2D-LC approach

Two-dimensional-liquid chromatography (2D-LC) approaches are much easier to automate than 2D-electrophoresis. However 2D electrophoresis has the advantage that separation is performed at the protein and not at the peptide level and... 

The 2D-LC Approach to Increase Peak Capacities Beyond These Limits... 

As explained in Sections 16.4 and 16.5, the comprehensive characterization of complex polymer systems is hardly possible by the SEC alone. SEC employs only one retention mechanism which simnltaneonsly responds to all molecular characteristics of sample. Similarly, also the coupling of the different retention mechanisms within one single column only exceptionally allows fulfilling this task. Evidently several retention mechanisms should be applied in a tandem approach that is within at least two different on-line chromatographic systems. This is the basic idea of the two- and multidimensional polymer HPLC. In the present section, the principles of two-dimensional polymer HPLC, 2D polymer HPLC or (2D-LC) will be briefly elucidated. There are several reviews available [23-31,249,250] dealing with the 2D polymers. It is anticipated that also the three- and multidimensional polymer HPLC will be developed in future. 

Relative protein quantitation is the basis of all types of differential proteome analyses. In the 2D-gel approach protein staining with either visible or fluorescent dyes provides a reliable and sensitive method to detect changes in protein expression or isoform abundance. In the multidimensional LC approach quantitation relies mostly on stable isotope labeling and ratios between light and heavy isotopomers are determined by MS or MS/MS at the peptide level. Labeling can be performed on the protein level by... 

The bottom-up approach very much resembles classical protein identification strategies. The proteins in the proteome are first separated by 2D-GE (Ch. 17.3), or in some cases by SCX, size-exclusion (SEC), or affinity (AfC) chromatography. Specific proteins are excised from the gel, blotted, or electroeluted. The protein is digested, and the digest is analysed by LC-MS. The EC separation involves either RPLC with microcapillary or nano-LC columns (Ch. 17.5.2), or 2D-LC with typically SEC or SCX in the first dimension and RPLC in the second (Ch. 17.5.4). Alternatively, the sample may be introduced via either direct-infusion nano-ESl (Ch. 17.2), CE-MS (Ch. 17.5.6), or a microfluidic device coupled to MS (Ch. 17.5.5). 

The important issue of 2D-LC represents the abovementioned transfer of column effluent between the Id and the 2d columns, which can be done either off-line or online. In the off-hne approach, the fractions from the Id column are collected and successively re-injected into the 2d column. In this case, the unit TF is just a fraction collector. The macromolecules within particular fractions from the Id column are immixed so that resulting overall separation selectivity may be challenged. Moreover, entire procedure is laborious and slow. Various approaches were elaborated for the online transfer of fractions from the Id column into the 2d column. Often, the fractions from the Id column are cut into small parts that are one-by-one gradually transported into the 2d SEC column for independent characterization. This is the method of choice if the first-dimension separation produces broader peaks, such as it does liquid chromatography under critical conditions of enthalpic interactions, LC CC (see section 11.8.3). The operation principle of such chop-and-reinject method is evident from Figure 22. In this case, the TF unit from Figure 21 is a switching valve. 

A 150 mm long column packed with sub-2 pm particles will, even in the best case, generate no plate number larger than 40 000. Does this then mean that even modern UHPLC cannot support peak capacities larger than 400 This is clearly not the case but nevertheless it is important to investigate approaches to advance LC toward such high peak capacities. A very effective way to boost peak capacity further is to add another dimension of separation, in other words to advance from ID-LC to 2D-LC. In the ideal case, the peak capacities of the two individual dimensions multiply for the total peak capacity of a 2D-LC method, following Eq. 2.20. 

Sommer et al. [72] used reversed-phase LC-MS or LC-MS/MS with a C18 capillary column to fully characterize the individual lipid species including fatty acid compositions after fractionation of different lipid classes utilizing normal-phase LC-MS on an offline setting. Similar approaches using 2D LC-MS online or offline were also used by Byrdwell for a total lipid analysis [73] as well as for others [74—77]. 

A 2D LC-MS approach was employed to examine plasma sterols between AD cases and controls [93], In an initial study with plasma samples from 10 AD cases and 10 controls, the investigators uncovered that the levels of desmosterol, a precursor of cholesterol, were significantly lower in AD patients relative to those of controls (p< 0.009). This finding was then further confirmed with 26 MCI individuals, 41 AD subjects, and 42 age-matched controls. Moreover, the researchers found that the reduced levels of desmosterol were well correlated with the severity of AD and that the changes in desmosterol levels could most closely represent the pathological progression of AD. 

Degradation product structure elucidation requires a multidimensional approach, typically involving MS and NMR analysis (and if needed, other spectroscopic techniques such as UV and IR and chromatographic techniques such as 2D-LC). Such multidimensional approaches can be greatly facilitated by collaborative efforts involving analytical, process, formulation, and spectroscopic scientists in a team setting. (Figure 2, Step 7) ... 

Large-scale protein identification by 2D-LC-MS/MS is also referred to as multidimensional protein identification technology (MudPIT) [73,74,220]. With this approach, protein identification relies solely on MS/MS data. It has been shown, however, that including the LC retention time as peptide-specific information can improve the quality of protein identification significantly. 

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