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Vol 33 No 3 LCGC Europe March 2020 Regular Issue PDF

Mobile phase flow is critically important, and must be carefully controlled. In this instalment of “GC Connections”, we discuss carrier gas flow and its importance in successful gas chromatographic (GC) analysis. We will begin with a short review of fundamental theories showing why flow is important, and move into a discussion of how flow rate is measured and controlled on modern instruments. Finally, we will discuss the effect of flow-related parameters, including carrier gas choice, the difference between constant pressure and constant flow modes, and some new thinking about flow rate optimization. We conclude with some takeaways that should assist gas chromatographers with effective carrier gas management.

	When a sample is injected into a column in any mode of chromatography, its molecules spend time in one of two phases, the stationary phase or the mobile phase, as illustrated in the very familiar equation 1:

 represents the total retention time, t’

 represents the adjusted retention time, or the time spent sorbed (not moving) in the stationary phase, and t

 represents the hold-up time, the time spent moving in the mobile phase, also defined as the time required for an unretained substance to traverse the column. The carrier gas flow rate obviously has a very strong impact on t

. We are also very familiar with equation 2, which represents the retention factor:

	Most experts in method development will suggest that the best separations occur when 

 is between about 2 and about 10. The lower limit allows enough total time in the column for effective separation. The upper limit comes from a diminishing return for better resolution as retention times get very long. As 

 = 2, the analyte spends one third of the retention time in the mobile phase. At 

 = 10, it spends about 9% (1/11) of the retention time in the mobile phase. In any case, precise flow control is important for retention times, retention factors, and all of the calculations that we base on them to be reproducible.

	Equation 3 relates the gas holdâup time to the average column volumetric flow rate:

	The numerator of equation 3 represents the total volume of the column (think of it as a long skinny cylinder). Note that the stationary phase film thickness is not considered; it usually does not contribute much to the total volume inside the fused-silica tube. Also, note that this is an average flow rate. In contrast to high performance liquid chromatography (HPLC), in which the liquid mobile phase is essentially non-compressible, the gas mobile phase in gas chromatography (GC) is compressible. According to Boyle’s Law, pressure and volume are inversely proportional. As the gas moves from the higher-pressure column head to the lower-pressure outlet, it expands, increasing in volume. Thus, the volumetric flow rate calculated by equation 3 is the average volumetric flow rate and it is usually expressed in units of cm3/min or mL/min.

	A related term, average linear gas velocity, which is simply the average speed (usually expressed in cm/s) of the gas flowing through the column, is given in equation 4:

	As we saw above, the gas is expanding as it moves along the column. Since the column constricts the gas flow, the velocity of the gas must also increase as it moves down the column. The average linear gas velocity is most commonly used to examine the effect of flow on peak width and the construction of “van Deemter” plots, the classical method for optimizing the flow rate, discussed below. In a previous “GC Connections” column, Hinshaw provided a detailed description of the relationships between carrier gas flow and velocity (1).

	There are several variables that determine the gas hold-up time and some complexity in how gas molecules move through the column. Blumberg has provided a detailed description and equations describing the fundamental basis of the gas hold-up time (2). The relationships involved are surprisingly complex, but can be summarized by stating that the gas hold-up time is a function of column dimensions (length and inside diameter), the inlet and outlet pressures, and the viscosity (resistance to flow) of the carrier gas. The viscosity of a gas is a function of temperature, and increases as temperature increases. This is the opposite behaviour seen with liquids, where the viscosity decreases with increasing temperature. Some instruments, data systems, and computer simulation programs use equations based on the these relationships to determine gas hold-up times from the known variables, lessening the need to physically measure t

. Finally, remember that the calculated flow rate and linear gas velocity are average values, and they assume isothermal operation throughout the run.

	In a recent “GC Connections” column, Hinshaw suggested keeping a manual syringe and a butane lighter handy as part of the supplies kit for any gas chromatograph (3). Injecting butane from a lighter serves two very important purposes. First, butane is not retained on most capillary columns at most temperatures used in GC, so it can be used as the analyte to directly measure the gas hold-up time, t

. Second, the shape of the butane peak, injected following a column change or other maintenance, provides a confirmation that the column is connected properly and the connections are leak-free. If the butane peak exhibits tailing or a poor peak shape, then there is likely a leak or a problem with the connections. Beware that butane may be retained on some thick film columns, so methane is preferred when using them.

	The technique of injecting butane was developed in times when most GC was performed on packed column instruments, with manual injection and a manually operated chart recorder as the data system. Classically, injecting butane or methane (we used Bunsen burner gas collected from the laboratory gas jets or in a small lecture bottle) requires training that may not be commonly known today. Also, if using an autosampler, the autosampler may need to be dismounted from the instrument and a manual method set up in the data system. This makes injecting butane, while it may be a necessity, potentially problematic. Interestingly, this problem was addressed as early as 1959 by Peterson and Hirsch, who developed an equation for determining the hold-up time from the retention times of three homologous fatty acid methyl esters (4). As recently as 2013, Wu and associates provided an excellent review and comparison of many techniques that have been used to determine gas hold-up times. They correctly pointed out that all substances are retained to some degree, and that variations in gas hold-up time measurements may lead to variations in calculations that are based on them, including retention factors, partition coefficients, and retention indexes (5).

	Figure 1 shows the retention time for several analytes injected neat, measured on a common capillary column under isothermal conditions at temperatures ranging from 40 to 250 °C. Butane was injected by drawing about 5 µL of vapour from a lighter (just hold down the handle on the lighter while inserting the syringe needle and moving the plunger). The liquids (pentane, hexane, and ether) were injected by placing about 100 µL of each into a standard 2 mL vial, sealing the vial and injecting 5 µL of the headspace vapour, using a standard syringe and the autosampler. The t

 values were also calculated using equation 5 and retention factors for tetradecane provided by the data system:

	With all variables other than temperature held constant, the retention time of an unretained substance, the gas hold-up time, should increase with temperature, related to the carrier gas viscosity. Viscosity of a gas is related to temperature by an exponential relationship that can approximate to a linear relationship in the temperature ranges used in GC (6). In short, if a substance is unretained, the relationship between retention time and temperature should be linear with a positive slope. In Figure 1, this is seen at all temperatures for butane, and at temperatures above about 150 °C for pentane and diethyl ether. The calculated value also almost exactly overlapped with the butane results over the entire range, indicating agreement between the measured and calculated values. As the temperature decreases, however, the pentane, hexane, and diethyl ether become clearly retained, eventually exhibiting the expected behaviour with the retention time becoming longer as the temperature is lowered. When measuring hold-up time, take care to ensure a symmetrical peak; if the peak is asymmetrical, the retention time will not be accurate.

	As we have seen above, the average carrier gas flow rate can be calculated by measuring the gas hold-up time, t

. However, the average flow rate is not, by itself, a very useful measure for most practical situations. For example, the split ratio, calculated as the split vent flow divided by the column flow, requires the column flow to be measured at the inlet to be accurate. In packed-column GC, where column flow rates were much larger than with today’s capillary columns, the average and inlet flow rates are calculated from the measured outlet flow rate by applying appropriate correction factors (7).

Also in packed column GC, the column flow rate is most easily measured by attaching a soap film flowmeter, as seen in Figure 2, to the column outlet. These are inexpensive, and are operated by generating a soap bubble from the bulb at the bottom, and measuring the time required for 1, 10, or 100 mL of gas to flow from the column. This time is converted to a flow rate in mL/min. Soap film flowmeters can also measure split vent flow, as seen in Figure 2, and detector gas flow rates in capillary column systems. Today, fully electronic flow meters are available from a number of vendors. An electronic flow meter and an electronic leak detector are both must-haves in a modern GC laboratory.

	Capillary column flow rates are more difficult to measure than packed column rates, as they are generally much lower, typically on the order of 1–2 mL/min and the measurement is often needed when the system is running. It is not wise to place the inlet of a flowmeter into a heated and running flame ionization detector (FID)! It is impossible to directly measure the outlet flow rate on an mass selective detector (MSD). Today’s electronically controlled systems calculate column flow rates automatically, using the known column dimensions, choice of carrier gas, inlet and outlet pressure, and temperature and relationships, such as those discussed in this article. Users should be certain that the column dimensions are accurately entered into the data system, and should double-check that they are correct if the system enters them automatically.

	Prior to the advent of the solid-state electronic pneumatic controls, used in most new GC instruments today, nearly all commercially available GC systems for both packed and capillary columns operated at constant column head pressure. For packed column systems, this allowed very simple pneumatics, essentially just a flow controller and a pressure regulator. Today’s capillary column systems with electronically controlled pneumatics can operate in two modes: constant pressure and constant flow. Shortly after the advent of electronic control of the pneumatic systems in gas chromatographs, Blumberg, Wilson, and Klee compared column performance characteristics in constant pressure and constant flow with temperature programming (8). They noted that there is little difference in overall column performance between the two options, so other considerations determine the choice of mode.

	In constant pressure operation, the column head pressure is constant throughout the run. This is the more common operating condition and is seen is most of the GC literature, especially papers and methods more than 10 years old and in many compendial methods. In a temperature-programmed run the carrier gas viscosity will increase with the temperature, causing a decrease in the flow rate as the run proceeds. This decrease is partly offset by an increase in volume (Charles’ Law) as the temperature increases. Overall, however, in most cases, the volumetric flow rate will decrease as temperature increases in a temperature-programmed run.

	In constant flow operation, the electronic flow controller increases the head pressure as the temperature is increased, to maintain a constant flow rate. Figure 3 shows chromatograms of the same sample, run under the same temperature program and same initial column head pressure. The chromatogram on top was run in constant pressure mode and the chromatogram on the bottom was run under constant flow mode. Note that the retention times are slightly shorter in the constant flow chromatogram, however, in agreement with Blumberg and colleagues, the peak widths and spacing are about the same. Not surprisingly, the effect on retention time is greater for the later eluting peaks.

 learning platform, describes the fundamentals of setting up gas flows in GC and discusses constant pressure and constant flow (9). Some ideas to consider when choosing which mode to use include:

		Detector performance. Mass-sensitive detectors such as FID and MSD may perform better with constant flow.

		Translating methods. Constant pressure may be preferred when a method is translated between differing column dimensions or stationary phases.

		Speed. Constant flow methods will often be slightly faster than constant pressure.

		Lower temperature. Later eluted peaks will be eluted at lower temperature in constant flow mode.

		Electronic pneumatic controlâbased techniques for ensuring systemâtoâsystem and laboratoryâtoâlaboratory reproducibility, such as retention time locking and method translation often require constant flow mode.

		When optimizing, adapting, or attempting to repeat a method from the literature, it is important to note which mode was used for the original method, and either ensure that you are using the same mode, or be prepared to perform additional optimizing.

	Carrier gases for GC must meet several requirements to be useful. The carrier gas must be inert, safe, dry, highly pure, suitable for the detector, and inexpensive. Helium, which readily meets all of these characteristics, has been the carrier gas of choice for most capillary GC work, especially gas chromatography–mass spectrometry (GC–MS), for decades, used in over 90% of recent research articles describing GC methods. Recently, however, several incidents of regional and global shortages have caused helium to be expensive or unavailable at times. As helium becomes less available, hydrogen and nitrogen emerge as leading alternatives because they meet all of the necessary characteristics. Table 1 provides a brief comparison of the three gases.

	As seen in Table 1, each gas has benefits and difficulties. Helium has been the gas of choice, because with moderate cost, it is highly pure, inert, and fast, gives high resolution, and is compatible with nearly all detectors, including mass spectrometers. With the rising cost of helium, many experts are recommending changing to hydrogen, which, given its lower molecular weight and viscosity than helium, can generally provide faster separations with similar or improved resolution. If obtained from cylinders, hydrogen is also moderately expensive, offers better resolution, and is often the carrier gas of choice when extremely narrow bore columns (0.1 mm inside diameter) are used, and for fast GC. Many laboratories are now using hydrogen generators, which offer high purity hydrogen, but with high up-front capital cost, ongoing maintenance, and potential downtime if there is a failure of the generator. If you consider using hydrogen, be sure to consult with your instrument manufacturer because modifications or adjustments may be needed in order to prevent hydrogen from leaking into the laboratory.

	Most experts do not recommend nitrogen for capillary column work. Although the cost is low, it is compatible with most detectors, and the viscosity is similar to helium, the high-molecular-weight leads to lower resolution through increased diffusion rates for analyte molecules in the gas phase. Furthermore, nitrogen is detected within the mass range for most benchtop mass spectrometers, so it is not generally compatible with MS detection. In December 2018, a helium shortage struck our facility, and for several months we were not able to purchase helium cylinders at any price. Lacking enough capacity in our hydrogen generators to run carrier gas as well as FID gas, we decided to switch to nitrogen and take our chances. Figure 4 shows overlaid FID chromatograms of a simple (C6-C20 n-alkanes) hydrocarbon mixture, using helium (black) and nitrogen (green) with a simple linear temperature program, a split injection, and a common capillary column operated in constant pressure mode.

	As seen in Figure 4, the two chromatograms are remarkably similar, with the main difference being slightly shorter retention times for nitrogen, due to the lower viscosity of nitrogen versus helium, as seen in Table 1. The peak widths and separation numbers (a measure of the spacing between the peaks) are nearly identical. This is not the result that most chromatographers would expect; we would have expected the nitrogen separation to exhibit significantly poorer performance. Remember that most of our common understanding about carrier gas flow and characteristics is based on isothermal analysis. Changing the temperature as the run proceeds impacts nearly every physical parameter that affects transport of molecules along the column. We addressed the overall peak widths seen in both chromatograms in a previous “GC Connections” column (10). In short, Figure 4 leads to the recommendation that, if using helium is becoming problematic, nitrogen should be considered, as it is very abundant, inexpensive to obtain, may give adequate separation performance, and will not damage the instrumentation if it does not work. With more helium shortages looming, there is already a lot of discussion and advertising around alternatives. When choosing an alternative to helium, chromatographers should carefully evaluate both hydrogen and nitrogen, and make the best choice for their own laboratory.

	Classically, most gas chromatographers optimize the flow rate for a separation by trial and error, by making a Van Deemter or Golay plot of the height equivalent to a theoretical plate (H) versus the average mobile phase velocity or the average flow rate, or by doing nothing and simply setting a column head pressure they have “always used”. Trial and error and doing nothing are obvious approaches and numerous references that discuss making van Deemter plots are available elsewhere (11). All three of these approaches have limitations; for trial and error and doing nothing, the limitations are obvious. Van Deemter and Golay plots have been heavily used for flow optimization since the early days of GC, and therein lies the limitation. The theory, while illustrative, was developed for the limited conditions of the inlet and outlet pressure ratio near unity and constant temperature (12,13). Quoting Golay, the equations “are applicable to columns of uniform cross-sections in which the input to exit pressure ratio is near unity”. This means that the commonly used Golay equation is only applicable in cases where the pressure drop between the column inlet and outlet is very small. In today’s capillary GC, this places a potentially severe limitation on the actual utility of the plots, given the common use of small-diameter columns and large pressure drops. It is generally not applicable to larger pressure drops and vacuum outlet detectors, such as MSD, common in today’s capillary GC. The Golay equation is also not applicable at all to temperature programming. In a book and a book chapter, Blumberg addresses this problem in detail, but a simplified optimization process to replace van Deemter and Golay plots is still eluding most chromatographers (14,15). Be careful when using classical van Deemter or Golay equation plots to select an optimum flow rate; they may not be applicable to your situation.

	Although measuring the carrier gas flow rate in GC is very straightforward, and optimizing it may be as simple as a trial-and-error approach, the underlying principles behind how the carrier gas flows through the column, and how this affects retention time and peak broadening, are not so simple and have been under discussion since the inception of GC. In thinking about carrier gas flow and gas hold-up time, some lessons and takeaways become apparent:

		Given that the gas hold-up time may be up to half of the time an analyte spends in the column, precise flow control is critical in capillary GC.

		Calculated flow rate and linear gas velocity are average values, and they assume isothermal operation throughout the run.

		When measuring gas hold-up time (tM), take care to ensure a symmetrical peak; if the peak is asymmetrical, the retention time will not be accurate, and the asymmetry indicates a problem with the inlet or gas flows.

		An electronic flow meter and an electronic leak detector are both mustâhaves in a modern GC laboratory.

		Be certain that the column dimensions are accurately entered into the data system, or double-check that they are correct if the system enters them automatically.

		When optimizing, adapting, or attempting to repeat a method from the literature, note which mode (constant pressure or constant flow) was used for the original method, and either ensure that you are using the same mode, or be prepared to perform additional optimizing.

		When choosing an alternative to helium, chromatographers should carefully evaluate both hydrogen and nitrogen, and make the best choice for their own laboratories.

		If you consider using hydrogen, be sure to consult with your instrument manufacturer, as modifications or adjustments may be needed in order to prevent hydrogen form leaking into the laboratory.

		Be careful in using classical van Deemter or Golay equation plots to select an optimum flow rate; they may not be applicable to your situation.

, C.F. Poole, Ed. (Elsevier, Amsterdam, The Netherlands, 2012), pp. 33–36.	

		H.M. McNair, J.M. Miller, and N.H. Snow, 

 (John Wiley and Sons, New York, New York, USA, 3rd Ed., 2019), pp. 234–235.	

		L.M. Blumberg, W.H. Wilson, and M.S. Klee, 

		“Theory and Instrumentation of GC Gas Supply and Pressure Control” CHROMAcademy. http://www.chromacademy.com (accessed January 2020).

 (John Wiley and Sons, New York, New York, USA, 3rd Ed., 2019), pp. 25–35.	

		J.J.J. van Deemter, F.J. Zuiderweg, and A. Klinkenberg, 

, D.H. Desty, Ed. (Academic Press, New York, New York, 1958), 36–55.	

, C.F. Poole, Ed. (Elsevier, Amsterdam, The Netherlands, 2012), pp. 19–79.	

Temperature Programmed Gas Chromatography

 (John Wiley and Sons, New York, New York, USA, 2010).

 is a graduate student in the Department of Chemistry and Biochemistry at Seton Hall University.

 is an analytical chemist at GQAS&T Pharma and Forensics Department, Bristol Myers Squibb, New Brunswick, New Jersey, USA.

 is the Founding Endowed Professor in the Department of Chemistry and Biochemistry at Seton Hall University, and an Adjunct Professor of Medical Science. Direct correspondence to: 

GC Connections

Go With the Flow: Thinking About Carrier Gas Flow in Gas Chromatography

The effect of carrier liquid composition on the molecular properties of uncross-linked and enzymatically cross-linked caseinate solutions was studied by asymmetrical flow field-flow fractionation (AF4). The sample solutions were prepared in 0.1M NaCl solution and subjected to AF4 analysis using three different elution buffers: 0.1 M NaCl, 0.01 M NaCl, and 0.01M CaCl

. When using 0.1 M NaCl as the carrier liquid, AF4 enabled the separation of the main casein aggregates from larger species (probably residual phospholipids) according to their molar masses, which increase smoothly over the elution times. In contrast, a decrease in the ionic strength of the carrier liquid caused significant changes in the elution properties of the caseinate solutions as a result of variations in the molecular conformations of the aggregates. Our findings extend the knowledge on the molecular properties of complex caseinate solutions as well as point out potential challenges when analyzing dynamic natural systems with an AF4 method.

	Caseinate, a water-soluble casein product, is a mixture of the four primary casein proteins, α

-, β-, and κ-caseins (molar mass ~ 2 × 10

 g/mol), in a ratio of approximately 4:1:4:1 (1). Solutions of caseinates possess excellent functional properties that are of fundamental importance for a wide range of applications in food (nutritive supplements), confectioneries (foam stabilizers), glues (thickener agents), and pharmaceutics (drug delivery) (2). Studies on the molecular properties of caseinate solutions have been widely performed using batch dynamic light scattering (DLS) techniques providing size distributions in terms of hydrodynamic radius (

) (3). It has been shown that, in a caseinate solution, individual caseins form a dynamic system of small aggregates with an average 

 of ~ 10 nm as well as larger aggregates with an 

 of ~ 200 nm as a consequence of noncovalent interactions (3,4). Although LS methods provide a fast measurement of aggregate size in solution, they only yield average values and can hardly give a proper molecular characterization in the case of polydisperse samples. Yet, exact information about molar mass and conformation of individual components in caseinates is crucial to better understand the aggregation behaviours of caseins in solution as well as to develop structure–function relationships. Therefore, a separation step prior to LS analysis is needed to overcome the difficulties in analyzing caseinate solutions.

	Asymmetrical flow field-flow fractionation (AF4) is an analytical technique that has, in the past decade, clearly shown to be a powerful alternative to columnâbased separation methods for the separation and characterization of colloidal properties of ultra-large milk protein aggregates under native conditions (5). In a recent work, we demonstrated the capabilities of AF4 to investigate the enzymatic cross-linking of non-micellar caseins with microbial transglutaminase (mTGase) (6). Through coupling of AF4 to multi-angle light scattering (MALS), DLS, and concentration detectors such as UV–vis and refractive index (RI), we were able to elucidate the impact of mTGase on the molecular structure of protein aggregates, which changes in a controlled manner during enzymatic treatments, from flexible to compact and much denser network-like objects (6).

	The advantageous characteristics of AF4 for the analysis of dynamic natural systems mainly come from the lack of stationary phase. The sample fractionation is performed in an empty channel offering the following benefits: a “gentle” separation mechanism with severely reduced pressures and shear forces; higher molar mass separation range (approximately 10

 g/mol); and the possibility to apply non-denaturing eluents for a non-destructive analysis of native proteins. The theoretical background and the different separation modes of AF4 have been comprehensively reviewed over the years (7,8,9), and the method was presented particularly for the separation of caseins in our previous work (5).

	The AF4 separation mechanism is based on differences in the diffusion coefficient (D), which is related to the hydrodynamic size (

) of the molecules according to the Stokes-Einstein equation (10):

 is the temperature, and η is the viscosity of the carrier liquid.

	Taking into account the AF4 retention theory (11) and combining with the previous equation, the elution time (

 is the channel flow velocity. The ratio between 

 defines the separation power, and it has to be optimized to obtain reliable size separation in a relatively short analysis time. Programmed cross-flow with linear or exponential decay over the time is used for the separation of highly distributed samples, whereas constant cross-flow enables the separation of monomers from aggregates (12). Besides the flow rates, however, other parameters in AF4 should be optimized to achieve good separation and characterization. These parameters include the membrane type (cut-off and material), channel thickness, sample volume injected, and focusing time (13). Additionally, the choice of carrier liquid is also crucial to obtain reliable AF4 data and should be selected according to the properties of the sample. For protein analysis, buffer solutions are commonly used to reduce electrostatic interactions that may affect the sample components and cause poor fractionation (5,7). Furthermore, the composition of the carrier liquid has been shown to affect the stability of delicate protein aggregates (14) as well as to induce changes in molecular structure of charged particles (15).

	This article aims to understand the impact of the AF4 carrier liquid on the molecular properties of uncross-linked and enzymatically cross-linked caseinate solutions. Molar masses, sizes, and conformation of aggregates as a function of changes in composition of the carrier liquids are characterized through coupling of AF4 to MALS, on-line DLS, and concentration detectors. The results are expected to provide more insights into the complex properties of caseinate systems as well as to extend knowledge of AF4 applications for the analysis of biological macromolecules.

	Chemicals: Acid casein powder was purchased by SigmaâAldrich Chemie GmbH, with a crude protein content of 868 g/kg (Kjeldahl method, N × 6.38 [IDF, 1979]). NaCl (anhydrous), NaOH, and NaN

 were purchased by Sigma Aldrich, whereas CaCl

 (dehydrated) was purchased by (Carl Roth GmbH + Co. KG). Microbial transglutaminase Activa MP from Streptomyces mobaraensis (mTGase; activity of 90 U/g of protein) was purchased by Ajinomoto Foods Europe SAS.

	Carrier Liquids and Caseinate Preparation: All the carrier liquids were prepared using deionized 18.2 M½-cm water (Lab Water Purification Systems - ELGA LabWater). Three different AF4 carrier liquids were prepared as follow:

	Dissolution of 11.69 g of NaCl in 2 L of deionized water to obtain a 0.1 M NaCl solution.

	Dissolution of 1.17 g of NaCl in 2 L of deionized water to obtain a 0.01 M NaCl solution.

 in 2 L of deionized water to obtain a 0.01 M CaCl

 was added to the solution for preservation, and the pH was adjusted to 6.8 using a freshly prepared solution of 1 M NaOH. Prior to AF4 experiments, the carrier liquids were filtered using 0.2 µm pore size cellulose acetate filters (Sartorius Stedim BioTech GmbH).

	For the preparation of caseinate solution, commercial casein powder was dispersed at 10 mg/mL in 0.1 M NaCl and neutralized at pH 6.8 using 1 M NaOH solution. Thereafter, the caseinate solution was stirred overnight at room temperature to ensure complete dissolution of caseins. For the enzymatic cross-linking, a part of the solution was treated with 3 U mTGase per g casein at 40 °C. The enzyme was inactivated by heat treatment (85 °C for approximately 10 min) after 3 h (cross-linked caseinate). The reference caseinate was treated in the same way without enzyme addition (uncross-linked caseinate) (6).

The AF4 instrument was an Eclipse Dualtec separation system (Wyatt Technology Europe GmbH) equipped with a flow reading unit and channel section. This consisted of a long channel (26.5 cm in length), poly(tetrafluoroethylene) (PTFE) spacer with a thickness of 490 μm, and regenerated cellulose (RC) ultra-filtration membrane with an average molecular weight cut-off (MWCO) of 10 kDa. The channel section was connected to an Optilab T-rEx (Wyatt Technology Europe GmbH) differential refractive index (dRI) detector with a wavelength of 658 nm, an 18-angle Dawn-Heleos II MALS detector (Wyatt Technology Europe GmbH) operating at a wavelength of 658 nm, and a UV detector (Agilent Technologies) set at wavelength of 280 nm. A Wyatt QELS module (Wyatt Technology Europe GmbH) integrated inside the Dawn provided online Rh distributions at a scattering angle of 99°. The carrier liquids were delivered by an Agilent Technologies 1200 series isocratic pump equipped with an in-line vacuum degasser, while an autosampler (1200 series, Agilent Technologies) ensured the injections into the channel. Between the pump and the channel a filter holder with a 100 nm pore size poly(vinylidene fluoride) (PVDF) membrane (Millipore Corp.) to prevent the contamination of the separation device was placed.

	The programmed flow for AF4 experiments is shown in Figure 1. The channel flow rate was maintained at 0.5 mL/min for the entire AF4 run. The sample concentration was 10 mg/mL, and the amount of sample solution injected into the channel was optimized to 30 µL, which was tested to ensure no overloading of the channel.

	The solution was injected during the focus–injection step with a duration of 4 min, whereas a focusing–relaxation step of 2 min was performed prior to elution mode. The total focusing time was 7 min with a cross-flow rate (focusing flow) of 3 mL/min. Similar to our previous study (6), the crossâflow was initially maintained at 2 mL/min for 5 min, and then decreased exponentially to reach 0.1 mL/min in 25 min in elution mode. The cross-flow was kept constant at 0.1 mL/min for 10 min afterwards. At the end of each run, the channel was rinsed with the carrier liquid for 25 min without crossâflow to eliminate any particles prior to the next injection.

	The control of the Agilent and Eclipse components (flows and pressures) as well as the acquisition and data analysis were carried out using the software Vision CSH package (Chromatography Software Hub, Version R1806; Wyatt Technology Europe GmbH). The AF4 experiments were performed in triplicate, that is, three injections for uncross-linked and cross-linked caseinate solutions.

After AF4 fractionation, light scattering techniques are widely used in the field of proteins because they provide direct information on size and structural properties of the individual molecules and aggregates. In particular, the combination of MALS with concentration detectors enables the determination of molar mass, or more specifically weight average molar mass (

) according to the following equations (16):

(θ) is the intensity of light scattered by the molecule at angle θ, c is the concentration of the sample solution, dn⁄dc is the specific refractive index increment of the scattering solution, and 

(θ) is the particle scattering function:

	where λ is the wavelength of the incident light in a given solvent, and u is the scattering vector (16).

 by MALS works generally well for protein aggregates of at least 1 × 10

 g/mol and the angular dependence of the light scattering, for example, the determination of 

 of at least 10 nm (diameter > λ/20 nm). The MALS data can be fitted using different models of which Berry is commonly utilized for casein particles (5,17). In this work, the determination of 

 was performed using Berry formalism fitting a 1st degree polynomial to data obtained at 29.6–130.4° scattering angles, and an extinction coefficient of 0.81 L/g for caseinate solution (1).

 can be determined by on-line DLS, which substantially measures the frequency of the intensity fluctuations related to molecular diffusion, and thus to the 

 (equation 1). A significant advantage of having both DLS and MALS is that the first one is more sensitive to smaller aggregates (≤ 10 nm), and therefore a complete size distribution can be obtained by combining both detectors.

	Additionally, assumptions about the molecular conformation of the proteins can be obtained from the dependency of radii on 

	The slope of a double logarithmic plot defines the scaling factor 

, which is theoretically defined for spheres (

	Differences in the molecular properties of uncross-linked and cross-linked caseinate solutions with variation in the ionic strength as well as ion type of the AF4 carrier liquid were systematically studied. The impact of 0.1 M NaCl, 0.01 M NaCl, and 0.01 M CaCl

 elution buffers on the detector responses as well as on the 

 distributions of uncross-linked and crossâlinked caseinate solutions is shown in Figure 2. The 

 distributions as well as their average values are reported in Figure S1 and Table S1 (see supplementary material: 

http://www.chromatographyonline.com/supplementary-information-effect-carrier-liquid-compositionâmolecular-properties-caseinate-solutions

 (conformation plots) as a function of the carrier liquid compositions are shown in Figures 3, 4, and 5. The fractograms show that the elution behaviours of both uncross-linked and cross-linked caseinate solutions are strongly affected by variations in the composition of the carrier liquid used for AF4 analysis. Using a carrier liquid with identical composition as the sample preparation (0.1 M NaCl) allows high resolution separation of the main casein aggregate (at 20–35 min elution time) from larger aggregates eluting when cross-flow is off (at elution time of 50–60 min). The broader UV response at lower elution times (approximately less than 20 min) reveals the presence of casein material that is too small for a complete MALS analysis. The late-eluting fraction with relatively low UV activity but higher MALS response is likely a result of aggregation between proteins and residual phospholipids that are characteristics of the commercial casein powder (6,19). In fact, the zwitterionic phase of phospholipids molecules has been shown to interact with hydrophobic facets of κ-caseins located on the external surface of casein micelles in milk (20,21). Consequently, experimental conditions (for example, heat treatments) during milk processing to obtain the raw casein material might generate largerâsized complexes containing both casein proteins and lipid fractions, which are the cause of a high MALS response after AF4 separation.

	Casein aggregates in the uncross-linked caseinate possess an average 

 of 4.8 × 105 g/mol, which increases up to 1.02 × 106 g/mol after mTGase cross-linking (Table S1, see supplementary material: 

http://www.chromatographyonline.com/supplementary-information-effect-carrier-liquid-composition-molecular-properties-caseinate-solutions

 measured by DLS is 12.6 nm and 12.2 nm after mTGase cross-linking; 

 values for this low elution time fraction cannot be calculated because the sizes are smaller than 10 nm and are behind the limits of MALS (Figure S1, Table S1, see supplementary material: 

). Assumptions on the molecular conformations for casein aggregates can be made plotting 

 as shown in Figure 3. According to the theory, 

 is related to the diffusion coefficient of a hard sphere with the same volume (equation 1), whereas 

 is related to the geometrical dimension of a specific particle (5). It can be seen that casein aggregates possess compact molecular conformation (

 = 0.40), which is rather unaffected by cross-linking reactions (

 = 0.38). Such observations are in line with our previous studies, where we proved the hypothesis that mTGase forms cross-links between casein molecules predominantly within the same aggregates (6,19) However, the unavailability of 

 distributions does not allow further considerations to be made on the molecular shape of caseinate aggregates in this study. For larger aggregates, the average 

 g/mol, whereas the two radii show similar average values after enzymatic reaction (Table S1; see also Figure S1, see supplementary material: 

 > 0.4) of all the conformation plots indicates large aggregate formation with an overall lower compactness (Figure 3).

	When the concentration of NaCl in the carrier liquid was decreased to 0.01 M, the peak associated to the larger aggregates shifted towards shorter elution times (Figure 2), resulting in a decrease of the average 

 g/mol (uncross-linked caseinate) and 1.5 × 10

 g/mol (cross-linked caseinate) (Table S1, see supplementary material: 

). Additionally, both radii also decreased at lower ionic strength but the differences were not as significant as in the case of 

). The conformation plots, however, show that the larger aggregates become more compact as the slopes decrease to values ≤ 0.40 (Figure 4). In contrast, the average values of 

 and the radii for the main casein aggregate fraction prior to and after mTGase cross-linking do not drastically change when decreasing the ionic strength of the carrier liquid (Table S1, see supplementary material: 

), but the structures become slightly expanded with increasing conformation slopes (Figure 4). Interestingly, for the cross-linked caseinate, an additional peak (approximately 30 min elution time; Figure 2) with average 

 of 13.1 nm (Table S1, see supplementary material: 

) was detected using MALS. For this fraction, the conformation plot based on the dependency of Rg on Mw gives a slope of 0.64, which is typical for flexible molecular structures. However, the molar mass range available for fitting the LS data is rather small because of poor separation and thus, the higher slope values might be a result of a low accuracy.

	On the other hand, using salt-depleted carrier liquids causes a reduction in charge screening effects and thus increases electrostatic interactions during the AF4 analysis. In this context, the focusing step prior to elution could partially play an important role in changing the retention properties of caseinate solutions: during the focusing–relaxation step, the sample components undergo higher pressures and become concentrated from the cross-flow in a narrow band on the ultra-filtration membrane. Consequently, interactive moieties of caseins and other species can form different agglomerates that significantly affect the AF4 performance and separation. However, the selection of focus flow conditions with respect to the type of buffer for AF4 analysis of caseinates requires more detailed investigations that are beyond the scope of this study.

	The elution behaviours of components in caseinate solutions are even more affected when using 0.01 M CaCl

 for AF4 separation (Figure 2). The presence of Ca

 appears to greatly increase the interactions between casein material and larger aggregates, resulting in the formation of highly disperse species. In other studies, ion Ca

 was added directly to caseinate solution, resulting in increased aggregation of individual caseins similar to native casein micelles (4,22). Furthermore, larger cross-linked casein aggregates were formed by mTGase in the presence of Ca

 as a consequence of more pronounced self-assembly of casein molecules (23). The average value of Mw was 1.6 × 10

 g/mol for the uncross-linked caseinate solution, and increased up to 8.7 × 10

 g/mol for the cross-linked caseinate solution (average values of 

 can be seen in Table S1, see supplementary material: 

). The size distributions, however, show different tendencies (Figure S1, see supplementary material: 

) and the average values of radii calculated for uncrossâlinked caseinate were larger than those ones obtained for the cross-linked caseinate solution (Table S1, see supplementary material: 

). When plotting the radii against the molar mass for the uncross-linked caseinate, reliable scaling slopes were obtained in the region of 

	In the region of molar masses between 5 × 10

 g/mol, no clear tendency was observed for the uncrossâlinked caseinate. This effect might be attributed to the formation of calcium bridges within the aggregates, resulting in a strong alteration of molecular conformations of the individual caseins. At higher 

, however, both radii steeply increased with increasing 

, which is typical for the formation of open structures (scaling factors > 0.6). In contrast, the dependency of both Rh and 

 for the cross-linked caseinate showed a decrease in conformation slopes, indicating the formation of more compact geometries. Whilst 

, resulting in a slope value of 0.45, the 

 deviated slightly from linear behaviour at 

 ≥ 2 × 108 g/mol as reflected in the conformation value of 0.41 (Figure 5). This effect might be caused by ions Ca

 of the carrier liquid that act on the external surface of cross-linked casein aggregates, and thus “mediate” stronger electrostatic interactions between individual caseins and residual phospholipids. In fact, it is rather unlikely that ions Ca

 can diffuse within the aggregates because of the presence of cross-links induced by mTGase. However, it should be noted that the amount of Ca

 in the buffer system was rather low but was sufficient to cause significant changes in AF4 performance during separation of caseinate components.

	This study examined the impact of the carrier liquid composition used for AF4 analysis on the molecular properties of uncrossâlinked and cross-linked caseinate solutions. The sample solutions were prepared using 0.1 M NaCl solution and the same experimental conditions, whereas three carrier liquids were chosen for AF4 experiments: 0.1 M NaCl, 0.01M NaCl, and 0.01M CaCl2. The characterization of the eluted fractions was achieved through coupling AF4 with MALS-UVâonline DLS detection systems. Whilst a carrier liquid with the same composition of the sample preparation (0.1 M NaCl) enabled a size separation of main casein aggregates from larger aggregates (probably phospholipids–protein complexes), the decrease in the ionic strength of the buffer systems affected the molecular conformation and, thus, the retention properties of both components. Such modifications are attributed to stronger interactions between caseins and residual lipids at lower salt concentration of the carrier liquid, resulting in the formation of different agglomerates during AF4 analysis.

	On the one hand, the obtained results extend the knowledge about aggregation behaviours and molecular properties of components in caseinate solutions. Furthermore, the importance of the carrier liquid composition when characterizing dynamic natural systems with AF4 has been demonstrated. In order to minimize changes in the molecular structures of the sample components and obtain a reliable AF4 separation, great care must be taken in the selection of the salt concentration and ion types in addition to flow conditions for method development.

	This research was funded by the Deutsche Forschungsgemeinschaft (DFG; Bonn, Germany) under the grant number LE1424/9-1 and RO3454/5-1. Microbial transglutaminase was kindly provided by Ajinomoto Foods Europe SAS (Hamburg, Germany).

	The manuscript was written through contributions from all authors. All authors have given approval to the final version of the manuscript.

	The authors have declared no conflict of interest.

		A. Pitkowski, D. Durand, and T. Nicolai, 

		A. HadjSadok, A. Pitkowski, L. Benyahia, T. Nicolai, and N. Moulai-Mostefa, 

		T. Huppertz, I. Gazi, H. Luyten, H. Nieuwenhuijse, A. Alting, and E. Schokker, 

		N. Raak, R.A. Abbate, A. Lederer, H. Rohm, and D. Jaros, 

		R.A. Abbate, N. Raak, S. Boye, A. Janke, H. Rohm, D. Jaros, and A. Lederer, 

		D. Perez-Rea, C. Zielke, and L. Nilsson, 

		M. Leeman, K.G. Wahlund, and B. Wittgren, 

		B. Kim, S. Woo, Y.-S. Park, E. Hwang, and M.-H. Moon, 

		J.R. Runyon, M. Ulmius, and L. Nilsson, 

Colloid. Surface. A: Physicochem. Eng. Aspects

, J. Roovers, Ed. (Springer, Berlin, Germany, Vol. 143, 1999), pp. 113–194.	

		N. Raak, L. Brehm, R.A. Abbate, T. Henle, A. Lederer, H. Rohm, and D. Jaros, 

		M. Sokolovski, T. Sheynis, S. Kolusheva, and R. Jelinek, 

		A. Crespo-Villanueva, B. Gumí-Audenis, F. Sanz, F. Artzner, C. Mériadec, F. Rousseau, C. Lopez, M.I. Giannotti, and F. Guyomarc’h, 

		A. Smialowskaa, L. Matia-Merinoa, B. Inghamb, and A.J. Carra, 

		N. Raak, C. Schöne, H. Rohm, and D. Jaros, 

 is a scientist in the analytical department of the Institute of Macromolecular Chemistry at IPF Dresden and head of the Polymer Separation Group.

 is a scientist in the Polymer Separation Group and leader of thermoanalysis as well as fieldâflow fractionation techniques at IPF Dresden.

 carried out his Ph.D. in the Polymer separation group at the IPF Dresden. He is currently working as an application scientist at Wyatt Technology Europe.

 worked as a lab technician and research engineer in the Polymer separation group at IPF Dresden.

Features

The effect of carrier liquid composition on the molecular properties of uncross-linked and enzymatically cross-linked caseinate solutions was studied by asymmetrical flow field-flow fractionation (AF4).

The Effect of Carrier Liquid Composition on the Molecular Properties of Caseinate Solutions Studied by AF4

How do bioinert and biocompatible liquid chromatography (LC) systems and columns improve separations of biomolecules? How do I know when these systems are required for my separation?

We are witnessing tremendous growth in the life science and biopharmaceutical research areas, and separation scientists have risen to the myriad challenges that have presented themselves in these industries. Increasingly, analytical workflows are being designed with special measures taken to improve separation performance for biomolecules. Thus, terms like bioinert, biocompatible, biocolumn, and large molecule liquid chromatography LC (bio-LC) have arrived on the liquid chromatography scene. For this month’s instalment of “LC Troubleshooting” I’ve asked two my research collaborators, Jordy Hsiao and Greg Staples, to join me to address these topics from the point of view of troubleshooting. In this first instalment, we’ll dive into some of the background behind these terms and their relevance to bioseparations, and how examples of paying attention to specific characteristics of LC systems and separation conditions can have dramatic effects on the separation of biomolecules.

	Several years ago, some of our own work focused on using hydrophilic interaction chromatography (HILIC) to separate biologically relevant small molecules. The preliminary results were quite exciting as we were able to achieve good separations of underivatized amino acids. However, we had difficulty detecting and obtaining good peak shapes for acidic metabolites containing phosphate groups or multiple carboxylate groups. These compounds are important because they are involved in many crucial cellular pathways (for example, the tricarboxylic acid cycle). At that time, we embarked on a journey to systematically study these effects, with the hope that a more detailed understanding of the observations would lead to better separations in the long run. It turned out to be a long trip, and the path is one that many analytical scientists have travelled. We began assembling other reports of deleterious interactions between biomolecules and high performance liquid chromatography (HPLC) systems, cataloging problems with phosphopeptides (1,2), phosphorylated glycans (3), monoclonal antibodies (mAbs) (4), and pharmaceutical compounds (5). From this survey, we observed that these problems were reported to occur on a variety of stationary phases in addition to HILIC, including reversed-phase, ion exchange, size-exclusion chromatography (SEC), and others. We were eventually able to develop robust, high performing separations of acidic metabolites. We hope some of the general lessons we’ve learned through these experiences can be leveraged by others to save significant time in tackling tricky biomolecule separations.

	Poor performance for separations of biomolecules can manifest in a number of ways. Biomolecules as a class of analytes comprise a vast array of molecules that vary in terms of both physicochemical properties and size, and it’s important to note that chromatography problems can arise for species that are either small (such as metabolites, glycans, peptides), or large (such as proteins).

		Peak tailing: Often, analytes are eluted with undesirable peak tailing, which can make quantitation difficult. Sometimes these peak shapes gradually improve over the course of days or weeks, while other times their poor shapes can remain the same or continue to worsen. A good example of such peak tailing for a biomolecule is the separation of the protein cytochrome C using SEC. Figures 1(a) and 1(b) show such a separation on a column housed in stainless steel (SS) versus a column housed in polyether ether ketone (PEEK). The tailing factor of the protein on the SS column is significantly worse than on the PEEK column.

LCGC Europe-03-01-2020

Vol 33 No 3 LCGC Europe March 2020 Regular Issue PDF

Go With the Flow: Thinking About Carrier Gas Flow in Gas Chromatography

The Effect of Carrier Liquid Composition on the Molecular Properties of Caseinate Solutions Studied by AF4

Troubleshooting LC Separations of Biomolecules, Part 1: Background, and the Meaning of Inertness

The Current Status and Future of Two- and Multidimensional Liquid Chromatography in Pharmaceutical R&D and QC