Nowadays a huge focus in research is understanding how diseases are developing on a molecular level. It is no easy feat to determine the precise molecular composition of an organ or a cell without destroying it. What if I told you that there is a method that can tell you the molecular composition of a sample quickly, and without damage, just by shining light on it? This method is called Raman Spectroscopy. This well-established technique stems from the Raman effect, discovered by Sir Chandrasekhara Venkata Raman in 1923, and for which he obtained a Nobel prize in 1930. While it is not a new technology, many scientists around the world are devoted to improving its applications to modern medicine.
Imagine yourself as a liver surgeon. A new patient comes in suffering from liver sickness, and you need to assess the severity of the disease. If you are lucky a simple medical analysis such as a blood analysis or an echography will do the trick. But more often than not, a more invasive method will need to be used such as a liver biopsy, which means taking a piece of the patient's liver and then sending it to an external lab that will take several days to tell you what your patient is suffering from. It takes a lot of time, and there is a high risk of human error in the interpretation of the result. In the future, with a Raman spectroscope coupled with artificial intelligence, the liver will be analyzed on the spot to tell you precisely how affected the patient is, and the best treatment to administer.
It sounds like an amazing new technology that could come out of a sci-fi movie, but this might be a reality sooner than you think! So, how does this Raman spectroscopy work?
Spectroscopy: the interaction of light and matter
I always hated biophysics when I was a student, but it turns out that the fundamentals of many biophysical techniques are actually quite easy to understand! So first, we should begin by explaining what spectroscopy is, to get all the complicated scientific explanations out of the way: spectroscopy is the study of the interaction between light (also called ‘electromagnetic radiation’) and a sample. Electromagnetic radiation consists of waves of energy made of photons. Whilst visible light is the most well-known, infrared, ultraviolet, microwave, radio, x-rays, and gamma rays are all types of electromagnetic radiation that you may know - and some of which you use daily on your brand-new smartphone or computer.
There are multiple types of spectroscopies, differentiated by the kind of interaction between the light and the sample. Raman spectroscopy is focused on the diffusion of light. When the light goes through a sample, some is absorbed by the sample, some is transmitted straight through the sample, and some is deviated by the sample - a process called diffusion. If the light that is diffused keeps the same energy as the light that is sent through the sample, it is referred to as an elastic diffusion (or Rayleigh diffusion). But if the light is of higher or lower energy, that is called the Raman effect. This change in energy shows an interaction between the light and the sample and is dependent on the molecular composition of the sample. The result is a spectrum of the sample, where each peak corresponds to a vibration mode of a molecule. These vibration modes are akin to a molecular fingerprint, a highly informative data that can be specific to a particular biomarker 1.
An old technology with new applications
At this point of the story, you must be wondering how we get to the point where this technology is used in a hospital. This only seems to be interesting for physicists at the moment. Well, for a long time, it has been true. The Raman effect was discovered in 1928 by Chandrashekhara Ventaka Raman, an Indian physicist who won a Nobel Prize for his findings in 1930. The invention of the laser in the 1960’s allowed the technology to be used by physicists on simple molecules. However, it was still very hard to use on biological samples that are composed of hundreds of thousands of complex different molecules. Raman spectroscopy is getting more popular nowadays because of the strong technological developments in spectroscopy, accompanied by the decreasing price of lasers and the evolution of informatics and artificial intelligence.
The power of modern computers allows the analysis of millions of Raman spectra for a single biological sample. When combined with artificial intelligence, algorithms can be taught to recognize specific molecules from the spectra and create molecular maps of the samples.
Many applications are being developed from this technique. For example, on skin tissue, Raman spectroscopy can quickly distinguish all the different layers of skin and even find out the age of the sample according to variations in the quality of collagen 2. It is also groundbreaking in clinical use; in surgery, new portable Raman probes can tell neurosurgeons the difference between brain tumor and healthy tissue, greatly reducing the risk of relapse 3. Finally, Raman spectroscopy can also be used on biofluids such as blood, saliva, and urine, which are easier and less invasive to obtain than tissue samples. For example, prediction models allow the detection of colorectal cancer from molecules present in the plasma of patients 4.
These are just a few examples. Hundreds of brilliant scientists are developing innovative uses of Raman spectroscopy to make sure that, if the day comes that you need a diagnosis, it could not only be fast and precise - but also highly specific to your issue, which means a personalized treatment. All of these improvements with a cheap and simple technique that might even be used on your smartphone!
1. Butler, H et al. (2016) Using Raman spectroscopy to characterize biological materials. Nat Protoc. http://doi.org/10.1038/nprot.2016.036
4. Ito H et al. (2020) Highly accurate colorectal cancer prediction model based on Raman spectroscopy using patient serum. World J Gastrointest Oncol. http://doi.org/10.4251/wjgo.v12.i11.1311
This article was specialist edited by Prof. Claire Mangeney and copy edited by Kyrie Grasekamp. This article results from a collaboration with ComSciCon France, a workshop on Scientific communication for PhD students.