Lipidomics, en route to accurate quantitation

    Lipidomics, en route to accurate quantitation Sin Man Lam, He Tian, Guanghou Shui PII: DOI: Reference:

S1388-1981(17)30032-X doi:10.1016/j.bbalip.2017.02.008 BBAMCB 58115

To appear in:

BBA - Molecular and Cell Biology of Lipids

Received date: Revised date: Accepted date:

3 December 2016 5 February 2017 15 February 2017

Please cite this article as: Sin Man Lam, He Tian, Guanghou Shui, Lipidomics, en route to accurate quantitation, BBA - Molecular and Cell Biology of Lipids (2017), doi:10.1016/j.bbalip.2017.02.008

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Sin Man Lam1#, He Tian1#, Guanghou Shui1,2*

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Lipidomics, en route to accurate quantitation

1. State Key Laboratory of Molecular and Developmental Biology, Institute of

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Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101.

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2. Lipidall Technologies Company Limited

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# These authors contributed equally to this work.

* Corresponding author:

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Dr Guanghou Shui

Tel: +86-10-6480-7781

Email : [email protected]

ACCEPTED MANUSCRIPT Abstract (<100 words) Accurate quantitation is prerequisite for the sustainable development of

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lipidomics via enabling its applications in various biological and biomedical

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settings. In this review, the technical considerations and limitations of existent

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lipidomics technologies, particularly in terms of accurate quantitation; as well as the potential sources of errors along a typical lipidomics workflow that could

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ultimately give rise to quantitative inaccuracies will be addressed. Furthermore,

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the pressing need to exercise stricter definitions of terms and protocol standardization pertaining to quantitative lipidomics will be critically discussed, as accuracy

may

substantially

impact

upon

the

persevering

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quantitative

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development of lipidomics in the long run.

Key words: lipidomics, accurate quantitation, internal standards, lipid extraction

ACCEPTED MANUSCRIPT Abbreviations used: liquid extraction (LE)

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methyl-tert-butyl ether (MTBE) butanol/methanol (BUME)

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sphingosine-1-phosphate (S1P)

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free fatty acids (FFA) phosphatidic acids (PAs)

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phosphoinositides (PIPs)

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lipopolysaccharide (LPS) sodium dodecyl sulfate (SDS) myolic acids (MAs)

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arachidonic acids (ARA)

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solid phase extraction (SPE)

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polyunsaturated fatty acids (PUFAs) lipoxygenase (LOX)

cyclooxygenase (COX)

cytochrome P450 epoxygenase (CYP450) phosphatidylcholines (PCs) phospholipase D (PLD) phosphatidic acids (PAs) butylated hydroxytoluene (BHT) mass spectrometry (MS) gas chromatography (GC) liquid chromatography (LC) electrospray ionization (ESI) atmospheric-pressure chemical ionization (APCI)

ACCEPTED MANUSCRIPT atmospheric-pressure photoionization (APPI) secondary ion mass spectroscopy (SIMS)

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matrix-assisted laser desorption ionization (MALDI) electron impact ionization (EI)

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high-resolution MS (HRMS)

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hydroxyeicosatetraenoic acids (HETEs) glucosylceramides (GluCer)

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galactosylceramides (GalCer)

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two dimensional LC (LC*LC) capillary electrophoresis (CE)

imaging MS (IMS)

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tandem MS (MS/MS)

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multiple reaction monitoring (MRM)

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triple quadrupole (QqQ)

high-resolution tandem MS (HRMS/MS) triacylglycerols (TAGs) wax esters (WEs)

phosphatidylinositol (PI) phosphatidylserine (PS) free fatty acids (FFAs) limit of detection (LOD)

limit of quantification (LOQ) coefficient of variation (COV) quality control (QC)

ACCEPTED MANUSCRIPT 1. Introduction Lipids, which represent vital components of cellular membranes and

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various lipid-transport and storage vesicles, can elicit a myriad of biochemical

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functions in a variety of cellular processes by virtue of their great diversity in

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structures and physiochemical properties [1,2]. The extensive investigation of biological lipidomes and lipid metabolism is coined lipidomics, a relatively new

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discipline emerged in 2003, which relies essentially on the principles of analytical

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chemistry primarily centralized on mass spectrometry [2]. As a comparably young branch in the realm of metabolomics, lipidomics has been broadly applied for

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investigation of cellular lipid metabolism in a plethora of organismal models and

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various diseases threatening public health [3,4,5,6,7,8,9,10,11,12], adding a new dimension of omics information critically relevant to forwarding biological

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research on top of the established fields of genomics and proteomics. The application of lipidomics to biological and biomedical research has been critically reviewed elsewhere [2], and hence will not be a major focus of this review. Herein, we discuss the technical considerations and limitations of existent lipidomic technologies, particularly in terms of accurate quantitation; as well as the potential sources of errors along a typical lipidomic workflow that could ultimately give rise to quantitative inaccuracies. Furthermore, the pressing need to exercise stricter definitions of terms and protocol standardization pertaining to quantitative lipidomics will be critically discussed. 2. Lipid extraction and sample pre-treatment

ACCEPTED MANUSCRIPT A robust lipid extraction method is prerequisite for accurate lipidome analysis. Liquid extraction (LE) represents the most commonly employed approach in

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lipidomics studies, and can entail a broad array of organic solvents such as

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chloroform, methanol, methyl-tert-butyl ether (MTBE), isopropanol and butanol (Figure 1) [13,14,15,16]. In terms of LE-based extraction methods, modified Bligh

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and Dyer’s method, modified Folch method, MTBE method, butanol/methanol

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(BUME) method as well as single-phase extraction denote some of the most frequently adopted approaches for extracting an assemblage of lipids from

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biological samples [2]. While the methods aforementioned are reasonably efficient in terms of conferring a general lipidome representative of the vast

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diversity of endogenous lipids, the exact choice of solvents and extraction protocols depend predominantly on the lipids of interest. For instance, single-

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phase extraction, which does not involve a phase-break, is generally more efficient in terms of harvesting a holistic inclusion of lipids with considerable differences in terms of polarities; as the absence of phase-break can serve to minimize the loss of extremely polar lipids of lower endogenous abundance e.g. lysophospholipids, sphingosine-1-phosphate (S1P) and free fatty acids (FFA) from the general lipidome. On the other hand, the extraction of lower chloroform phase after phase-break in the modified Bligh and Dyer’s method, when executed correctly with minimal contaminants from tissue remnants and watersoluble contaminants from the upper aqueous phase, serves to reduce aqueous impurities that may introduce undesirable background matrix in subsequent mass spectrometric analysis.

ACCEPTED MANUSCRIPT Besides selecting an appropriate extraction approach, some lipids with unique properties demand specialized protocols for efficient extraction from biological

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samples. For instance, phosphatidic acids (PAs), S1Ps, and phosphoinositides

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(PIPs) are preferentially extracted in medium with acidic pH, the lack of which may severely compromise the retrieval of these lipids during the process of lipid

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extraction with a two-phase system [17,18]. On another note, hydrolysis is

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required to effectively release specific lipids such as lipid A and mycolic acids, which are covalently bonded to other macromolecules, into the extraction

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solvents. Lipid A, for instance, is a critical functionality of the lipopolysaccharide (LPS) moiety in the cell envelope of Gram-negative bacteria comprising a di-

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glucosamine sugar backbone substituted with varying number of fatty acyl chains; and deemed to elicit crucial functions in microbial pathogenesis [19]. As lipid A is

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covalently bonded to the core polysaccharide and O-antigen [20], isolation of lipid A from crude LPS necessitates mild acid hydrolysis in 1% sodium dodecyl sulfate (SDS) supplemented with 10 mM sodium acetate acidified with hydrochloric acid [20,21]. On the other hand, myolic acids (MAs) esterified to the cell wall of mycobacterium, previously demonstrated to exert immunomodulatory function in host-pathogen interactions [22], require alkaline hydrolysis using potassium hydroxide or tetrabutylammonium hydroxide (TBAH) from defatted mycobacterial cells for effective release into the extraction medium [22,23]. While hydrolysis is requisite for satisfactory yield of the special lipids aforementioned, caution needs to be exercised if other lipids sensitive to hydrolytic reactions are also under study. For example, phospholipids are generally susceptible to hydrolysis under

ACCEPTED MANUSCRIPT prolonged exposure to strongly acidic or basic medium; and thus if the extraction process requires extreme pH treatment to cater for such special lipids, it may be

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advisable to first retrieve the majority of phospholipids in a first round of

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extraction under mild pH before further adjusting the pH to extract special lipids. Therefore, while the conventional protocols listed in Figure 1 are generally

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effective in harboring lipid extracts of considerable chemical complexities

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representative of the overall endogenous lipid profiles, separate protocols of lipid extraction are often required when lipids of significantly differing physiochemical

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reflective of in vivo lipid levels.

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properties are study interests; in order to ensure satisfactory yield accurately

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While LE represents a somewhat universal extraction method in lipidomics, lipids of extremely low endogenous abundance may require enrichment via solid

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phase extraction (SPE), which simultaneously serves to minimize background matrix, to ensure satisfactory detection upon mass spectrometric analysis. Eicosanoids collectively refer to a group of bioactive lipid mediators derived primary from arachidonic acids (ARA), or other polyunsaturated fatty acids (PUFAs), principally via three pathways, including the lipoxygenase (LOX), cyclooxygenase (COX) and cytochrome P450 epoxygenase (CYP450) pathways [24]. Routine extraction of eicosanoids from biological samples often entails passage through specific SPE columns, such as the Phenomenex Strata-X [25] or Waters Oasis-HLB cartridges [24], to remove the prevailing amount of phospholipids present in biological samples that renders subsequent detection of eicosanoids.

ACCEPTED MANUSCRIPT Apart from the inherent physiochemical properties of lipids per se, the biological nature of the samples involved, coupled with the unique properties of

For

instance,

Zien

et

al

had

previously

established

that

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times.

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extant endogenous enzymes, also warrants the adoption of tailored protocols at

phosphatidylcholines (PCs) represent the predominant in vivo substrate for

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Arabidopsis phospholipase D (PLD), which rapidly converts PCs to phosphatidic

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acids (PAs) upon tissue injury within 30 min of wounding [26]. As such, the inherently strong PLD activity in plant tissues necessitates pre-treatment of

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tissues with hot isopropanol containing 0.01% butylated hydroxytoluene (BHT) to effectively quench PLD activity. Otherwise, the extracted lipid profiles may

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contain artificially high levels of PAs accompanied by lower than usual levels of PCs. Furthermore, we have tested the lipid profiles from a variety of plant

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species including Arabidopsis, canola and capsicum; and concluded that even transporting harvested samples on dry ice for cross-laboratory analysis or prolonged storage at – 80 oC is not sufficed to effectively inhibit plant PLD activity (unpublished observations). In our opinion, the optimal protocol for plant lipid extraction entails immediately submerging plant tissues in hot isopropanol containing 0.01% BHT right upon harvesting of fresh tissues from their respective culturing conditions, which will produce undistorted lipid profiles truly reflective of the endogenous levels of PCs and PAs. Given the critical role of PCs as predominant membrane lipids [27], as well as the established function of PAs as signal transducer of water stress in plants [28], it is evident that strict adherence

ACCEPTED MANUSCRIPT to sample pre-treatment protocol is indispensable in deriving biologically meaningful lipid profiles in plants.

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Hence, the execution of a correct procedure of sample pre-treatment and lipid

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extraction is an obligatory precondition to an accurate lipidome; as improper

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extraction protocols may lead to erroneously skewed lipid profiles, leading to incorrect biological interpretations. As such, an in-depth knowledge of the

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physiochemical properties of the wide array of biological lipids is therefore

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prerequisite in designing an optimal extraction protocol that caters for the differing lipids of interests in each study. We have outlined above several

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precautionary notes pertaining to lipid extraction that may have substantial

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impact upon the resulting quantitative profiles. The precise selection and execution of lipid extraction protocols, however, depends critically on the lipids of

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interest in different studies, as well as the nature of the biological samples. In general, the Bligh and Dyer’s method displays superior performance in yielding neutral lipids, while the Folch’s method produces higher efficiency in extracting polar lipids. The choice of lipid extraction protocols should therefore also take into consideration the relative proportions of these major lipid classes inherent in the biological samples under study. Comparative evaluation of lipid extraction protocols for common biological matrices including plasma, tissues and cells have been extensively reviewed elsewhere [29,30]. 3. Mass spectrometric data acquisition Mass spectrometry (MS) hyphenated with prior chromatographic separation such as gas chromatography (GC) and liquid chromatography (LC), as well as

ACCEPTED MANUSCRIPT shotgun MS represent two dominant camps of analytical approaches in quantitative lipidomics (Figure 1) [31]. The application of the latter for quantitative

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lipidomics has been critically reviewed elsewhere, and herein we will emphasize

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on the concerns pertaining to LC-MS analysis in quantitative lipidomics. Common ionization techniques in MS include electrospray ionization (ESI), atmospheric-

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pressure chemical ionization (APCI), atmospheric-pressure photoionization

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(APPI), secondary ion mass spectroscopy (SIMS), matrix-assisted laser desorption ionization (MALDI), and electron impact ionization (EI) (Figure 1) [1,2].

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In particular, ESI is a soft ionization technique that enables the detection of phospholipids, sphingolipids and glycerolipids in their intact forms with minimal

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loss of structural information, primarily attributed to the relatively low energy used during the ionization process, and is currently the most commonly adopted

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ionization method in various applications of lipidomics [1]. Prior LC separation serves to segregate the various classes of lipids in biological samples, thereby reducing the effect of ion suppression from lipids of high abundance on minor lipid species; thus improving the signal detection of the latter. Furthermore, LC separation also functions to scale down unexpected contaminants possibly introduced during preceding steps of sample preparation, resulting in a cleaner matrix and thus smaller background interferences during ionization. A major criticism on the use of LC separation, particularly gradient separation, is the varying matrix condition for individual lipid species during their respective elution, which may lead to differing ionization efficiency along the gradient, since matrix effect is known to be a key determinant in the ion response

ACCEPTED MANUSCRIPT of an analyte [31]. Nonetheless, a well-designed lipidomic gradient usually allows the various lipid classes to be sufficiently spaced out from one another, while

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individual species within the same class (i.e. sharing common internal standards

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for normalization during quantitative calculations) are eluted within a relatively narrow time window (typically within 2-3 min) (Figure 2A) [13]. In other words, a

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good LC separation not only minimizes ion suppression across lipid classes of

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appreciably different abundances, but also serves to keep individual species within one lipid class and their corresponding internal standards eluting within a

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narrow time window to secure a comparable matrix, thereby ensuring accurate quantitation. Moreover, the application of an appropriate LC gradient can at times

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isolate structural isomers and enantiomers with identical elemental compositions that are unresolvable even with high-resolution MS (HRMS), rendering their

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accurate identification and quantification. Some excellent examples of lipid isomers include members of the eicosanoid family, such as individual species of hydroxyeicosatetraenoic

acids

(HETEs)

(Figure

2B);

as

well

as

glucosylceramides (GluCer) and galactosylceramides (GalCer) (Figure 2C). These lipid isomers often elicit distinct physiological functions [7,32], and their respective quantitation is imperative for elucidation of novel insights to lipid biology. Hence, chromatographic separation can confer higher specificity and sensitivity in terms of metabolite detection compared to shotgun MS, principally via curtailing ion suppression and matrix complexities.

ACCEPTED MANUSCRIPT Two dimensional LC (LC*LC) and capillary electrophoresis (CE) represent two burgeoning areas of technological developments in LC separation. While the

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concept of LC*LC is not new, its wider application in the laboratory analytical

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workflow has been largely circumscribed by its higher demand imposed upon instrumentation, method design and data analysis [33]. Nonetheless, LC*LC

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holds potential in terms of its high resolving power and extended coverage of

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metabolites bearing a wider range of polarities within a single analytical platform. These advantages can cut down on sample volume requirement and analysis

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time, thereby uplifting analysis throughput [34]. On the other hand, CE utilizes high voltage to create an electrophoretic flow of ions within a capillary of a

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relatively narrow bore (i.d. 20-200 µm) to achieve separation of analytes. The major advantages of CE compared to conventional LC lie in its ability to

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efficiently separate analytes with a range of molecular sizes; as well as the minimal sample volume required (typically in the range of nanoliters), which represents one of the smallest sample volume requirements in modern separation technologies.

In terms of post-separation data acquisition, users are presented with the options of adopting either non-targeted analysis, which confers unbiased characterization often at the expense of quantitative accuracy and the possibility of ambiguous lipid assignment; or targeted analysis using triple quadrupole (QqQ) that usually renders unambiguous identification (i.e. specificity) on top of superior sensitivity and quantitative accuracy, despite a pre-defined coverage of lipids designated by the multiple reaction monitoring (MRM) library (Figure 1). In our

ACCEPTED MANUSCRIPT opinion, while non-targeted analysis may be useful in the preliminary characterization of novel lipidomes, as well as in the discovery phase of

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lipidomics research to sieve out potentially perturbed lipid classes; this technique

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is far from ideal as a standalone in clinical research settings to characterize disease biomarkers and establish biological thresholds for defining disease

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conditions. Indeed, the rather loose application of non-targeted MS with a routine

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(and often non-ideal) gradient for quantification of a multitude of metabolites vastly differing in physiochemical properties based on a single internal standard,

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and the subsequent setting of disease thresholds based on such purportedly quantitative data may seriously undermine the credibility of omics for clinical

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research [35]. Therefore, compared to non-targeted analysis, QqQ-MS in the MRM mode not only facilitates the detection of lipids with very low abundance by

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virtue of its higher sensitivity, but also confers higher quality of MS data both in terms of specificity and quantitative accuracy [36]. Imaging MS (IMS) represents a unique technique in MS-based data acquisition due to its exclusive capacity in conferring information pertaining to the spatial distribution of analytes. In IMS, spatial information can be retrieved over two or three spatial dimensions; and with the advent in ionization sources, the lateral spatial resolution offered by IMS has been greatly extended to cover ranges spanning from nano- to kilo-meters [37]. Notably, IMS holds the potential to revolutionize surgical procedures by serving as molecular microscopes to map aberrant tissue regions based on the presence of onco-metabolites, which serves to guide surgical procedures and intraoperative decisions that traditionally

ACCEPTED MANUSCRIPT depended on frozen section pathology [38]. Nonetheless, quantitative analysis in IMS is generally inferior compared to traditional extraction and analysis via

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conventional LC/MS approaches, albeit spatial distribution of metabolites will be

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destroyed in the latter [37].

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4. Identification of candidate lipids

The unambiguous identification of novel/unknown lipids has remained an

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outstanding challenge in lipidomics. In lipidomics, tandem MS (MS/MS) with ESI

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in both positive and negative ion modes can confer information pertaining to lipid head groups; while MS/MS in negative ion mode can yield information on fatty

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acyl chain length and degree of unsaturation. A primary challenge in MS-based

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lipid identification essentially lies in the unambiguous characterization of fatty

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acyl compositions (i.e. the precise positions of the double bonds). With technical advances, however, the double bonds can be located via high energy collisioninduced dissociation, specific multistage fragmentation approaches, ozoneinduced

dissociation,

and/or

using

prior

GC-based

separation

[39,40].

Nonetheless, much still remains to be resolved for unambiguous characterization of lipid isomers and enantiomers, such as determination of the precise snpositions of fatty acyls, which may have considerable implications on the biological functions of distinct lipid species given the stereospecific preferences of the wide array of endogenous phospholipases [41]. HRMS can offer users with unrivaled resolution and the critical advantage of accurately defining the exact elemental compositions of individual ion peaks over MS with unit resolution. For example, HRMS can potentially resolve between the

ACCEPTED MANUSCRIPT monoisotopic peak of a species with the second isotopologue of an adjacent species with just one additional double bond based on accurate masses per se,

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which is infeasible on MS with unit resolution. In addition, the proper use of

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authentic standards with LC separation is critical in preventing potentially erroneous identification of molecular fragments as parent ions. Furthermore,

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developing a robust tandem MS approach based upon HRMS (i.e. HRMS/MS)

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may represent an ideal, efficient and reliable solution for novel and complex lipid characterization, in particular for new biological systems that have remained

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underexplored. Currently, a number of existent databases allow the identification of unknown lipids via comparing their HRMS/MS spectra with that of

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characterized species recorded in these databases, such as Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG)

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[42,43].

5. Analytical design for large sample cohorts A predominant application of lipidomics lies in the analysis of large sample cohorts in biomedical and clinical research [44]. Two major drawbacks, however, are ingrained in traditional workflow commonly utilized in clinical sample collection and analysis. First, clinical designs often entail collection of biological samples over extended duration that could last for several months or years, depending on the nature of the study, and subsequently analyzing the samples in a single batch on MS to achieve maximal reduction of interferences arising from fluctuations in MS detection (i.e. inter-batch effects). The prolonged storage of samples would inevitably lead to artefactual alterations in lipid profiles that could

ACCEPTED MANUSCRIPT consequently impact upon quantitative accuracy, particularly for lipids susceptible to oxidative damages. Second, extended and continuous MS run often

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culminates in appreciable deposition of inorganic and/or organic residues on the

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source probe, which will substantially diminish ion response and compromise signal detection as the run continues, leading to intra-batch variation in signal

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intensities. Indeed, it may be advisable to immediately process and analyse

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freshly collected samples on the MS in smaller batches using a common internal standard cocktail across all samples to be analysed within the same study. The

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spiked internal standards can serve as a correction for inter-batch effects in ion response to guarantee comparability of data across different batches, while

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running samples in smaller batches allows for frequent cleaning of the ion source to ensure signal stability. Alternatively, lipid extraction can be executed in smaller

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batches as soon as the clinical samples are collected, since purified lipid extracts usually exhibit greater biochemical stability than the biological samples per se. Furthermore, developing novel LC conditions that result in smaller deposition of chemical residues on the source probe while ensuring ideal separation among various lipid classes for extended run duration may be obligatory. 6. Choice of internal standards The strict requirement for internal standards to achieve accurate quantitation on the MS is a recognized fact, which is principally attributed to the lack of a definite association between ion counts (i.e. intensity) of any molecular ion with its absolute concentration. Nonetheless, specific requirements pertaining to the

ACCEPTED MANUSCRIPT nature and number of internal standards need to be satisfied in order to attain accurate quantitation.

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Typically, lipids containing odd-chain fatty acids or stable isotope-labelled

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analogs are spiked into the samples as internal standards for quantitation. The

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use of comparable lipids comprising odd-chain fatty acids is based on preliminary observations that such fatty acyls are usually found in low endogenous

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abundance (typically <1 %) in higher organisms (e.g. mammals); but caution

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needs to be exercised for distinct tissues such as the brain, which may contain notably higher proportion of such odd-chain fatty acids [9]. Furthermore,

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standards based on odd-chain fatty acids will not be applicable for organism

comprise

appreciable

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models such as plants, microbes and Caenorhabditis elegans, which inherently amounts

of

odd-chain

fatty

acids

(unpublished

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observations). Thus, there is no simple rule to justify the application of such internal standards, and the conditions for distinct organismal models and tissue/cell types must be pre-determined to exclude the presence of odd-chain acids before the corresponding standards can be applied. This highlights the critical importance of constructing an all-encompassing lipidome inventory for each model organism commonly utilized in lipid research [1], since these valuable information can come in handy for researchers in their choice of internal standards for different studies. On another note, stable isotope-labeled analogs denote ideal internal standards for accurate quantitation, as their identical chemical properties would translate to equal ion response factors to the corresponding endogenous species. Nevertheless, it is advisable to first verify

ACCEPTED MANUSCRIPT the purity of these isotopically-labeled standards prior to use, as impurities in stable isotope-labelling may potentially lead to confounding peaks with adjacent

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endogenous species, especially in the absence of HRMS, resulting in

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quantitative errors.

As for the number of standards, in order to correct the differences in ion

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response factors attributed to varying acyl chain lengths for individual species

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within each lipid class, two or more internal standards corresponding to each class of lipids being analysed is usually recommended.

This is particularly

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important for neutral lipids such as the triacylglycerols (TAGs) and wax esters

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(WEs) [45], since the absence of polar head groups would translate greater

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contribution of fatty acyl chain lengths and unsaturation to the overall polarity of individual species, and henceforth the resulting ion response on MS [31]. Furthermore, neutral lipids often span a greater range than polar lipids in terms of fatty acyls chain lengths, given their predominant roles as energy stores and/or extracellular structural components [1]. As such, three or more internal standards spanning an effectively wide range in acyl chain lengths may at times be required for accurate quantitation of each class of neutral lipids. Besides the overall polarity of the lipid molecules of interest, another critical issue to consider in accurate quantitation using MS/MS is the fragmentation pattern of the compound of interest relative to the internal standard for which normalization is based upon. The use of MS/MS greatly extends the linear dynamic range of an analytical method by virtue of its reduced baseline drift and

ACCEPTED MANUSCRIPT lowered background noise, primarily attributed to double filtering offered by tandem MS. It must be noted, however, that fragmentation of individual species

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within the same lipid class during collision induced dissociation can be varied,

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principally due to the distinct kinetics of dissociation and the differing thermodynamic stability of resultant product fragments. As a result, care needs to

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be exercised in the selection of internal standards during quantitation in order to

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curtail variations introduced by the tandem MS factor [31]. This is especially relevant for lipid classes for which MRMs for individual species are not

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constructed upon the loss of a common fragment, but distinct fragments instead

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(e.g. individual fatty acyls attached to each species).

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For example, under the ESI mode, PC species consistently generates a fragment at m/z 184 corresponding to choline moiety in the positive ion mode,

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while individual phosphatidylinositol (PI) species produces the characteristic inositol fragment at m/z 241; and phosphatidylserine (PS) undergoes neutral loss of a common fragment at m/z 87 in the negative ion mode. Construction of MRM libraries based

on these

common fragments

(and hence

comparable

fragmentation patterns) poses minimal issues to the quantitation accuracy of species within a lipid class as long as the internal standard(s) used for normalization belong to the same lipid class as the compound of interest. In such scenarios, the major concern for the choice of internal standards will be insuring that the internal standard co-elutes with the endogenous compounds to ensure comparable matrix and ionization efficiency (see Section 3). In contrast, lipid classes including free fatty acids (FFAs) and WEs lack such characteristic

ACCEPTED MANUSCRIPT fragments common to all species within the entire lipid class, and their MRMs are usually based on the loss of distinct fatty acyls. In such situations, caution needs

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to be taken in the choice of appropriate internal standards for normalizing

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individual species within the same lipid class. In our earlier analysis of WEs in the human tear fluid [45], for instance, it was found that while all WE species form

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precursor ammonium adducts [M+NH4]+, the subsequent fragmentation patterns

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differ appreciably between WE species comprising saturated fatty acids versus that containing unsaturated (i.e. monounsaturated and polyunsaturated) fatty

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acids. While WEs comprising unsaturated fatty acyls produce [RCO]+ and [R=C]+ as the major fragments, species containing saturated fatty acids yield

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protonated fatty acids [RCOOH2]+ as the predominant fragment (Figure 3). In such cases, the use of isotopically-labelled standards containing saturated or

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unsaturated fatty acids for the respective quantitation of their corresponding endogenous compounds may be necessary to ensure accurate quantitation. 7. Method establishment and validation Prior to the application of an analytical method for quantitative evaluation, rigorous experimental validation needs to be conducted to validate its quantitative accuracy. A set of reference standard compounds representative of the endogenous lipid species present needs to be utilized for establishment of calibration curves and determination of linear dynamic range. In this section, we will use WEs in the human tear fluid as an example to discuss the general workflow in the establishment of a quantitative lipidomic method based upon LCMRM [45]. First, a preliminary analysis usually in the form of survey MS scan (e.g.

ACCEPTED MANUSCRIPT enhanced MS scanning, Q1 scanning, neutral loss scanning or precursor ion scanning) should be conducted to derive the endogenous species present in the

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biological samples of interest. The precise strategy to be adopted will depend on

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the specific properties and fragmentation patterns of the lipid class of interest. In the case of WEs in human tear fluid, HRMS survey scanning was conducted on

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the LTQ Orbitrap XL hybrid Fourier Transform MS coupled with an Accela HPLC

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system to retrieve molecular ions denoting WEs in the human tear fluid (Figure 4). LC-MS/MS was then executed to derive the fragmentation patterns of individual

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WE molecular ions, as well as the elution patterns of individual WE species on the specified gradient. Appropriate internal standards for quantitation were

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selected or synthesized, which should (1) be found in negligible concentration endogenously, (2) possess comparable fragmentation patterns relative to the

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endogenous species to be normalized against, and (3) elute within a time window close to the endogenous species (typically within 2-3 min) to be normalized against if a binary gradient is utilized. In the case of human tear fluid WE analysis, palmitoyl 13

palmitate

(C16:0/C16:0)

and

13

C718:1(oleic

acid-1,2,3,7,8,9,10-

C7)/C26:0 were utilized as the internal standards for WEs containing saturated

and unsaturated fatty acyls, respectively. Next, a series of reference WE compounds including C18:0/C18:0, C18:1/C22:0, C18:1/C22:0, C22:0/C18:0 representative of endogenous tear WEs were used for quantitative method validation. Linear dynamic range was obtained using serial dilutions of endogenous WE mix (i.e. 1 ng/mL, 10 ng/mL, 50 ng/mL, 100 ng/mL, 1 µg/mL, 10 µg/mL, 20 µg/mL, 50 µg/mL and 100 µg/mL) spiked with fixed concentrations of

ACCEPTED MANUSCRIPT internal standards. Linear regression analysis of normalized intensities of endogenous WEs to their corresponding internal standards displayed squared

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correlation coefficient (R2) >0.999 for concentrations ranging from 1 ng/mL to 100

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µg/mL, spanning at least three orders of magnitude. This represents the valid linear dynamic range for accurate quantitation based on the established method,

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and it is crucial to keep the approximated concentrations of all future samples to

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be quantitated within the linear dynamic range to ensure quantitative accuracy. The limit of detection (LOD; defined as three times signal-to-noise ratios) and the

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limit of quantification (LOQ; usually defined as ten times signal-to-noise ratios), which is typically the lower limit of the linear dynamic range, can also be derived

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from these validation experiments [31]. LOD and LOQ serve to provide an approximated gauge for the minimal endogenous concentrations that can be

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detected and accurately quantitated based on the established method, respectively. The LOD, LOQ and linear dynamic range typically function as analytical figures of merits for assessment of analytical capacities and sensitivities

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classes/species. Apart from analytical sensitivity and the linear dynamic range, method reproducibility is also greatly emphasized in analytical chemistry. Method reproducibility is typically measured in terms of coefficient of variation (COV), which can be calculated by repeatedly injecting an identical sample across batches analysed on the same day (intra-day variations) or different days (interday variations). An assessment of COV is of utmost importance for quantitative methods to be applied in analytical settings on a large scale, e.g. clinical cohort

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8. Post-acquisition data processing

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To date, intensive research efforts have been dedicated to derive

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bioinformatics algorithms that can effectively correct the astronomical MS datasets acquired from extended run of massive clinical cohorts principally via

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removal of systematic biases [19,46,47,48,49,50]. Such non-biological errors

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may arise from retention time drift, ion suppression, and fluctuations in MS sensitivity during prolonged duration of analysis. Often, the addition of identical

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internal standards to samples across different batches can serve as an effective

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means of data correction for differences in ion response. On the other hand,

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retention time drift is usually minor (<0.1 min) provided that the chromatographic condition and pre-analysis sample treatment/extraction protocols are ideallydesigned, and can be easily corrected via automated integration of individual ion peaks during quantitative calculations. External calibration methods, such as that based upon the injection of identical quality control (QC) samples to correct big datasets acquired over several months or even years, were also found to confer remarkable improvement in terms of data reproducibility [47,51,52,53]. Nonetheless, the effectiveness of such data correction hinges significantly on the QC injection frequency. Furthermore, correction based on QC imposes stringent requirements on the chemical stability and reproducibility of the QC samples per se, which may be difficult to achieve at times, especially if chemically labile lipids represent study interests.

ACCEPTED MANUSCRIPT The above discussion has thus far focused on the use of MS/MS with unit resolution for accurate quantitation. In situations under which accurate

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quantitation is required for HRMS survey analyses, the determination of

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compound-specific response factors based on specific internal standards may become infeasible, given the astronomical number of both known and unknown

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analytes obtained in each survey scan. In such cases, accurate quantitation may

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rely upon extensive post-acquisition data processing for correction of measured intensities. For example, it was demonstrated that the response factor

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distributions in complex biological mixtures basically adopt a log-normal distribution, with deviations only observed in the low-response end of the

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distribution. Assuming that the distribution of response factors will be consistent for a given analytical technique and sample type, it was proposed that the

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original concentration distribution of endogenous analytes can be accurately determined from a fitted response factor distribution without the need of authentic standards catering for every class of analytes present in the biological mixture [54].

9. Perspectives Accurate quantitation is the sine qua non of analytical chemistry, and is indispensable for the sustainable development of lipidomics via enabling its wide applications in the biological as well as biomedical settings. As critically discussed in this review, specific conditions needs to be fulfilled along the entire lipidomics workflow in order to qualify the data as accurately quantitative (Figure 1). Over the years, lipid analysis has evolved tremendously, from the application

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characterized biological lipidomes, often culminating to hundreds or even

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thousands of individual species in number (Figure 5). The emergence and advancement in MS technologies is the major catalyst for these changes, and

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with the continual improvements in methodology, coupled with the accompanying

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augmentations in accuracy, specificity, sensitivity as well as the number of detectable lipid species, lipidomics is transiting steadily from being solely

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qualitative towards accurate quantitation. With the outpour in lipidomics studies over the recent years, perhaps it is timely for researchers to lay out proper

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definitions of terms and analytical considerations for lipidomics data to be deemed sufficiently quantitative in order to render their applications in the larger

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clinical settings (e.g. in the definition of disease biomarker thresholds). In essence, quantitative accuracy may substantially impact upon the persevering development of lipidomics in the long run. Acknowledgements

This work was financially supported by grants from the National Natural Science Foundation of China (31371515, 3150040263) and “100 Talents Program of Chinese Academy of Sciences”. This work was also financially supported by the Special Financial Grant awarded to S.M. Lam from the China Postdoctoral Science Foundation (Grant No: 2014T70137).

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ACCEPTED MANUSCRIPT Figure Legends

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Figure 1. Factors that may impact upon quantitative accuracy along a typical lipidomics workflow. Various factors along the routine workflow can potentially introduce sources of errors that may compromise the ultimate quantitative accuracy.

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Figure 2. Illustrations of the quantitative edge rendered by liquid chromatography-mass spectrometry. (A) Total ion chromatogram from a brain lipid extract, individual lipid classes were spaced out while species within the same class eluted within a relatively narrow time window. (B) Chromatographic separation of individual isomeric species of the HETEs family. (C) Chromatographic separation of GluCer d18:1/8:0 and GalCer d18:1/8:0 structural isomers.

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Figure 3. MS/MS spectra for wax esters. Wax esters comprising unsaturated fatty acyls produced [RCO]+ and [R=C]+ as the major fragments (A); while wax esters containing saturated fatty acyls produced [RCOOH2]+ as the predominant fragment (B). Figure is modified from Lam et al [45].

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Figure 4. General workflow in the establishment of a quantitative lipidomic method based upon LC-MRM. Figure 5. The transformation of lipidomics towards quantitative accuracy. Advancements in mass spectrometry has fueled the rapid methodological developments in lipidomics towards the ultimate goal of quantitative accuracy, a key determinant towards the continual growth and expansion of lipidomics as an emerging branch of omics science.

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Technical precautions during lipid extraction that may affect quantitation is discussed. Criteria for selecting internal standards for accurate quantitation is listed. General workflow in establishing LC-MRM quantitative lipidomic method is presented. Quantitative accuracy impacts on the persevering development of lipidomics.

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