare for a variety of reasons. A major one being the difficulty of finding animals and environments which can be manipulated in a controlled manner. Drs. Calsbeek and Cox didn’t let this stop them as they utilized a group of small islands in the Caribbean, each small enough to throw a ball end to end, as their test beds. There they removed the resident brown anole lizards and replaced them with experimental animals which had been carefully measured, tested for stamina, and tagged to identify at the end of the experiment.

Now that they had their experimental populations they needed to set up islands that tested the hypothesis that competition played a larger selective role than predation in island anole lizard populations. They established islands that had low and high density populations, and for each density type they setup islands inhabited by lizard-eating birds alone, lizard-eating birds and snakes, as well as islands free of predators (accomplished with a generous covering of netting as seen below). An unmodified control island was also monitored as a natural reference population.

They distributed the lizards in May and four months later at the end of the breeding season, September, they came back to capture the survivors and census the population. While this was difficult work they were able to collect a large amount of data which confirmed the hypothesis that competition is a more powerful selective force in these populations. They saw no real phenotypic differences in the lizards on islands experiencing differing predation, but saw that lizards surviving on crowded islands were significantly bigger and had greater stamina than those on less crowded islands (seen below). This indicated competition between lizards pushed the population while predation did not.  

While their clever experiment does a great job explaining the relative importance of selective pressures on this particular species of lizards on islands in the Caribbean it may not say anything about natural selection in other species. Despite that this paper remains awesome because as much as I like to see technology change the way we do science, I still appreciate a well constructed experiment to answer tough questions.

(Nature: Experimentally assessing the relative importance of predation and competition as agents of selection)

Having spent a few years working on cell based assays for screening small molecules I became aware of how limited traditional in vitro cell culture can be in modeling biological systems. Traditional tissue culture while fairly easy to do and manipulate for experiments, often produces two-dimensional growth with gene expression, signaling, and morphology that can be dramatically different from those found in vivo. This can make in vitro studies clinically irrelevant. In vivo work while a more accurate model has it’s own drawbacks such as cost and ease of manipulation. Therefore, it would be ideal to develop methods which can make in vitro tissue culture produce in vivo results.

That aim is what makes this paper by Souza et al. in Nature Nanotechnology so impressive to me. In this paper they describe a method for culturing cells three-dimensionally by magnetically levitating cells grown in the presence of a hydrogel consisting of gold, magnetic iron oxide nanoparticles, and filamentous bacteriophage. 

Dr. Souza’s group tested their hydrogel with glioblastoma cells as seen in the figure above. Application of a magnetic field allows the cells to counteract gravity floating in the media and allowing for three-dimensional growth. The field also concentrated cells resulting in cell to cell interactions consistent with previous work on tissue engineering scaffolds designed to provide a cell growth advantage. In addition, the shape of the magnetic field can also be used to shape cell growth.

While the ability to promote three-dimensional growth without biodegradable porous scaffolds or protein matrixes is remarkable, the truly impressive part of this technique is that the cells exhibit differential protein expression that more closely resembles that of in vivo tumor xenografts as seen in the figure below thanks to their new growth conditions.
The ability of these three-dimensional cultures to mimic in vivo samples effectively is remarkable and the simplicity of this technology could provide a less time intensive and cost effective solution to traditional experimental methods. It’d certainly be nice to someday be able to design in vitro assays which produce truly clinically relevant data. Maybe with techniques like this one we’ll be able to accomplish that soon.

(Source – Nature Nanotechnology : Three-dimensional tissue culture based on magnetic cell levitation)

Science News recently put out a very interesting article about the numerous mistakes that many a doctor and scientist have made in evaluating statistics. Given the importance of statistical analyses in research today (who doesn’t worship at the “altar of p < 0.05”?), I was pretty shocked at how poor a typical scientist’s statistical training is.

The article highlights a few key common misconceptions to watch out for:

1. The opposite of a false-positive is not necessarily a true-positive: How many times have you heard the explanation that a p-value of 0.05 means that “it is at least 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance”? Well, that’s an understandable but unfortunate error. A p-value of 0.05 implies that there is a 5% chance that the result observed is what you would get in that particular experiment if the opposite of what you believe is true: in other words, the probability of a false-positive. However, it does not mean a 95% chance that the hypothesis is correct. Or, to use an example from the Science News article: suppose there is a drug test which detects steroid use in athletes. This test has a 5% false-positive rate (kind of like having a p-value of 0.05). Suppose a specific athlete comes back with a positive drug test. What is the chance that he or she is actually a steroid user? By now, you know the answer is not 95%, but what is it? The actual answer depends on how many athletes actually use steroids. Or, to use real numbers, suppose 5% of a group of 400 tested athletes are actual steroid users. Therefore:
   • There are 20 athletes (5%) who use steroids and 380 (95%) who don’t use
   • Of the 20 athletes that are actual steroid users, the test will correctly identify 19 (95%) as users
   • Of the 380 athletes that are not actual steroid users, the test will incorrectly identify 19 (5%) as steroid users.

   The final result? Of the 38 athletes identified as steroid users, 50% (19) will be false positives! So, even though the test/experiment’s results have a p-value of 0.05, the actual probability that the hypothesis is correct was only 50%, not 95%. Keep that in mind the next time you hear someone use a p-value to assert they have a better than 95% shot at being correct.

2. Statistical significance does not necessarily mean actually significant: The poster-children for this type of error are the numerous articles floating around that random food/environmental factor XYZ causes a “significantly increased risk” of cancer/heart disease/death/something bad. Just because something observed is highly unlikely to be explained away by chance, doesn’t mean that the actual impact itself is significant by any actual sense of the word. An increase in a risk might be real, but if its an increase in risk of cancer from 0.01% to 0.011%, I’d hardly call that significant.
3. Large numbers of experiments means large numbers of false positives. The archetype for this type of error are the large genome-wide studies done to find genetic fingerprints which tend to go with a particular disease. If you are studying 20,000 genes with a survey tool which has a p-value of 0.05, elementary multiplication suggests that you’ll find at least 1,000 genes (5%) showing up as hits which aren’t actually related to the disease at all! This isn’t to say that so-called genome-wide association studies are all bunk (the best studies will use multiple means to verify and assess if a gene is related to a given condition), but it should be the first thing that you think about when evaluating claims on a large data set based on standard statistical tools.
4. “Statistically significant” isn’t always statistically significant. Imagine two clinical trials comparing Drug A and Drug B with placebo. Although data shows that both Drug A and Drug B provide improvements over placebo, only Drug A demonstrates a statistically significant improvement. Does this then mean that Drug A is statistically significantly better than Drug B, which the trial suggested does not provide a statistically significant improvement over placebo? The answer, obviously, is that it depends on the level of improvement. The point of that little mental exercise, however, is that the status of being “statistically significant” doesn’t confer any special significance or power when making a different comparison. An illustration of the real-world consequences of this comes from an example from the Science News article:
   

   A number of studies have suggested that children and adolescents taking antidepressants face an increased risk of suicidal thoughts or behavior… One set of such studies, for instance, found that with the antidepressant Paxil, trials recorded more than twice the rate of suicidal incidents for participants given the drug compared with those given the placebo. For another antidepressant, Prozac, trials found fewer suicidal incidents with the drug than with the placebo. So it appeared that Paxil might be more dangerous than Prozac.

   But actually, the rate of suicidal incidents was higher with Prozac than with Paxil. The apparent safety advantage of Prozac was due not to the behavior of kids on the drug, but to kids on placebo — in the Paxil trials, fewer kids on placebo reported incidents than those on placebo in the Prozac trials.

   As the previous examples makes it clear, our ability to compare two forms of statistical comparison is extremely limited and subject to all sorts of extra considerations. This is one reason why many statisticians are skeptical of meta-analyses (studies which combine the data from multiple studies), and clearly illustrates why scientists and doctors everywhere need to bone up on their statistical training and their reading of the fine print on studies they use.

Read the list carefully, and don’t make these mistakes!

(Image credit)

If you’re an astronomy buff, February 14 means a lot more than just Valentines Day. It also marks the fateful day (HT: NASA Jet Propulsion Laboratory), in 1990, when the Voyager I spaceprobe took a “family portrait” of all the planets of our solar system that it could see as one last parting gift before it shut down its camera and continued its journey towards “interstellar space”:

The diagram above shows the 60 frames that Voyager I took. The pictures aren’t high-resolution beauties (as a result of needing to use optical tricks to correct for the amazing brightness of the sun and the light it scatters, and smearing from the long exposure times needed to capture Neptune and Uranus), but it is still amazing to think that this is the only family portrait mosaic of the solar system ever taken. Closeups on the 6 prominently visible planets are below (left to right and top to bottom are Venus, Earth, Jupiter, and Saturn, Uranus, Neptune):

More details are at the NASA JPL page, but I will leave you all with this bit from Carl Sagan:

This was the image that inspired Carl Sagan, the the Voyager imaging team member who had suggested taking this portrait, to call our home planet "a pale blue dot."

As he wrote in a book by that name, "That’s here. That’s home. That’s us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives. … There is perhaps no better demonstration of the folly of human conceits than this distant image of our tiny world."

Happy 20 year anniversary to the grandest family portrait humanity has ever taken, and happy Valentine’s Day to all.

(Image credits – NASA JPL)

Here at Bench Press we’re fans of PLoS because they strive to expand access to the world’s scientific and medical literature with their open access stance as well as other experimental endeavors such as PLoS Currents: Influenza. That’s why when I checked in on PLoS Biology I was intrigued by a new collection titled Genomics of Emerging Infectious Diseases.

The collection is a series of essays, perspectives, and reviews discussing the potential genomics research holds in understanding emerging infectious diseases. While I haven’t had a chance to read through very much of the collection yet, one perspective written by Rajesh Gupta, Mark H. Michalski, and Frank R. Rijsberman suggests an interesting plan for infectious disease research. They suggest beginning an Infectious Disease Genomics Project (IDGP), much like the Human Genome Project.

The IDGP would be:

a coordinated, large-scale, international effort focused on the genomes of pathogens, vectors, hosts, and reservoirs and linked to end-point surveillance and response systems. Such a project could coordinate activities in four specific areas: generating data, linking data, analyzing data, and applying data.

The figure above illustrates some of the specific things the authors envision the IDGP being able to coordinate. Ideally the IDGP provides:

• A “roadmap” for researchers to follow in sequencing and monitoring emerging pathogens that allow researchers worldwide to aid in global efforts while continuing critical research on local diseases.
• Advanced data management in an easy to use, open-source, real-time interface. With an emphasis on linking as much data with relevant details (e.g. literature references).
• A centralized analytical toolbox with dynamic databases allowing for collaboration worldwide in addition to improved access for researchers in resource-limited settings.
• Ability to incorporate emerging technologies and provide access (e.g. new assay methods, next generation sequencers).

Personally I find the IDGP very intriguing simply from the standpoint of developing a framework for worldwide scientific collaboration. If this were successful it could provide a model for future projects. On a practical level, providing a network of this sort for scientists to rely on could at least increase the speed at which emerging diseases are detected. Increasing the speed of detection is always a good thing when dealing with novel pathogens with pandemic potential. It’ll be interesting to see what the scientific community thinks about beginning an IDGP.

Readers any thoughts?

(Source – PLoS Biology: Can an Infectious Disease Genomics Project Predict and Prevent the Next Pandemic?)

I suspect that most people who enter the sciences are inspired by tales of the great scientists of yesteryear: bold luminaries who, through brilliance and ingenuity, helped uncovered the laws which govern the universe. For me, one of the most inspiring stories was that of Albert Einstein who, as a mere clerk in a Swiss patent office, published four papers which shook the foundations of physics in the span of one year! After all, who becomes a scientist who doesn’t have the dream of making a discovery or two so great that you become recognized as Person of the Century?

But, is this conception of science as a world where scientific Davids slay the Goliaths of orthodoxy and ignorance too romantic to be accurate? Is science still a field driven by brilliant individuals? This is a question which was top of mind for those attending the meeting of the International Astronomical Union, held in Rio de Janeiro from Aug 3-14, 2009 (HT: The Economist).

While theoreticians and well-funded groups in certain fields may still be able to comfortably push the “lone ranger” model of scientific research, in many areas (especially astronomy), the scientific frontier is being increasingly dominated by massive endeavors which consume enormous amounts of resources. After all, if your brilliant idea requires long, uninterrupted access to the Hubble Space telescope (i.e. the Hubble Deep field), you either get in line and save up, or you try to convince the rest of the astronomical community that your idea is worth pursuing (over their own, other, projects). This need to allocate very limited resources to a wide range of demands in astronomy has led to what The Economist refers to as “managerialism”:

The present is a “golden age” [for astronomy]. The rate of discoveries has been increasing, along with the means to keep up with the details. That has, in turn, led to bigger and more expensive telescopes, and the introduction of management techniques intended to ensure the smooth running of large projects. But it is that managerialism that is beginning to worry some of the more thoughtful members of the union. They fear that although it brings short-term benefits, it may, in the long run, crush individual flair.

This same clash between the desire to foster scientific Davids, but the need to build scientific Goliaths in order to use the latest and greatest (and most expensive) equipment is probably not unique to astronomy. After all, advances in technology have made possible new types of visualization (i.e. Imaging Mass Spectrometry to visualize how and where molecules move within a cell), new collections of vast amounts of data (i.e. the Diseasome), and even new ways of analyzing these new vast collections of data (i.e. the Millennium Simulation).

So is the “lone ranger” scientist doomed to have to one day ride off into the sunset? I don’t think so.

As we’ve discussed many times here at Bench Press, there are still plenty of innovative and relatively low-cost things that enthusiasts and scientists can do to push scientific inquiry. While there is no doubt that a lot of good can and will come out of big projects requiring costly equipment (I’m looking at you, LHC!), I think we’re far from the point where all experiments and models require multi-billion dollar investments.

Furthermore, while more expensive technology has made it more expensive to do experiments at the cutting edge, the advance of technology has made many other forms of inquiry much cheaper. For instance, technology has now made it possible for more and more people to collaborate and have access to data and the computational tools needed to analyze and report on it. If you had told Watson and Crick back in 1953, that every researcher would one day be able to as easily search a public database of nearly every gene and DNA/RNA sequence known for a match as they could read a book, they probably would’ve thought you were insane. And yet, today, I can not only randomly and arbitrarily search as many sequences as I want by using the NIH’s BLAST tool, I can quickly and cheaply deploy my own computing cluster using Amazon EC2 or, for specific types of computational workloads, even a graphics card/GPU!

I also think that, on some level, the fears about growing managerialism come from people who dramatically underestimate the value of collaboration between multiple scientists who can bring multiple specialties to the table, and the new ease of collaboration enabled by tools like Google Wave and Friendfeed.

In any event, even the field of astronomy seems to be trying to swing the pendulum back in favor of the Davids and Lone Rangers of the world:

Dr White suggests astronomers should ensure small science can flourish alongside its larger counterpart by, for example, ensuring that telescopes designed to look for big fish can also be used for projects that might be considered as small fry.

Another way to encourage gifted individuals might be to reform the way time on telescopes is allocated. The IAU’s new president, Robert Williams of the Space Telescope Science Institute in Baltimore, Maryland, is a supporter of this idea. He reckons decisions about who gets what observing time should be made by the directors of observatories, answerable to a governing body, rather than by groups of the great and good, as tends to happen now.

Williams is a particularly good authority on this – as he was one of those responsible for allotting the time necessary for Hubble’s Deep Field to be captured.

Viva la Lone Ranger!

(Image credit – Einstein) (Image credit – the Lone Ranger)

In a day and age where scientific exploration seems to require very expensive apparatuses, its hard to remind people that they can do their own mini-scientific inquiries relatively cheaply, with cell phones rigged as smart-sextants, Foucault pendulums, a gaming setup, or a widely available program like Mathematica .

It’s in that spirit that I was very happy to learn about the efforts of two enterprising MIT students who, in what they appropriately called Project Icarus, were able to take high-altitude pictures from the “edge of space” with a setup that included a weather balloon filled with helium, a cheap digital camera (Canon A470), a pre-paid phone with GPS (Motorola i290), an antenna (to extend the range of the phone, some basic tracking/geography software like Google Earth and Accutracking, and a styrofoam beer cooler to insulate the setup such that, collectively, cost them only a mere $148!

The results? Over a 5 hour period, the setup went 17.5 miles up (to the “edge of space”), where the balloon popped, and fell to the earth over a 40 minute period and landed about 20 miles away from the launch site. And, as for the pictures, well, you can see them yourself in the students’ MIT directory or in the timelapse video they put together (below).

If that doesn’t inspire you to do a little exploration of your own, I don’t know what will!

Not to be outdone, British students launched teddy bears into near-space using a similar technique, but included temperature sensors (to measure the temperature extremes), insulating “spacesuits” (to protect the bears from freezing solid), and even a parachute to gently glide the bears down to Earth!

(Note: if you’re in the US and want to do something similar, please make sure to (a) contact the FAA, (b) use the University of Wyoming’s balloon trajectory estimator to make sure that your balloon won’t land in a densely populated region, and (c) make sure the balloon can land gently without injuring anyone or your equipment)

(Photo’s from 1337arts site) (Teddy Bear photo’s from DailyMail)

I have a great deal of respect for the early pioneers of chemistry — not just because they were intelligent and inquisitive and spawned entire fields of research, but mainly because they were able to do this while never having the ability to see what they were studying. So, although the early experimenters could conduct experiments to indirectly validate or invalidate their hypotheses on a macro-scale (like shaking a tree to see what fruit fell out rather than actually looking up at the tree to see the individual fruit), the fact that they could never see or manipulate or count molecules meant that most of their work resided in the domain of thought experiments.

And, although the scientific community now take the existence of atoms and molecules for granted, I think the early Avogadros of chemistry would have been especially gratified by the recent work at IBM’s research facility in Zurich to use atomic force microscopy to actually see molecules of pentacene (five fused aromatic 6-carbon rings, pictured below)

The results are detailed both on IBM’s press page as well as in the Aug 28 issue of Science. But, in graphical terms, this is the scientific community’s current best picture of pentacene:

Amazing isn’t it? More of the technical details are presented in the video IBM put together in conjunction with the press release (below), but in a nutshell, atomic force microscopy uses a well-defined atomic tip to “feel” out the electronic surface of a molecule. The ability to do this and even be able to resolve the respective hydrogen atoms is a testament to IBM’s ability to put together an incredibly stable (both to mechanical and thermal fluctuations) and precise setup.

From IBM’s perspective, this breakthrough allows them to continue to push ahead on the advanced nanotechnology and semiconductor research which they depend on to churn out next-generation electronics, but for the scientific community, these advances could result not only in better atomic force microscopy experimental techniques, but potentially also a new way to understand and study the chemical reactions and structures which have such great influence over our lives.

Publication: Science 28 August 2009: Vol. 325. no. 5944, pp. 1110 – 1114; DOI: 10.1126/science.1176210

(Image credit – Pentacene chemical diagram) (Image credit – AFM picture)