Landmark Literature: 2015 (part2)
Just imagine how many analytical problems would be solved if we could increase the resolution of liquid chromatography (LC) from hundreds to thousands or perhaps a hundred thousand compounds?
Martin Gilar , Peter Griffiths |
By Martin Gilar, Principal Investigator, Waters Corporation, Milford, Massachusetts, USA
The potential of multidimensional separations has always interested me. Just imagine how many analytical problems would be solved if we could increase the resolution of liquid chromatography (LC) from hundreds to thousands or perhaps a hundred thousand compounds? Two-dimensional (2D)-LC has that potential, which is why groups led by Peter Carr, Dwight Stoll, Peter Schoenmakers, Pavel Jandera, Paola Dugo, and Mark Schure, among others investigated it. Whether the goal was practical or academic, the drive towards multidimensional separations is noticeable. Instrument manufacturers picked up the trend and they now offer systems suitable for targeted or comprehensive 2D-LC.
The paper that caught my eye features several “big hitters” in separation science – Gert Desmet, Sebastiaan Eeltink, and Peter Schoenmakers, with their talented students Bert Wouters, Herman Terryn, and Jelle De Vos – who investigated the concept of spatial 2D-LC as a first step towards the ultimate goal of 3D-LC (1). (Schoenmakers has presented his 3D-LC separation vision at several HPLC conferences.) It isn’t a simple concept. For example, if the first dimension separation is performed within an hour, the second dimension separation must be done within tens of seconds, and the third should not exceed seconds. Schoenmakers has proposed a concept of spatial chromatography that overcomes this problem. Similar to thin layer chromatography (TLC), he proposes that the sample components are separated spatially in two dimensions and then resolved in the third dimension using a “separation cube”.
The concept of spatial separation evaluated in the paper is a stepping-stone towards a 3D-LC solution. The concept is equally as intriguing as challenging to execute. However, collectively, the authors have the necessary expertise to do it. They first describe a design and construction of a planar chip for the 2D separations. They borrow the expertise for chip building from Desmet’s earlier microfluidic work and add to it. Formulating separation media in the chip channels was a problem the authors dealt with by filling the channels with a monomeric mixture to create the monolith stationary phase in-situ. They also had to work out additional technical details such as connection to pumps, isolation of the flow in the first dimension, and the transfer of the analytes to second dimension. They stop at this point without showing any actual 2D separations, but they promise more results in subsequent papers. It remains for us to see how well the 2D-LC chip works with test mixtures and real life samples.
Several problems with the 2D-LC chip need solving. In the current version, both the first and second separation dimension use the same monolithic stationary phase. This means that orthogonal separation selectivity needs inducing by the mobile phase, for example by altering the pH, ion-pairing, or solvent type in both separation dimensions. Also, the spatial separation concept works well for small molecules in the isocratic separation mode, but may fail for proteins and other (bio)polymer applications. The solution to the problem – gradient elution – does not translate well from time domain to spatial chromatography. In other words, weakly retained peaks will inevitably leave the chip boundary, while the more strongly retained compounds will remain clustered at the 2D-LC chip inlet. Executing a gradient within a single column volume could evenly spread the peaks along the separation plane, but such separations are a tricky proposition.
The authors aim to apply the 2D-LC chip to protein separation (similar to 2D gel electrophoresis). If the 2D-LC chip offers a straightforward interface to mass spectrometry, this is a worthy proposition.
Despite the technical difficulties, this paper describes exciting research. The potential of multi dimensional separations always has interested me and I hope the prospect of a hundred thousand-peak capacity will be possible in the not too distant future.
Martin’s Landmark Paper
B Wouters et al., “Design of a microfluidic device of comprehensive spatial two-dimensional liquid chromatography”, J Sep Sci, 38, 1123–1129 (2015). PMID: 25598051.
Hyperspectral Disease Diagnosis
By Peter Griffiths, Professor of Chemistry Emeritus, University of Idaho, Owner, Griffiths Consulting LLC, Moscow, Idaho, USA.
The diagnosis of cancers at an early stage is critical for the long-term survival of patients. For solid cancers, such as lung, breast and prostate cancer, this is currently accomplished by staining tissue samples with hematoxylin and eosin (H&E) dyes followed by histopathological examination; time to results is typically days rather than hours. Furthermore, diagnoses performed in this way are quite subjective. Indeed, if four histopathologists examine a stained tissue sample, there could be four different diagnoses! Clearly, a technique that is faster, more accurate and less subjective than H&E staining is needed.
For at least two decades, vibrational spectroscopists have attempted to demonstrate the feasibility of using infrared spectroscopy in medical diagnosis. In the early studies, a FT-IR microspectrometer equipped with a single-element detector was used in the mapping mode, where spectra were measured sequentially, with the sample being moved in steps of a few micrometers. Although the results showed promise, the time required to acquire enough spectra to allow tissue samples to be fully classified was too long. Furthermore, an insufficient number of samples were usually tested, so that any results were rarely statistically significant. As a result, optimism for such measurements was not justified.
Hyperspectral imaging achieved by the interface of mercury cadmium telluride array detectors to a standard continuously-scanning FT-IR spectrometer allows thousands of spectra of tissue samples to be measured in a couple of minutes with a spatial resolution of between 1 and 10 μm. The spectrum measured at each pixel can be classified by several different chemometric algorithms (sometimes known as chemical imaging). Several research groups have demonstrated the applicability of such methods in predicting different types of cancer. For example, groups led by Rohit Bhargava at the University of Illinois (USA), Max Diem at Northeastern University (USA) and Nick Stone at Exeter University (UK) have all made remarkable progress in area.
My paper of choice details the results of a very careful collaborative study of the prediction of prostate cancer recurrence by scientists from the US Center for Interventional Oncology at the National Institutes of Health, the Department of Pathology at the University of Illinois at Chicago, and the Computer Science, Bioengineering, and Electrical and Computer Engineering, departments and Cancer Center of the University of Illinois, Urbana-Champaign (1). Their results significantly outperformed those found using the most commonly applied approaches – H&E staining with classification using the Kattan nomogram or the Cancer of the Prostate Risk Assessment (CAPRA-S) score.
The paper stands out because of the combination of very high-quality spectroscopy and data processing and the collaboration of scientists from different disciplines. The paper described an approach that, in the words of its abstract “provides a histologic basis to a prediction that identifies chemical and morphological features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.”
In contrast to magnetic resonance spectroscopy, where the MRI technique was rapidly commercialized and adopted in hospitals worldwide within 10 years of demonstrating its feasibility in the laboratory, the uptake of vibrational spectroscopic techniques for medical diagnosis has been slow. Nonetheless, I believe that, within the next decade, the techniques described in this paper could displace current staining techniques for histopathological analysis – or, at the very least, I expect that they will be used alongside them.
Peter’s Landmark Paper
JT Kwak et al., “Improving prediction of prostate cancer recurrence using chemical imaging”, Scientific Reports 5, Article number: 8758 (2015). PMID: 25737022.