WO2005085838A2 - Computational analysis of mass spectroscopic lipid data - Google Patents

Abstract

The present invention provides methods for developing a lipid profile from cells using a computational statistical analysis of a lipid data array derived from mass spectroscopy. The methods have particular utility in diagnosing disease, screening drugs, and identifying new lipids and lipid-linked pathways.

Description

DESCRIPTION COMPUTATIONAL ANALYSIS OF MASS SPECTROSCOPIC LIPIDOMIC ARRAYS

BACKGROUND OF THE INVENTION The government owns rights in the present invention pursuant to grant number GM58516 of the National Institutes of Health Grants, grant number U54-GM62114 of The Alliance for Cellular Signaling, and LIPID 1VIAPS grant U54-GM69338. The present application is related to a U.S. provisional application entitled "Lipid Analysis", by Brown and Forrester, filed on March 2, 2004, the entire content of which is hereby incorporated by reference. The present application also claims benefit of priority to U.S. Provisional Application Serial No. 60/549,583, filed March 2, 2004, the entire contents of which are hereby incorporated by reference.

1. Field of the Invention The present invention relates generally to the fields of cell biology, biochemistry and mass spectroscopy. More particularly, it concerns methods of analyzing lipid arrays in cells by mass spectroscopy and computational statistics.

2. Description of Related Art From the early 1960's through the 1980's, the pioneering research of Eugene Kennedy defined the major pathways of phospholipid synthesis. Long viewed as simply the building blocks of the semi-permeable membrane whose primary functions were compartmentalization and ion content regulation, one of the first hints of the central importance of lipids in cellular signaling came when Ho in and Hokin (1953) showed that treating avian pancreatic slices with cholinergic drugs resulted in both the secretion of amylases and the concomitant turnover of membrane phospholipids. The significance of this observation initially went unrecognized because of a lack of appreciation of the diverse cellular roles of phospholipids and the inherent difficulties in their measurement. Elucidation of the inositol-mediated calcium gating opened up a new era in the study of lipid biomolecules (reviewed in Berridge. 1993). It is now understood that lipids play important roles as second messengers in signal transduction processes as well as participating in membrane topology by creating specialized domains of specific lipid and protein complexes in both normal and diseased cells (Ramanadham et ah, 1998; Bevers et al, 1999; Shen et al, 2001; Ivanova et al, 2001; Han and Gross, 2003; Alb et al, 2003). Still, an appreciation of the involvement of phospholipids in cellular processes has lagged well behind that of nucleic acids and proteins in many fields. The burgeoning interest in the role of phospholipids in cellular functions has spurred research in methodology for their identification and measurement. Until recently, lipid analysis has not benefited from paradigm shifts produced from new technologies with the same kind of impact as monoclonal antibodies for protein isolation and the various cDNA microarrays technologies available in molecular genetics. The ability of the researcher to delineate the role played by phospholipids in biological processes would be greatly enhanced if the technology to monitor dynamic changes in the cellular lipid composition during these events was available with only a reasonable expenditure of effort and cost. The increasing sophistication of biological modeling schemata is intimately linked to the ability to acquire large, relevant sets of measurements from cellular phenomena. Moreover, the greatest promise of such schemata lies in their ability to form integrative models from readouts that previously seemed like disparate collections of data, facilitating quantum leaps in the understanding of biological systems possible. These considerations make the ability to comprehensively measure changes in phospholipids a critical addition to the armamentarium of cellular biologists, complementing existing technologies such as gene arrays and proteomics. Mass spectrometry has emerged as a forerunner in developmental technologies with the ability to analyze comprehensive changes in membrane lipid composition. Advances in ionization techniques have made routine the preparation and analysis of biological extracts with this highly versatile class of instruments. Lipid chemists have thus utilized mass spectrometry as a potent structural proof technique for many years. However, in most applications, these methods require intricate multi-step preparations and relatively large amounts of sample. Lipid analysis has also been hampered by the complexities involved in resolving lipid extracts at the individual species level, obscuring the amount of intra-class specificity involved with their in situ biological reactions. Moreover, many bioactive lipids occur in extremely low cellular concentrations and are difficult to resolve and measure. Thus, despite advances in mass spectrometry that have created a powerful tool for measuring lipids, there remains a need for refined methodologies that permit assessing of global changes in the cellular lipome with high sensitivity in even the most complex biological systems.

SUMMARY OF THE INVENTION Thus, in accordance with the present invention, there is provided a method of obtaining a lipid profile from a biological sample comprising (a) providing a biological sample comprising lipids; (b) subjecting essentially all of the lipids in the sample to simultaneous mass spectroscopy; (c) obtaining a lipid data array from step (b); and (d) subjecting the lipid data array to statistical analysis to obtain a lipid profile for the biological sample. The method may further comprise a step (e) of comparing the lipid profile with one or more standard or control lipid profiles. The biological sample may be a fluid sample, such as blood, serum, gastric fluid, cerebrospinal fluid, saliva, urine, or semen, or a cell or tissue sample, such as from dermis, muscle, lung, heart, pancreas, kidney, liver, intestinal muscosa, ovary, testis, brain, cervix, uterus, prostate or bladder. The biological sample may be processed to remove non-cellular material. The method may further comprise a second analysis on the biological sample, such as an analysis of gene transcripts, analysis of a proteins, or analysis of cell signaling molecules. The biological sample may be diseased or suspected of being diseased, and the lipid profile may be compared to (i) a lipid profile from a comparable healthy biological sample and/or (ii) a lipid profile from a diseased biological sample of a known disease state. The method may thus further comprising diagnosing, prognosing or classifying a disease state in the biological sample based on the lipid profile. The diseased sample may be infected with a virus, a bacterium, parasite or a fungus, or be from a hyperproliferative tissue sample, benign or cancerous. The biological sample may be a tissue culture sample, for example, where the tissue culture sample has been contacted with one or more test substances, and the lipid profile is compared to the lipid profile of a comparable tissue culture sample that has not been contacted with the test substance(s). The test substance may comprise a DNA, an RNA, a polypeptide, a peptide or small molecule. Alternatively, the tissue culture may be subjected to an environmental stimulus, such as a drug, a hormone, a cytokine, a transmitter, a toxin, an enzyme, an ion, temperature change, light, electrical stimuli, pH, oxidation, or mechanical stress. In another embodiment, the biological sample is derived from a living organism, such as a non- human experimental animal or a human. The organism may also have been contacted with one or more test substances, and the lipid profile is compared to the lipid profile of a comparable tissue culture sample that has not been contacted with the test substance(s) or environmental stimuli, as described above. The mass spectroscopy may be electrospray ionization mass spectroscopy. The analysis under step (d) may comprise data smoothing and self-normalization of the data array. The method may further comprise determimng significance of both time and non-time components by statistical process control. The method may also further comprising exception handling. In still yet another embodiment, there is provided a method of identifying the presence of an unknown lipid species in a biological sample comprising (a) providing a biological sample comprising lipids; (b) subjecting essentially all of the lipids in the sample to simultaneous mass spectroscopy; (c) obtaining a lipid data array from step (b); (d) subjecting the lipid data array to statistical analysis to obtain a lipid profile for the biological sample; and (e) comparing the lipid profile to one or more known lipid profiles, wherein the existence of a unmatched member of lipid profile identifies the sample as containing an unknown lipid species. The method may further comprise identifying the unknown lipid species, for example, by collision induced mass spectroscopy and/or nuclear magnetic resonance. It is contemplated that any method or composition described herein can be implemented with respect to any other method or composition described herein. The use of the word "a" or "an" when used in conjunction with the term "comprising" in the claims and/or the specification may mean "one," but it is also consistent with the meaning of "one or more," "at least one," and "one or more than one." These, and other, embodiments of the invention will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following description, while indicating various embodiments of the invention and numerous specific details thereof, is given by way of illustration and not of limitation. Many substitutions, modifications, additions and/or rearrangements may be made within the scope of the invention without departing from the spirit thereof, and the invention includes all such substitutions, modifications, additions and/or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

?FIG. 1 - A Shewhart Control Chart for the group means of five data sets each with four repetitions is shown. The individual measurements are indicated with a (•) while the intra group averages with a (*) and are connected with a solid line.

FIGS. 2A & 2B - Data collected from the mass spectrometer in the 618.0 to 620.5 m/z range before and after smoothing. (FIG. 2A) Unsmoothed data connected with a solid line indicating separate peaks in the 619.0 to 620.0 range with a total separation of 0.28. (FIG. 2B) The result of the Kernel Density Estimate shows a smoothed version of this data with a single representative peak in this range occurring near 619.5.

FIG. 3 - Control chart for data in two experimental conditions. The left panel contains the control chart generated from the data in Figure 2. The right panel extends the limits found in the basal condition and plots the data sets and their means for the stimulated condition. Means meeting the defined criteria as out-of-control are indicated by a circle.

FIG. 4 - An example of one round of analysis is shown in this excerpt from the lipid array of

RBL-2H3 mastocytoma cells challenged with antigen over the indicated time course (1, 3, 6, 9,

12. 15, 20, 30 min). This excerpt is from the positive ion scanning mode for the m/z range of

518.4 to 525.3. These were shown by CID MS/MS to correspond with several species of LPC.

Array cells scored with a 1 (in red) indicate a time point where the stimulated data showed a statistically significant increase over the basal condition.

FIG. 5 - Cumulative count of peaks versus absolute significance score. The number of peaks observed (solid triangle) at a particular absolute significance value is seen to decrease rapidly as the score increases, with 87 being highly significant. This number is computed for each peak as the absolute value of the sum for the five individual experiments. A peak indicated as increasing or decreasing in four of the five tests would be plotted here as a four. Cumulative count (solid square) displays total number of peaks with that significance score or less.

FIGS. 6A-B - Excerpts from the WEHI-231 Lipid Arrays. (FIG. 6A) Negative ion mode. (FIG.

6B) Positive ion mode. The second column of each table indicates the mass-to-charge ratio (m/z), and the first column contains lipid species identified by CID MS/MS. The remaining five columns show the significance score, which is the number of experiments (out of 10) in which the indicated peak (m/z) was shown to be statistically distinct from the basal at that respective time point, with positive numbers representing increasing and negative numbers indicating a decreasing signal. An array cell containing the number -7 is interpreted to mean the indicated species was observed to decrease in 7 out of the 10 trials at that time point. These cells are color coded by signal frequency with deep blue or red indicating 6 or more occurrences of 10 (shown highly significant through simulation) and light blue or red indicating 5 occurrences (shown significant through simulation). Green cells, representing a -4 to 4 significance score, indicate statistical stability between basal and stimulated conditions.

FIGS. 7A-B - Excerpts from Lipid Arrays. (FIG. 7A) Positive mode Lipid Array. (FIG. 7B)

Negative mode Lipid Array.

FIG. 8 - Degranulation in mast cells. Comparison between RBL-2H3 and B6A4C1 treated with antigen and Ca²⁺ Ionophore (A23187). FIG. 9 - Polvphosphomositides. Stimulation of RAW 264.7 cells with zymosan (50 μg/ml for 15 min) led to elevated Pl-monophosphate (PIP) and Pl-diphosphate (PIP2) levels compared to basal.

FIG. 10 - Fragmentation and identification of Hpid species. Individual lipid species from the total cell extract were isolated and fragmented using ESI-MS/MS. Positive mode analysis was utilized in the identification of three phospholipid classes. Negative mode analysis was used to assign five classes and to determine fatty acid compositions.

FIG. 11 - Fragmentation table for phosphatidylserines. Using negative mode ESI-MS/MS, 33 PS and lyso-PS species were identified. The numbers in parentheses following fragment ions (FA:D) refer to the total number of fatty acid carbons (FA) and fatty acid carboncarbon double bonds (D). GP = glycerophosphate.

FIG. 12 - Library of identified glvcerophospholipid species. All of the glycerophospholipids identified by ESI-MS/MS fragmentation in positive and negative modes are summarized. PC and PE compounds with lower case e or p refer to plasmanyl and plasmenyl (alkyl ether and plasmalogen) subspecies, respectively. When plasmanyl and plasmenyl PE or PC species are separated by and or (a o), this indicates that one or both species were detected at that m/z. FIG. 13 - An excerpt from a lipid array in the m/z range of 885.6 to 892.5. Data were collected from WEHI-231 cells challenged with 0.13 ocM anti-IgM. Analysis using CID MS/MS determined that this area contained phosphatidylinositol lipids with 38 carbons in several double- bond configurations. The array shows these species decreasing over the time course after the stimulation, as indicated by the negative score.

FIGS. 14A & 14B - Exceφts from lipid arrays. (FIG. 14A) Positive mode lipid array. (FIG. 14B) Negative mode lipid array. The first column contains lipid species identified by ESI MS/MS. The second column indicates the mass-to-charge ratio (m/z) of the observed compounds. The remaining five columns are for the five time points (1.5, 3, 6, 15, and 240 minutes). The values in these columns represent the significance score, which is the sum of that cell for the 10 individual experiments, with positive numbers representing increasing signal and negative values indicating a decreasing signal. Therefore, an array cell containing the number -8 is interpreted to mean the indicated species was observed to decrease in 8 of the 10 trials and remain stable in the other 2, or that the species decreased in 9 of the experiments and increased in 1 at that time point. These scores are color coded by signal frequency with deep blue or red, indicating an absolute score of 6 or more from a possible 10 (shown highly significant by computer simulation). Lighter shades of red and blue indicate significant stimulations (-5 or 5). Green colored cells, representing a -4 to 4 significance score, indicate statistical stability between basal and stimulated conditions.

FIG. 15 - Summary of glvcerophospholipid changes following AIG stimulation of WEHI-231 cells (positive mode).

FTG. 16 - Summary of glvcerophospholipid changes following AIG stimulation of WEHI-231 cells (negative mode).

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS It is now understood that lipids play an active role in the physiology of the cell. Recent reports advocate the involvement of lipids in signaling pathways modulating cell survival, proliferation, and migration, as well as in pathophysiological disease states including inflammation, angiogenesis, and cancer (Mills and Moolenaar, 2003; Pyne and P?yne, 2000). The number of distinct species of lipids in any given cell type remains unknown, however. Thus, there is an intense interest in probing the lipome of mammalian cells, both to identify completely all relevant species, and to further an understanding of the complex roles played by lipids in cellular processes - both normal and abnormal. Traditionally, phospholipid species and lipid classes have been characterized by a combination of various analytical techniques, including thin-layer chromatography (TLC), gas chromatography (GC) and high performance liquid chromatography (HPLC) (Christie, 2003). In most applications, these methods require intricate multi-step preparations and relatively large amounts of sample. Lipid analysis has also been hampered by the complexities involved in resolving lipid extracts at the individual species level, obscuring the amount of intra-class specificity involved with their in situ biological reactions. Moreover, many bioactive lipids occur in extremely low cellular concentrations and are difficult to resolve and measure. Advances in mass spectrometry have created a vehicle for the measurement of global changes in the cellular lipome through their high sensitivity and ability to comprehensively examine complex biological extracts. The development of electrospray ionization mass spectrometry (ESI-MS) has made possible the detection and identification of thermally labile biological molecules such as phospholipids. The "soft" ionization used in ESI does not cause extensive fragmentation, is highly sensitive, accurate, and reproducible. Thus, this method is well suited for analyzing a broad range of phospholipids without elaborate chromatographic separation. However, evaluating the vast amounts of data resulting from these measurements is a rate limiting step in the assessment of phospholipid composition, requiring the development and application of computational algorithms for mass spectrometry data. The present invention describes a process referred to herein as computational lipidomics, an analytical technique coupling mass spectrometry with statistical processing to facilitate the comprehensive analysis of hundreds of lipid species from cellular extracts. As a result of this approach, the inventors are able to generate lipid data arrays that permit examinatio of qualitative changes occuring in lipid compositions in experimental or disease states. This permits analyses that are similar to proteomic and genomic analyses, and can actually be used in conjunction with such analyses to augment information obtained. The details of the invention are provided in the following sections.

I. Lipids Glycerophospholipids are the basic building blocks of cellular membranes, and their chemical structure and diversity are well suited for this important physiological role. These molecules consist of a glycerol backbone having a polar phosphate-head group attached to the third carbon, and acyl, alkyl, or alkenyl moieties attached at the sn-1 and sn-2 positions. The different head groups determine the glycerophospholipid classes, and the variety of fatty acyl chains contribute to the diversity of species within a class. While all glycerophospholipids contain a glycerol backbone, the diversity of head groups, acyl chains, and degree of unsaturation can produce hundreds of different lipid species existing within a given cell. This enormous number of structural combinations allows for a large variety of physical and chemical membrane properties, including membrane permeability, fluidity, and curvature. Local variations in the concentrations of particular lipid species mediate these properties through the formation of lipid rafts and the regulation of lipid-protein interactions. As residents of the plasma membrane, glycerophospholipids also serve as a substrate pool for the production of second messengers regulating cellular signaling events at the initial site of receptor activation. Recent reports advocate the involvement of lipids in signaling pathways modulating cell survival, proliferation, and migration, as well as in pathophysiological disease states including inflammation, angiogenesis, and cancer (Mills and Moolenaar, 2003; Pyne and P?yne, 2000). For example, lysophosphatidic acid (LPA), produced by the enzymatic activity of a variety of phospholipases, including phospholipases Al, A2 and the lysophospholipase D autotaxin, plays a vital role in a variety of cellular and biological actions that increase motility and invasiveness of cells (Mills and Moolenaar, 2003). In addition, evidence supports the involvement of LPA and the lysophospholipid sphingosine-1 -phosphate (S-l-P) in hematopoeisis or stem cell trafficking, a process crucial in stem cell biology and bone marrow transplantation (Whetton et al, 2003). S-l-P has also been implicated in angiogenesis, a critical process of cancer progression (Wang et al, 1999). Second messengers derived from precursor phospholipids also act as mediators in inflammation and neurodegeneration. Studies report that prostanoid production from arachidonic acid (AA), the starting molecule of the pro-inflammatory eicosanoids, is important in regulating vital aspects of the inflammatory response seen in arthritis and asthma (Heller et al, 1998). h addition, AA regulates neural membrane biology, including protein-lipid interactions and trans- synaptic signaling, including abnormalities in these pathways that have been described as contributing to the pathophysiology of Alzheimer's disease (Bazan et al, 2002). The diverse roles of lipids in cell functions and disease processes have stimulated renewed interest in phospholipids and encouraged development of improved methods to determine comprehensive changes in membrane lipid composition.

II. Biological Samples and Processing Biological sample to be used for analysis in the present invention may derive from virtually any source, including those that are essentially acellular, such as plasma, and cellular samples. Cellular samples may comprise an isolated cell or cultured cells populations, but they also may comprise cells, tissues, or organs derived from living or recently deceased organisms. The only requirement is that the sample contain useful amounts of lipid.

III. Mass Spectroscopy Mass spectrometry (MS), because of its extreme selectivity and sensitivity, has become a powerful tool for the quantification of a broad range of bioanalytes including pharmaceuticals, metabolites, peptides, proteins, nucleic acids and lipids. By exploiting the intrinsic properties of mass and charge, compounds can be resolved and confidently identified. Until the 1980s the primary ionization source for mass analysis was electron impact or chemical ionization. The challenges arising from sample desorption and ion formation associated with these ionization methods limited researchers to small molecules and excluded many of the larger thermally labile molecules found in biological systems. The introduction of the "soft" ionization techniques including fast atom bombardment (FAB), matrix-assisted laser desorption ionization (MALDI), and electrospray ionization (ESI) revolutionized the ionization capabilities for mass analysis. The use of electrospray mass spectrometry on phospholipid extracts has made the routine analysis of these complex biological samples possible (Kerwin et al, 1994; Kim et al, 199 ; Han and Gross, 1994; 1995; 1996; Smith et al, 1995; Brugger et al, 1997; Fridriksson et al, 1999; Khaselev and Murphy, 2000; Ivanova et al, 2001; Hsu and Turk, 2003). These types of MS quantification rely on internal standards that undergo the same processes as the analyte (ESI followed by tandem MS (MS/MS) (Chen et al, 2001; Zhong et al, 2001); matrix assisted laser desorption ionization (MALDI) followed by time of flight (TOF) MS (Bucknall et al, 2002; Mirgorodskaya et al, 2000; Gobom et α/., 2000)).

A. ESI ESI is a convenient ionization technique developed by Fenn and colleagues (Fenn et al, 1989) that is used to produce gaseous ions from highly polar, mostly nonvolatile biomolecules, including lipids. The sample is injected as a liquid at low flow rates (1-10 μL/min) through a capillary tube to which a strong electric field is applied. The field generates additional charges to the liquid at the end of the capillary and produces a fine spray of highly charged droplets that are electrostatically attracted to the mass spectrometer inlet. The evaporation of the solvent from the surface of a droplet as it travels through the desolvation chamber increases its charge density substantially. When this increase exceeds the Rayleigh stability limit, ions are ejected and ready for MS analysis. A typical conventional ESI source consists of a metal capillary of typically 0.1-0.3 mm in diameter, with a tip held approximately 0.5 to 5 cm (but more usually 1 to 3 cm) away from an electrically grounded circular interface having at its center the sampling orifice, such as described by Kabarle et al. (1993). A potential difference of between 1 to 5 kV (but more typically 2 to 3 kN) is applied to the capillary by power supply to generate a high electrostatic field (10⁶ to 10⁷ N/m) at the capillary tip. A sample liquid carrying the analyte to be analyzed by the mass spectrometer, is delivered to tip through an internal passage from a suitable source (such as from a chromatograph or directly from a sample solution via a liquid flow controller). By applying pressure to the sample in the capillary, the liquid leaves the capillary tip as a small highly electrically charged droplets and further undergoes desolvation and breakdown to form single or multicharged gas phase ions in the form of an ion beam. The ions are then collected by the grounded (or negatively charged) interface plate and led through an the orifice into an analyzer of the mass spectrometer. During this operation, the voltage applied to the capillary is held constant. Aspects of construction of ESI sources are described, for example, in U.S. Patents 5,838,002; 5,788,166; 5,757,994; RE 35,413; and 5,986,258.

B. ESI/MS/MS In ESI tandem mass spectroscopy (ESI MS/MS), one is able to simultaneously analyze both precursor ions and product ions, thereby monitoring a single precursor product reaction and producing (through selective reaction monitoring (SRM)) a signal only when the desired precursor ion is present. When the internal standard is a stable isotope-labeled version of the analyte, this is known as quantification by the stable isotope dilution method. This approach has been used to accurately measure pharmaceuticals (Zweigenbaum et al, 2000; Zweigenbaum et al, 1999) and bioactive peptides (Desiderio et al, 1996; Lovelace et al, 1991). Newer methods are performed on widely available MALDI-TOF instruments, which can resolve a wider mass range and have been used to quantify metabolites, peptides, and proteins. Larger molecules such as peptides can be quantified using unlabeled homologous peptides as long as their chemistry is similar to the analyte peptide (Duncan et al, 1993; Bucknall et al, 2002). Protein quantification has been achieved by quantifying tryptic peptides (Mirgorodskaya et al, 2000). Complex mixtures such as crude extracts can be analyzed, but in some instances sample clean up is required (Nelson et al, 1994; Gobom et al, 2000).

C. Lipid MS The generation of lipid arrays requires the identification of individual species represented by peaks in the mass spectrum. This identification process begins with a full scan in both positive and negative mode to determine the m/z values referring to a molecular ion's monoisotopic molecular weight. The detection and resolution of phospholipid classes is based on the ability of representative molecules to acquire positive or negative charges under the electrospray high energy source. The primary type of ionization for each molecule is based on its chemical structure, and this leads to differences in the ability to detect particular classes in the two ionization modes. Zwitterionio phospholipids such as phosphatidylcholine (PC), lysophosphatidylcholine (lysoPC), phosphatidylethanolamine (PE) and sphingomyelin (SM) can be detected in either positive or negative ionization mode. They are more efficiently detected in positive mode with the exception of PE, which is detected equally well in either mode. By contrast the anionic phospholipids phosphatidylserine (PS), phosphatidylinositol (PI), phosphatidic acid (PA), and phosphatidylglycerol (PG) are negatively charged at neutral pH and produce molecular ion peaks detected only in negative mode. The structural analysis of the individual molecular ion peaks is accomplished by tandem mass spectrometry (MS/MS). This involves subjecting an ion of interest to collision induced dissociation (CID) where the molecule is fragmented due to the interaction with a collision gas. The resulting fragments can then be used to generate a product or "daughter" ion spectrum where headgroup and/or fatty acyl chain compositions are disclosed. The results of this fragmentation are dependent on the instrument used and the chemistry of the molecule. Glycerophospholipids detected in positive ionization mode reveal mainly headgroup information upon CID either as a headgroup fragment peak (as in PC, LPC or SM) or as a fragment peak formed by neutral loss of the headgroup (as in PE and lysoPE). Fragmentation in negative mode yields sn-1 and sn-2 fatty acid residues, thus providing structural information on the acyl chain composition. There are also ions characteristic to the headgroup or its fragments, as well as common fragments for all glycerophospholipids (e.g., m/z 19 for PO3-, m/z 91 for H2PO4-, m/z 153 for [glycerophosphate-

Based on the fragmentation patterns observed for each set of cellular extracts, a "fragmentation library" can be created allowing easy consultation and determination of phospholipid species within that cell type (Milne et al, 2003). Lipid species are denoted with the abbreviation for the phospholipid headgroup preceded by xx:y, where xx represents the total carbon number in the fatty acid side chain(s) and y represents the number of double bonds in them. The methodology described above makes possible the detection of over 300 phospholipid species (Milne et al, 2003). The immense amount of data resulting from these techniques is a rate limiting step, and necessitates the use of a computational analysis, as discussed in the following section.

IV. Computational Analysis of Lipid MS Data As discussed above, in order to fully exploit the use of MS in examining the lipome of a cell, it is necessary to identify and measure hundreds of lipids simultaneously from biological extracts. The existing art does not permit such analysis. Therefore, the present invention relies in part on a new analytical approach that combines statistical and computational techniques, which can be applied to high-throughput in vivo screening of compounds to assay their global effect on the cellular lipome. The goal of the mathematical formulation presented here is the comprehensive analysis of qualitative changes in cellular lipid concentrations, as measured by mass spectrometry, between two experimental conditions as a biological system evolves through time. Computer programs were developed using the S-Plus Version 3.3 for Windows programming suite. The programs were constructed around two central concepts, which are detailed below.

A. Data Normalization Utilizing peak intensities for assaying relative amounts of components in an observed mass spectrum proves troublesome, unless the compounds have been carefully prepared and calibrated against an internal standard (Siuzdak, 1996). Sample-to-sample fluctuation observed in these intensities for a particular m/z ratio can be large, even in apparent exact replicates. Moreover, ionization efficiencies for compounds vary, even within species of the same class, and do not correlate directly in a known way a priori to their molar concentration. This generated the need for the development of a normalization methodology involving the creation of a unit-less number that would prove to be a more robust measure of the signal strength at a particular m/z value with respect to the overall pattern observed. The goal of this normalization is to create conditions where the experiments are comparable to one another in a statistical sense, thereby allowing the construction of a qualitative map of lipid changes at the cellular level. The results of this analysis are then expanded, for example, through the inclusion of internal standards for changing compounds to determine the magnitude of the changes. Two choices for this unit-less number were obvious candidates: signal strength in standard statistical units, and signal ranks. In the first method, the mean and standard deviation of the intensities observed at all m/z values in the scanning range are computed. These statistics are then used to transform the signal intensity at each of the m/z values as I* = (I - mean)/SD, with the transformed intensity represented as the number of standard deviations the signal occurs above or below the mean signal strength. Thus, a signal with intensity equal to the mean intensity of the data set would receive a score of zero, and any signal with intensity below the mean would receive a negative score. This noπnalization scheme has the effect of equating the first two statistical moments in each data set, (i.e., the transformed data has a mean of zero and a variance of 1). The second method involves utilizing the rank of the signal in comparison with all the other points in the data set as the transformed intensity measure. Thus, for a scanning range of 1000 m/z with a gradation of 0.10, the observed intensities would be mapped in a one-to-one correspondence with the integers 1 to 10,000. This method has proven to be highly robust against the wide changes in signal magnitude observed. After completion of the normalization, the transformed intensity signals are then carried into the second part of the analysis.

B. Use of Shewhart Control Charts Shewhart Control charts are statistical devices used to detect process changes in complex systems as they evolve through time. Their primary function is to sort out random variation (noise) from special cause variation (signal) as a process evolves along a time axis. The basic procedure involves drawing samples of size n from the process under study at various time points and computing a statistic of interest such as the sample mean. These values are then plotted along the time axis and a set of Control Limits are calculated for the statistic computed. These limits represent the expected variability in the statistic, and are computed from the process output assuming the underlying distribution remains stable. As a result, a kind of running hypothesis test is constructed. FIG. 1 is a control chart for the sample mean constructed from five data sets each containing four measurements taken with respect to time. The means of the sets are connected with a solid line. The chart also shows the grand mean as well as the lower and upper control limits, LCL and UCL respectively. These limits are constructed as the three ό limits for the variance in the sample mean as estimated from the average sample standard deviation. For a statistical grounding in the construction of these limits, even if the distribution of the individual measurements is not Gaussian, as long as it is essentially unimodal the means of these observations should be reasonably approximated with a Gaussian distribution as a consequence of the Central Limit Theorem. The area between these limits represents the expected variability in the mean of four observations of the process, not the individual measurements. A process is said to be "in-control" if it exhibits only random variation, i.e., all points (means in this case) are within the control limits and no non-random patterns are present. To aid in the detection of non-random patterns, the areas between the control limits and the grand mean are divided into three zones labeled A, B, and C as they proceed toward the center of the chart. These zones represent one-ό distances from the grand mean and can be utilized to provide additional statistical tests for signal drift, as the sample means should fall within the two C zones with probability 0.68 and within the C and B zones taken together with probability 0.95. For example, if two means within a cluster of three adjacent time points occur in zone A, this would be taken as an indication that the signal mean has underwent a shift in its underlying distribution. If a process is found to be in-control, it is concluded to be stable over the given time course, and the control limits generate a profile for the variation in the measured statistic. The signal represented in FIG. 1 is in-control over the time course studied. While the group means are within the control limits, some of the individual measurements are not. From a biological standpoint, m/z transformed signal values shown to be in-control over the time course in the basal condition represent molecules in which metabolic cellular events are negligible, as measured by mass spectrometry.

C. Computational Analysis The analysis programs used in the construction of the lipid arrays utilize the concepts developed above. Data is collected from the mass spectrometer as Raw files in the Xcalibur software package from ThermoFinnigan. These files are translated into text files for reading into S-Plus. hi the first part of the analysis, the data from each spectrum are smoothed using a Nadaraya- Watson kernel regression estimate to remove the shoulder effect which produces extraneous peaks. An example is given in FIGS. 2 A & 2B. After smoothing, each data set is normalized using one of the transformations described above. The computer then parses the data set, looking for "Low-High-Low" patterns and flagging each "High" point as a peak, collecting them as a data table. During this concatenation, the locations (m/z ratio) of the peaks are averaged to compensate for measurement error in the mass spectrometer. In the next stage of the analysis, the program constructs a Shewhart control chart for the mean of the transformed signal at each peak identified in the basal condition, and tests to determine if the signal is "in-control." This includes parsing the data for means that occur beyond the control limits as well as using the control chart zones to look for non-random patterns. The analysis then uses the control limits obtained from the basal condition to examine the output from the stimulated case at all m/z values where the signal is found to be "in-control". In these instances, the signal has been stable over the time course in the basal condition, and using the basal control limits for comparison, allows for a pooling of the information contained there. Thus, the data is plotted from the stimulated condition on a control chart generated from the basal data (FIG. 3). The analysis uses the rules described above to examine the stimulated data for non- random variation as compared with the profile generated in the basal condition. This includes parsing for time points beyond the control limits as well as searching for non-random patterns which can be deduced from the control chart zones. As seen in the right panel of FIG. 3, the third and fourth time points are indicated as "out-of-control" as they consecutively appear in the A zone of the extended control chart. Thus, this m/z value would be scored as having increased at both the third and fourth time points in the stimulated data set. Two other possibilities also require explanation here. In the first case, the basal data behaves in an "out-of-control" manner, (i.e., contains some non-random time related variation). The second possibility involves peaks that appear in different frequencies within the basal and stimulated conditions. When the basal condition exhibits "out-of-control" variation, extending the control limits would be inappropriate, hi this instance, a Welch modified two sample t-test is performed at each of the time points to determine if differences exist in the means between the two conditions at the given time. In the second case, a binomial test is performed, with the null hypothesis that a peak has an equal chance of appearing in either the basal or stimulated case, to determine if the observed difference in the number of occurrences in the two conditions is significant. Generation of Lipid Arrays. After testing for statistically significant differences between experimental conditions at each time point-peak combination, the results are grouped into a comprehensive array containing the m/z values observed as peaks and the time course, on the vertical and horizontal axes respectively. An excerpt of a lipid array is shown in FIG. 4. Peaks that have been identified by tandem mass spectrometry are documented as specific lipid species. Each m/z and time point combination found to be increasing is scored with a positive one (+1), while those decreasing are assigned a negative one (-1). Statistically stable combinations are scored with a zero. These arrays can be color coded to enhance readability, and in many cases provide a striking display of cellular lipid changes through time after challenge with a biological agonist. When examining a system that includes a large number of time point-peak combinations, a significant opportunity for false-positives is created. This is illustrated by considering that if 1000 different peaks are analyzed over five time points, it generates 5000 chances for a false positive. If the a value is set at .05, one would anticipate 250 false indicators on the lipid array occurring by chance alone. Methods involving the reduction of the false positive rate by effectively decreasing the a value (e.g., the Bonferroni inequality) have been found to be extremely detrimental to the sensitivity of the detection process. As an alternative, this effect can be countered by repeating the experiment multiple times and summing the cells from the resulting arrays. Since random eπors are unlikely to occur in the same position, after several repetitions the result is seen to converge as a stable map of lipid changes.

V. Diagnosing Disease States In one embodiment of the present invention, mass spectroscopy of lipid content is applied to determine alterations in lipid compositions that are predictive, diagnostic or prognostic of disease states. Typically, such assays will be run on biological samples taken from an organism of interest, i.e., an organism suspected of or at risk of a disease state. Cell containing samples are obtained using standard methods, include phlebotomy, biopsy, swabs or scrapings from a body cavity (oral, rectal, vaginal, urethral, nasal, ear, post-operative bed), waste (feces, urine) or secretory fluid (saliva, mucous, semen, vaginal fluid, ocular fluid). The analysis will be compared against known diseased and/or healthy samples from comparable sources. In addition, one may assess the response of an organism to a drug provided to treat a disease state by similar methods. The organism will be treated and one or more samples will be taken following the treatment (multiple samples to examine a time course). The comparison may be made with a standard (diseased and/or healthy), but can be with a sample from the same organism prior to treatment, thereby permitting a true "control" sample.

A. Infectious Diseases 1. Fungal Diseases The methods of the present invention may be used with regard to fungal diseases. Fungal diseases are caused by fungal and other mycotic pathogens (some of which are described in Human Mycoses, (1979); Opportunistic Mycoses of Man and Other Animals (1989); and Scrip's Antifungal Report (1992)); fungal diseases range from mycoses involving skin, hair, or mucous membranes, such as, but not limited to, Aspergillosis, Black piedra, Candidiasis, Chromomycosis, Cryptococcosis, Onychomycosis, or Otitis externa (otomycosis), Phaeohyphomycosis, Phycomycosis, Pityriasis versicolor, ringworm, Tinea barbae, Tinea capitis, Tinea corporis, Tinea cruris, Tinea favosa, Tinea imbricata, Tinea manuum, Tinea nigra (palmaris), Tinea pedis, Tinea unguium, Torulopsosis, Trichomycosis axillaris, White piedra, and their synonyms, to severe systemic or opportunistic infections, such as, but not limited to, Actinomycosis, Aspergillosis, Candidiasis, Chromomycosis, Coccidioidomycosis, Cryptococcosis, Entomophthoramycosis, Geotrichosis, Histoplasmosis, Mucormycosis, Mycetoma, Nocardiosis, North American Blastomycosis, Paracoccidioidomycosis, Phaeohyphomycosis, Phycomycosis, pneumocystic pneumonia, Pythiosis, Sporotrichosis, and Torulopsosis, and their synonyms, some of which may be fatal. Known fungal and mycotic pathogens include, but are not limited to, Absidia spp., Actinomadura madurae, Actinomyces spp., Allescheria boydii, Alternaria spp., Anthopsis deltoidea, Apophysomyces elegans, Arnium leoporinum, Aspergillus spp., Aureobasidium pullulans, Basidiobolus ranarum, Bipolaris spp., Blastomyces dermatitidis, Candida spp., Cephalosporium spp., Chaetoconidium spp., Chaetomium spp., Cladosporium spp., Coccidioides immitis, Conidiobolus spp., Corynebacterium tenuis, Cryptococcus spp., Cunninghamella bertholletiae, Curvularia spp., Dactylaria spp., Epidermophyton spp., Epidermophyton floccosum, Exserophilum spp., Exophiala spp., Fonsecaea spp., Fusarium spp., Geotrichum spp., Helminthosporium spp., Histoplasma spp., Lecythophora spp., Madurella spp., Malassezia furfur, Microsporum spp., Mucor spp., Mycocentrospora acerina, Nocardia spp., Paracoccidioides brasiliensis, Penicillium spp., Phaeosclera dematioides, Phaeoannellomyces spp., Phialemonium obovatum, Phialophora spp., Phoma spp., Piedraia hortai, Pneumocystis carinii, Pythium insidiosum, Rhinocladiella aquaspersa, Rhizomucor pusillus, Rhizopus spp., Saksenaea vasiformis, Sarcinomyces phaeomuriformis, Sporothrix schenckii, Syncephalastrum racemosum, Taeniolella boppii, Torulopsosis spp., Trichophyton spp., Trichosporon spp., Ulocladium chartarum, Wangiella dermatitidis, Xylohypha spp., Zygomyetes spp. and their synonyms. Other fungi that have pathogenic potential include, but are not limited to, Thermomucor indicae-seudaticae, Radiomyces spp., and other species of known pathogenic genera. These fungal organisms are ubiquitous in air, soil, food, decaying food, etc. Histoplasmoses, Blastomyces, and Coccidioides, for example, cause lower respiratory infections. Trichophyton rubrum causes difficult to eradicate nail infections. In some of the patients suffering with these diseases, the infection can become systemic causing fungal septicemia, or brain/meningal infection, leading to seizures and even death.

2. Viral Diseases The methods of the present invention may be used to diagnose, predict responses to and progression of viral diseases. Viral diseases include, but are not limited to influenza A, B and C, parainfluenza (including types 1, 2, 3, and 4), paramyxoviruses, Newcastle disease virus, measles virus, mumps virus, adenoviruses, adeno-associated viruses, parvoviruses, Epstein-Barr virus, rhinoviruses, coxsackieviruses, echoviruses, reoviruses, rhabdoviruses, lymphocytic choriomeningitis, coronavirus (SARS virus), polioviruses, human immunodeficiency viruses (HIV-1 and -2), cytomegalovirus, papillomaviruses, virus B, varicella-zoster, poxviruses, rubella, rabies, picornaviruses, rotavirus, Kaposi associated herpes virus, herpes simple viruses type 1 and 2, hepatitis viruses (including types A, B, and C), Korean hemorrhagic fever virus, and respiratory syncytial virus (including types A and B).

3. Bacterial Diseases The methods of the present invention may be used with respect to bacterial diseases. Bacterial diseases include, but are not limited to, infection by the 83 or more distinct serotypes of pneumococci, streptococci such as S. pyogenes, S. agalactiae, S. equi, S. canis, S. bovis, S. equinus, S. anginosus, S. sanguis, S. salivari s, S. mitts, S. mutans, other viridans streptococci, peptostreptococci, other related species of streptococci, enterococci such as Enterococcus faecalis, Enterococcus faecium, Staphylococci, such as Staphylococcus epidermidis, Staphylococcus aureus, particularly in the nasopharynx, Hemophilus influenzae, pseudomonas species such as Pseudomonas aeruginosa, Pseudomonas pseudomallei, Pseudomonas mallei, brucellas such as Brucella melitensis, Brucella suis, Brucella abortus, Bordetella pertussis, Neisseria meningitidis, Neisseria gonorrhoeae, Moraxella catarrhalis, Corynebacterium diphtheriae, Corynebacterium ulcerans, Corynebacterium pseudotuberculosis, Corynebapterium pseudodiphtheriticum, Corynebacterium urealyticum, Corynebacterium hemolyticum, Corynebacterium equi, etc. Listeria monocytogenes, Nocordia asteroides, Bacteroides species, Actinomycetes species, Treponema pallidum, Leptospirosa species and related organisms. The invention may also be useful against gram negative bacteria such as Klebsiella pneumoniae, Escherichia coli, Proteus, Serratia species, Acinetobacter, Yersinia pestis, Francisella tularensis, Enterobacter species, Bacteriodes and Legionella species and the like. 4. Protozoan Diseases The methods of the present invention may be used to diagnose, predict responses to, and monitor responses to protozoan diseases. Protozoan or macroscopic diseases include, but are not limited to, infection by organisms such as Cryptosporidium, Isospora belli, Toxoplasma gondii, Trichomonas vaginalis, Cyclospora species, for example, and for Chlamydia trachomatis and other Chlamydia infections such as Chlamydia psittaci, or Chlamydia pneumoniae, for example.

B. Non-Infectious Diseases 1. Cancer Another important diagnostic, prognostic and predictive application of the present invention is in the field of cancer. Apparently normal may be assessed for the presence of cancerous features, or for the possibility of transformation into neoplastic or even cancerous growth. Apparently benign hyper- or neoplastic can also be assessed to confirm the non-cancer nature, and also for the tendency to become cancerous. Finally, cancerous tissues may be confirmed as cancerous, graded, staged and predicted to progress, recur, metastasize or respond to a therapy. Appropriate controls include treated and untreated tissues, both normal, benign hyper- or neoplastic, and cancerous, from a similar organism. Cancer cells that may be identified by or correlate with changes in gene expression in hair follicle cells as measured using methods of the present invention include, but are not limited to, cells from the bladder, blood, bone, bone marrow, brain, breast, colon, esophagus, gastrointestine, gum, head and neck, pancreas, kidney, liver, lung, nasopharynx, neck, ovary, prostate, cervix, skin, stomach, testis, tongue, or uterus. In addition, the cancer may specifically be of the following histological type, though it is not limited to these: neoplasm, malignant; carcinoma; carcinoma, undifferentiated; giant and spindle cell carcinoma; small cell carcinoma; papillary carcinoma; squamous cell carcinoma; lymphoepithelial carcinoma; basal cell carcinoma; pilomatrix carcinoma; transitional cell carcinoma; papillary transitional cell carcinoma; adenocarcinoma; gastrinoma, malignant; cholangiocarcinoma; hepatocellular carcinoma; combined hepatocellular carcinoma and cholangiocarcinoma; trabecular adenocarcinoma; adenoid cystic carcinoma; adenocarcinoma in adenomatous polyp; adenocarcinoma, familial polyposis coli; solid carcinoma; carcinoid tumor, malignant; branchiolo-alveolar adenocarcinoma; papillary adenocarcinoma; chromophobe carcinoma; acidophil carcinoma; oxyphilic adenocarcinoma; basophil carcinoma; clear cell adenocarcinoma; granular cell carcinoma; follicular adenocarcinoma; papillary and follicular adenocarcinoma; nonencapsulating sclerosing carcinoma; adrenal cortical carcinoma; endometroid carcinoma; skin appendage carcinoma; apocrine adenocarcinoma; sebaceous adenocarcinoma; ceruminous adenocarcinoma; mucoepidermoid carcinoma; cystadenocarcinoma; papillary cystadenocarcinoma; papillary serous cystadenocarcinoma; mucinous cystadenocarcinoma; mucinous adenocarcinoma; signet ring cell carcinoma; infiltrating duct carcinoma; medullary carcinoma; lobular carcinoma; inflammatory carcinoma; Paget's disease, mammary; acinar cell carcinoma; adenosquamous carcinoma; adenocarcinoma w/squamous metaplasia; thymoma, malignant; ovarian stromal tumor, malignant; thecoma, malignant; granulosa cell tumor, malignant; androblastoma, malignant; sertoli cell carcinoma; leydig cell tumor, malignant; lipid cell tumor, malignant; paraganglioma, malignant; extra-mammary paraganglioma, malignant; pheochromocytoma; glomangiosarcoma; malignant melanoma; amelanotic melanoma; superficial spreading melanoma; malignant melanoma in giant pigmented nevus; epithelioid cell melanoma; blue nevus, malignant; sarcoma; fibrosarcoma; fibrous histiocytoma, malignant; myxosarcoma; liposarcoma; leiomyosarcoma; rhabdomyosarcoma; embryonal rhabdomyosarcoma; alveolar rhabdomyosarcoma; stromal sarcoma; mixed tumor, malignant; mullerian mixed tumor; nephroblastoma; hepatoblastoma; carcinosarcoma; mesenchymoma, malignant; brenner tumor, malignant; phyllodes tumor, malignant; synovial sarcoma; mesothelioma, malignant; dysgerminoma; embryonal carcinoma; teratoma, malignant; struma ovarii, malignant; choriocarcinoma; mesonephroma, malignant; hemangiosarcoma; hemangioendothelioma, malignant; kaposi's sarcoma; hemangiopericytoma, malignant; lymphangiosarcoma; osteosarcoma; juxtacortical osteosarcoma; chondrosarcoma; chondroblastoma, malignant; mesenchymal chondrosarcoma; giant cell tumor of bone; ewing's sarcoma; odontogenic tumor, malignant; ameloblastic odontosarcoma; ameloblastoma, malignant; ameloblastic fibrosarcoma; pinealoma, malignant; chordoma; glioma, malignant; ependymoma; astrocytoma; protoplasmic astrocytoma; fibrillary astrocytoma; astroblastoma; glioblastoma; oligodendroglioma; oligodendroblastoma; primitive neuroectodermal; cerebellar sarcoma; ganglioneuroblastoma; neuroblastoma; retinoblastoma; olfactory neurogenic tumor; meningioma, malignant; neurofibrosarcoma; neurilemmoma, malignant; granular cell tumor, malignant; malignant lymphoma; Hodgkin's; paragranuloma; malignant lymphoma, small lymphocytic; malignant lymphoma, large cell, diffuse; malignant lymphoma, follicular; mycosis fungoides; other specified non-Hodgkin's lymphomas; malignant histiocytosis; multiple myeloma; mast cell sarcoma; immunoproliferative small intestinal disease; leukemia; lymphoid leukemia; plasma cell leukemia; erythroleukemia; lymphosarcoma cell leukemia; myeloid leukemia; basophilic leukemia; eosinophilic leukemia; monocytic leukemia; mast cell leukemia; megakaryoblastic leukemia; myeloid sarcoma; and hairy cell leukemia. 2. Genetic Diseases Various other disease states may be diagnosed or predicted by virtue of analyzing lipid profiles. Polygenic genetic diseases include diabetes (Types I and II) and gout. Mendelian disorders include Marfan's Syndrome, familial hypercholesteremia, neurofibromatosis, cystic fibrosis, phenylketonuria, galactosemia, albinism, Wilson's Disease, glycogen storage disorders, lipid storage disorders, and mucopolysaccharidoses. X-linked disorders include Ehlers-Danlos Syndrome. Autosomal disorders include Down's Syndrome, Edward's Syndrome, Patau's Syndrome, and Le cri du chat Syndrome. Sex-linked disorders include Klinefelter's Syndrome, ?XXY males, and Turner's Syndrome. Controls will be from patients with known genetic abnormalities, as well as normal subjects.

3. Other Disease States Other diseases which may be diagnosed or predicted in accordance with the present invention include gestational diabetes, Gaucher's Disease, Tay-Sach's Disease, Niemann-Pick Diases, Hurler' s Syndrome, Hunter's Syndrome, atherosclerosis, arterioscloerosis, ischemic heart disease, congestive heart failure, Bruton's Disease, DiGeorge's Syndrome, Severe Combined Immunodeficiency, Wiskott-Aldrich Sydrome, Systemic Lupus Erythematosus, Rheumatoid Arthritis, Scleroderma, Polymyositis, Sjogren's Syndrome, Wegener's Granulomatosis, Mixed Connective Tissue Disease, amyloidosis, Sickle Cell anemia, thalassemia, aplastic anemia, Hodgkin's Disease, non-Hodgkin's lymphoa, thrombocytopenia, eosinophilic granuloma, myasthenia gravis, chronic glomerulonephritis, Alzheimer's Disease, Pick's Disease, Senile Dementia, Parkinson's Disease, amyotrophic lateral sclerosis, multiple sclerosis, and Schilder's Disease. Controls will be from patients with known disease states, as well as normal subjects.

VI. Screening Drugs The present invention further comprises methods for screening drugs for the ability to modulate lipid composition of a cell. These assays may comprise random screening of large libraries of candidate substances; alternatively, the assays may be used to focus on particular classes of compounds selected with an eye towards a given desired effect. To identify an modulator of lipid composition, one generally will determine the contact a cell or cell culture with a modulator and compare the lipid profile with that of a similar cell or culture that has not been contacted with the candidate substance. For example, a method generally comprises: (a) providing a candidate modulator; (b) contacting the candidate modulator with a cell; (c) obtaining lipids from said cell; (d) performing mass spectroscopy and statistical analysis on said lipids to obtain a computational lipid array; and (d) comparing the array obtained in step (d) with an array obtained in the absence of the candidate modulator, wherein a difference between the profiles indicates that the candidate modulator is, indeed, a modulator of the lipid composition of the cell.

Contacting of cells may occur using isolated cells ororgans, or be performed in the context of living organisms. It will, of course, be understood that all the screening methods of the present invention are useful in themselves notwithstanding the fact that effective candidates may not be found. The invention provides methods for screening for such candidates, not solely methods of finding them.

A. Modulators As used herein the term "candidate substance" refers to any molecule that may potentially modify cellular lipid content. The candidate substance may be a protein or fragment thereof, a small molecule, or even a nucleic acid. The candidate substance may be selected randomly, with no prior knowledge or reason to suspect the existence of lipid-modulating activity. Alternatively, one may select a compound based on some attribute - structural or functional - that is more likely to give it the desired function. This includes the practice of using lead compounds to help develop improved compounds is known as "rational drug design." The goal of rational drug design is to produce structural analogs of biologically active polypeptides or target compounds. By creating such analogs, it is possible to fashion drugs which are more active or stable than the natural molecules, which have different susceptibility to alteration, or which may affect the function of various other molecules. In one approach, one would generate a three-dimensional structure for a target molecule, or a fragment thereof. This could be accomplished by x-ray crystallography, computer modeling, or by a combination of both approaches. It also is possible to use antibodies to ascertain the structure of a target compound, activator, or inhibitor. In principle, this approach yields a pharmacore upon which subsequent drug design can be based. It is possible to bypass protein crystallography altogether by generating anti-idiotypic antibodies to a functional, pharmacologically active antibody. As a mirror image of a mirror image, the binding site of anti-idiotype would be expected to be an analog of the original antigen. The anti-idiotype could then be used to identify and isolate peptides from banks of chemically- or biologically-produced peptides. Selected peptides would then serve as the pharmacore. One may acquire, from various commercial sources, small molecule libraries that are believed to meet the basic criteria for useful drugs in an effort to "brute force" the identification of useful compounds. Screening of such libraries, including combinatorially-generated libraries (e.g., peptide libraries), is a rapid and efficient way to screen large number of related (and unrelated) compounds for activity. Combinatorial approaches also lend themselves to rapid evolution of potential drugs by the creation of second, third, and fourth generation compounds modeled on active, but otherwise undesirable compounds. Candidate compounds may include fragments or parts of naturally-occurring compounds, or may be found as active combinations of known compounds, which are otherwise inactive. It is proposed that compounds isolated from natural sources, such as animals, bacteria, fungi, plant sources, including leaves and bark, and marine samples may be assayed as candidates for the presence of potentially useful pharmaceutical agents. It will be understood that the pharmaceutical agents to be screened could also be derived or synthesized from chemical compositions or man-made compounds. Thus, it is understood that the candidate substance identified by the present invention may be peptide, polypeptide, polynucleotide, small molecule inhibitors or any other compounds that may be designed through rational drug design starting from known inhibitors or stimulators. Other suitable modulators include antisense molecules, ribozymes, and antibodies (including single chain antibodies), each of which would be specific for the target molecule. Such compounds are described in greater detail elsewhere in this document. For example, an antisense molecule that bound to a translational or transcriptional start site, or splice junctions, would be candidate inhibitors.

B. In vitro Assays In vitro assays generally use cells in culture and can be ran quickly and in large numbers, thereby increasing the amount of information obtainable in a short period of time. A variety of vessels may be used to run the assays, including test tubes, plates, multi-well plates, dishes and other surfaces such as dipsticks or beads. Contacting generally will involve merely adding a candidate substance to the cell culture medium, although other modulators will require additional steps, such as transport into cell cytoplasms or nuclei. Cells may also be comprised in intact tissues or even intact organs. Methods are known to those of skill in the art to maintain tissues and organs in vitro and ex vivo for extended periods of time. Tissues of interest include skin (epithelium), cornea, intestinal mucosa, brain, heart, lung, stomach, liver, pancreas, spleen, prostate, ovary, uterus, testes or muscle. The tissue may also comprise a disease tissue such as a benign neoplasm or a cancer tissue.

C. In vivo Assays In vivo assays involve the use of various animal models, including those that are disease models, such as animals with genetic defects leading to development of disease states. An example of such a model is a transgenic animal, cells of which are engineered to develop a disease as part of normal development or upon appropriate external signaling. Due to their size, ease of handling, and information on their physiology and genetic make-up, mice are a preferred embodiment, especially for transgenics. However, other animals are suitable as well, including rats, rabbits, hamsters, guinea pigs, gerbils, woodchucks, cats, dogs, sheep, goats, pigs, cows, horses and monkeys (including chimps, gibbons and baboons). Assays for modulators may be conducted using an animal model derived from any of these species. Treatment of animals with test compounds will involve the administration of the compound, in an appropriate form, to the animal. Administration will be by any route that could be utilized for clinical purposes. Also, measuring toxicity and dose response can be performed in animals in a more meaningful fashion than in in vitro or in cyto assays.

VII. Identifying Cellular Response Pathways In yet another embodiment, the present invention may be utilized to identify cellular response and signaling pathways within cells, as well as linking particular lipids (known and unknown) to such pathways. The pathways may be selected from any pathway in a cell, but in particular, include those involved in responses to drugs or toxins,. They may also be related to synthetic pathways, as such, involve enzymes that produce or process various substances. The pathways may be involved in cellular signaling, such as those that respond to hormones, cytokines, ions or transmitters. They may also be pathways involved in response to environmental stimuli, such as toxins, temperature change, light, electrical stimuli, pH, oxidation, or mechanical stress. The assays will follow the general format of (a) providing a test cell, (b) subjecting the test cell to a condition that activates or is suspected to activate a pathway, and then monitoring the lipid response of the cell. The cell may be isolated or may be part of a larger community of cells (tissue culture, tissue sample). Step (b) may be performed in vitro or in vivo.

VIII. Identifying New Lipid Species In yet another embodiment, the present invention provides for new methods of identifying unknown lipid species. In general, the method will follow that described above for lipid analysis, where a biological sample comprising lipids is provided and all lipids therein are subjected to simultaneous mass spectroscopy. This will produce the lipid data array as discussed previously. The data array is then subjected to statistical analysis to obtain a lipid profile for said biological sample, also as described above, followed by comparison of the lipid profile to one or more known lipid profiles. Any deviation may indicate the existence of an unknown lipid species. Subsequent steps in the method involve the further isolation and/or characterization of the new lipid species. For example, the method may further comprise collision induced mass spectroscopy and/or nuclear magnetic resonance, both of which are well known in the art.

IX. Examples The following examples are included to further illustrate various aspects of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques and/or compositions discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.

EXAMPLE 1 To demonstrate the ability of the mathematical formulation as described above to identify subtle differences between similar biological extracts, a proof-of-concept experiment was preformed. This experiment was designed to determine the efficacy of the mathematical algorithm in locating the components of a chemically defined cocktail of lipids added to cellular extracts and to assess the resulting false alarm rate. The admixture was constructed from commercially available phospholipid preparations obtained from Avanti Polar Lipids, Inc. The chosen lipid standards were supplemented at the indicated concentrations: 34:1 PA (75 μg/ml), 16:0 LPC (250 μg/ml), 34:2 PI (100 μg/ml), 16:0 PE (200 μg/ml), 28:0 PE (200 μg/ml), and 32:0 PE (200 μg/ml). The 34:2 PI standard was a complex mixture isolated from soy plant extracts composed of a major 34:2 PI species and minor amounts of 34:3, 36:4, 36:5, and 36:6 PI as well. HL-60 pellets containing ~10 x 10⁶ cells were extracted using a modified Bligh and Dyer procedure and dried in a speed-vac. Samples were resuspended in a chloroform/methanol solution into which the chemically defined standards were added. The samples were analyzed via direct injection in the negative ionization mode. Five sets of data were generated, each consisting of three repetitions that included HL-60 extracts in the absence and presence of the external standards. The data were analyzed using a modified version of the procedure described above where the means of the transformed signals were compared using a Welch modified two sample t-test (lack of a time course precludes the use of control charts) with the alpha value for the individual tests set at 0.005. Lipid arrays were constructed for the five experiments and the results were combined to form a summary array of the observed lipid changes. There were a total of 887 peaks identified and analyzed by the software. Peaks that were found to be changing in the spiked sample condition were subjected to CID MS/MS to determine their molecular species. Of the 887 peaks analyzed, 87 (9.8%) exhibited a unidirectional change in four or five of the experiments performed, which was defined to be highly significant. Of these 87 peaks, a total of 59 were indicated as increasing while 28 were found to be decreasing. All of the six standards were located in the spectra, including the complete collection of PI lipids, each receiving a significance score of 5 with the exception of 32:0 PE and 36:6 PI which were scored with a 4. Many of the remaining increasing peaks were subsequently identified using CID MS/MS, and several were found to be gas-phase dimers of the various standards added. Of the total collection of peaks found to be increasing, 20 were found to be consistent with the addition of the standards. This yields a false alarm rate of 67/887 = 7.6%. The results are summarized in FIG. 5.

EXAMPLE 2 MS Analysis of WEHI-231 cells. WEHI-231 cells were challenged with the B-cell receptor (BCR) agonist anti-IgM (AIG, 0.13 μM). This data was collected in conjunction with The Alliance for Cellular Signaling (AfCS) as one of several ligand-induced cellular response assays. The AfCS consists of seven experimental laboratories coupled with a bioinformatics core, focused on the overall goal of understanding the relationships between the inputs and outputs of signaling cells in a context-dependent manner. To achieve this goal, the AfCS laboratories have been applying existing and developmental technologies to acquire data from a large collection of cellular events. This data is to be reduced into a set of theoretical models to aid in the understanding of ligand response pathways. For additional information on the AfCS project, visit the website at www.signaling-gateway.org. The WEHI-231 samples were extracted using a modified Bligh and Dyer procedure and analyzed as described elsewhere (Milne et al, 2003). Identification of the individual glycerophospholipids present in the total lipid extracts (both basal and AIGstimulated) was accomplished by tandem mass spectrometry (ESI-MS/MS). Collision-induced dissociation (CID) of the peaks of interest yielded fragmentation patterns, which were used to identify the lipid(s) present at a particular m/z value. Simultaneously, over 300 glycerophospholipids have been detected and identified in WEHI-231 total lipid extracts. Instrumentation. Mass spectra were acquired on a Finnigan TSQ Quantum triple quadrupole mass spectrometer (ThermoFinnigan, San Jose, CA) equipped with a Harvard Apparatus syringe pump and an electrospray ionization source. Samples were analyzed at an infusion rate of 10 μL/min in both negative and positive modes over the range of m/z 400 to 1200. The data was collected at a resolution of 0.07 m/z units, producing 11428 intensity measurements per run. Mass spectrometer parameters were optimized with 1,2-dioctanoyl-sτ.- Glycero-3-phosphoethanolamine (16:0 PE). Mass spectral data were collected using the Xcalibur software package (ThermoFinnigan). Glycerophospholipid Changes in Basal versus AIG-stimulated WEHI-231 Cells. Stimulation of the AfCS WEHI-231 BCR with AIG (0.13 μM) resulted in robust changes in glycerophospholipid concentrations. Lipid arrays were constructed for both the positive and negative ESI modes from 10 sets of samples. Each data set contained 4 replicates of paired samples that included a control (basal) and matched ligand (AIG) stimulated sample at each of five time points: 1.5, 3, 6, 15, and 240 minutes. Thus, each summary array was constructed from the summation of the 10 individual arrays and contains a total of 400 samples. Excerpts from the positive (A) and negative (B) mode arrays are shown in FIGS. 6A-B. There were a total of 935 peaks identified in the positive mode and 827 in the negative mode. Out of 935 peaks in the positive mode, 810 (86.6%) remained statistically stable at all time points. Similarly, 722 (87.3%) of the negative peaks remained stable at all time points. From a temporal perspective, lipids identified in both the positive and negative scanning modes showed little change after stimulation at the early time points (1.5 and 3 min.), modest changes at 6 min, and the greatest differences were observed at 15 minutes. The majority of these lipid changes were seen to return to their basal state 4 hours after stimulation. In the array for the positive ion mode, highly significant decreases were observed for many PC and or PE species at the 6 and 15 minute time points, with concomitant increases in several lysoPC compounds (FIG. 6A). In the negative ionization mode, clusters of phosphatidylinositols (PI) and phosphatidylserines (PS) showed a highly significant decrease at the 15 minute time point. Corresponding increases in lyso- PI, lyso-PS, and glycerophosphatidic acid (GPA) species were also observed. An excerpt demonstrating the PS fraction of this result is shown in FIG. 6B. Activation of the BCR by AIG in mature B cells results in a wide variety of cellular changes that include cell proliferation, differentiation, increased metabolic rate, and changes in cellular adhesion properties. Additionally, these cells exhibit germinal center reactions, immunoglobulin isotype class switch DNA recombination, and somatic hypermutation of immunoglobulin V regions. Interestingly, although stimulation of the BCR results in activation and proliferation of mature B cells, it results in apoptosis in immature B cells. A detailed characterization of other changes induced is provided on the AfCS website. Many of the lipid changes observed during the stimulation of the BCR seem consistent with the associated cellular behavior. For example, highly significant decreases in the various PI species were observed and are likely in response to the PLC activation (i.e., hydrolysis and subsequent biosynthesis of PIP₂). The decreases in the PS lipids with the increase in LPS may be functionally linked to initiation of the apoptotic pathway in the B-cell. The interpretation of the lipidomic profile generated, and its relationship to the overall signaling pathway will provide exciting new challenges for the signaling community, but are likely to implicate unanticipated participants in familiar biological processes.

EXAMPLE 3 Mast cell degranulation. Lipid changes during Antigen receptor (FcεRI) mediated degranulation in mast cells were measured and compared with changes induced by Ca²⁺ ionophore (A23187). Experimental conditions included 2 cell types (RBL-2H3 and mutant B6A4C1) stimulated with antigen (0.1 μg/ml) and A23187 (10 μmol) for a period of 3, 6, 9, 12, 15, 20, 30 and 60 minutes and comparing the changes in the detected phospholipids with basal (untreated) samples. The analysis shows changes in individual molecular species of membrane glycerophospholipids from 2 x 10⁵ cell equivalents of lipid extract from RBL-2H3 and mutant RBL mastocytoma cells (B6A4C1) for comparison. Phospholipids were extracted using Bligh/Dyer extraction procedure under acidic conditions. The chloroform phase of the extraction, containing most of the glycerophospholipids, was carefully removed and the solvent was evaporated. The resulting lipid film was immediately dissolved in CH3OH:CHC (9:1), containing 1% ? H₄OH to ensure protonation, and analyzed by mass-spectrometry. Mass spectra were acquired on a Bruker Esquire-LC 00146 ion trap mass spectrometer (ITMS) (Bruker Daltonics, Billerica, ?MA) equipped with an ESI interface. All samples were sprayed in positive and negative mode, resulting in a variety of molecular ion signals in the range of m z 500-1000. Excerpts from the generated positive (A) and negative (B) mode Lipid Arrays are illustrated in FIGS. 7A-B. Phospholipids are presented with the class abbreviation preceded by xx:y, where xx is the total carbon atoms in the fatty acid chains and y is the number of double bonds. The presentation of the data in lipid arrays allows for an easy visualization of the lipid changes (FIGS. 7A and 7B). The "positive" changes, associated with an increase in a hpid molecular species are shown in red, the "negative" changes are indicated in blue, and the species remaining unchanged are presented in green. This presentation format allows for quick identification of changes from a large and complex data set. It also has the potential to reveal patterns of changes in lipid species correlated with various cell processes and diseases. Mast cells are involved in allergic inflammation responses through release of bioactive molecules, such as phospholipids, during degranulation. The fusion between secretory vesicles (granules) and the plasma membrane is the final stage of exocytosis. Therefore, the distribution of the structural membrane components (such as phospholipids) should play an important role in regulating secretion. The inventors have previously demonstrated that production of bioactive lipids by exogenously added phospholipases to permeabilized mast cells (RBL-2H3) leads to release of the granule contents through plasma membrane. The use of "broken cell systems" showed that regulated secretion can be achieved in vitro in the absence of cytosolic factors via phospholipase activation (Ivanova et al, 2001; Cohen and Brown, 2001). Despite the demonstration that changes in phospholipid composition as a result of exogenously added bacterial phospholipases stimulate secretion, it does not reveal the mechanism as to how these changes participate in secretion initiated by the physiological mast cell receptor, FcεRI. To understand the involvement of phospholipid changes during exocytosis, the inventors compared the differences in membrane phospholipid composition as a result of degranulation triggered by antigen and Ca2+- ionophore (A23187) in two cell lines - RBL-2H3 mast cells and a mutant RBL cell line, B6A4C1. These cells derived from the RBL-2H3 mastocytoma line are defective in FcεRI-coupled secretion, receptor-dependent calcium immobilization, ganglioside transport, and stimulated phospholipase activities. Although defective in antigen-stimulated reaction, B6A4C1 cells degranulate in response to Ca²⁺ ionophore A23187. The changes in cell membrane phospholipid composition as a result of physiological stimulation proved to be more subtle than those occurring during phospholipase stimulation and, therefore, more difficult to detect via conventional mass spectrometry analysis. Comparison of the degranulation in RBL-2H3 and B6A4C1 cells shows both cells initiate exocytosis when cells are challenged with Ca2+ ionophore (FIG. 8, lower panel) and exhibit a two-phase curve with the second phase apparent by the 10 min time point. There are two types of general response patterns observed in these Lipid Arrays. The first type of response was related to the stimulus used and the second is dependent on cell type (i.e., wild-type vs. mutant). As shown in the positive Lipid Array (FIG. 7A), some species of PC were found to change in response to ionophore, where little or no responses were seen in response to antigen. The second type of response illustrated in FIG. 7B shows reproducible changes in PI species as a result of ionophore as well as antigen stimulation in the RBL-2H3 cells. These responses are correlated with degranulation. In contrast, there was little or no detection of changes in the B6A4C1 cells in response to antigen. As inferred from FIG. 8, these pattern changes are found under conditions in which exocytosis occurs. This paradigm provides an opportunity to identify those phospholipids that are changing systematically with degranulation and to directly address the roles of specific phospholipid species and clusters of species in antigen-mediated degranulation. Some recent studies suggest that phospholipid distribution in plasma membrane plays a role in mast cells exocytosis (Kato et al, 2002). Data from these MS Lipid Arrays show alterations in membrane phospholipids which otherwise would go undetected because of the very small changes associated with a physiological response. Statistical analysis of mass spectrometry data allows for assessment of patterns of changes in phospholipids. The first striking observation is the variety of phospholipids changing in RBL-2H3 and B6A4C1 cells treated with A23187 compared to the other panels of the arrays in positive and negative mode (FIGS. 7A and 7B). Despite the similar pattern of changes between the two cell lines treated with Ca²⁺ ionophore, overall more lipids change in RBL-2H3 than in B6A4C1 cells. It is worth mentioning the decrease in PI species perhaps associated with PLC activation (hydrolysis and subsequent biosynthesis of PIP2) (FIG. 7B). The fact that most changes in lipid profiles occur almost immediately is consistent with Takenawa et al. (1983) describing a rapid increase in PI turnover due to degranulation. The increase in LPC species in the degranulating cells can be attributed to the activation of PLA2 and associated with PC hydrolysis (FIG. 7A). Decrease in PC species (especially those containing long fatty acyl chains) is likely a result of PLD activation, an increase in PA species also resulting and would be consistent with previous patterns observed when degranulation was induced by bacterial phospholipases (Ivanova et al, 2001) in a broken mast cell assay. In contrast to RBL-2H3 cells, mutant B6A4C1 cells fail to degranulate in response to antigen treatment (FIG. 8, upper panel). The comparative lack of changes in membrane lipid composition of B6A4C1 cells treated with antigen compared to the changes in RBL-2H3 is consistent with the degranulation data. There is also a different pattern of lipid changes between RBL-2H3 cells treated with ionophore and antigen suggesting that perhaps the exocytosis triggered by these two stimulants occurs via slightly different mechanisms. Polyphosphoinositides. Lipidomics permits the detection and identification of important membrane signaling molecules which were previously difficult to fully characterize by other methods. An illustrative example of this includes the class of compounds known as polyphosphoinositides. Traditionally, Pl-monophosphate (PIP) and Pl-diphosphate (PIP₂) species have been analyzed as a deacylated "pool" of inositides using ³²P radiolabeling and HPLC. Estimates of total PIP and PIP2 are possible with this procedure, but molecular species analysis (i.e., fatty acid information) in the context of other lipid species changes has been challenging to obtain. However, using ESI-MS/MS, the inventors were able to identify approximately twenty polyphosphoinositides (including acyl content on several species), hi the example shown below j 3 x 10⁶ cells were challenged with 50 μg/ml zymosan (a protein-carbohydrate complex derived from yeast cell wall) for 15 minutes followed by aspiration and pelleting. Inositides were then extracted using a modified Bligh Dyer extraction procedure. Mass spectral analysis was performed on a Finnigan TSQ Quantum triple quadrupole mass spectrometer (ThermoFinnigan, San Jose, CA) equipped with a Harvard Apparatus syringe pump and an electrospray source. Samples were analyzed at an infusion rate of 10 μl/min in negative mode over the range of m/z 400 to 1100. Using this procedure, the inventors were able to demonstrate that zymosan stimulation of RAW 264.7 cells leads to elevated levels of a wide variety of PIP and PIP₂ species compared to basal levels (FIG. 9). Interestingly, a recent report also utilized ESI-MS to provide a detailed characterization of molecular species analysis of PIP lipids extracted from rat brain (Wenk et α/., 2003).

E?XAMPLE 4 A. Methods and Protocols Cell Extraction and Reconstitution. Phospholipids were extracted using a modified Bligh and Dyer procedure (25). Pellets containing 3 x 106 cells were extracted with 800 μL of 0.1 N HC1: MeOH (1:1) and 400 μL CHC13. The samples were vortexed (1 min) and centrifuged (5 min, 18,000 g). The lower phase was then isolated and evaporated (Labconco CentriNap Concentrator, Kansas City, MO), followed by reconstitution with 80 μL MeOH: CHC13 (9:1). Prior to analysis, 1 μL of ? H4OH was added to each sample to ensure protonation. Lipid standards were obtained from Avanti Polar Lipids (Alabaster, AL). Mass Spectrometry Analysis of Phospholipid Cell Extracts. Mass spectral analysis was performed on a Finnigan TSQ Quantum triple quadrupole mass spectrometer (ThermoFinnigan, San Jose, CA) equipped with a Harvard Apparatus syringe pump and an electrospray source. Samples were analyzed at an infusion rate of 10 μL/min in both positive and negative modes over the range of m/z 400 to 1200. Instrument parameters were optimized with 1, 2-dioctanoyl-5rø-glycero-3-phosphoethanolamine (16:0 PE). Data were collected with the Xcalibur software package (ThermoFinnigan) and analyzed by a software program developed by the inventors, discussed above.

B. Results Identification of the individual glycerophospholipids present in the total lipid extracts (both basal and AIGstimulated) was accomplished by tandem mass spectrometry (ESI-MS/MS). Resolution and characterization of glycerophospholipids in an unprocessed total lipid extract are based on the predisposition of each lipid class to acquire positive or negative charges under the source energy. A single molecular ion is present with a mass-to-charge ratio (m/z) that refers to the monoisotopic molecular weight. Collision-induced dissociation (CID) of the peaks of interest yielded fragmentation patterns, which were used to unambiguously identify the lipid(s) present at a particular m/z value (FIG. 10) provides an illustration of this procedure). For tandem mass spectrometry, both positive and negative mode ionization were utilized. Traditionally, degree of structural information obtained as a result of this analysis varies by the type of instrumentation used. In negative ionization mode, triple quadrupole instruments tend to yield sn-l and sn-2 fatty acid residue fragments, whereas ion traps form more lyso-lipid fragments. Positive ion ESI-MS/MS spectra from ion trap instruments are more likely to create lysό-PC fragmentation products, which reveal the fatty acid composition of the lipid. However, under these triple quadrupole MS experimental conditions, only glycerophospholipid head group information was routinely obtainable from positive mode fragmentation. Three lipid classes were analyzed in positive ESI mode: phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), and sphingomyelins (SMs). The choline containing species, PCs and SMs, both show a characteristic m/z 184 phosphocholine head group peak, as well as an [M+H-59]+ peak corresponding to the neutral loss of (CH3)3N. In addition to the diacyl PC compounds, a large number of plasmanyl and plasmenyl phosphocholines were also identified. All together, over 100 choline containing lipids were identified. Fragmentation of phosphatidylethanolamines exclusively yielded one peak, an [M+H-141]+ ion from the neutral loss of the phosphoethanolamine head group. Again, plasmanyl and plasmenyl lipids were a large proportion of the over 40 PE species identified. Five lipid classes were detected in negative ESI mode: phosphatidylinositiols (Pis), phosphatidylserines (PSs), phosphatidylglycerols (PGs), glycerophosphatidic acids (PAs), and PEs. Negative mode fragmentation of these species yielded a wealth of structural information. In each case, head group fragmentation, lyso-lipid formation, and fatty acid fragments aided in the lipid identification process. Phosphatidylinositol fragmentation generated a wide variety of product ("daughter") ions. Four types of lysophosphatidic acid and lysophosphatidylinositols, phosphatidic acid, and five characteristic head group fragments were used in identifying the 27 observed PI and lyso-PI species. In a similar fashion, 33 distinct species of PS and lyso-PS compounds were identified from their phosphatidic (PA) and lysophosphatidic acid (LPA) fragments. A negative mode fragmentation library of the phosphatidylserines is provided as an example in FIG. 11. Fragmentation tables for the remaining phospholipid classes (for both fragmentation modes) can be viewed online at www.signalinggateway.org/ reports/vl/DAOOll/D A0011.htm. Phosphatidylcholine compounds were not identified during the routine negative mode scans. However, it was found that these compounds were detectable after the addition of ammonium acetate. Two important categories of signaling lipids were not included in this analysis. Diacylglycerol (DAG) was not routinely detected under the optimized conditions for triple quadrupole MS described here; however, DAG species can be detected using a Fourier transform ion cyclotron resonance (FT-ICR) instrument (Ivanova et al, 2001). The inventors have also found that DAG can be detected using a triple quadrupole MS but requires formation of a sodium adduct. hi the current study, well over 200 glycerophospholipids have been detected and unambiguously identified in WEHI-231 total lipid extracts. A tabular listing of all identified lipids for both positive and negative MS modes is shown in FIG. 12. Lipid Arrays. Next a computational analysis of these data are performed (as described elsewhere in the document), leading to the construction of an array containing the m/z ratios for peaks from with mass spectrometry (observed as peaks on the vertical and the time points on the horizontal axes) that displays the comprehensive changes in lipid species between two experimental conditions (e.g., addition of a ligand at a given concentration) over the defined time course. Lipid species that have been identified by CID MS/MS techniques as being present in the sample are assigned their corresponding m/z values. Each m/z and time point combination found to be increasing is scored with a one (1), while those decreasing are assigned a negative one (-1). Statistically stable combinations are scored with a zero (0). These arrays are color coded to enhance readability and in many cases provide a striking display of cellular lipid changes in time after challenge with a biological agonist. An excerpt of a lipid array is shown in FIG. 13. The number of peak/time point combinations examined in the system can create a significant opportunity for false positives. This is illustrated by considering that if 1000 different peaks are analyzed over 5 time points, 5000 chances for a false-positive are created. If the alpha value is set at 0.05, one would anticipate 250 false indicators on a lipid array of this size occurring by chance alone. This effect can be countered by repeating the entire experiment multiple times and summing the cells from the resulting arrays. Thus, if the experiment is repeated five times, each cell in the summary array will have a score between -5 and 5. Scores occurring toward the extremes (-5 and 5) indicate species that are fluctuating under stimulation with high statistical significance. Since random errors are unlikely to occur in the same position, after several repetitions the result is seen to converge to a stable map of lipid changes. Glycerophospholipid Changes in Basal Versus AIGStimulated WEHI-231 Cells. Stimulation of the AfCS WEHI-231 B-cell receptor with 0.13 μM anti-IgM ligand resulted in robust changes in glycerophospholipid concentrations. Lipid arrays were constructed from 10 sets of samples. Each data set contained four exact replicates of paired samples that included a control (basal) and matched ligand-stimulated sample at each of five time points: 1.5, 3, 6, 15, and 240 minutes. Thus, each array was constructed from 400 samples. The lipid species were identified using both the positive (array 1, supplemental material) and negative (array 2, supplemental material) ESI modes. Excerpts from the positive (A) and negative (B) mode arrays are shown in FIGS. 14A and 14B. In array one, only a few changes in concentration were observed during the 1.5- and 3- minute time points. However, highly significant decreases were observed for many phosphatidylcholine and/or phosphatidylethanolamine species at the 6- and 15 -minute time points, with corresponding increases in several lyso-PC compounds. By the fourth hour, the cells had mostly returned to their prestimulated states. A list of lipids having significant or highly significant changes is summarized in FIG. 15. The temporal trend in array two was shifted towards the later time points. Little movement was observed during the 1.5-, 3-, or 6-minute experiments. But, clusters of phosphatidylinositols and phosphatidylserines were observed to decrease highly significantly at the 15-minute time point. Corresponding increases in lyso-PI, lyso-PS, and glycerophosphatidic acids were also recorded. A summary of observed changes for the entire array is shown in FIG. 16.

All of the compositions and methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. W/hile the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and methods, and in the steps or in the sequence of steps of the methods described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the scope of the invention as defined by the appended claims.

X. References The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference:

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Claims

1. A method of obtaining a lipid profile from a biological sample comprising: (a) providing a biological sample comprising lipids; (b) subjecting essentially all of said lipids in said sample to simultaneous mass spectroscopy; (c) obtaining a lipid data array from step (b); and (d) subjecting said lipid data array to statistical analysis to obtain a lipid profile for said biological sample.

2. The method of claim 1 , further comprising: (e) comparing said lipid profile with one or more standard or control lipid profiles.

3. The method of claim 1 , wherein said biological sample is a fluid sample.

4. The method of claim 3, wherein said fluid sample is blood, serum, gastric fluid, cerebrospinal fluid, saliva, urine, or semen.

5. The method of claim 1 , wherein said biological sample is a cell or tissue sample.

6. The method of claim 5, wherein said cell or tissue sample is obtained from dermis, muscle, lung, heart, pancreas, kidney, liver, intestinal muscosa, ovary, testis, brain, cervix, uterus, prostate or bladder.

7. The method of claim 1, wherein said biological sample is diseased or suspected of being diseased, and said lipid profile is compared to (i) a lipid profile from a comparable healthy biological sample and/or (ii) a lipid profile from a diseased biological sample of a known disease state.

8. The method of claim 7, further comprising diagnosing, prognosing or classifying a disease state in said biological sample based on said lipid profile.

9. The method of claim 7, wherein said diseased sample is infected with a virus, a bacterium, parasite or a fungus.

10. The method of claim 7, wherein said diseased tissue sample is a hyperproliferative tissue sample.

11. The method of claim 10, wherein said hyperproliferative tissue is benign.

12. The method of claim 10, wherein said hyperproliferative tissue is cancerous.

13. The method of claim 1 , wherein said biological sample is processed to remove non- cellular material.

14. The method of claim 1, wherein said biological sample is a tissue culture sample.

15. The method of claim 14, wherein said tissue culture sample has been contacted with one or more test substances, and said lipid profile is compared to the lipid profile of a comparable tissue culture sample that has not been contacted with said test substance(s).

16. The method of claim 15, wherein said test substance comprises a DNA, an RNA, a polypeptide, a peptide or small molecule.

17. The method of claim 14, wherein said tissue culture has been subjected to an environmental stimulus.

18. The method of claim 17, wherein said environmental stimulus comprises a drug, a hormone, a cytokine, a transmitter, a toxin, an enzyme, an ion, temperature change, light, electrical stimuli, pH, oxidation, or mechanical stress.

19. The method of claim 1, wherein said biological sample is derived from a living organism.

20. The method of claim 19, wherein said organism has been contacted with one or more test substances, and said lipid profile is compared to the lipid profile of a comparable tissue culture sample that has not been contacted with said test substance(s).

21. The method of claim 20, wherein said test substance comprises a DNA, an RNA, a polypeptide, a peptide or small molecule.

22. The method of claim 19, wherein said organism has been subjected to an environmental stimulus.

23. The method of claim 22, wherein said environmental stimulus comprises a drug, a hormone, a cytokine, a transmitter, an enzyme, a toxin, an ion, temperature change, light, electrical stimuli, pH, oxidation, or mechanical stress.

24. The method of claim 1 , wherein mass spectroscopy is electrospray ionization mass spectroscopy.

25. The method of claim 1 , wherein said biological sample is from a non-human experimental animal.

26. The method of claim 1, wherein said biological sample is from a human.

27. The method of claim 1, wherein step (d) comprises data smoothing and self- normalization of said data array.

28. The method of claim 27, further comprising determining significance of both time and non-time components by statistical process control.

29. The method of claim 28, further comprising exception handling.

30. The method of claim 1, further comprising a second analysis on said biological sample.

31. The method of claim 30, wherein said second analysis comprises analysis of gene transcripts, analysis of a proteins, or analysis of cell signaling molecules.

32. A method of identifying the presence of an unknown lipid species in a biological sample comprising: (a) providing a biological sample comprising lipids; (b) subjecting essentially all of said lipids in said sample to simultaneous mass spectroscopy; (c) obtaining a lipid data array from step (b); (d) subjecting said lipid data array to statistical analysis to obtain a lipid profile for said biological sample; and (e) comparing said lipid profile to one or more known lipid profiles, wherein the existence of a unmatched member of lipid profile identifies said sample as containing an unknown lipid species.

33. The method of claim 32, further comprising identifying said unknown lipid species.

34. The method of claim 32, wherein identifying comprises collision induced mass spectroscopy.

35. The method of claim 34, further comprising nuclear magnetic resonance.