A liquid chromatography–two-dimensional mass spectrometry (LC–MS-MS) system that achieves a 72-s injection-to-injection cycle time was created by making several small modifications to common components. The system was used to quantify a wide range of chemical compounds in various absorption, distribution, metabolism, and excretion (ADME) assays with an analytical success rate >90%. Method considerations included the amount of carryover when reducing the number of needle washes, potential loss of signal intensity with increased flow rate, and ideal column pore size to perform runs consisting of more than 2000 injections. These modifications enable almost any laboratory to operate in a high-throughput manner without having to purchase state-of-the-art equipment. In addition, replacing the mass spectrometer with a more sensitive system opens up the possibility of pooling up to six samples per injection, achieving a net injection-to-analyte time of 12 s, which is comparable to the most advanced LC–MS-MS systems currently on the market. 

The desire to provide new treatments for unmet diseases or conditions, along with the opportunity to provide improvements in safety and efficacy for existing therapies, highlight the urgency to bring new drug entities (NDEs) to market. Research and development costs to provide these solutions have risen to an estimated $1.8 billion per NDE (1). These needs and costs highlight the importance of bringing a product to market quickly to provide patient benefit as early as possible.  

Current efforts to speed up the drug discovery process have included a dramatic increase in chemical synthesis over the past decades through the rise of combinatorial chemistry and the outsourcing of chemical synthesis (2). It is important to note that with this increase in chemistry production comes the demand to provide supporting information critical to the development of a safe, effective, and marketable drug. Supporting information includes measurements of potency, metabolism, elimination pathways, and toxicity, to name just a few. These data are used to promote candidates with acceptable properties to the next stage in the discovery funnel and to drive the study of structure-activity-relationships (SAR). The faster these data can be obtained, the more impact they will have on selecting the right drug candidates earlier in the process, therefore reducing the overall time from discovery to market. Many of the measurements listed above require the use of liquid chromatography–mass spectrometry (LC–MS) for the detection of the drug candidates or other assay endpoints. Therefore, having access to LC–MS instrumentation that can rapidly screen compounds is a distinct advantage, because so many of the assays depend on it for their data endpoints.  

Currently, there are several "out of the box" platforms available to increase MS sample analysis throughput. Although such systems are convenient and powerful, their price can exceed several hundreds of thousands of dollars. Complicating the matter further is the fact that many research budgets are either shrinking or remaining stagnant (3,4). Scientists are now being asked to do more with less and to make do with the instrumentation that is currently in their laboratories. This often means working with instrumentation that is not state-of-the-art or purchasing readily available used equipment from auction sites such as DoveBid or Labequip. At the time this article was written, there were three AB Sciex API-3000 mass spectrometers, 11 Agilent 1100 high performance liquid chromatography (HPLC) binary pump systems, and seven CTC Analytics HTS autosamplers available for purchase on DoveBid. This article demonstrates how to create an LC–MS-MS system using only these three common components that approaches a 1-min injection–injection cycle time, without the use of column swapping, multiple LC systems, or aftermarket scheduling software. 

The system used in these studies comprised an AB Sciex API-3000 mass spectrometer with a turbo ion spray source from Applied Biosystems (Foster City, California), an Agilent 1100 Series binary pump with a vacuum degasser (Santa Clara, California), a CTC Analytics model HTS-PAL autosampler from LEAP Technologies (Carrboro, North Carolina), and a Valco Cheminert two-position, 10-port switching valve from VICI (Houston, Texas). Fortis Pace C18 columns (30 mm × 2.1 mm) were obtained from Resolution Systems (Holland, Michigan) and were used for all tests unless otherwise stated. Mobile phase A consisted of 0.1% formic acid in water and mobile phase B was 0.1% formic acid in acetonitrile. All solvents were HPLC grade from Sigma-Aldrich (St. Louis, Missouri). A test solution of carbutamide, carbamazepine, imipramine, and verapamil in 50:50 acetonitrile–water was used throughout the process to gauge relative retention times. The compounds were purchased from Sigma-Aldrich. 

A baseline for comparative purposes was established using a commonly used gradient and flow rate. The initial method details are as follows: mobile phase A was held at 95% for the first 0.3 min, then decreased to 5% over the next 0.5 min, and held at that level for 0.7 min. Mobile phase A was then increased over the next 0.1 min back to 95% and held for an additional 1.4 min, yielding a 3.0-min method. The flow rate was constant at 500 µL/min for the duration of the method. The column was a Fortis C18 (30 mm × 2.1 mm, 5-µm 

). Tuning of the compounds to obtain precursor and product ion, collision energy, and declustering potential information was performed beforehand and used to generate the multiple reaction monitoring (MRM) methods. All compounds were ionized in positive mode. Using these standard conditions, the analytes were eluted between 2.3 and 2.6 min (Figure 1a) with an injection-to-injection time of 3.4 min after Analyst software "think time" and autosampler arm movement were included.  

Figure 1: Comparison of retention times by method. Blue = carbutamide, red = imipramine, green = verapamil, grey = carbmazepine. Shown are (a) a 3-min method at a 500-ÂµL/min flow rate with a 400-ÂµL delay volume representing the mixing chamber; (b) a 2-min method at 500 ÂµL/min with the mixing chamber removed; (c) a 1.5-min method at 500 ÂµL/min with a 0.2-min gradient; and (d) a 0.95-min method at 1000 ÂµL/min with a 0.2-min gradient.			

The first modification investigated was a reduction of the system dead volume. We began with the biggest source of dead volume in this system: the HPLC instrument. Agilent has previously documented how to reduce the dead volumes of its 1100 and 1200 series HPLC systems (5). The detailed procedure involves bypassing both the mixing chamber and dampener. For the work done in these studies, only the mixing chamber was bypassed because the dampener is required to read and report the pressure on the 1100 series HPLC system. In the case of the 1200 series HPLC system, the dampener is not used to measure the system pressure and can therefore be bypassed as an additional option for reducing delay volume. This single adjustment reduced the retention time, as compared to the baseline method, by almost a full minute, with the analytes eluted between 1.4 and 1.7 min (Figure 1b). This would allow an analyst to end the method at 2.0 min, yielding a method capable of 2.4-min injection-to-injection times. It should be noted that to recreate the 400-µL mixing chamber volume on the HPLC system of the initial method, a piece of wide-bore tubing of the same volume was used because our mixing chambers were previously discarded and unavailable for use in testing. 

Peaks were being eluted around 1.5 min into the method, so the next alteration was to the gradient profile. We wanted to take advantage of the software "think time" between when one injection ends and the next one starts by returning to original conditions at the very end of the method, allowing the system to re-equilibrate during that "think time." Various gradient profiles were tested before settling on the following: Mobile phase A was held at 95% for the first 0.05 min, decreased to 2% over the next 0.1 min, where it was held for 0.55 min, then increased back to 95% over the next 0.05 min for an additional 0.75 min, resulting in a 1.5-min method. This adjustment reduced the retention times of the analytes to 0.9 and 1.2 min (Figure 1c), and yields a method capable of a 1.9-min injection-to-injection time. 

The next parameter that was adjusted was the flow rate. There is no doubt that increasing the flow rate reduces the elution time. At some point, however, the higher flow may result in a loss of sensitivity. The vendor states that the operating flow range for the API-3000 ESI source is 40–1000 µL/min. The potential loss of sensitivity at higher flow rates was tested by repeat injections of the analytes at flow rates of 500, 750, and 1000 µL/min. The peak areas at each flow rate were then averaged and compared (Figure 2). Increasing the flow rate from 500 to 750 µL/min resulted in an average reduction in response of 21%. Carbutamide had the largest reduction, at 25%, and imipramine had the lowest reduction, at 16%. The flow rate of 1000 µL/min resulted in an average reduction in response of 34%. Carbamazepine had the largest reduction in peak area, at 43%, and verapamil had the lowest reduction, at 24%. Additional tests were performed using in-house compounds under these same conditions, and similar results were obtained. These data were subsequently used to determine the overall reduction in sensitivity and the effect on our ability to detect compounds in our absorption, distribution, metabolism, and excretion (ADME) assays. This analysis determined that moving from 500 to 1000 µL/min would have minimal impact (<5%) on the number of compounds not detected, and the benefit of reduced analytical run times far exceeded the small reduction in the percentage of compounds not detected.  

Figure 2: Comparison of signal intensity of four test solutions at multiple flow rates. Signal 			

A common belief is that the loss of sensitivity at higher flow rates can be overcome by splitting the flow postcolumn and doubling the injection volume. Although that may work in some applications, we found that in our case, this option yielded a signal that was 35% less than expected (data not shown). Because of those findings, flow splitting was not implemented in our methodology. Updating the method with the new flow rate of 1000 µL/min resulted in analyte retention times between 0.50 and 0.60 min (Figure 1d). The new flow rate enabled the method to end at 0.95 min (instead of the previous 1.5 min), yielding a method with an injection-to-injection time of 1.35 min (81 s).  

Two modifications were considered with regard to the autosampler: the number of wash cycles and the time spent opening and closing the refrigerated sample tray door for each injection. Incorporating the previously described modifications resulted in a method of 1.35 min, which may not be enough time to complete our usual wash cycle of two needle and two valve washes. A test was performed to determine the carryover when the number of wash cycles is reduced. The wash station used the 100-µL L-MARK syringe (LEAP Technologies) that is standard with the system. Because carryover can be both compound- and system-dependent, we replaced the analyte test solution previously described with 93 in-house compounds, representing a wide range of chemicals used in our drug discovery operations. Tests were performed reducing the number of washes from four down to one, and the average carryover was compared (Table I). Wash solution A consisted of 33:33:33 (v/v/v) isopropyl alcohol–methanol–water and wash solution B was 50:50 (v/v) isopropyl alcohol–acetonitrile. The syringe filling speed for all tests was 50 µL/s and the injection speed was 25 µL/s. Each test consisted of two analyte injections followed by two blank injections (10 µL each). The four-wash condition consisted of two needle washes and two valve washes, one from each wash solution, filling the needle to its maximum capacity (100 µL). The three-wash experiment used two valve washes and only one needle wash of solution A, whereas the two-wash experiment used two valve washes only. The one-wash experiment consisted of one valve wash with solution A.  

Table I: Comparison of the carryover and wash cycle duration when the number of autosampler washes was reduced from four to one			

The experiments above revealed that there was minimal difference in the amount of carryover seen between the four- and three-wash conditions, 0.36 and 0.39%, respectively. However, there was a 15-s reduction in wash cycle time (54 to 39 s). The two-wash condition reduced the wash cycle time down to 32 s, but there was a slight carryover increase to an average of 0.47%. When only one wash cycle was used, the average carryover increased approximately threefold to 1.43%, but the cycle time was reduced markedly, down to only 19 s. Defining what an acceptable level of carryover would be is a topic open for debate. For in-vitro ADME studies, our laboratory prefers to see an average carryover of <1%. Considering this criteria, only the single-wash cycle would be outside our limits. Given that our injection-to-injection time was currently 57 s, we decided to use the three-wash cycle option to keep the possibility open to reducing this run time even further. 

The second adjustment we made to the autosampler was the manner in which it retrieved the samples. Initially, the autosampler would open and close the refrigerated sample tray for each injection. A method that injects samples from the same plate, with samples that are thermally stable at room temperature, could save several seconds in cycle time by leaving the tray open between injections. The closing of the tray between injections is an adjustable setting on the autosampler. After the restore mode was changed from "sample" to "auto," the drawer would remain open until an injection from a different drawer was required. Although this change had no bearing on the method time, it did decrease the injection-to-injection time by ~9 s. After implementing this last modification, a method that achieved a 72-s injection-to-injection time was achieved.  

C18 was chosen as our stationary phase, with the belief that it would give us the most flexibility in retaining the diverse library of compounds we are responsible for testing. Also, because our goal was to decrease the method time, a smaller column was selected (30 mm × 2.1 mm). Our larger-capacity assays generate analytical runs as large as six 384-well plates for a single experiment, which equates to 2304 injections. Given the size of these large experiments, sample preparation is kept simple by using protein precipitation, centrifugation, and direct injection from the crash plate. The first columns tested had particle sizes smaller than 5 µm (3.5 or 2.6 µm) from various vendors. The decreased particle size resulted in higher resolution and therefore higher sensitivity based on a higher signal-to-noise ratio. However, given the relatively "dirty" samples generated from our simple sample preparation, it was discovered that the columns with smaller particle sizes did not consistently last through a 2000-injection run without clogging, causing failure of the system as a result of high back pressure. Moving to a 5-µm column largely eliminated problems with column clogging during a run and we believe it is a suitable particle size for large analytical runs using pumps not designed for ultrahigh-pressure liquid chromatography (UHPLC).    

We were able to reduce our original injection-to-injection cycle time from 3.4 to 1.2 min with simple modifications of our original system components. Multiplying this reduction over a large run of 2304 injections results in a time savings of 84.5 h (130.5 vs. 46 h total run time), or 3.5 days. Over the course of the experiments summarized above, we found that even the tiniest changes resulted in considerable time savings during a run. The simple act of leaving the sample tray open between injections saved a mere 9 s. Although that doesn't sound like much at first, that small savings shaved 5.75 h from the total run time. An important fact to keep in mind is that a 46-h run time ensures that if a scientist starts the analysis on Monday, he or she could process the data on Wednesday. A run that takes longer would require the scientist to wait until the following day to process the data, thus losing a day to report results. The main reason this work was performed was to to achieve an overall reduction in the cycle time from an assay request to data released. 

The 72-s method described in this article has been used for our intrinsic clearance assays. We believe these conditions provide us with a robust method that can be universally applied as a first line methodology for LC–MS-MS analysis because we are able to achieve a 94% analytical success rate over a diverse range of chemicals (with a molecular weight range from 150 to 1200 g/mol, and a cLogP range from –3 to 9). We believe the approach is easily transferable to other LC–MS-MS systems, and that similar modifications to reduce system dead volumes are possible, regardless of the HPLC instrument used. Additional modifications, such as column selection, tubing size and length, and software adjustments, could enable the method to be optimized even further. 

Having the option to upgrade the mass spectrometer to a more sensitive platform would open up several new opportunities for analytical cycle time reduction. First, the overall success rate of detecting compounds would increase as a result of the higher sensitivity of the instrument. Secondly, and a more intriguing advantage would be the ability to pool samples for analysis as a result of the higher scan rates, as well as the higher sensitivity of the instrument. It is reasonable to project that the sensitivity obtained from singlet samples analyzed on an API-3000 instrument could be maintained using those same samples pooled into groups of three, four, or even six by using an API-4000 instrument for analysis. Given this scenario, one would obtain data for six analytes over 72 s, or 12 s per analyte. A 12-s per analyte process is now within the same cycle time range as the most state-of-the-art LC–MS systems currently on the market.  

Incorporating a few simple, minor adjustments to a common LC–MS system resulted in the ability to obtain a 72-s injection-to-injection time. The system was devoid of any advanced or upgraded equipment such as column switchers, multiple HPLC systems, or aftermarket scheduling software. The system is robust, capable of long (2000+) injection sequences, and can be applied as a universal approach with a high success rate (>90%) over a diverse range of chemicals. 

The authors wish to thank Sean Orlowicz of Agilent Technologies for helpful discussions regarding ways to reduce system dead volume, and Sonia de Morais, director of drug metabolism at Abbott Laboratories, for enabling our work and content review. 

 is a Senior Scientist II, both in Global Pharmaceutical Research and Development at Abbott Laboratories, in Abbott Park, Illinois. Please direct correspondence to: 

(1) S.M. Paul, D.S. Mytelka, C.T. Dunwiddie, C.C. Persinger, B.H. Munos, S.R. Lindborg, and A.L. Schacht, 

(3) T. Randall, "Merck, Pfizer Research Strategies Diverge on Spending," 

(February 3, 2011). Retrieved July 8, 2011, from 

www.bloomberg.com/news/2011-02-03/merck-pfizer-strategies-diverge-on-the-value-of-spending-on-drug-research.html

(4) Rate of funding for biomedical research slowing, decreasing in recent years. 

 (January 12, 2010). Retrieved July 6, 2011, from 

http://www.sciencedaily.com/releases/2010/01/100112165236.htm

(5) "Switching Between Standard and Low Delay Volume Configurations Using a Software-Controlled, High-Pressure Valve for Optimized Separation Performance," Publication Number 5989-7732EN, Agilent Technologies (Santa Clara, California, April 1, 2008). 

Articles

How to create a liquid chromatography–tandem mass spectrometry (LC–MS-MS) system using mass spectrometers, a high performance liquid chromatography (HPLC) binary pump system, and an autosampler

Creating a High-Throughput LC–MS-MS System Using Common Components

Matrix-assisted laser desorption–ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool in histopathological characterization and represents a modern analytical technique, enabling two-dimensional detection of molecular components of biological samples. Using this method, it is possible to investigate the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules in biological systems by in-situ analysis of cell cultures and tissue sections. Recently, MALDI-IMS has become an essential tool for tissue analyses in life science applications, offering global analysis of tissue samples. An advantage of this imaging technique is the acquisition of local molecular expression profiles up to the microscopic level, while maintaining the topographic integrity of the tissue by avoiding time-consuming extraction, purification, or separation steps, respectively. With MALDI-IMS it is possible to determine the distribution of hundreds of unknown compounds in a single measurement, allowing rapid probing of the tissues' biochemistry. Moreover, MALDI-IMS results include qualitative and semiquantitative information, providing unique chemi-morphological information about the tissue status, which represents an important benefit for future analytical interpretation of pathological changes of a tissue. This article summarizes the most recent advances in sample preparation, instrumentation, and data-processing techniques for MALDI-IMS.

Today, bioanalytical tools based on mass spectrometry (MS) are essential elements in the discovery for new biomarkers and for new drugs that can improve the quality of human life. Rapid assessment of these components' properties is a critical step for enhancing the success rate to find new biomarker or new drug candidates (1). Biomarker discovery is the result of a complex series of comparative proteomics experiments and starts with a diagnosis step on tissue, followed by a differential expression analysis and then marker identification, using orthogonal analytical techniques, among which MS plays a key role (2). In most biomarker discovery and drug discovery studies, high performance liquid chromatography (HPLC)–MS-MS has become one of the standard tools for qualitative and quantitative determination of cell components. However, HPLC–MS-MS is not able to provide answers to certain questions regarding the distribution of certain biomarkers or drugs within individual organs or tissues (3–7). Autoradiography, fluorescence spectroscopy, and immunohistochemistry are still the methods of choice for determining the distribution of certain cell components in organs or tissues. But these techniques have some limitations, because they require the synthesis of radioactive isotopes, fluorescent tags, or specific antibodies, and a direct differentiation between the distribution of an administrated drug and its metabolites is not possible. Over the past few decades a new analytical method, which is generally called 

matrix-assisted laser desorption–ionization (MALDI) imaging mass spectrometry (IMS) 

 for examination of tissue sections and for visualizing the acquired data, has been introduced. Figure 1 illustrates the MALDI-IMS process, which was described, in parallel to the MALDI profiling procedure, by the laboratory of R.M. Caprioli in 1997 (8). 

Figure 1: Workflow diagram of sample preparation, MALDI-IMS measurement, and data analysis. Shown are (a) isolation of the medial tissue slice of the prostate specimen; (b) and (c) cutting the prostate sample, after snap-freezing in liquid, and mounting onto ITO glass slides; (d) matrix application with an ImagePrep station; and (e) MALDI-IMS measurement, data analysis, interpretation, and comparison with the histological images.			

Today, MALDI-IMS of tissue sections represents an alternative method for the identification of new biomarkers and for the detection of known compounds with no additional labels or affinity tags (9–11). Because of its unmatched sensitivity and high specificity, MALDI-IMS is a powerful tool for the investigation of the spatial distribution of biomarkers in biological samples through the in-situ analysis of thin tissue sections (12,13). Therefore MALDI-IMS, which is a functional microscopic imaging technique, can display the spatial distribution of unlabeled molecules in tissue sections and makes it possible to locate proteins, peptides, lipids, and metabolites by their mass signals in the tissue. MALDI-IMS can contribute to both diagnosis and identification steps. This interplay between those two uses brings a new perspective to the field of biomarker discovery. Technically, this new method uses a soft ionization technique that is capable of transfering intact bio-molecular ions into the gas phase. The result of a MALDI-IMS experiment contains chemical and spatial information (2,13,14). Hence, MALDI-IMS represents a medium-range technique for lateral resolution and sensitivity compared to LC–MS and has a limited dynamic range, mainly as a result of the inability to perform separation techniques on tissue (2,15,16). 

MALDI-IMS Tissue Sample Acquisition and Preparation

The chemical representativity and spatial integrity of an IMS experiment depends on sample acquisition, preparation, IMS, and interpretation of the acquired data. In all sample-handling steps throughout the acquisition and preparation process, the spatial and chemical integrity of all compounds must be maintained to avoid delocalization and degradation of the analytes (17). For MALDI-IMS experiments, handling and preparation steps of the sample after procurement are very important to preserve the native condition of the tissue. The main focus should be on the treatment of the tissue specimens peri- and postoperatively, sectioning of the tissue, tissue storage after sectioning, sample transfer to a MALDI target, tissue fixation, and matrix application (18).  

Typically, the tissue is snap frozen in liquid nitrogen immediately after tissue collection, conserved at –80 °C, and then cut in a cryostat. Embedding substances such as optimal cutting temperature (OCT) polymer or Tissue-Tek should be avoided to ensure trouble-free measurements, because these embedding materials can smear across the sample during the cutting procedure, resulting in a disturbance of the chemical integrity. For that reason OCT-free cryosectioning of the frozen tissue samples is recommended (19,20). It has also been reported that embedding in gelatin (21) and agarose (19) could be a good alternative to OCT or Tissue-Tek to facilitate handling of small or fragile samples like biopsies. The thickness of the tissue sections is normally between 10 and 20 µm, which represents the optimal thickness for handling and analyzing in the high vacuum environment of the mass spectrometer. Sections thinner than 10 µm can be very fragile and are difficult to manipulate, whereas sections thicker than 20 µm need more time to dry and can adversely affect the performance of the mass analyzer (18). Concerning tissue conservation, MALDI-IMS studies of biological samples do not only involve analysis on fresh frozen-, but also on formalin-fixed tissue. Archived formalin-fixed paraffin-embedded (FFPE) tissue blocks represent a considerable source of information regarding morphological features and molecular content and enable the investigation of the spatial distribution of potential biomarkers. FFPE material has the advantage of preserving molecular features for decades and is compatible with immunochemistry. The main disadvantage of FFPE tissue samples is the formalin induced protein cross-linking, which in general has been assumed to render them unsuitable for proteomic studies. The strategies of analyzing FFPE samples using MALDI-IMS are generally based on in-situ enzymatic digestion of the tissue section after paraffin removal and can be performed on a specific area of the tissue as well as on a very small area. The combination of automated microdigestion of a predefined tissue area with automated microspotting of MALDI matrix according to the same area enables the analysis of the tissue using MALDI-IMS. Nevertheless, the disadvantage of enzymatic digestion treatments in the preparation of FFPE samples is the impairment of an efficient ionization of the proteins and peptides (22,23). 

When using frozen material, after cutting the tissue and applying it to the slide, a fixation step before matrix deposition is necessary. One method for fixation is similar to that used in standard histology. First, the tissue is immersed in 70% ethanol, which precipitates the protein content of the tissue, removes salts and impurities, and seems to improve the crystallization process when α-cyano-4-hydroxycinnamic acid (CHCA) is used as matrix. But with the preliminary washing step, a loss of alcohol-soluble compounds can be expected (20). There is also the finding that the removal of lipids by organic solvents such as chloroform, hexane, acetone, toluene, or xylene increases the MS signal of proteins and peptides (24). However, there is no evaluation of chemical modifications, damage, or loss of molecules as a result of these treatments, and no validated technique is available to evaluate the level of molecule delocalization on a biological tissue depending on a particular treatment. 

Regardless of whether frozen or FFPE tissue is used, there are two main techniques for tissue analyses using MALDI-IMS: the direct imaging approach and blotting analysis approach, which is less frequently applied (8,25). For the blotting method, targets covered with C18-coated resin beads are used. The first step in this method is the fixation of the blotting membrane on the sample plate. Then, the tissue is blotted onto the membrane, washed with water, dried, and covered with matrix. Preliminary trypsin digestion is also possible. After matrix application, the membrane is analyzed in a mass spectrometer to detect proteins or peptides, but with this technique no direct comparison with the histological features of the measured probe is possible. Given that limitation, the direct imaging approach is more often used, since a direct correlation of MALDI imaging results with the histological features of the probe is possible. On that account, the right choice of the appropriate sample plate is an important aspect for a direct MALDI-IMS experiment. The sample plate of choice is often a transparent conductive slide such as glass slides coated with indium-tin-oxide (ITO) coated, which allows direct correlations between MS images and histological features of the measured tissue section. Glass slides coated by a thin layer of ITO were shown to be compatible with MALDI analysis and with optical microscopy (21,26). 

Matrix Selection and Application for MALDI-IMS

The possibility to delocalize molecules on a biological surface efficiently is essential for an IMS experiment and is highly dependent on the matrix depositon procedures. Deposition of a matrix solution can induce delocalization of the compounds in the area covered by the droplet and following crystallization. Therefore, the type of matrix and the deposition process play a preeminent role in a successful MALDI-IMS experiment, and require the choice of the matrix that fits best to the molecular class of interest. The optimum interplay of the type of matrix and the different molecular classes has been determined empirically (27). 

For frozen and FFPE tissue analyses, it has been reported that the most used matrices are α-cyano-4-hydroxycinnamic acid (CHCA) and sinapinic acid (SA). SA provides the best signals for higher molecular weight proteins, whereas CHCA is more suitable for lower molecular weight peptides (20). Matrices like 2,5-dihydroxybenzoic acid (2,5-DHB) and 3,4,5-trihydroxyacetophenone (THAP) must be avoided for MALDI-IMS experiments, because they crystallize in the form of long and fine needles, thereby inducing a very significant delocalization of the molecules.  

Matrix application protocols for MALDI-IMS include manual methods and automated sample preparation devices. The major disadvantage of manual procedures such as spraying, using an airbrush or thin-layer chromatography (TLC) sprayer, or dipping the tissue sections into matrix-containing solutions is poor reproducibility (20). Higher reproducibility can be achieved using automated sample preparation devices, which include two classes: spotting and spraying devices.  

Spotting devices apply small droplets of matrix solution in a grid onto the tissue. In correlation to the actual droplet size, the distance can be enlarged significantly by using a spotting device (28). Spotting devices also can be used to add chemical reagents onto the tissue in a controlled way to perform chemical modifications or digestions on the tissue (29). Disadvantages of robotic spotting procedures are the preparation time, which increases quadratically to the tissue area, and the lateral resolution, which is in the range of 200–500 µm.  

Compared to spotting devices, a higher resolution can be achieved by using automated spraying preparation devices such as pneumatic spraying and vibrational spraying devices. Vibrational spraying devices use vibrational vaporization of the matrix with a piezo-electricspray head, which moves a pinhole sheet next to the matrix reservoir to eject small droplets with an average diameter of 20 µm. The matrix aerosol is deposited onto the tissue section under controlled conditions and the entire matrix deposition process is monitored by an optical scattering-light sensor. This sensor monitors the light scattered from matrix crystals to control deposition periods and intervals, matrix layer thickness, wetness, and drying rate in real time. Therefore, this device provides highly reproducible sample preparations for MALDI imaging in a fully automated, push-button process, and represents a significant advantage for routine operation, enabling excellent spectra quality at high image resolution of 25 µm (13). With the pneumatic spraying devices, a constant flow of heated sheath gas is delivered conjointly with the matrix spray, which results in a very rapid evaporation and in consequence minimal analyte delocalization and highly efficient cocrystallization for a superior MS signal. The heated capillary with a pneumatic spray moves in predefined patterns over the tissue sample and sprays with a higher and more reproducible quality compared to a manual pneumatic spray. A disadvantage of this method is the missing control of parameters such as drying rate and wetness of the tissue, which are not monitored during the preparation. Consequently, the settings must be tuned in advance (13). 

Histological Staining of MALDI-IMS Tissues

For interpretation of MALDI-IMS results, it is necessary to correlate the allocation of MALDI results with the shape and histological features of the probe. Therefore, there is an urgent need to superimpose the MALDI images onto a macroscopic or microscopic optical image of the sample that was acquired before the MALDI measurement. With a macroscopic optical image it is possible to recognize the outline of the tissue and to define the measurement area, but for the interpretation of the histological features a microscopic image with an appropriate resolution is required. Thus, it is necessary to use stained tissue sections for the analysis of the histological features. However, there are different techniques for the correlation of histology with the MALDI-IMS result: a histological staining of a consecutive section, a histological staining of the measured section before the MALDI-IMS experiment, or a histological staining of the measured section after the MALDI-IMS experiment. With the consecutive-section approach, which allows the use of any staining protocol, one section is used for the MALDI imaging, and another for the histological staining (13). Therefore, hematoxylin-eosin (HE) staining is preferred because it yields a high degree of information about the sample. The HE staining method is popular in histology and is widely used in medical diagnosis. This method involves application of hemalum, a complex formed from aluminium ions and oxidized hematoxylin, which colors the nuclei of cells in blue. The nuclei staining by hemalum does not require the presence of DNA and probably results from binding of the dye–metal complex to arginine-rich basic nucleoproteins such as histones. The hemalum staining is followed by counterstaining with an aqueous or alcoholic solution of eosin Y. Eosin Y colors intracellular or extracellular structures in various shades of red, pink, and orange (30,31). The main difficulty with the consecutive-section approach is the fact that the MALDI image is derived from a different section than the histological image and this only enables a correlation to tissue features with a vague matching. As a result, it remains guesswork if the features seen in the histology are properly matched with the molecular information — at least for exact correlation (13). Histological staining of the measured section before the MALDI-IMS integrates histology and MALDI-IMS and allows an unambiguous correlation of histomorphology and MALDI-IMS. Several commonly used histological dyes such as cresyl violet and methylene blue were tested for compatibility with MS analysis and show a good preservation of spectra quality, whereas HE staining compromises the quality. Disadvantages of this method are the subjection of the sample to additional handling steps before the measurement and the limited choice of stains to those that are compatible with MALDI (26). Therefore, the staining of the sample after the MALDI measurement is the adequate method and allows the use of the best staining for morphological features (HE) and an unambiguous correlation with the MALDI-IMS results (32). Figure 2 shows the histomorphological integrity after MALDI-IMS measurement. 

Figure 2: Schematic illustration of the histomorphological integrity after MALDI-IMS measurements. Shown are (a) a frozen sample of a human prostate tissue on a cryostat steel plate; (b) a frozen tissue section (12 Âµm) mounted onto a conductive glass slide for MALDI-IMS scanned at high resolution; (c) a tissue section after MALDI-IMS measurement (measured region) and MALDI matrix coating on the tissue section; and (d) and (e) the HE stained MALDI-IMS slide. It is observable that the histomorphology after MALDI-IMS is well-preserved as presented on higher magnification in (e).			

Visualization and Statistical Analysis of MALDI-IMS Results

Analysis and visualization of MALDI-IMS results are challenging and may become a limitation when a large number of datasets have to be evaluated peak-by-peak. Therefore, many tools have been developed to visualize and analyze these high-dimensional data cubes using various data-processing strategies for visualization. These tools enable fast MS imaging of large surfaces at high-spatial resolution and thus aid in the understanding of various diseases and disorders (13,33,34). The analysis of MALDI-IMS data sets includes region-of-interest analysis, choice of intensity scales for image display (color palette, linear, logarithmic), image overlay, binning of spectra

 and image data for improved signal-to-noise, intensity profiles (variation of intensity with time or space), and chemical libraries for analyte identification (17). Available software programs improve the quality of information extracted from the large data sets, reduce the dimensionality to more practical levels, allow different imaging data sets to be aligned and compared, and address data management, including determination of statistical significance and relative abundance between particular protein species. Large numbers of patients, each having unique aspects to their disease, are needed for clinical studies, which points out that individualized medicine is founded on this exciting but enormously complex molecular diversity. Therefore, bioinformatic approaches are necessary to identify individualized molecular patterns that can improve diagnosis and prognosis. It has been reported that data analysis of MALDI-IMS results consists of several steps: 					

Selecting proteins that are differentially expressed among histomorphologically predefined groups (for example, healthy and control). The selection of such proteins is based on identifying peaks that significantly differ between the groups using, for instance, statistical significance tests. Such tests include Student's 

-test or the non-parametric Wilcoxon rank sum for comparing two groups, and, analysis of variance (ANOVA) and the non-parametric Kruskal-Wallis test for comparing more than two groups (18).

Reducing the complexity of MALDI-IMS datasets with principal component analysis (PCA), which transforms the original coordinate system defined by peak intensities to a coordinate system that better explains the variance in the dataset. Principal component scores can be used to reconstruct images that contain most of the information, but do not classify the spectra (13).

Unsupervised classification by hierarchical clustering, which clusters the spectra pair-wise according to similarity until a dendrogram is obtained that contain all spectra. Each branch of the dendrogram can be considered a class of spectra and it is possible to select those dendrogram nodes that reflect certain histological features (35).

In Figure 3, a possible visualization and statistical analysis approach of a MALDI-IMS data set is represented.  

Figure 3: Visualization and statistical analysis of a prostate tissue MALDI-IMS result. Shown are (a) an HE-stained tissue section of human prostate cancer with marked regions of interest (ROIs), in which benign = blue, cancer = red, stroma = green; (b) immunohistochemical validation with marked regions; (c) a MALDI imaging result shown in 2D ion density map representation; and (d) score plots of first, second, and third principal component. Each colored data point of the plot represents one region of interest (ROI). The data sets were chosen from tumor- (red), stroma- (green), and benign tissue region (blue), which are coregistered with the scanned HE slide. This coregistration allows a parallel assessment of the histopathological features and the proteome profiles obtained by MALDI-IMS. (e) The MALDI-IMS result in false color representation. (f) A hierarchical clustering of the human prostate dataset achieved by MALDI-IMS.			

Several approaches for protein identification of tissue samples, often based on MS tools, coexist in the field of proteomics (36–40). Medical and biomedical proteomics are commonly used to answer three questions: 					

How can a specific disease and its stage be diagnosed?

How can we follow a treatment or evaluate the prognosis?

What is the mechanism of a disease, the role of protein complexes, and so forth?

The first two questions are the purpose of comparative proteomics in which the nature and concentration of proteins present in "healthy" versus "diseased" samples are compared, especially for biomarker research. The last question is targeted by functional proteomics (41–43). In Figure 4, a classical scheme for biomarker research is illustrated. Batches of samples labeled either "healthy" or "diseased" are separately submitted to protein identification and result in lists of proteins. In the classical approach, resulting protein lists are compared and the proteins that do not match — that is, those that are present, absent, or under- or over-expressed in comparison to the other lists — are potential biomarkers for identification. Identified biomarker candidates then must be confirmed by numerous repetitive experiments by increasing the cohort of samples. After confirmation,  the biomarkers must be validated by histological stainings for morphological features and by immunohistochemistry for validation on the protein level by specific antibodies, but such a protocol does not consider the usual high heterogeneity of tissues (2,36). This fact is confirmed for samples diagnosed "diseased," which usually consist of a mixture of healthy and diseased cells without possibility to discriminate. Thus, comparing protein lists from samples labeled "diseased" and "healthy" allows only a selection of biomarker candidates. This limitation can be avoided by laser capture microdissection (LCM). LCM is helpful to select and dissect with a high discrimination between "diseased" and "healthy" samples. But there are limits to the determination of different tissue types, especially at early stages of disease development where only small changes in morphology are present. Other disadvantages of LCM are the necessity of high precision by the operator, the consumption of time, and the small yield of material, which presents a challenge for a full-blown proteomics study (2,44,45). 

Figure 4: Workflows for biomarker discovery in (a) classical comparative proteomics and (b) MALDI-IMS.			

Compared to the classical approach, MALDI-IMS proteomics illustrated in Figure 4, where the analysis is done post-MS by the examination of the tissue distribution of each 

 species, is devoid of prior knowledge and classification. After acquisition of MALDI-IMS results, regions of interest can be defined and analyzed, resulting in classes of compounds. Those masses, which are not homogeneously distributed on defined ROIs, can subsequently be used for tissue classification and identification. In a subsequent step, comparison of generated mass images with images of the experimental sections, stained after MALDI-IMS analysis, allows direct linking the molecular information. Thus, the areas analyzed by MALDI-IMS can be set in correlation to the histopathologically diagnosed tissue type. Hence, the use of MALDI-IMS facilitates the generation of biomarker candidate lists and can be used as a new biomarker discovery tool. The advantage of MALDI-IMS as a biomarker discovery tool is that classification only relies on mass analysis and statistical tools but not on a preliminary diagnosis. The mentioned approaches are capable of detecting many compounds at a time and show great promise in clinical proteomics (2,46,47). However, there are two approaches for protein identification: the top-down approach, which involves ionization and gas-phase fragmentation of the protein of interest inside a mass spectrometer (48), and the bottom-up approach, which uses MS to identify peptides obtained from protease digestion of that protein (49). Resulting mass spectra of the bottom-up approach are searched against theoretical protein–peptide databases for corresponding sequence patterns using conventional algorithms such as Mascot and Sequest (50,51). Incorporating proteomics methodologies into MALDI-IMS protocols, which increases the relevance of this new method to proteomics enhances detection sensitivity and protein identification capabilities. Classical proteomics protocols start with sample collection, followed by sample homogenization, washing, extraction, purification, separation (or separations), enzymatic digestion, further separation steps, preparation for MS analysis, identification of peaks of interest, data interpretation, and validation (52–54). For MALDI-IMS experiments, sample homogenization is not relevant and separation steps like liquid chromatography (LC) or gel electrophoresis must be incorporated for a subsequent identification of peptides and proteins of interest determined by MALDI-IMS. Methods for washing, separation, enzymatic digestion, MS-MS on-tissue and top-down experiments via electron transfer dissociation (ETD) and its related proton transfer reaction (PTR) for identification are possible and have already been shown to improve MALDI-IMS results (2,20,29,55,56). Enzymatic digestion for protein identification coupled with direct tissue analysis involves automatically depositing a spotted array of enzymatic solution onto the tissue at room temperature followed by matrix deposition onto the array for subsequent MALDI-IMS analysis. It has been reported that this approach for the in-situ identification of proteins requires less time than conventional strategies and comparison of the protein image to the image of its subsequent peptides increases identification confidence (29). It also has been demonstrated that direct identification via MS-MS on biological tissues will be a very important tool for MALDI-IMS experiments in the future. The identification potential of MS-MS on biological tissues is used to differentiate between isobaric ions (57–59), to produce specific mass images, usually on targeted molecules like drugs (60), and to identify nontargeted ions on tissues, with the focus on low-molecular-weight ions, especially for specific lipid imaging (57,61). The disadvantage of this technique is that it is not currently sufficient to detect intact proteins. Therefore, a direct analysis strategy of intact proteins has been used. There is increasing interest in this strategy, because of the possibility of a complete protein characterization including termini and modifications. ETD and the corresponding proton transfer (PTR) are considered to be alternative methods for the fragmentation of peptides and proteins. With the ETD-PTR method, it is possible to identify intact peptides and proteins without prior enzymatic digestion. In the first step of this approach, potential biomarker candidates are detected via MALDI-IMS and then are identified via ETD-PTR. Therefore, tissue is extracted and purified and proteins of interest are then identified via ETD-PTR. Such an approach has been tested by Walch, Rauser, and coworkers (55). However, MALDI-IMS can innovate the proteomics world and integrates a comparative proteomics workflow. 

The authors would like to thank Wolfgang Horninger, Jasmin Bektic, and Viktor Skradsky of the European Prostate Center Innsbruck (Innsbruck, Austria) for providing prostate samples for the biobank. We also gratefully acknowledge Arnd Ingendoh and Konstantin Halikias from Bruker for their aid. We owe thanks also to C. Seifarth from the Department of Urology, Medical University of Innsbruck for sectioning and for assistance with sample preparation. The authors are grateful for the support of the IMGuS project from the Austrian National Foundation for Research, Technology and Development (AWS) and the GENAU project. For support in recording data, we thank M. Becker and S. Schneider, Bruker Daltonik GmbH, Bremen, Germany. 

 are with the Department of Urology at the Medical University of Innsbruck, in Innsbruck, Austria. 

 are with the Institute of Pathology, Helmholtz Zentrum München, in Neuherberg, Germany. 

M. Handler, M. Netzer, M. Osl, M. Seger, B. Pfeifer,

are with the Institute of Electrical, Electronic, and Bioengineering at the University for Health Sciences, Medical Informatics and Technology, in Hall, Austria. 

are with the Division of Clinical Biochemistry at the Medical University of Innsbruck, in Innsbruck, Austria. Address correspondence about this article to to C.W. Huck, Head of Spectroscopy Group, at the Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innrain 52a, 6020 Innsbruck, Austria, Tel: +43 512 507 5195; Fax: +43 5012 507 2965; or email: 

(2) L. MacAleese, J. Stauber, and R.M. Heeren, 

(4) B.L. Ackermann, M.J. Berna, and A.T. Murphy, 

(5) G. Hopfgartner, C. Husser, and M. Zell, 

J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 

(8) R.M. Caprioli, T.B. Farmer, and J. Gile, 

(9) Rohner, T.C., D. Staab, and M. Stoeckli, 

A summary of the most recent advances in sample preparation, instrumentation, and data-processing techniques for MALDI-IMS

Matrix-Assisted Laser Desorption-Ionization Imaging Mass Spectrometry for Direct Tissue Analysis

A generic drug has the same active ingredient (or ingredients) as the reference listed drug (RLD) or branded drug (1). In many generic firms, analytical development strategies are similar to those used at branded drug firms because the analytical methods for generic drug companies must comply with the International Conference on Harmonization (ICH) and Food and Drug Administration (FDA) guidelines that also govern branded drug manufacturers (2). The different stages of generic development are active pharmaceutical ingredient (API) selection, deformulation, preformulation, and formulation development. This article will discuss API selection, drug product development, and mass spectrometry instrumentation.

Choosing an active pharmaceutical ingredient (API) supplier is a significant task in generic drug development. Figure 1 shows the different stages of generic development and how chromatography and mass spectrometry (MS) relate to each development phase (3). For sterile injectable development, the main quality attributes for an API are assay and impurity profiles. Figure 2 depicts assay and impurity profiles with respect to the International Conference on Harmonization (ICH), the Food and Drug Administration (FDA), and the European Medicines Agency (EMEA) guidelines and the separation technique commonly employed (4). One recent development in MS, which is helpful in the API selection process, is differential analysis software developed for use with liquid chromatography–mass spectrometry (LC–MS) data. Differential analysis can be used to look for very small differences in samples such as differences between two lots of APIs or differences in APIs from two different vendors. This kind of analysis is particularly important when minute differentiation among samples is required, such as in the determination of low-level impurities. 

Figure 1: Different stages of generic drug product development and their relation to chromatography and mass spectrometry.			

ICH Q3A addresses the quantitation and characterization of organic impurities in APIs. Quantitation of these impurities includes high performance liquid chromatography (HPLC) and ultrahigh-pressure liquid chromatography (UHPLC) methods. Characterization includes LC–MS or gas chromatography–mass spectrometry (GC–MS) analysis. ICH Q3C sets guidelines for specific residual solvents that are usually determined by GC–flame ionization detection (FID) or GC–MS. The ICH guidance document sets thresholds for reporting, identification, and quantitation as shown in Table I. When an impurity presents a probable high toxic risk or a specific structure alert to genotoxicity, new or higher levels of the impurity cannot be introduced as compared to products with existing market authorizations (1,4). 

Figure 2: Assay and impurity profiles of APIs with respect to the FDA, ICH, and EMEA guidelines and separation technique commonly employed.			

Chromatography and MS are extensively used in the formulation development stage of generic drug products. The ICH Q3B guidelines (Table II) address the reporting threshold, identification threshold, and qualification threshold for impurities in the drug product. In a drug product, impurities can be degradation products of the active ingredient or by-products of drug excipient incompatibility or external contaminants. Out of all of these, contaminants are the most difficult to characterize. During the development phase, chromatography and MS support drug excipient interaction studies, material compatibility studies, structure elucidation and identification studies, extractable–leachable studies, ink migration studies, and filter selection studies. Chromatography and MS also aid in unique formulation process development studies such as oxygen reduction in the headspace, bulk hold time, and lyophilization cycle development. Chromatography is a common practice for stability studies, API and drug product release, and in-process controls for the finished product. 

Table I: ICH Q3A guidelines for reporting, identification, and qualification threshold limits of API			

As part of an abbreviated new drug application (ANDA) filing, the impurity profile of the generic drug is compared to the reference listed drug (RLD). If an impurity is observed in the generic drug at a level in excess of the ICH Q3B guidelines, then it must be compared with the RLD to justify a higher specification. A particular impurity at the same relative retention time (RRT) observed in a branded and generic product can be deemed identical if UV spectra, MS spectra, and MS-MS spectra are the same. The exceptions to this rule are diastereomeric impurities and impurities that are structural isomers. Even though these impurities are different structurally, they may still be eluted at the same retention time. In those cases, going beyond MS-MS and carrying out a deuterium-exchange LC–MS

  experiment may be helpful to differentiate them.  

Table II: ICH guidelines for reporting, identification, and qualification threshold limits of drug products			

The principal tools used for the elucidation of an unknown structure in any drug development are chromatography and MS. When a method is modified to make it compatible with LC–MS, the peaks of interest will likely have different retention times than in the original HPLC method, and may have a different elution order. Peak tracking between the release method and the adapted LC–MS method often requires that MS is run in sequence with a complementary detection method such as a diode-array detection (DAD) that can provide peak-identifying spectra for comparison between both methods.   

The following is an example of how to use MS to elucidate structures of degradation products. In this example, two unknowns were observed in the stability study. These unknowns were 16 mass units higher than the API. A careful observation in the degradation chemistry revealed that the degradation product either can be through pathway A (Figure 3), where it forms hydroxide or it can be through pathway B (Figure 3), where it forms N-oxide. Hydroxide and N-oxide are identical in mass, because they are isobaric in nature. In this case, standards were not available of either N-oxide or hydroxide to compare the retention time or UV spectra or MS-MS spectra. In cases like this, deuterium-exchange chromatography combined with MS can be used to confirm structures of isobaric compounds. For the hydroxide, a deuterium-exchange chromatography–MS experiment would produce an MS peak four mass units higher than the peak for the protonated parent species. For the N-oxide, mass spectra from a deuterium-exchange chromatography–MS analysis would reveal an MS peak three mass units higher than the original protonated parent species. Figure 3 shows the deuterium-exchange mass spectra for both compounds. As observed from the mass spectra, deuterium exchange can be a powerful tool in establishing tentative structures, or may provide orthogonal proof of similarity (5). As a reminder to readers, the clinical side of the drug development process is not within the scope of this article and will not be discussed here. 

Figure 3: Structural elucidation of isobaric degradation products by deuterium-exchange LCâMS. 			

Recently, one issue that has been observed is that the use of columns with new column technology leads to on-column degradation, particularly in columns with particle sizes smaller than 3 µm. In Figure 4a, the on-column degradation of the drug product (DP) sample resulted in a broad, poorly defined baseline disturbance. In Figure 4b, the on-column degradation of the DP sample resulted in well defined Gaussian peaks that could easily be misinterpreted as actual impurity peaks. In the first case, the baseline disturbance is thought to be caused by degradation across the column bed. In the second case, the well-defined peaks are an indication that the degradation is occurring on the column frit and the degradation product enters the column along with the API in the narrowly defined injection plug. The degradation peaks are subsequently resolved from the active as if they were present in the analytical solution. These on-column degradation peaks were not present in brand new columns and tend to increase with the age of the column. In both cases, the resulting peaks were identified via LC–MS as oxidative degradation products of the active compound. After a tentative structure and reaction mechanism was established, a corrective process to remediate this problem was developed. It is believed that during the analysis either peroxide- or metal-mediated oxidation is taking place. The metal-mediated oxidation was controlled by flushing the column with a solution of EDTA, thereby removing the metal ions present in the column by complexation. 

Figure 4: Example of on-column degradation peaks.			

Many molecules without chromophores are often encountered in the generic drug development process. For these molecules, UV detection is not a technique of choice. So chromatography with alternate detection systems, such as refractive index (RI) detection, charged aerosol detection (CAD), evaporative light scattering detection (ELSD), reagent-free ion chromatography (RFIC), or MS can be used for developing stability indicating methods. RI detectors do not normally have enough sensitivity to detect low-level impurities. ELSD can be a powerful technique for analyzing large molecules. The utility of CAD is dependent on how well a particular molecule can maintain a charged state. Ion chromatography works best if the analytes are in ionic form. An example chromatogram of a generic drug with no chromophores analyzed with chromatography coupled with different detection systems is shown in Figure 5. In this particular case, CAD and ELSD were not able to provide enough sensitivity to identify the impurities in comparison to RFIC. 

Figure 5: Chromatograms of a generic drug without chromophore using different detection systems.			

MS can be a very important tool in developing fat-soluble vitamins as well, where traditional electrospray ionization (ESI) generates poorly protonated spectra. In such cases it is difficult to get protonated spectra for impurities and active ingredients. Cation-enhanced MS was shown to be very effective dealing with vitamin D derivatives (6). The sample solutions of vitamin D derivatives spiked with different molar concentrations of silver nitrate ranging from 2 mM to 6 mM were injected directly into an ESI-MS system operating in positive mode. The representative total ion chromatogram with and without silver nitrate is shown in Figure 6. The addition of silver ions drastically improved the signal corresponding to the vitamin D derivative; however, the signal response was not linear in the tested range of 2–6 mM of silver ions. The corresponding mass spectrum is shown in Figure 6. Picogram sensitivity can be achieved using this technique. The corresponding degradation products or impurities of the vitamin D derivatives can be identified using this technique. 

Figure 6: Cation-enhanced MS determination of vitamin D derivative with silver ions.			

LC coupled with MS detection is the most advanced and powerful tool to date in the drug development process, from regular impurity identification to genotoxic impurity quantitation. The recent advances in MS have increased the selectivity and sensitivity capabilities of commercially available instruments. There are different kinds of mass spectrometers, which vary by design and ionization sources. Each instrument design has its own advantages and disadvantages. For example, triple-quadrupole mass spectrometers can provide femtomolar detection limits for certain compounds, whereas ion-trap instruments can perform multiple MS

  experiments (7). However, these traditional mass spectrometers were designed to provide unit mass resolution, and hence often pose challenges for the characterization of structurally similar compounds. More recently developed instruments such as quadrupole time-of-flight (QTOF), linear orbital trap, and multiple TOF instruments provide superior mass resolution and are capable of exact mass determinations. A hybrid quadrupole–ion trap instrument provides greater specificity and quantitation and MS-MS sensitivity, thereby offering simultaneous qualitative and quantitative results (8). QTOF instruments offer enhanced selectivity, high mass accuracy of fragmented ions, and high dynamic mass range (for some instruments) (9). Linear trap combined with orbital trap instruments can do exact MS measurement with multiple accurate MS fragmentations (10). Application of these mass instruments provides quality information on elemental composition and structural characteristics with greater specificity for identifying even the least abundant compounds in complex matrices in the drug development process. With regard to ionization, newer techniques like desorption electrospray ionization (DESI) and direct analysis in real time (DART) MS promise to make the sample preparation step very simple or nonexistent (11). Improvements in ion mobility spectrometry (another form of MS) offer unique solutions for cleaning and decontamination studies. 

Even though chromatography and MS play an integral role in any drug development, their role in generic development is even bigger because of the shorter development timelines. The future of generic analytical developments promises to be more robust with the use of newer column technology, UHPLC systems, and improvements in MS instrumentation.   

The input from various colleagues from the pharmaceutical industry at large are acknowledged, particularly the members of the Product and Process Development (PPD) department at Boehringer Ingelheim (BI) Ben Venue Laboratories (Bedford, Ohio). Many helpful discussions with Mr. Greg Fernengel, Drs. Dong Wen, Richard Thompson, Robert Sullivan, and encouragement, support, and guidance provided by Dr. William Larkins are also acknowledged. The authors would also like to thank Ms. Maegan Little for proofreading the manuscript and the members of the PPD who carried out the work in the laboratory.  

Disclaimer: The views provided in this articles are from authors only and not from the BI or any of its associates. 

 are with Boehringer Ingelheim Ben Venue Laboratories in Bedford, Ohio. Please direct correspondence to 

(3) A. Roy et al., "Analytical Strategies in the Development of Generic Drug Products: Role of Chromatography and Mass Spectrometry," presented at Pittcon 2011, Atlanta, Georgia.  

(5) A. Roy et al., "Impurity Analysis in Pharmaceuticals," presented at Pittcon 2010, Orlando, Florida. 

(10) R.H. Perry, R.G. Cooks, and R.J. Noll, 

A discussion of active pharmaceutical ingredient (API) selection, drug product development, and mass spectrometry instrumentation

Analytical Strategies in the Development of Generic Drug Products: The Role of Chromatography and Mass Spectrometry

A review of recent research related to the coupling of ambient ionization mass spectrometry (MS) with various separation techniques is presented. Ambient ionization, referring to a mode of atmospheric pressure ion generation in which sample introduction is segregated from the generation of ionizing radiation under ambient conditions, has generated a great deal of interest in recent years. A plethora of techniques incorporating spray-based and chemical ionization–based source designs have been demonstrated for analysis of analytes from headspace, solution, and solid samples. An overview of some of the more common techniques is given, especially in light of their potential for coupling with different modes of separation, in both on- and off-line formats. This is an emerging area of research that has a strong potential for novel applications, especially given the capability for generating sensitive mass spectral signals from complex samples. In many cases, however, the possibility for coupling efficient separations with current ambient ionization MS exists, but more work remains to comprehensively demonstrate the effectiveness of such hyphenated techniques so that they can be used routinely. 

This review highlights the combination, or potential combination, of various separation methods with ambient ionization mass spectrometry (MS) (1). The coupling of separation devices with MS has been recognized as an extremely useful approach for real-world samples analysis, in which complex mixtures are the norm. Typical liquid-phase separation techniques, including high performance liquid chromatography (HPLC), thin-layer chromatography (TLC), and capillary electrophoresis (CE), have been successfully combined with various MS interfaces, such as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) to create high performance analytical methods (2). 

 refers to several relatively new ionization techniques that are rapidly advancing toward use in many fields for direct in-situ screening of analytes with minimal or no sample preparation. The popularity of these new ionization techniques is indicated by the explosive growth of the new designs and applications in this area. The ability to separate and independently optimize the sample introduction and the ionizing radiation provides significant advantages compared to more-traditional ionization techniques. Even so, without removing large background interferences, we should be aware that high-resolution and mass-accurate mass detectors are required to unambiguously discriminate the target mass from background noise. This is why additional hyphenation of ambient ionization with separation devices can promote these new techniques for wider applications. Significant advances can be found with these hyphenated techniques, compared with traditional coupling combinations (for example, HPLC and ESI), to a large extent; however, relatively few demonstrations and applications of this promising technology currently exist. 

From the standpoint of separation science, factors that can restrict the range of the analysis in current separation–MS combinations still exist in various aspects. For example, nonvolatile buffers such as borate and phosphate that are adopted for better separation in chromatography can cause ion suppression in ESI (3). Also, nonpolar solvents, typically used in normal-phase liquid chromatography, are poorly compatible with ESI due to their low conductivity or low dielectric constants (4). On the contrary, this is another regime where ambient ionization sources could broaden the compatibility between separations and MS. With respect to matrix tolerance and samples present in non-ESI-friendly solvents, some ambient ionization techniques have been shown to provide a higher tolerance to such conditions, compared with traditional atmospheric pressure ionization (5). 

Here, we cover the developments and trends of recent research achievements in ambient ionization techniques. Some popular cases will be illustrated with regard to experimental arrangements, mechanisms of ionization, and applications. Following this, some successful combinations between ambient ionization and separation techniques will be narrated. Finally, the potential for this coupling in future work to handle complex sample analysis will be discussed. 

Recent Developments in Ambient Ionization 

Given the virtue of increased throughput and less stringency in sample form, ambient ionization techniques have become an increasingly useful part of the analytical tool box. Ambient ionization has been widely used in environmental, food, flavor, fragrance, forensic, homeland security, and pharmaceutical areas for trace and real-time analysis (6). A large number of setups have been proposed in the past few years and this effort has been accompanied by an equally long list of new acronyms to remember. Generally speaking, ambient ionization techniques can be classified into two groups based on their associated ionization process. Nominally, these include spray-based (ESI source-modified) and electric discharge-based (APCI source-modified) setups (7–9). Some other variations exist, such as those inspired by atmospheric pressure photoionization (APPI) (10) and a multimode ionization technique that integrates the benefits of both ESI-based and APCI-based ionization techniques (11). 

Spray-based ambient ionization techniques often take advantage of conventional ESI to generate charged species, applying it to various situations for gas, liquid, and solid samples analysis, with and without sample pretreatment. Desorption electrospray ionization (DESI) is one of the most prominent and commonly used examples of spray-based ambient ionization techniques. It provides direct solid and liquid sample analysis. The complete design and application was reported by Cooks and coworkers in 2004 (12). The general configuration is shown in Figure 1a. Success with DESI was first demonstrated with small molecules that can be both polar and nonpolar on solid surfaces. The variation of spray solvents (from various aqueous to nonaqueous solvents) plays a critical role for target analyte selection based partly on analyte polarity and solubility (13). Although DESI from solid samples is most common, DESI from liquid samples and flowing liquid samples also has been reported (14). 

Figure 1: Schematics of popular and established spray-based ambient ionization sources: (a) desorption electrospray ionization (DESI) (adapted from reference 12) and (b) extractive electrospray ionization (EESI) (adapted from reference 29).			

A droplet pickup model is hypothesized to be the predominant sampling process in DESI. The sample surface is first wetted by the spray, forming a thin film of liquid on top of the sample. Analytes partition into this thin film and subsequent spray droplets "pick up" the analyte into the ESI droplets, which will continue to desolvate, subdivide, and ultimately lead to release of analyte ions into the gas phase. DESI has been used to analyze a multitude of different analyte types, including nonvolatile pyrolysis products (15), polymer oligomers (16), inks (17), carbohydrates (18), peptides (19), explosives (20–22), human breast cancer tissue (23), porcine and rabbit adrenal glands (24), lipids in mouse brain tissue (25,26), and counterfeit antimalarial drugs (27), among many others (28). Commercial DESI sources are available that are designed to interface to mass spectrometers produced by a variety of manufacturers. 

Extractive electrospray ionization (EESI) is another popular spray-based ambient ionization technique that was also introduced by the Cooks group (29). EESI, as shown in Figure 1b, typically is suited for analysis of liquid samples or aerosol samples that can be nebulized. The sample solution is isolated from the ESI source and its high voltage by virtue of its introduction through a second nebulizer. Neutral sample droplets intersect with charged aerosols from the ESI source in front of the MS system inlet (30). 

Applications have included the analysis of undiluted urine (29), milk (31), diet effect on biofluids (32), perfume classification (33), and human breath (34), all with minimal or no sample preparation. Zhu (35) and McCullough (36) described the use of EESI for monitoring chemical reactions. Sampling was designed to ensure that the bulk reaction mixture was sampled rather than just the headspace by using a second nebulizer to create an aerosol of the bulk solution using a Venturi pump. The compatibility with liquid samples or aerosols makes EESI a good partner with separation techniques such as liquid chromatography.  

In 2008, a modification of the EESI sampling method that uses a neutral desorption (ND) sampling gas beam to gently acquire analytes from sample surfaces, called NDEESI, was proposed by Chen and colleagues (37). This development broadened the EESI application base to trace analysis on biological surfaces and has been demonstrated for use with cheese (38), skin, tissue, muscle, and blood vessels (39). 

Chemical Ionization–Based Ambient Ionization   

Several significant chemical ionization–based ambient ionization techniques have been reported, including direct analysis in real time (DART) (40), desorption atmosphere pressure chemical ionization (DAPCI) (41), flowing atmospheric pressure afterglow (FAPA) (42), low-temperature probe (LTP) (43), dielectric barrier discharge ionization (DBDI) (44), and microplasmas (45), among others. They basically involve the generation of an electrical discharge across a pair of electrodes in the presence of a flowing gas, such as nitrogen, helium, or vapor in air. Under the high electric field or radiofrequency excitation, a series of ionized molecules, radicals, excited state neutrals, and electrons will be produced that, in turn, can be used to ionize analytes of interest.   

DART, schematically depicted in Figure 2, was one of the first ambient ionization sources brought to the market and has been demonstrated for numerous applications, including counterfeit pharmaceutical analysis (46), drug discovery (47), organic reaction monitoring (48), serum metabolite fingerprinting (49), hormone detection (50), and malaria control (51). DART has also been used to directly characterize analytes in conjunction with separations by TLC (52) and HPLC (53).  

Figure 2: Direct analysis in real time (DART) ion source (adapted from reference 40).			

Desorption atmospheric pressure chemical ionization (DAPCI) was first reported in 2005 (41). It was originally investigated as a complementary method to study the ionization process in DESI. DAPCI uses moisture in air or nitrogen to generate the charged species following their passage across a discharge needle. Subsequently, Chen and colleagues successfully applied this technique to the analysis of food, skin, and clothing (54). Additionally, sheath gas is not necessary for good performance in DAPCI, so the analysis of samples in powder form can also be realized (55). 

Hyphenation of Ambient Ionization with Various Separation Methods 

Ambient ionization (for example, DESI and DART), which provides versatile interfaces between liquid phase separations (such as HPLC) and MS, has been highlighted by recent reports (52,53,56). Significant advantages were demonstrated in terms of the ability to tolerate harsher buffer salts and high flow rates, increase sensitivity and selectivity, and achieve high throughput. These applications are very promising and should help increase interest in coupling ambient ionization with a wider array of separation techniques, in general. In an era where chromatographic separations are being pushed to higher dimensions, the added specificity and versatility of coupling to MS detection through ambient ionization sources will serve to further multiply capabilities. Existing and potential hyphenation of ambient ionization with various separation methods are listed in Table I. Further details and highlights of these achievements and their potential are discussed below. The reader should understand that this may not serve as an exhaustive coverage of this technology. 

Figure 3: Schematic of a prototype HPLCâDESI-MS apparatus (adapted from reference 61). 			

The coupling of liquid chromatography with MS through a DESI source was recently demonstrated by Chen and colleagues (Figure 3). Characteristics include a wide range of elution flow rates, online derivatization via reactive DESI (62), and further combination with electrochemistry experiments (61). Three neurotransmitter compounds (3 mg/mL, each), norepinephrine, normetanephrine, and dopamine were separated in isocratic mode at pH 3.0, and fused-silica tubing was used to deliver fractions for DESI analysis. Ionization could be readily generated through the interaction between the charged spray and the HPLC eluent. HPLC flow rates as high as 1.8 mL/min were used in this experiment without a loss of sensitivity. 

Table I: Overview of work highlighting the coupling of ambient ionization with separation techniques			

Besides coupling with HPLC, DESI in combination with TLC is a more natural marriage because of the surface analysis format. Surfaces such as nanoporous silicon, ultrathin-layer chromatography plates, polymethyl methacrylate (PMMA), and polytetrafluoroethylene (PTFE) have been investigated and compared by Cooks and colleagues (70). For all four materials, limits of detection (LODs) were obtained in the femtomole to picomole (picogram to nanogram) range.  

Van Berkel and colleagues have taken advantage of this simple and fast method for the direct analysis of gangliosides from TLC plates. Importantly, they demonstrated the potential for automation of this technique by coupling normal-phase TLC with DESI-MS through a computer-controlled scanning device (71). Other applications for the analysis of porcine brain lipids (72) and peptides (73) were also reported. Of course, a significant advantage is that DESI can be used to sample and analyze separation bands without removing or extracting them from the plate. Additionally, the sampling area can be sufficiently small so that the plate can be retained for further analysis by other techniques, if desired.  

Similar to a TLC surface, preconcentration and purification techniques such as solid-phase extraction (SPE) and single droplet microextraction (SDME) in which extracted analytes can be enriched on a bead or a frit, respectively, are also amenable to DESI analysis. Direct coupling of SPE with DESI was shown to improve the overall sensitivity up to six orders of magnitude if enough sample was collected (60). Rhodamine 116 was used as the analyte in this study. The lower limit of detection obtained (100 fg/mL–100 pg/mL) was comparable to ESI. For SDME, single aqueous or organic droplets, enriched with trace amounts of methamphetamine (MA) from water, served as a good substrate for DESI analysis (74). The limit of detection (LOD) for MA in the droplet was determined to be 51 ng/mL. 

Another classic separation technique is CE. CE is used for separation of ionic species in solution, and recently a unique coupling with DESI was reported by Zare and colleagues (75). Two sets of analyte mixtures, rhodamine dyes and cardiac drugs prepared in high concentrations (10–25 mM) of buffer solutions, were separated by CE and then deposited on a rotating stage (Figure 4a). The researchers were able to show that ion suppression and MS inlet contamination were minimal, despite the high salt concentration. Additionally, the rotating stage format can be used in automated and high-throughput systems. 

Figure 4: (a) Schematic of capillary electrophoresis coupled with DESI through a rotational spotting/sampling stage (adapted from reference 75). (b) Multichannel device that can be interfaced to DESI for high-throughput analysis (adapted from reference 76).			

The capability for increased throughput has always been recognized as an important aspect to consider for a new method to be developed, especially if it is intended for use in industry. To this end, a high throughput device was developed by Ma and colleagues (76) that features a multichannel device with 16 parallel capillaries that can be interfaced with DESI-MS detection (Figure 4b). Of course, the potential for HPLC separations in the capillaries is a significant advantage. The authors indicated that if each sample analysis requires 1.6 min, then an overall throughput of 600 samples/h can be achieved with this technology.  

From these examples, it is clear that DESI has enjoyed the most development in terms of interfacing separations with MS detection through an ambient ionization source. Clearly, the ability to sample directly from separation media in various automatable and high-throughput formats, in some cases alleviating potential band broadening effects associated with elution, makes these techniques extremely attractive for use in both routine and high impact research scenarios. 

Of all of the ambient ionization techniques, DART is second only to DESI in the number of studies in which it has been coupled to various separation formats. Similar to DESI, the coupling of HPLC with DART exhibited significant tolerance to buffer salts in the chromatographic separation (53). Phosphate buffer concentrations as high as 120 mM were used in this combination without evidence of any source contamination or ion suppression. Limits of detection of 20–55 µg/L were achieved for a series of parabens as analytes in the negative ionization mode in this study. The outlet of the HPLC was connected to a stainless steel or fused-silica capillary, and the effluent was directed into the ionizing radiation (Figure 5a). Flow rates as high as 1 mL/min were introduced through the needle into the DART source for MS analysis. Fraction collection is possible by the HPLC-DART combination. After MS analysis, further characterization of the HPLC fractions can be carried out.  

Figure 5: Schematics showing the coupling of (a) HPLC (53) and (b) HPTLC (52) to a DART ion			

High performance TLC is also easily coupled with DART-MS (52), as depicted in Figure 5b. Strips of plates containing the separation bands were cut and the edge of each band was exposed to the metastable gas ion beam. Optimal response was observed at a distance of approximately 1 mm from the MS inlet at an angle of about 160° vertical to the gas flow. The coupling was applied in qualitative and quantitative analysis of isopropylthioxanthone. Quantitation was difficult initially, but good linearity (

 = 0.9892) and repeatability (RSD < 5.4%, 

 = 6) was achieved for caffeine detection by introducing an isotopically labeled internal standard. Spatial resolution was shown to be better than 3 mm in this coupling system. 

EESI (29) and nanoEESI (67), continuous flow infrared matrix-assisted laser desorption electrospray ionization (CF IR MALDESI) MS (66), and continuous flow-extractive desorption electrospray ionization (CF-EDESI) MS (69) have been demonstrated for on-line analysis of liquid samples in recent literature. A natural next step is to couple these with separation techniques, like HPLC and CE. Some work to this end is undoubtedly already being undertaken.  Besides, DAPCI, which can ionize an analyte without need for a charged spray and nebulizer gas, holds significant potential for combinations with gas chromatography separations. 

The capability to independently tune and optimize sample introduction and ionizing radiation in ambient ionization MS holds great promise for further developments in this area. Additionally, since such a high degree of tolerance to solution conditions that hamper traditional API techniques has been demonstrated, the coupling of separations to MS through these ambient ionization sources is immensely attractive. In the earlier years of HPLC, before MS detection was developed into a routine approach, many separations were developed that featured high concentrations of nonvolatile salts. It appears that these high quality separations could now be revisited. As separations advance to higher complexity and dimensionality, the fewer the limitations with detection, the better. With robustness, versatility, and increased throughput capabilities, it can be expected that the number of studies featuring the combined use of high efficiency separations in virtually all phases and formats with the vast array of ambient ionization techniques continually being developed, will amass at a significant rate.  

 are with the Department of Chemistry & Biochemistry at the University of Texas at Arlington.

(1) G.A. Harris, A.S. Galhena, and F.M. Fernandez, 

(3) J.P. Antignac, K.D. Wasch, F. Monteau, H.D. Brabander, F. Andre, and B.L. Bizec, 

(6) F.M. Green, T.L. Salter, P. Stokes, I.S. Gilmore, and G. O'Connor, 

(7) M. Huang, C. Yuan, S. Cheng, Y.T. Cho, and J. Shiea, 

A review highlighting the combination, or potential combination, of various separation methods with ambient ionization mass spectrometry

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We report the first observations of aqueous surfaces by time-of-flight secondary ion mass spectrometry (TOF-SIMS) via a self-contained microfluidic module compatible with a vacuum. The interface uses a microfluidic channel with a 3-µm diameter window into the flowing fluid beneath it. This window supports the liquid against the vacuum by the liquid's surface tension and limits the high-density vapor region traversed by the probe beams to only a few micrometers. We demonstrate detection of aqueous surfaces such as deuterium water and sodium iodide (NaI) solution through the small aperture by TOF-SIMS. A molecular signal (C

 of glutamic acid also was observed. TOF-SIMS coupled with the interface provides a molecular recognition capability, making it a great choice to detect short-lifetime reaction intermediates in aqueous solutions. This novel microfluidic interface makes multimodal vacuum-based analysis of liquid surfaces possible. 

This article describes a breakthrough using a microfluidic interface to conduct sensitive time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis and study liquid surfaces in situ under vacuum conditions. Applications in TOF-SIMS so far have been limited to solid surfaces or low-vapor-pressure liquid surfaces.  

TOF-SIMS has been applied in applications such as polymers (1), pharmaceuticals (2), semiconductors (3), environmental chemistry (4), inorganic surfaces (5), corrosion and monitoring of organic coating processes, catalysis (6), investigation of surface contamination (7), geology (8), and archaeology (9). However, a lot of chemistry takes place at the interface of liquid phases with gases in environmental, industrial, and biological systems. The surfaces of these aqueous phases and films (when <1-nm thick) have unique kinetics and thermodynamics that are distinct from those of the bulk materials (10–12). Although TOF-SIMS has already been used to investigate ionic liquids (13), lubricants (14,15), and liquid crystals (16,17), which all have very low vapor pressure, no one has been able to analyze high-vapor-pressure aqueous solutions (to the best of our knowledge). Because TOF-SIMS is vacuum-based, one cannot easily probe these high-vapor-pressure interfaces. Our goal is to permit studies of high-vapor-pressure liquid surfaces in TOF-SIMS. 

TOF-SIMS offers great sensitivity and attractive spatial resolution as an advanced vacuum-based surface analytical technique. It uses a focused ion beam to raster over the surface, ejecting molecules, atoms, and molecular fragments (neutral and ionic) from the top layer, and a TOF mass spectrum is obtained for each pixel (see Figure 1). TOF-SIMS gives high mass resolution (

 > 10,000) and high lateral resolution (<100 nm), a great advantage in exploring complex samples. Automated charge compensation permits the study of insulators. Simultaneous auxiliary ion sputtering permits depth profiling and 3D chemical maps. Negative or positive emitted ions can be monitored. Sensitivity is typically in a range of 0.1–10 ppm. 

 Figure 1: A schematic of the TOF-SIMS instrument.			

In this article, we will describe the first aqueous surface detections by TOF-SIMS enabled by a microfluidic interface designed for operation in vacuum conditions. We demonstrate new TOF-SIMS applications using different liquid samples prepared in the laboratory. Directions for using this unique technique in future developments are also presented. 

A mixture of polydimethylsiloxane (PDMS) prepolymer and curing agent (Sylgard 184, Dow Corning Co., Midland, Michigan) with a 10:1 ratio (w/w) was used to make the microfluidic device via a soft photolithography technique (18). The microchannel was 80-µm wide and 2.97-mm long, and the narrow part of the channel in the middle was 10-µm wide and 30-µm long. The depth of the whole channel was 8 µm. The flow channel was enclosed using a silicon nitride (SiN) window that contained either a 100-nm-thick SiN membrane (0.5 mm × 0.5 mm) and a frame with 200-µm thick silicon (7.5 mm × 7.5 mm) or a 500-nm-thick SiN membrane (1.5 mm × 1.5 mm) and a frame with 200-µm thick silicon (7.5 mm × 7.5 mm) (Norcada Inc., Canada) by oxidizing the membrane and frame in an oxygen plasma (PX-250; March Plasma Systems, Concord, California) for 1 min and immediately bringing them into conformal contact (18) under a stereomicroscope (SMZ-U, Nikon, Japan). The assembly was then placed in an oven at 75 °C for 2 h to form an irreversible bond. Above the channel on the SiN window, a hole approximately 2.5 µm in diameter was drilled through the membrane by using the TOF-SIMS depth profiling feature (see Figure 2). 

Figure 2: Fabrication of the vacuum-compatible microfluidic interface. (a) A schematic showing the assembly of the microfluidic block; (b) a cross-section view of the PDMS block showing the aperture; and (c) an optical image showing the top view of the SiN window with the aperture as a part of a finished device.			

The aqueous vacuum interface module is shown in Figure 3. It is composed of a PDMS microfluidic block; an electro-osmotic pump (model number 3000126, Dolomite, United Kingdom); a battery (Saft, Li-SOCl

); and PTFE connecting tubing (Valco Instruments Co. Inc., Houston, Texas). The PDMS microfluidic block was coated with a thin layer of gold film, except for the membrane window. The continuous flow in the channel was obtained by an electro-osmotic pump that was driven by a battery. The wire connections were covered by ultrahigh vacuum (UHV)-compatible fiberglass sleeving. A Viton tube was intersected with a short capillary (360 µm o.d. and 74 µm i.d., Polymicro Technologies, Phoenix, Arizona) to form a tee. Various aqueous solutions were introduced into the system using a syringe pump (Harvard Apparatus, Holliston, Massachusetts). 

Figure 3: A photo of the interface including: (a) PMDS block containing the microchannel, (b) electro-osmotic pump, (c) battery, (d) one of two PTFE tube fluid reservoirs, ( e) connecting wires to battery, and (f) pressure relief tee (see text).			

A commercial electro-osmotic pump was adapted for the dynamic microchip design. By applying a voltage gradient along the water flow path inside the pump, the liquid is moved in uniform "plug flow" and is able to run on low voltages. For our flow and channel back pressure, it requires only about 3 V bias. Different solutions were used. Because the pumping mechanism requires deionized (DI) water, the liquid of interest is placed past the DI water and separated by a small section of inert perfluorodecalin liquid as an immiscible spacer.  

This pump is designed to be used with open-to-air reservoirs connected to microfluidic systems; thus, it is not well configured to be inserted directly in-line on our microfluidic device.

Problems such as electrodes springing fluid leaks in vacuum have been encountered. To solve these problems, two aluminum blocks with O-rings were used to seal the pump between them.  

A TOF-SIMS V spectrometer (IONTOF GmbH, Münster, Germany) was used to study the aqueous interface in this work. A pulsed 25-keV Bi

 (beam size: ~250 nm) ion beam with an incident angle of 45° off the normal was used as the primary ion beam for all measurements. The main chamber operating vacuum pressure during measurements was 2.5–5.5 × 10

 mbar. The interface device is normally preconditioned in a vacuum chamber for leak checks before usage. The instrument can be easily run on Bi cluster ions (for example, Bi

), which may give higher sensitivity for molecular ions. The SIMS measurements were performed at the beam current of 1.0 pA with a beam width of 130 ns and chopped at 20 kHz. Images shown in this article were taken for a total integration time of 65.5 s, over a scan area of 10 µm × 10 µm, with 256 × 256 pixels.  

Various liquids, including DI water, deuterium water (Sigma-Aldrich Corp., St. Louis, Missouri, 99.9%), sodium iodide (Sigma-Aldrich Corp., 99.99%), and 0.5% glutamic acid aqueous solutions, were prepared in the laboratory and filled in the system by a syringe pump. 

A breakthrough using a microfluidic interface to conduct sensitive time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis and study liquid surfaces in situ under vacuum conditions is described here.

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Probing Aqueous Surfaces by TOF-SIMS