Principles of Shotgun Lipidomics

Principles of Shotgun Lipidomics Xianlin Han Diabetes and Obesity Research Center Sanford-Burnham Medical Research Institute Lake Nona Orlando, FL 32827

What is shotgun lipidomics? Original definition (in analogous to shotgun proteomics): Han & Gross, Mass Spectrom. Rev. 24 (2005), 367

What is shotgun lipidomics? Original definition (in analogous to shotgun proteomics): Han & Gross, Mass Spectrom. Rev. 24 (2005), 367 ASMS 2004

What is shotgun lipidomics? Original definition (in analogous to shotgun proteomics): Han & Gross, Mass Spectrom. Rev. 24 (2005), 367 Currently it is generally referred to any approach/platform after direct infusion (but not include loop injection) Unique features: (1) High throughput; (2) MS analysis under a constant concentration of lipid solution without any time constraints; (3) Molecular species of a lipid class are commonly displayed in an identical mass spectrum

Classification of shotgun lipidomics Lipid class-specific tandem MS-based shotgun lipidomics (Brugger et al., PNAS 94 (1997), 2339; Welti et al., JBC 277 (2002), 31994; etc) High mass resolution-based shotgun lipidomics (the platform developed by Dr. Shevchenko s group) Multi-dimensional MS-based shotgun lipidomics (the platform developed by our group; Mass Spectrom. Rev. 31 (2012), 134) Total ion mapping by using high mass accurate/resolution mass spectrometers (AB Sciex platform) Ion-mobility based shotgun lipidomics (Waters Platform)

Cellular lipids are very complex J. Lipid Res. 46 (2005) 839 We divide lipids into eight categories (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides) containing distinct classes and subclasses of molecules.

Glycerophospholipids: ~ 30 Classes of Glycerophospholipids: ~ 100 ~ 10 (14 to 22 C, 0 to 6 DBs, 3 linkages)

Principle of shotgun lipidomics (In general) To maximally exploit the differences in the physical and chemical properties between the classes of lipids and the uniqueness of a cellular lipid class in combination with the relationship between the tandem MS techniques after direct infusion Han, Yang, & Gross, Mass Spectrom. Rev. 31 (2012), 134

Principles of shotgun lipidomics (In specific) 1. Different charge properties of lipid classes can be used to selectively ionize different lipid categories 2. Interchangeable relationship among product-ion analysis, neutral-loss scanning (NLS), and precursor-ion scanning (PIS) can be exploited to effectively identify lipid species 3. Unique structural construction of the majority of lipids (i.e., building blocks) can be employed to efficiently identify lipid species in combination of NLS and PIS 4. Unique physical property of lipids (i.e., hydrophobicity) should be kept in mind for quantification of lipids 5. Quantitation of lipid species can be achieved through direct ratio comparison (ratiometrics) of their ion peaks to a selected internal standard in the full MS scan after de-isotoping Han, Yang, & Gross, Mass Spectrom. Rev. 31 (2012), 134

Principles of shotgun lipidomics (In specific) 1. Different charge properties of lipid classes can be used to selectively ionize different lipid categories Anionic lipids (AL) which intrinsically carry net negative charge(s) under weakly acidic conditions (e.g., ph < 5): PG, PS, PI, PA, cardiolipin, sulfatide, acyl-coa, PG Weak anionic lipids which do not carry net a negative charge under acidic conditions, but become anionic under physiological conditions (i.e., ph ~ 7): PE, free fatty acid, ceramide PE Charge neutral, but polar lipids: PC, SM, cerebroside, acylcarnitine, MAG, DAG, TAG, PC

ESI/MS analyses of a lipid mixture in the negative- and positive-ion modes 15:0-15:0 PG 22:6-22:6 PG Anionic lipids (1) 15:0-15:0 PE 20:4-20:4 PE Weak anionic lipids (15) 14:1-14:1 PC 18:1-18:1 PC Charge neutral but polar lipids (10) JASMS 17 (2006), 264

ESI/MS analyses of a lipid mixture in the negative- and positive-ion modes Negative-ion mode Positive-ion mode Before addition of LiOH After addition of LiOH PG:PE:PC, 1:15:10 JASMS 17 (2006), 264

Imaginary analysis of lipids by electrophoresis pi = 7 - PC TAG PE FA AP + - PC TAG + FA/PE/AP + AP/PE/FA TAG PC -

Imaginary analysis of lipids by electrophoresis Intrasource separation of cellular lipid classes pi = 7 - PC TAG PE FA AP + A lipid extract of a biological sample Negativeion mode Anionic lipid molecular species - PC TAG + FA/PE/AP Addition of LiOH Weak anionic lipid molecular species A mildly basic lipid extract + AP/PE/FA TAG PC - Charge neutral but polar lipid molecular species

Intrasource separation of cellular lipid classes Mass spectra of a cellular lipidome A lipid extract of a biological sample Negativeion mode Anionic lipid molecular species Addition of LiOH A mildly basic lipid extract Weak anionic lipid molecular species Charge neutral but polar lipid molecular species

Product-ion analysis, neutral loss scanning, and precursor-ion scanning are interchangeable Production analysis Suppose: 1. M 1 -m 1a =M 2 -m 2a =M 3 - m 3a =constant (i.e., m a ) 2. M 1c =m 2c =m 3c =constan t (i.e., m c ) 3. m 2b =m 3b

Product-ion analysis, neutral loss scanning, and precursor-ion scanning are interchangeable Han, Yang, & Gross, Mass Spectrom. Rev. 31 (2012), 134

Building blocks of glycerol-based lipids General structure of glycerol-based lipids

Building blocks of sphingolipids General structure of sphingolipids

Identification of lipid building blocks by PIS and NLS to identify molecular ion peaks

Analysis of lyso-choline glycerophospholipids

Analysis of lyso-choline glycerophospholipids Structure of lysopc 1-acyl lysophosphatidylcholine (sn-2 lysopc) 2-acyl lysophosphatidylcholine (sn-1 lysopc)

Analysis of lyso-choline glycerophospholipids JACS 118 (1996), 451

Analysis of lyso-choline glycerophospholipids JACS 118 (1996), 451 J. Chromatogr. B 877 (2009), 2924

Analysis of lyso-choline glycerophospholipids LysoPC molecular species in the lipid extracts of rat myocardium* 13 ion peaks and 19 species *pmol/mg of protein J. Chromatogr. B 877 (2009), 2924

Lipids go aggregation!!! Lipids can go aggregation in a manner depending on concentration, solvent, lipid classes, etc. due to the nature of hydrophobicity. The aggregates cannot be well ionized and most of the lipids in the aggregated states go to waste Therefore, ionization efficiency becomes molecular species dependent (i.e., the numbers of carbon atoms and double bonds present in acyl chains) in the aggregated states The lipid concentration at which aggregation occurs in CHCl 3 /MeOH: ~ 200 pmol/ml in 2:1, 50 pmol/ml in 1:1, and 10 pmol/ml in 1:2

An easy method to test whether we are analyzing lipids in an aggregate state y = ax + b

An easy method to test whether we are analyzing lipids in an aggregate state log(y - b) = log(a) + log(x) r 2 =??? y = ax + b J. Lipid Res. 50 (2009), 162

Quantification by mass spectrometry 1. There is no absolute quantification in mass spectrometry as in optical spectroscopy where the Beer-Lambert law (A = ξl[c]) can be followed 2. Only relative quantification in comparison to an internal standard is possible under certain conditions I = ax + b or I-b = ax where I is ion intensity, b is spectral background, a is the response factor, and x is the molar concentration I s /I u = x s /x u Where I >> b and a s a u Ideally isotope-labeled internal std, a s = a u So what s in reality? Yang & Han, Metabolites 1 (2011), 21

The response factor a The a should include all possible factors, affecting the quantification, from ionization to detection, a = a1.a2.a3.a4. a1, ionization efficiency; a2, concentration factor (in ideal solution or not); a3, tandem MS factor; a4, matrix factor if ionization occurs under different conditions;

Quantification in shotgun lipidomics The linear dynamic range of concentration (intensity vs. concentration) (The upper limit of a linear dynamic range for lipid analysis is the aggregated state of a lipid solution) One internal standard of a lipid class for quantitation of the abundant molecular species in the class (I s /I u = x s /x u where I >> b and a s a u ) Dynamic range of relative intensities in comparison to a selected internal standard (low abundance species??)

ESI/MS analyses of a lipid mixture in the negative- and positive-ion modes Negative-ion mode Positive-ion mode Before addition of LiOH After addition of LiOH PG:PE:PC, 1:15:10 JASMS 17 (2006), 264

Quantification in shotgun lipidomics Isotopologues Isotope Ratio Peak Intensity (area) M 1 I M M+1 0.0109n 0.0109nI M M+2 0.0109 2 n(n-1)/2 0.00594n(n-1)I M M+3 0.0109 3 n(n-1)(n-2)/6 2.16x10-7 n(n-1)(n-2)i M M M+1 De-isotoping of 13 C isotopologues based on monoisotopic ion peak intensity (I M ): I total = I M (1 + 0.0109n + 0.00594n(n-1) + ) M+2

Quantification in shotgun lipidomics 0.8 pmol/μl 0.16 pmol/μl 4 pmol/μl 16 pmol/μl Han, Yang, & Gross, Mass Spectrom. Rev. 31 (2012), 134

Quantification in shotgun lipidomics 0.1 pmol/µl 0.4 pmol/µl Koivusalo et al., J. Lipid Res. 42 (2001), 663 An equimolar mixture of 14 PC species including 6 diunsaturated ones, ranging from C34 to C56 of total carbon atoms.

Quantification in shotgun lipidomics Full MS scan for quantitation of abundant and non-overlap species The internal standard of a lipid class is spiked during lipid extraction Analysis should be conducted at a low concentration Correction for differential 13 C isotopologue distributions Quantitation of abundant species (S/N > 5) after correction for chemical background and baseline drift. JASMS 22 (2011), 2090

Quantification in shotgun lipidomics Quantification of low abundance species (a two step procedure) Facts: The dynamic range is very limited in quantitation with full MS scan; the overlapped and/or low abundance species can not be determined. Solution: Use the NLS and/or PIS for building blocks, which extends the dynamic range substantially, detects the low abundance species, and resolves the overlap ones. Concerns: Any NLS or PIS analysis of a lipid class depends on the fragmentation pathways of individual species, thereby depending on the physical properties of individual species. Solution: 1. Use endogenously determined species in the full MS (i.e., first step) as standards to quantify those structurally similar species (i.e., second step of quantification) 2. Use multiple PIS/NLS to refine the accuracy in quantitation.

Quantification in shotgun lipidomics Full MS spectrum NLS of 183 u

Classification of Shotgun Lipidomics Lipid class-specific tandem MS-based shotgun lipidomics (Brugger et al., PNAS 94 (1997), 2339; Welti et al., JBC 277 (2002), 31994; etc) High mass resolution-based shotgun lipidomics (the platform developed by Dr. Shevchenko s group) Multi-dimensional MS-based shotgun lipidomics (the platform developed by our group; Mass Spectrom. Rev. 31 (2012), 134) Total ion mapping by using high mass accurate/resolution mass spectrometers (AB Sciex platform) Ion-mobility based shotgun lipidomics (Waters Platform)

Drawbacks of shotgun lipidomics 1. Isomers possessing identical fragmentation patterns (e.g., chiral isomers, GluCer and GalCer in the positive-ion mode) can t be separately analyzed at the current stage; 2. This technology needs pre-characterization of a lipid class of interest by product-ion analysis for the identification of a novel lipid class. 3. Ion suppression???

Principles of shotgun lipidomics (In specific) 1. Different charge properties of lipid classes can be used to selectively ionize different lipid categories 2. Interchangeable relationship among product-ion analysis, neutral-loss scanning (NLS), and precursor-ion scanning (PIS) can be exploited to effectively identify lipid species 3. Unique structural construction of the majority of lipids (i.e., building blocks) can be employed to efficiently identify lipid species in combination of NLS and PIS 4. Unique physical property of lipids (i.e., hydrophobicity) should be kept in mind for quantification of lipids 5. Quantitation of lipid species can be achieved through direct ratio comparison (ratiometrics) of their ion peaks to a selected internal standard in the full MS scan after de-isotoping (Key: no aggregation) Han, Yang, & Gross, Mass Spectrom. Rev. 31 (2012), 134