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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)

Despite all the cool and meaningful innovations we’ve discussed on this blog, few come as close in terms of impact on a scientific field as the Hubble Space Telescope. And this weekend, you can help celebrate it’s birthday!

Officially launched on April 24, 1990 (can you believe that was 20 years ago!?), it has provided one of humanity’s best looks into deep space and has, among other things:

• Helped refine the field’s understanding of Hubble’s Law and the Hubble Constant
• Showed that the expansion of the universe was not decelerating, but accelerating, suggesting the existence of dark energy
• Helped to establish the existence of massive black holes at the center of galaxies and their relationships
• Provided sharp images of the impact of comet Shoemaker-Levy 9 into Jupiter
• Collect data on extrasolar planets and protoplanetary discs
• Furthered the study of Wolf-Rayet Stars, suspected to be the precursors of Gamma-ray bursts, the most powerful energy bursts known in the universe
• The mindblowing look 13 billion years into the past known as the Hubble Deep Field

And, potentially, most important of all: the gorgeous pictures of deep space (from Space Telescope Science Institute’s HubbleSite website).

Happy 20th birthday, Hubble!

(Image credits – Hubble Site via Space Telescope Science Institute)

Here at Bench Press we’re always interested in new initiatives that harness the advantages of the internet. We’ve covered various powerful distributive computing initiatives as well as breakthrough collaborative endeavors in scientific research. So I was intrigued when I saw buzz on Twitter about the Obama administration’s attempt to crowd source suggestions for scientific policy.

Through the American Association for the Advancement of Science (AAAS) and associated non-profit Expert Labs, the Obama administration wants to hear what grand challenges scientists envision taking on.

Expert Labs has a nice video explaining the reasoning behind this grand experiment in policy crowd sourcing.

After a quick search on Twitter I’m a bit curious as to how Expert Labs plans to parse all the data they’re going to get from this call to arms, but I’m optimistic that some interesting insights can be gleaned as to the opinions of Americans on the directions science should be headed in. More data never hurt right? If you’re interested in submitting an idea follow the directions here, you’ve got until April 15th!

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)