E ditorial For reprint orders, please contact [email protected]
Data processing for 2D-LC: where are we heading? “Examining the 2D-HPLC dataset as a whole, instead of the sum of its parts, is the correct approach to 2D-chromatography data analysis…” Keywords: 2D-HPLC n computer vision n data analysis n space × space
The underlying foundation of any analytical method is to collect information. The current state of technology allows scientists to collect vast quantities of data that describe the chemical processes taking place; data systems must be in place to extract information rapidly and with a high degree of confidence. When screening very complex mixtures, such as those extracted from natural products, there is an abundance of compounds that overwhelm traditional separation processes. Resolution of these complex mixtures is realized with 2D-HPLC, which has the capacity to separate chemically similar compounds that would otherwise co-elute [1]. 2D-HPLC Multidimensional HPLC operates by transferring aliquots of the first dimension eluent to the second dimension via a switching valve or fraction collector. The sample mixture is separated twice with two entirely different, but chemically compatible, separation systems. The data generated is comprised of several 1D-HPLC chromatograms, one for each aliquot transfer, which are pieced together to form the 2D-HPLC dataset. In recent years 2D-HPLC experimental procedures have been closely investigated and refined, and the capacity to separate very complex mixtures is being realized. However, the development of computer tools to extract information from these separations has not been explored to the same extent and must be rectified before widespread adoption of 2D-HPLC is achieved [2].
first method is an extension of 1D-HPLC data analysis, which examines the data by finding peaks in each independent separation step [3]. Since multiple fractions per first dimension peak are transferred to the second dimension, each adjacent chromatogram is compared and local peaks are grouped together [4]. This method taps into decades of research into peak deconvolution and integration, and can be applied to each fraction with no modification [5]. However, difficulties arise when trying to assign peaks in adjacent chromatograms that could join any of several peak clusters. This has recently been addressed by introducing Bayesian statistics to assign groups on the basis of peak shape descriptors [6]. The second method for 2D-HPLC ana lysis is a holistic approach developed with a basis in computer imaging. The drain method floods the 3D contour plot with a hypothetical liquid that is subsequently emptied [7]; as the water is drained peak apices are revealed and recorded. Unfortunately, only 18% of the expected components within a sample will be eluted as single peaks [8], so peak-deconvolution strategies must be employed to extract the most information. However, the drain algorithm is unable to distinguish between peaks without a local minimum, and therefore has limited applicability to the most complex separation problems.
The current state of 2D-HPLC analysis There are two fundamentally different approaches for the extraction of information from 2D-HPLC data and these methods differ in how the data are conceptually viewed. The
Enhancing multidimensional resolution Examining the 2D-HPLC dataset as a whole, instead of the sum of its parts, is the correct approach to 2D-chromatography data analysis; however, this cannot be applied to time × time separations (i.e., the separation is recorded by measuring the time taken for the analytes to pass through the separation media). According to the Murphey–Schure–Foley rule, the optimum
10.4155/BIO.13.272 © 2013 Future Science Ltd
Bioanalysis (2013) 5(23), 2867–2869
Paul G Stevenson Centre for Chemistry & Biotechnology, Faculty of Science, Engineering & Built Environment, Deakin University, Geelong, VIC 3216, Australia Tel.: +61 3 5227 2241 Fax: +61 3 5227 1040 E-mail: paul.stevenson@ deakin.edu.au
ISSN 1757-6180
2867
E ditorial |
Stevenson number of fractions that are transferred to the second dimension is three to four per peak [9]. This number is well justified but does not allow for a true representation of the 3D peak. For instance, the description of a traditional chromatographic peak in the second dimension might comprise hundreds of data points, thanks to the rapid data acquisition of modern UV spectrometers. However, the same peak is described by only four points in the first dimension because of the fractioning frequency, thus it is impossible to properly describe the peak shape in both dimensions. The retention time in the first dimension can only be reported within a time range and peak size represented as cumulative peak area, not volume [10]. It is unrealistic to perform 2D-HPLC and achieve the same amount of data in both dimensions, as sample dilution would provide concentrations in the second dimension that are less than the limits of detection, and total analysis time is governed by the number of fractions. Moving away from HPLC The only way to reach the full potential for 2D-chromatographic data analysis is to perform multidimensional separations in the space domain; for example, on thin-layer chromatography (TLC) plates, and record a high-resolution image of the completed separation [11]. With this in mind, it is inevitable that multidimensional chromatography will move away from time × time domain and towards space × space separations, assuming TLC plates can be developed with sufficient peak capacity to separate very complex mixtures [12]. Detection of peaks in HPLC is most commonly done by measuring the intensity of a chemical as the bulk solution passes through the detector. When the separation is completed on a TLC plate, the detection options are expanded; for instance, the analysis of these plates could be completed with a MALDI-MS, which ejects chemicals from a physical surface with a laser beam [13]. To complete a high-resolution analysis in a short time, a low-resolution grid can be first screened for peaks, and areas of interest can be further scanned from several directions to develop a high-resolution map of the peak profile. Alternatively, MRI, which is a nondestructive analysis technique, has been used to detect chemicals on a TLC plate [14]. As MRI is primarily used to scan 3D objects, for example, living tissue, at a resolution sufficient to locate clusters of abnormal cells, it is
2868
Bioanalysis (2013) 5(23)
not unrealistic that this could be transferred to the detection of chemicals within a 3D separation space. This imaging is well-established in medical science, and it would not be difficult to modify current analysis approaches for 2D or 3D chromatography. Furthermore, cluster analysis methods, such as the mean shift algorithm [15], could be used to locate peaks in higher order dimensions and deconvolute any that still coelute. It has been postulated that peak capacities in a space × space × space could reach up to 50,000 peaks within 2 h [16].
“The future of 2D-HPLC data analysis will be directly tied to the development of higher-order dimension separations in the space domain; it will be space separations that provide the massive peak capacities to fully resolve complex samples within a reasonable amount of time.” Conclusion The future of 2D-HPLC data analysis will be directly tied to the development of higher order dimension separations in the space domain; it will be space separations that provide the massive peak capacities to fully resolve complex samples within a reasonable amount of time. Experimentally, it will be much easier to use magnetic resonance detection, rather than designing a custom device to monitor the bulk liquid as it passes through the 3D structure, although the technology has not been developed to perform these kinds of separations. It is vital that researchers not only focus on time × time modes of 2D-HPLC, but also have the vision to develop the concepts behind the detection, quantification and deconvolution of 3D chemical clusters separated by higher order multidimensional separation media to be prepared for the next generation of multidimensional chromatography applications. Financial & competing interests disclosure The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
future science group
Data processing for 2D-LC: where are we heading?
| E ditorial
References 1
2
3
4
5
6
Giddings JC. Two-dimensional separations: concept and promise. Anal. Chem. 56(12), 1258A–1270A (1984). Stoll DR, Li X, Wang X, Carr PW, Porter SEG, Rutan SC. Fast, comprehensive twodimensional liquid chromatography. J. Chromatogr. A 1168(1–2), 3–43 (2007). Vivó-Truyols G, Torres-Lapasió JR, Van Nederkassel AM, Vander Heyden Y, Massart DL. Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection. J. Chromatogr. A 1096(1–2), 133–145 (2005). Peters S, Vivó-Truyols G, Marriott PJ, Schoenmakers PJ. Development of an algorithm for peak detection in comprehensive two-dimensional chromatography .J. Chromatogr. A 1156(1–2), 14–24 (2007). Fell AF, Scott HP, Gill R, Moffat AC. Novel techniques for peak recognition and deconvolution by computer-aided photodiode array detection in highperformance liquid chromatography. J. Chromatogr. A 282(0), 123–140 (1983). Vivó-Truyols G. Bayesian approach for peak detection in two-dimensional chromatography. Anal. Chem. 84(6), 2622–2630 (2012).
future science group
7
Reichenbach SE, Ni M, Kottapalli V, Visvanathan A. Information technologies for comprehensive two-dimensional gas chromatography. Chemometr Intell. Lab. Syst. 71(2), 107–120 (2004).
8
Davis JM, Giddings JC. Statistical theory of component overlap in multicomponent chromatograms. Anal. Chem. 55(3), 418–424 (1983).
9
Murphy RE, Schure MR, Foley JP. Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70, 1585–1594 (1998).
10 Stevenson PG, Guiochon G. Cumulative area
of peaks in a multidimensional high performance liquid chromatogram. J. Chromatogr. A 1308(0), 79–85 (2013). 11 Cebolla VL, Membrado L, Domingo MP
et al. Quantitative applications of fluorescence and ultraviolet scanning densitometry for compositional analysis of petroleum products in thin-layer chromatography. J. Chromatogr. Sci. 37(6), 219–226 (1999). 12 Urbanova I, Svec F. Monolithic polymer layer
with gradient of hydrophobicity for separation of peptides using two-dimensional thin layer chromatography and MALDI–TOF-MS detection. J. Sep. Sci. 34(16–17), 2345–2351 (2011).
www.future-science.com
13 Fuchs B, Schiller J, Süß R, Schürenberg M,
Suckau D. A direct and simple method of coupling matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI–TOF MS) to thinlayer chromatography (TLC) for the analysis of phospholipids from egg yolk. Anal. Bioanal. Chem. 389(3), 827–834 (2007). 14 Teisseyre TZ, Urban J, Halpern-Manners
NW et al. Remotely detected NMR for the characterization of flow and fast chromatographic separations using organic polymer monoliths. Anal. Chem. 83(15), 6004–6010 (2011). 15 Georgescu B, Shimshoni I, Meer P. Mean
shift based clustering in high dimensions: a texture classification example. Proceedings of: Ninth IEEE International Conference on Computer Vision. Nice, France, 13–16 October 2003. 16 Davydova E, Schoenmakers PJ, Vivó-Truyols
G. Study on the performance of different types of three-dimensional chromatographic systems. J. Chromatogr. A 1271(1), 137–143 (2013).
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Data processing for 2D-LC: where are we heading? “Examining the 2D-HPLC dataset as a whole, instead of the sum of its parts, is the correct approach to 2D-chromatography data analysis…” Keywords: 2D-HPLC n computer vision n data analysis n space × space
The underlying foundation of any analytical method is to collect information. The current state of technology allows scientists to collect vast quantities of data that describe the chemical processes taking place; data systems must be in place to extract information rapidly and with a high degree of confidence. When screening very complex mixtures, such as those extracted from natural products, there is an abundance of compounds that overwhelm traditional separation processes. Resolution of these complex mixtures is realized with 2D-HPLC, which has the capacity to separate chemically similar compounds that would otherwise co-elute [1]. 2D-HPLC Multidimensional HPLC operates by transferring aliquots of the first dimension eluent to the second dimension via a switching valve or fraction collector. The sample mixture is separated twice with two entirely different, but chemically compatible, separation systems. The data generated is comprised of several 1D-HPLC chromatograms, one for each aliquot transfer, which are pieced together to form the 2D-HPLC dataset. In recent years 2D-HPLC experimental procedures have been closely investigated and refined, and the capacity to separate very complex mixtures is being realized. However, the development of computer tools to extract information from these separations has not been explored to the same extent and must be rectified before widespread adoption of 2D-HPLC is achieved [2].
first method is an extension of 1D-HPLC data analysis, which examines the data by finding peaks in each independent separation step [3]. Since multiple fractions per first dimension peak are transferred to the second dimension, each adjacent chromatogram is compared and local peaks are grouped together [4]. This method taps into decades of research into peak deconvolution and integration, and can be applied to each fraction with no modification [5]. However, difficulties arise when trying to assign peaks in adjacent chromatograms that could join any of several peak clusters. This has recently been addressed by introducing Bayesian statistics to assign groups on the basis of peak shape descriptors [6]. The second method for 2D-HPLC ana lysis is a holistic approach developed with a basis in computer imaging. The drain method floods the 3D contour plot with a hypothetical liquid that is subsequently emptied [7]; as the water is drained peak apices are revealed and recorded. Unfortunately, only 18% of the expected components within a sample will be eluted as single peaks [8], so peak-deconvolution strategies must be employed to extract the most information. However, the drain algorithm is unable to distinguish between peaks without a local minimum, and therefore has limited applicability to the most complex separation problems.
The current state of 2D-HPLC analysis There are two fundamentally different approaches for the extraction of information from 2D-HPLC data and these methods differ in how the data are conceptually viewed. The
Enhancing multidimensional resolution Examining the 2D-HPLC dataset as a whole, instead of the sum of its parts, is the correct approach to 2D-chromatography data analysis; however, this cannot be applied to time × time separations (i.e., the separation is recorded by measuring the time taken for the analytes to pass through the separation media). According to the Murphey–Schure–Foley rule, the optimum
10.4155/BIO.13.272 © 2013 Future Science Ltd
Bioanalysis (2013) 5(23), 2867–2869
Paul G Stevenson Centre for Chemistry & Biotechnology, Faculty of Science, Engineering & Built Environment, Deakin University, Geelong, VIC 3216, Australia Tel.: +61 3 5227 2241 Fax: +61 3 5227 1040 E-mail: paul.stevenson@ deakin.edu.au
ISSN 1757-6180
2867
E ditorial |
Stevenson number of fractions that are transferred to the second dimension is three to four per peak [9]. This number is well justified but does not allow for a true representation of the 3D peak. For instance, the description of a traditional chromatographic peak in the second dimension might comprise hundreds of data points, thanks to the rapid data acquisition of modern UV spectrometers. However, the same peak is described by only four points in the first dimension because of the fractioning frequency, thus it is impossible to properly describe the peak shape in both dimensions. The retention time in the first dimension can only be reported within a time range and peak size represented as cumulative peak area, not volume [10]. It is unrealistic to perform 2D-HPLC and achieve the same amount of data in both dimensions, as sample dilution would provide concentrations in the second dimension that are less than the limits of detection, and total analysis time is governed by the number of fractions. Moving away from HPLC The only way to reach the full potential for 2D-chromatographic data analysis is to perform multidimensional separations in the space domain; for example, on thin-layer chromatography (TLC) plates, and record a high-resolution image of the completed separation [11]. With this in mind, it is inevitable that multidimensional chromatography will move away from time × time domain and towards space × space separations, assuming TLC plates can be developed with sufficient peak capacity to separate very complex mixtures [12]. Detection of peaks in HPLC is most commonly done by measuring the intensity of a chemical as the bulk solution passes through the detector. When the separation is completed on a TLC plate, the detection options are expanded; for instance, the analysis of these plates could be completed with a MALDI-MS, which ejects chemicals from a physical surface with a laser beam [13]. To complete a high-resolution analysis in a short time, a low-resolution grid can be first screened for peaks, and areas of interest can be further scanned from several directions to develop a high-resolution map of the peak profile. Alternatively, MRI, which is a nondestructive analysis technique, has been used to detect chemicals on a TLC plate [14]. As MRI is primarily used to scan 3D objects, for example, living tissue, at a resolution sufficient to locate clusters of abnormal cells, it is
2868
Bioanalysis (2013) 5(23)
not unrealistic that this could be transferred to the detection of chemicals within a 3D separation space. This imaging is well-established in medical science, and it would not be difficult to modify current analysis approaches for 2D or 3D chromatography. Furthermore, cluster analysis methods, such as the mean shift algorithm [15], could be used to locate peaks in higher order dimensions and deconvolute any that still coelute. It has been postulated that peak capacities in a space × space × space could reach up to 50,000 peaks within 2 h [16].
“The future of 2D-HPLC data analysis will be directly tied to the development of higher-order dimension separations in the space domain; it will be space separations that provide the massive peak capacities to fully resolve complex samples within a reasonable amount of time.” Conclusion The future of 2D-HPLC data analysis will be directly tied to the development of higher order dimension separations in the space domain; it will be space separations that provide the massive peak capacities to fully resolve complex samples within a reasonable amount of time. Experimentally, it will be much easier to use magnetic resonance detection, rather than designing a custom device to monitor the bulk liquid as it passes through the 3D structure, although the technology has not been developed to perform these kinds of separations. It is vital that researchers not only focus on time × time modes of 2D-HPLC, but also have the vision to develop the concepts behind the detection, quantification and deconvolution of 3D chemical clusters separated by higher order multidimensional separation media to be prepared for the next generation of multidimensional chromatography applications. Financial & competing interests disclosure The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
future science group
Data processing for 2D-LC: where are we heading?
| E ditorial
References 1
2
3
4
5
6
Giddings JC. Two-dimensional separations: concept and promise. Anal. Chem. 56(12), 1258A–1270A (1984). Stoll DR, Li X, Wang X, Carr PW, Porter SEG, Rutan SC. Fast, comprehensive twodimensional liquid chromatography. J. Chromatogr. A 1168(1–2), 3–43 (2007). Vivó-Truyols G, Torres-Lapasió JR, Van Nederkassel AM, Vander Heyden Y, Massart DL. Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals: Part I: Peak detection. J. Chromatogr. A 1096(1–2), 133–145 (2005). Peters S, Vivó-Truyols G, Marriott PJ, Schoenmakers PJ. Development of an algorithm for peak detection in comprehensive two-dimensional chromatography .J. Chromatogr. A 1156(1–2), 14–24 (2007). Fell AF, Scott HP, Gill R, Moffat AC. Novel techniques for peak recognition and deconvolution by computer-aided photodiode array detection in highperformance liquid chromatography. J. Chromatogr. A 282(0), 123–140 (1983). Vivó-Truyols G. Bayesian approach for peak detection in two-dimensional chromatography. Anal. Chem. 84(6), 2622–2630 (2012).
future science group
7
Reichenbach SE, Ni M, Kottapalli V, Visvanathan A. Information technologies for comprehensive two-dimensional gas chromatography. Chemometr Intell. Lab. Syst. 71(2), 107–120 (2004).
8
Davis JM, Giddings JC. Statistical theory of component overlap in multicomponent chromatograms. Anal. Chem. 55(3), 418–424 (1983).
9
Murphy RE, Schure MR, Foley JP. Effect of sampling rate on resolution in comprehensive two-dimensional liquid chromatography. Anal. Chem. 70, 1585–1594 (1998).
10 Stevenson PG, Guiochon G. Cumulative area
of peaks in a multidimensional high performance liquid chromatogram. J. Chromatogr. A 1308(0), 79–85 (2013). 11 Cebolla VL, Membrado L, Domingo MP
et al. Quantitative applications of fluorescence and ultraviolet scanning densitometry for compositional analysis of petroleum products in thin-layer chromatography. J. Chromatogr. Sci. 37(6), 219–226 (1999). 12 Urbanova I, Svec F. Monolithic polymer layer
with gradient of hydrophobicity for separation of peptides using two-dimensional thin layer chromatography and MALDI–TOF-MS detection. J. Sep. Sci. 34(16–17), 2345–2351 (2011).
www.future-science.com
13 Fuchs B, Schiller J, Süß R, Schürenberg M,
Suckau D. A direct and simple method of coupling matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI–TOF MS) to thinlayer chromatography (TLC) for the analysis of phospholipids from egg yolk. Anal. Bioanal. Chem. 389(3), 827–834 (2007). 14 Teisseyre TZ, Urban J, Halpern-Manners
NW et al. Remotely detected NMR for the characterization of flow and fast chromatographic separations using organic polymer monoliths. Anal. Chem. 83(15), 6004–6010 (2011). 15 Georgescu B, Shimshoni I, Meer P. Mean
shift based clustering in high dimensions: a texture classification example. Proceedings of: Ninth IEEE International Conference on Computer Vision. Nice, France, 13–16 October 2003. 16 Davydova E, Schoenmakers PJ, Vivó-Truyols
G. Study on the performance of different types of three-dimensional chromatographic systems. J. Chromatogr. A 1271(1), 137–143 (2013).
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