Lipids (2014) 49:609–619 DOI 10.1007/s11745-014-3903-x
METHODS
Development of an Automated Multi-Injection Shotgun Lipidomics Approach Using a Triple Quadrupole Mass Spectrometer John A. Bowden • Jackie T. Bangma John R. Kucklick
•
Received: 7 January 2014 / Accepted: 26 March 2014 / Published online: 12 April 2014 Ó AOCS (outside the USA) 2014
Abstract Shotgun lipidomics is a well-suited approach to monitor lipid alterations due to its ability to scan for varying lipid types on a global, class and individual species level. However, the ability to perform high-throughput shotgun lipidomics has remained challenging due to timeconsuming data processing and hardware limitations. To increase the throughput nature of shotgun lipidomics, an automated shotgun lipidomics approach is described utilizing conventional low flow gradient liquid chromatography (LC) analysis (post-injection) coupled with multiple sample injections per sample (on a lipid scan per injection basis). The proposed automated multi-injection approach resulted in a reproducible lipid scanning period of 2.5 min (in a 4.5 min total data acquisition period), thereby Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology; nor does it imply that the materials or equipment identified are necessarily the best for the purpose.
Electronic supplementary material The online version of this article (doi:10.1007/s11745-014-3903-x) contains supplementary material, which is available to authorized users. J. A. Bowden (&) J. R. Kucklick Hollings Marine Laboratory, National Institute of Standards and Technology, 331 Fort Johnson Road, Charleston, SC 29412, USA e-mail:
[email protected];
[email protected] J. R. Kucklick e-mail:
[email protected] J. T. Bangma Department of Obstetrics/Gynecology, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 634, MSC 619, Charleston, SC 29425, USA e-mail:
[email protected]
providing a sufficient scanning period for performing either mass spectrometric or tandem mass spectrometric analyses. In addition to being simple, robust and reproducible, this approach was also constructed to be cost-effective by using common LC instrumentation and customizable as the data acquisition period can be tailored to perform different scan types, period lengths and scan numbers. Combined with a strategy to create multiple lipid-specific aliquots per sample, the overall approach provides a simple and efficient platform to perform high-throughput lipid profiling. Keywords Shotgun lipidomics Lipid profiling Triple quadrupole mass spectrometry Flow-injection analysis Multi-injection shotgun lipidomics Tandem mass spectrometry Direct-infusion Abbreviations Ptd2Gro Cardiolipins Cer Ceramides CE Cholesteryl esters DAG Diacylglycerols ESI Electrospray ionization FFA Free fatty acids FIA Flow-injection analysis FS Full scan CerGal Galactosylceramides LC Liquid chromatography LysoPtdCho Lysophosphatidylcholines LysoPtdEtn Lysophosphatidylethanolamines MS Mass spectrometry MRM Multiple-reaction Monitoring NL Neutral loss PtdOH Phosphatidic acid PtdCho Phosphatidylcholines PtdEtn Phosphatidylethanolamines
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PtdGro PtdIns PtdSer PI CerPCho ST MS/MS TAG
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Phosphatidylglycerols Phosphatidylinositols Phosphatidylserines Precursor ion Sphingomyelins Sulfatides Tandem mass spectrometry Triacylglycerols
Introduction Lipids present a unique target for disease state biomarkers, as they are diverse, abundant and ubiquitous in living organisms (present at membrane, cellular, tissue and systemic levels) [1–4]. Due to the complexity and diversity of lipids present in most samples, methods capable of screening the lipidome (i.e., lipidomics) from a global, class and/or individual species perspective are necessary to monitor lipid-based biomarkers and metabolites. Since the incorporation of mass spectrometry (MS) into lipidomics, there have been numerous reports of using changes in the lipidome to further increase the understanding of disease etiology of Alzheimer’s [5–8], diabetes/obesity [8–11], atherosclerosis [12, 13], metabolic syndrome [9, 10], cancer [8, 14] and other lipid related diseases [8, 15, 16]. Over the past several decades, increases in these pathophysiological states and others, in both wildlife and humans, have sparked a continued demand for expanding the scope and scale of current lipidomics measurements. Specifically, studies have focused on advancing the efficiency of lipidomics, including the improvement of lipid measurements [17–19], development of efficient and reliable data processing programs [20–22] and the incorporation of automation for high-throughput analyses [23– 26]. For the multi-class characterization of lipid content in a sample using MS, an important consideration is whether or not to include chromatographic separation prior to electrospray ionization (ESI)–MS. Accordingly, lipid content is often either characterized post liquid chromatographic separation (LC-lipidomics) or by direct-infusion which employs no chromatographic separation (shotgun lipidomics). LC-lipidomics provides distinct benefits, including enhanced ability to detect trace lipids, reduced isobaric interferences and reduced ion suppression effects. Thus, for targeted lipidomics, techniques incorporating liquid chromatography (LC) separation are commonly preferred [27– 29]. However, for applications investigating large groups of diverse lipid classes in one sample, shotgun lipidomics may be the best approach. Originally developed by Han and Gross [30–33], shotgun lipidomics provides the
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capability of determining lipid species across different polarities and/or structural characteristics within a single analysis, an endpoint commonly difficult to achieve using LC-based methods. However, unlike LC-lipidomics, where method automation can be easily controlled using sample batching and LC/MS software, the incorporation of automation to shotgun lipidomics can be challenging. A critical factor in the execution of automated shotgun lipidomics is largely centered on how the sample introduction mechanism is controlled. Traditional shotgun lipidomics is typically achieved one lipid scan at a time by ESI–MS with direct infusion using a syringe pump. Multiple lipid scans per sample can be consecutively collected over the entirety of the sample present in the syringe. While manual shotgun lipidomics is operationally the simplest approach, it is not suited for high-throughput analysis as the operator must be present to set up each lipid scan for each lipid extract. Thus, recent research has been focused on developing new strategies to automate sample introduction. A promising and expanding strategy for automation is the implementation of new NanoMate introduction systems [23, 25, 34]. Coupled to microfluidics-based electrospray and controlled by software, this technique was shown to be an effective platform for automated shotgun lipidomics. Alternatively, automated shotgun lipidomics using the capabilities of existing LC/MS equipment could potentially be a more accessible approach for laboratories. With certain LC/MS systems, vendor software provides the operator the ability to control sample introduction rate via an LC autosampler syringe, thus providing the flexibility to perform flow-injection analysis (FIA) [35–38], thereby eliminating the need for direct manual infusion. For this setup, the autosampler syringe can be controlled to draw and dispense the sample at a specific rate into the solvent stream and into the ESI– MS source, thereby creating a sample acquisition period which can span several minutes, depending upon sample volume and the syringe dispensing rate. During sample acquisition, multiple lipid scanning periods, with userdefined scan numbers and MS parameters, are queued and acquired for a single sample injection. Because the sample injection and lipid scanning periods are explicitly controlled, this process can be repeated for several sample injections using sample batching software. While this approach is a promising alternative for automated shotgun lipidomics, some LC/MS systems and the accompanying software do not provide the necessary functionality to perform this type of automated acquisition. In general, the inability to perform FIA-based lipidomics originates from an inability to control either the sample dispensing rate during injection (an important factor in obtaining extended acquisition periods) or the start of data acquisition. In these cases, FIA can still be performed; however, the total
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Fig. 1 Schematic of the automated multi-injection shotgun lipidomics method used for lipid scanning. The developed automated multiinjection shotgun lipidomics approach described was based on exploiting the capabilities of basic LC systems, which included the use of an LC pump, an autosampler syringe and sample batching/ processing software. After an initial solvent purge (2 mL/min), the automated method was initiated by delivering solvent at a flow rate of 40 lL/min through PEEK-sil tubing to the instrument. This occurred while the autosampler drew up 60 lL of sample from an autosampler vial containing solvent (blank) or sample. After injection, data
acquisition began at contact closure of the autosampler syringe. To allow for approximately 2.5 min of sample acquisition (from a 60-lL injection), a low flow rate gradient was employed. Initially, the solvent flow was set to 40 lL/min and held until 0.5 min (before sample reaches mass spectrometer), and then the flow was decreased to 20 lL/min. The flow was held at 20 lL/min for 2.5 min and then the flow was ramped to 40 lL/min at 3.5 min. Flow was maintained at 40 lL/min for the last min. This process was then repeated for subsequent sample injections
sample is injected all at once, providing only seconds of acquisition time. For these systems, while lipid scanning can be automated, the abbreviated acquisition period (seconds) severely limits the scanning potential per sample injection, resulting in a reduced number of lipid scans acquired per injection. Thus, the intention of the present study was to develop an alternative automated shotgun lipidomics using LC instrumentation to overcome these shortcomings and provide extended acquisition periods. In this work, a simple and cost-effective approach for automated lipidomics was demonstrated by effectively mimicking traditional LC/MS analysis. The goal was to produce a method that provided adequate sample flow and acquisition time in order to perform shotgun lipidomics on a sample set with minimal operator intervention. The overall approach was constructed to include (1) a low flow gradient post-injection to delay the sample from immediately reaching the ESI–MS source (Fig. 1), (2) multiple sample injections per sample (on a lipid scan per injection basis) and a strategy to characterize the lipidome by creating and analyzing multiple lipid extract aliquots per sample. In addition, several aspects of the developed approach were further discussed in this report, including an examination into the optimal mass spectrometric parameters required for the detection of several lipid classes, the flexibility to customize the data acquisition period obtained using the automated method, and the comparability between the developed automated multi-injection approach to the manual approach using a lipid extract of Standard Reference Material (SRM 1950) Metabolites in Human Plasma.
Materials and Methods Materials For standard preparations and lipid extraction, HPLC-grade chloroform (C, Alfa Aesar, 99? %, Ward Hill, MA) and methanol (Honeywell, Muskegon, MI) were used. The deionized water was obtained from a MilliQ Gradient A10 (Billerica, MA). A lithium chloride (Sigma Aldrich, Milwaukee, WI) saline solution was prepared by adding approximately 2.11 g to 1 L of deionized water. A 10 mM (mmol/L) lithium hydroxide (Strem Chemicals, [95 %, anhydrous, Newburyport, MA) solution was prepared by adding 12 mg in 50 mL methanol. Acetyl chloride (Alfa Aesar, 99? %, Ward Hill, MA), which was used for cholesteryl derivatization, was prepared daily by adding 1 mL of acetyl chloride to 5 mL of chloroform (% volume fraction). A vial of SRM 1950 Metabolites in Human Plasma (NIST, Gaithersburg, MD) was used in the automated lipidomics validation study. SRM 1950 Lipid Extraction Three separate 100-lL aliquots of SRM 1950 were transferred into 16 mm 9 100 mm glass culture tubes, and lipids were extracted using the Modified Bligh-Dyer extraction [39]. Briefly, to the 100 lL of sample, 700 lL of LiCl saline solution was added. To this, 3 mL of 2:1 methanol/chloroform (volume fraction) was added and vortexed, followed by the addition of 250 lL of the internal standards (IS) 17:0 cholesteryl ester (CE, Nu-
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Chek Prep, Elysian, MN) and 14:1–14:1 phosphatidylcholine (PtdCho, Avanti Polar Lipids, Alabaster, AL) at a molar concentration of 149 and 44 lmol/L, respectively. The sample was vortexed repeatedly and placed on a shaker for 1 h at room temperature. After the 1 h, 1 mL of chloroform and 1 mL of LiCl saline solution were added, and the sample was vortexed and centrifuged at 209.4 rad/s (2,000 rpm) for 10 min. The bottom chloroform layer was removed, and the sample was extracted twice more with 2 mL of chloroform, pooling the extracts. The extract was evaporated to dryness and reconstituted with 500 lL of 1:1 methanol/chloroform (volume fraction), which was divided into three aliquots. For aliquot A analysis, 100 lL of the reconstituted lipid extract was transferred to an autosampler vial. To this vial, 1.4 mL of 4:1 methanol/chloroform (volume fraction) was added, along with 50 lL of LiOH (10 mmol/L). The sample was vortexed prior to analysis. The remaining lipid extract was reserved for aliquot B and C analysis (see below for more explanation regarding the analysis of the three aliquots). The vials were stored at -80 °C until analysis of CE and PtdCho in the lipid extract was performed via the manual or the automated multi-injection shotgun lipidomics approach. Manual Lipidomics Approach The manual shotgun lipidomics approach was performed using a Harvard Apparatus 11-plus syringe pump (Holliston, MA) and a 1-mL Hamilton syringe to draw and dispense the sample. The sample was introduced into the ESI– MS source at a flow rate of 10 lL/min through PEEK-sil tubing (Sigma Aldrich, 50 cm 9 1/16 in. 9 100 lm). The mass spectra obtained for each lipid scan were averaged over a 2.5-min period after signal stability was established (approximately 1 min). An example total ion current (TIC, top) and mass spectrum (bottom) obtained using this approach for monitoring CE (using the neutral loss (NL) of 368.5 U) in the SRM 1950 lipid extract (aliquot A) is shown in Fig. 2a. In Fig. 2a (top), a 2.5-min acquisition window (between 1.0 and 3.5 min) was selected for data analysis. Development of Automated Multi-Injection Lipidomics Approach The automated multi-injection shotgun lipidomics approach was developed using an Agilent 1100 LC and Autosampler (G1329A) and an AB Sciex API 4000 triple quadrupole mass spectrometer (equipped with a TurboV electrospray ionization source). Operation of the LC and MS was controlled using Analyst software (v.1.52). After an initial solvent purge (2 mL/min), the automated method
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was initiated by delivering solvent (4:1, methanol/chloroform, volume fraction) at a flow rate of 40 lL/min through PEEK-sil tubing to the instrument. After a sample acquisition batch was set up in Analyst (containing a queue of the lipid scans to be analyzed for each sample aliquot A through C, as shown in Table 1), the autosampler drew up 60 lL of sample from an autosampler vial containing solvent (blank) or sample and injected the sample into the solvent stream. Data acquisition began at contact closure of the autosampler syringe. After injection, to allow for approximately 2.5 min of sample acquisition (from a 60 lL injection), a low flow rate gradient was employed, as shown in Fig. 1. Initially, the solvent flow was set to 40 lL/min and held until 0.5 min (before sample reaches mass spectrometer), and then the flow was decreased to 20 lL/min. The flow was held at 20 lL/min for 2.5 min and then the flow was ramped to 40 lL/min at 3.5 min. Flow was maintained at 40 lL/min for the last min (after the sample has been analyzed, typically concludes within the 4.5 min total acquisition period). This process was then repeated for subsequent sample injections. An example TIC (top) and mass spectrum (bottom) obtained using this approach for monitoring CE from an SRM 1950 lipid extract (using the NL 368.5 U) is shown in Fig. 2b. In Fig. 2b (top), a distinct 2.5-min acquisition window (1.0–3.5 min) was produced and was used for subsequent data analysis. Three Aliquot Comprehensive Lipidomics Strategy The overall lipidomics strategy, as shown in Fig. 3, encompassed seven discrete steps, including sample handling/aliquoting, addition of internal standard, lipid extraction, extract reconstitution, extract aliquoting, mass spectrometric analysis and data handling/interpretation. The MS and MS/MS scan modes used to detect the different lipid species are listed in Table 1. For lipid extract aliquoting, the proposed strategy was set to separate extracts into three aliquots, with each aliquot processed for specific lipid classes. Aliquot A was examined after the addition of lithium hydroxide (LiOH). Lithium was chosen as the adduct employed in this study due its ability to provide enhanced fragmentation (and consequently an enhanced MS/MS detection), in comparison to other adducts, such as sodium and ammonium adducts [40–43]. With the addition of Li? adducts, an enhanced detection of CE, diacylglycerol (DAG), triacylglycerol (TAG), PC, lysophosphatidylcholine (LysoPtdCho), and sphingomyelin (CerPCho) species was obtained. Additionally, while under alkaline conditions, free fatty acid (FFA), phosphatidylethanolamine (PtdEtn), lysophosphatidylethanolamine (LysoPtdEtn), and ceramide (Cer) species were also detected in aliquot A in negative mode. However, base
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Fig. 2 a Stable TIC (2.5 min) and mass spectrum (for CE, NL 368.5 U) obtained from manual shotgun lipidomics method (total acquisition time of 4.5 min). b Stable TIC (2.5 min) and mass spectrum (for CE, NL 368.5 U) obtained from automated multiinjection shotgun lipidomics method (total acquisition time of
4.5 min). For both a and b, the 2.5 min (1.0 to 3.5 min) acquisition period was used for subsequent data analysis. Asterisk denotes the detection of sodiated adducts for the highly concentrated 18:1 and 20:4 CE species
addition can lead to a reduction in response for those lipids possessing a charge under neutral conditions [31, 44], thus a second aliquot (B) was created. The lipids scanned in aliquot B, with no base added, were phosphatidic acid (PtdOH), phosphatidylinositol (PtdIns), phosphatidylglycerol (PtdGro), phosphatidylserine (PtdSer), cardiolipin (Ptd2Gro) and sulfatide (ST). A third aliquot (C) was designated for the analysis of cholesterol. Cholesterol does not ionize well in electrospray and must be converted to cholesterol acetate via acetyl chloride derivatization for detection [45]. The distribution and dilution of each aliquot A through C must be adjusted and optimized on a per lipid scan and per sample basis. In practice, when using the three aliquot strategy with the automated multi-injection shotgun lipidomics approach (60 lL per injection), most lipid
species were detected in total sample volumes of 1.5, 1 and 0.5 mL, for aliquots A, B and C, respectively. Real-Time Mass Spectrometric Tuning of Lipid Scans Each lipid scan (MS or MS/MS) proposed for aliquots A, B, and C (Table 1) was real-time tuned using the API 4000 triple quadrupole and manual direct-infusion of lipid standard. The lipid scans (for each aliquot, A through C), listed as part of the overall comprehensive analysis strategy in Table 1, represent a combination of lipid scans previously utilized in literature [22, 31, 33] and those that gave the greatest response analyzing lipid standards by ESI–MS. For tuning, each of the representative lipid standards was prepared to approximately 2 lmol/L (in 1:1 methanol/
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614 Table 1 Shotgun lipidomics injection order and scan details for aliquots A, B and C
The lipids detected in aliquot A, B and C were detected after the addition of LiOH, no adduct addition, and after acetyl chloride derivatization, respectively. The intensities obtained using the MRM scan modes were acquired by monitoring a specific parent mass for each compound with a corresponding fragment mass (as shown in supplemental information, Table S5). The full scans were collected for investigatory purposes (to check for background interferences and potential unknown lipids). All FA-containing TAG scans were acquired under identical conditions
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Injection order
Scan
m/z Window
Lipid class monitored
1
?Full scan
400–1,000
All; investigatory scan
2
?Full scan
550–750
DAG
3
?NL 368.5
550–750
CE
4
?NL 59.1
650–875
PtdCho/CerPCho
5
?NL 189.1
650–875
PtdCho
6
?NL 59.1
450–600
LysoPtdCho
7
?NL 213
650–850
CerPCho
8
?NL 228.2
750–950
14:0 FA-containing TAG
9
?NL 254.2
750–950
16:1 FA-containing TAG
10
?NL 256.2
750–950
16:0 FA-containing TAG
11
?NL 268.2
750–950
17:1 FA-containing TAG
12
?NL 280.2
750–950
18:2 FA-containing TAG
13
?NL 282.2
750–950
18:1 FA-containing TAG
14 15
?NL 284.2 ?NL 304.3
750–950 750–950
18:0 FA-containing TAG 20:4 FA-containing TAG
16
?NL 328.3
750–950
22:6 FA-containing TAG
17
-Full scan
200–1,000
All; investigatory scan
18
-Full scan
200–400
FFA
19
-PI 196.0
625–825
PtdEtn
20
-NL 256.2
500–700
Cer
21
?MRM
Specific scan
DAG
22
-PI 196.0
400–550
LysoPtdEtn
Aliquot A
Aliquot B 1
-Full scan
200–1,000
All; investigatory scan
2
-PI 153.0
400–1,050
Ptd(OH, Gro, Gro2, Ins, Ser)
3
-NL 87.0
650–900
PtdSer
4
-PI 97.0
700–1,000
ST
5
-NL 36.0
650–950
CerGal
6
-NL 241.2
650–950
15:0 FA-containing lipids
7 8
-NL 255.2 -NL 279.2
650–950 650–950
16:0 FA-containing lipids 18:2 FA-containing lipids
9
-NL 281.2
650–950
18:1 FA-containing lipids
10
-NL 283.2
650–950
18:0 FA-containing lipids
11
-NL 303.2
650–950
20:4 FA-containing lipids
12
-NL 327.2
650–950
22:6 FA-containing lipids
?NL 368.5
420–470
Cholesterol scan 1
Aliquot C 1
chloroform). Solutions were then introduced into the instrument by manual direct infusion at a flow rate of 10 lL/min. After a stable flow and TIC signal was established, the appropriate lipid scan mode (FS, NL, PI, MRM, as listed in Table 1), was selected along with an appropriate mass range, scan rate and an average set of compound and source specific mass spectrometric parameters. Compound specific parameters were collisional energy (CEn, 25 eV), declustering potential (DP, 90 V), entrance potential (EP, 5 V), and collision cell exit potential (CXP,
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5 V). Source specific parameters were collisionally activated dissociation (CAD, 4), curtain gas (CUR, 137 kPa, 20 psi), gas 1 (GS1, 68.9 kPa, 10 psi), gas 2 (GS2, 68.9 kPa, 10 psi), source temperature (TEMP, 450 °C), interface heater (ihe, on), and ion spray voltage (IS, 5,000 V). As solutions were introduced into the mass spectrometer, the signal was allowed to stabilize (approximately 30 s to 1 min). MS and MS/MS tuning parameters were then manually changed one-at-a-time using userdefined increments, while simultaneously monitoring
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Fig. 3 Proposed workflow diagram of the developed comprehensive lipidomics approach, from sample handling to data analysis/processing. The section in grey highlights the different aliquots (and sample conditions) required to detect specific lipid classes
changes in TIC response. In response to a change in one of the MS or MS/MS parameters, if the TIC signal increases, then the setting is more optimal (likewise, if the TIC decreases, then the setting is less optimal). An example of the real-time tuning strategy for the optimization of collisional energy for the detection of CE is shown in Figure S1. The final MS and MS/MS tuning parameters for each lipid scan from each aliquot A to C are listed in Tables S1 and S2 (for compound specific parameters) and S3 and S4 (for source specific parameters) in the supplemental information. DAG were monitored using two scan modes, FS and MRM. For the MRM detection of DAG, data were obtained by monitoring a specific parent mass for each compound with a corresponding fragment mass, specifically for DAG it monitors the loss of a lithiated fatty acyl chain (as shown in supplemental information, Table S5). For DAGs with a heterogeneous pair of fatty acids, it is recommended to perform multiple MRM scans to ensure absolute identification. Integration of the Three Aliquot Strategy with the Automated Multi-Injection Shotgun Lipidomics Approach A schematic of the multi-lipid class analysis procedure is shown in Fig. 3. For each sample in aliquot A, a set of 24 injections were queued (which included a wash injection, a blank solvent injection, and 22 injections for lipid scanning, as shown in Table 1). Likewise, there were a total of 14 and 3 injections for aliquots B and C, respectively (also shown in Table 1). In each of the sequences (for A, B or C), the first injection that was queued was a wash injection. The wash injection consisted of 60 lL of a blank solvent
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solution (4:1 methanol/chloroform, volume fraction) that ran for 15 min at a flow rate of 200 lL/min. The wash injection was included to reduce potential sample crosstalk. The 15 min wash period was found to be sufficient to clean the system between samples. After the wash injection, a solvent blank was run and data was acquired in the full scan mode for 2.5 min. This blank sample was included to check that the system was absent of lipid crosstalk prior to the first lipid extract injection. Following the blank acquisition, the first sample injection was performed. For aliquot A, the first sample injection was a positive full scan (as listed in order in Table 1). This was then followed by the remainder of the injections for lipid scanning, which was subsequently followed by a wash injection prior to the start of the next sample. In general, all lipid scan data for aliquot A was collected and completed for a sample set prior to the acquisition of lipid scan data from aliquot B (and C). The decision of aliquot analysis order should be examined and optimized on an application-to-application basis. The system was flushed before switching from aliquot to aliquot within a sample set. Comparison Between Manual and Automated Shotgun Lipidomics Approaches A comparison of the data acquired using the manual and automated multi-injection shotgun lipidomics approach was performed using aliquot A of the lipid extract from SRM 1950 (n = 3). The centroided intensities acquired for all of the individual CE (using the neutral loss scan of 368.5 U) and PtdCho species (neutral loss scan of 189.1 U) were collected and divided by the intensities obtained for the spiked internal standards (17:0 CE and 14:1–14:1 PtdCho). The resultant intensity ratios between the two methods were compared and error was provided as the standard deviation from the mean. Customization of Total Scan Number During Data Acquisition Period An examination into the comparability of data obtained using different total scan numbers was performed for CE species using aliquot A of the lipid extract from SRM 1950 (n = 3). To compare to the target scan number employed with the method described in this work (100 scans), two additional scan totals were investigated (40 scans and 180 scans). To achieve data acquisition periods capable of collecting 40 scans and 180 scans (keeping flow and scan rate constant), sample injection volumes of 30 and 100 lL were utilized, along with longer total acquisition periods (3 and 7 min), respectively. The resultant intensity ratios using the different total scan numbers were compared and error was provided as the standard deviation from the mean.
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Results and Discussion Automated Multi-Injection Shotgun Lipidomics While strategies are described for automated shotgun lipidomics that use commercially available sample introduction systems, approaches capable of performing highthroughput automated shotgun lipidomics without the need to purchase new instrumentation are of great interest. Beyond manual syringe-based lipidomics, the most compatible solution for performing automated shotgun lipidomics is to exploit the capabilities of LC (specifically the use of an LC pump and autosampler). The proposed alternative automated shotgun lipidomics approach described is fundamentally based on executing a series of short LC runs (with no column) wherein one lipid scan is acquired per sample injection. To overcome the fact that some LC systems do not afford control over sample injection dispensing rate and acquisition start time, execution of the automated multi-injection shotgun lipidomics approach was achieved by implementing a low flow gradient post-injection (Fig. 1). The reduced flow rate delayed the sample from immediately reaching the ESI– MS source and widened the sample plug, providing a stable 2.5 min period for data acquisition within a 4.5 min total acquisition period (Fig. 2b). An acquisition period of 2.5 min was selected because it routinely afforded approximately 100 scans to be averaged for quantitation. The generation of approximately 100 scans within the 2.5 min data acquisition period was determined by a series of experiments focused on optimizing the amount of sample injected, the solvent flow rate, and the acquisition scan rate. Beyond the aspect of automation, the multi-injection approach had other inherent advantages. In addition to being simple and robust, the approach was also designed to be LC-instrument independent, i.e., capable of being applied to other LC instruments (provided that the system has capable LC and autosampler hardware), thus allowing a greater adaptation of this approach across systems. For those systems optimized for high flow rates (0.1–1 mL/ min), a flow splitter could be employed to allow adaptation of this approach. Further, because the multi-injection approach was so simplistic and only required the basic ability to implement a low flow gradient for direct-infusion, it also provided a degree of customization, as the data acquisition period can be tailored to perform different scan types under various scan conditions (i.e., scan rate). In addition, the developed method provided a level of flexibility in the total number of lipid scans obtained during the data acquisition period, as shown in Fig. 4. In Fig. 4, when investigating the number of scans per injection needed to produce a reliable and reproducible
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Fig. 4 Intensity ratios obtained for CE using different scan numbers acquired during the data acquisition period (40 scans, 100 scans, and 180 scans). This figure illustrates the total number of averaged lipid scans required to produce reliable signal intensity and reproducible MS/MS spectra. The different scan numbers were obtained by changing sample injection amount and total acquisition time (scan rate was not changed, 1.5 s). The error is shown as the standard deviation of the mean, n = 3. Intensity ratios were determined by dividing the intensity of each lipid species with the intensity of the internal standard
lipid signal within the total data acquisition period, it was shown that for an acquisition period in which only 40 scans were collected for data analysis (30-lL injection), the data obtained exhibited no statistical difference to the data obtained when 100 (condition employed in this study) and/or 180 scans were collected and analyzed (Fig. 4). This aspect implied that the method was capable of being scaled up or down in terms of data acquisition period and sample amount injected (as shown in Figure S2a to S2c). Thus, for those samples with trace lipid content and therefore cannot be diluted by large volumes of solvent, smaller injection volumes can be utilized with the automated method without a negative impact on data analysis or the possible number of lipid scan types employed for a single aliquot. It should also be noted that the multi-injection approach was highly reproducible, as shown in Fig. 4 and because solvent was continually flowing between injections (in addition to washing cycles); the method was efficient in providing a low incidence of sample crosstalk. Implementation of the Three Aliquot Strategy with the Automated Multi-Injection Approach To complement the multi-injection shotgun lipidomics approach, a strategy is described to permit the data acquisition of a wide variety of lipid classes from a single sample. It should be noted that although shotgun lipidomics allows for the direct infusion of most lipid classes obtained from lipid extraction, the successful ionization and detection of certain lipids can rely heavily on solution conditions. The comprehensive aspect of this shotgun lipidomics method is obtained by dividing sample extracts into three
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distinct aliquots A, B and C (each having distinct solvent conditions), to expand the total number of lipid classes characterized from a single sample (as shown in Table 1). Initially, an examination of the appropriate sample volume (pre-extraction) and dilution factors (post-extraction) for aliquots A through C needed to be considered and optimized. In general, because lipids are abundant in extracts, diluting into larger volumes was required to avoid lipid aggregation [33]. As an example, for a 100-lL sample, we found that after diluting plasma/serum lipid extracts by a factor of 10–20 with 4:1 methanol/chloroform (volume fraction), we obtained a sample solution that (1) produced a significant response for most lipid species and (2) provided a total sample volume sufficient to perform the multiinjection shotgun lipidomics approach (with 60 lL per injection). For those lipids which are present in trace quantities, the option of creating separate aliquots can be achieved by diluting the sample lipid extract into smaller solvent volumes. For aliquot A, after the addition of LiOH, both positive [M?Li]? adducts and negative ion [M-H]- species of several lipid classes were detected using the automated multi-injection approach. In general, for a 100 lL plasma sample extracted and reconstituted to 500 lL 1:1 methanol/chloroform (volume fraction), a 100–200 lL aliquot of the extract diluted to 1.5 mL of 4:1 methanol/chloroform (volume fraction) was sufficient for the detection of most lipids (ex., CE, TAG, DAG, PtdCho, LysoPtdCho, CerPCho, and FFA). It should be noted that the automated approach exhibited a reduced presence and abundance of sodiated CE adducts in comparison to the manual approach (as shown in Fig. 2a, b). For those lipids hampered by the slightly alkaline conditions in aliquot A, a separate aliquot B was implemented. For aliquot B, it was determined that a 200–250 lL aliquot of the lipid extract diluted to 1 mL of 4:1 methanol/chloroform (volume fraction) was typically sufficient for the detection of these lipids (charged phospholipids, ex., PtdSer). Lastly, after an acetyl chloride derivatization, free cholesterol was determined as cholesteryl acetate in aliquot C. A 50 lL aliquot of the extract diluted to 0.5 mL of 4:1 methanol/ chloroform (volume fraction) was sufficient for the detection of cholesterol. It should be noted that the exact aliquot distribution and/or dilution factor needs to be developed and tailored for each specific biological sample. While a complete characterization of a biological sample using the three aliquot strategy (with the automated method) is not included in this publication, it has been proven to be effective with on-going shotgun lipidomics analyses (data not shown). Overall, the approximate total run time (which includes the wash and blank injections) for aliquots A, B and C were 130, 80, and 25 min, respectively.
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Fig. 5 Intensity ratios of CE (top) and PtdCho (bottom) obtained using the manual and automated multi-injection shotgun approaches for a lipid extract of SRM 1950. The error is shown as the standard deviation of the mean, n = 3. Intensity ratios were determined by dividing the intensity of each lipid species with the intensity of the internal standard. The letters D and P in front of each PtdCho represent the diacyl and plasmenyl forms
Comparison Between Manual and the Multi-Injection Automated Shotgun Lipidomics Approaches Since the triplicate analysis was performed on the same sample extract (just different aliquots), it was important to demonstrate that the intensity ratio and lipid profile obtained from the manual and automated multi-injection shotgun lipidomics approaches were statistically similar, as shown in Fig. 5. While the automated multi-injection shotgun lipidomics method performed well using lipid standards, a brief examination was included to demonstrate that the data acquired using the developed method was at least equal to or better than the data obtained using the manual shotgun lipidomics approach. The response of lipid classes in human plasma lipid extract (SRM 1950), using both the manual and automated method for the detection of CE and PtdCho, are shown in Fig. 5. It should be noted that although one internal standard was employed in this study, it is recommended to employ more than one IS per lipid class for quantitation. Intensity ratios, relative standard error and mass spectra exhibited no statistical difference. The results demonstrate comparability between the manual and automated lipidomics approaches for the individual CE and PtdCho species. An examination of the other lipid groups resulted in a similar finding when comparing the
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manual and automated multi-injection approaches (data not shown). From a high-throughput perspective, the manual shotgun lipidomics approach is largely limited by (1) the amount of operator time required to manually set up each lipid scan for each sample, (2) the possible sample-to-sample inconsistencies present during data acquisition and (3) the timeconsuming practice of cleaning the syringe between samples. The proposed automated multi-injection lipidomics method addresses each of these concerns. The principal advantage of the automated approach is savings in usertime, since the method is fully automated (using the LC pump, autosampler syringe and MS software); the operator does not need to be present for data acquisition. In addition, sample-to-sample data is acquired under consistent conditions, and sample carryover is reduced by implementing wash and blank injections within the sample queue. This publication addresses the automated multi-injection method in regards to (1) strategizing an aliquot strategy capable of providing a framework necessary to characterize the lipidome from a single sample, (2) demonstrating the flexibility of the method (can alter which lipid scans employed, data acquisition period and target scan number) and (3) validating the use of the automated multi-injection method in comparison to the manual lipidomics approach. The final automated method was shown to be reproducible, cost-effective (using common LC instrumentation), and customizable (the data acquisition period can be tailored for specific analyses). The overall approach provided a simple and efficient platform to perform high-throughput shotgun lipidomics.
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