Time-of-flight secondary ion mass spectrometry imaging of subcellular lipid heterogeneity: Poisson counting and spatial resolution

Adipose tissue composition determines its computed tomography radiodensity

OBJECTIVES

Adipose tissue radiodensity in computed tomography (CT) performed before surgeries can predict surgical difficulty. Despite its clinical importance, little is known about what influences radiodensity. This study combines desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and electrospray ionization (ESI) with machine learning to unveil how chemical composition of adipose tissue determines its radiodensity.

METHODS

Patients in the study underwent abdominal surgeries. Before surgery, CT radiodensity of fat near operated sites was measured. Fifty-three fat samples were collected and analyzed by DESI-MSI, ESI, and histology, and then sorted by radiodensity, demographic parameters, and adipocyte size. A non-negative matrix factorization (NMF) algorithm was developed to differentiate between high and low radiodensities.

RESULTS

No associations between radiodensity and patient age, gender, weight, height, or fat origin were found. Body mass index showed negative correlation with radiodensity. A substantial difference in chemical composition between adipose tissues of high and low radiodensities was observed. More radiodense tissues exhibited greater abundance of high molecular weight species, such as phospholipids of various types, ceramides, cholesterol esters and diglycerides, and about 70% smaller adipocyte size. Less radiodense tissue showed high abundance of short acyl-tail fatty acids.

CONCLUSIONS

This study unveils the connection between abdominal adipose tissue radiodensity and its chemical composition. Because the radiodensity of the fat around the surgical site is associated with surgical difficulty, it is important to understand how adipose tissue composition affects this parameter. We conclude that fat tissue with a higher content of various phospholipids and waxy lipids is more CT radiodense.

CLINICAL RELEVANCE STATEMENT

This study establishes the connection between the CT radiodensity of adipose tissue and its chemical composition. Clinicians may use this information for preoperative planning of surgical procedures, potentially modifying their surgical approach (for example, performing partial nephrectomy openly rather than laparoscopically).

KEY POINTS

• Adipose tissue radiodensity values in computed tomography images taken prior to the surgery can potentially predict surgery difficulty. • Fifty-three human specimens were analyzed by advanced mass spectrometry, molecular imaging, and machine learning to establish the key features that determine Hounsfield units' values of adipose tissue. • The findings of this research will enable clinicians to better prepare for surgical procedures and select operative strategies.

Single-Cell Metabolic Profiling of Macrophages Using 3D OrbiSIMS: Correlations with Phenotype

Macrophages are important immune cells that respond to environmental cues acquiring a range of activation statuses represented by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at each end of their spectrum. Characterizing the metabolic signature (metabolic profiling) of different macrophage subsets is a powerful tool to understand the response of the human immune system to different stimuli. Here, the recently developed 3D OrbiSIMS instrument is applied to yield useful insight into the metabolome from individual cells after in vitro differentiation of macrophages into naïve, M1, and M2 phenotypes using different cytokines. This analysis strategy not only requires more than 6 orders of magnitude less sample than traditional mass spectrometry approaches but also allows the study of cell-to-cell variance. Characteristic metabolites in macrophage subsets are identified using a targeted lipid and data-driven multivariate approach highlighting amino acids and other small molecules. The diamino acids alanylasparagine and lipid sphingomyelin SM(d18/16:0) are uniquely found in M1 macrophages, while pyridine and pyrimidine are observed at increased intensity in M2 macrophages, findings which link to known biological pathways. The first demonstration of this capability illustrates the great potential of direct cell analysis for in situ metabolite profiling with the 3D OrbiSIMS to probe functional phenotype at the single-cell level using molecular signatures and to understand the response of the human body to implanted devices and immune diseases.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data

RATIONALE

Non-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time. We will inspect the results for MSI data analysis as these different mathematical approaches impose different characteristics on the data and the resulting decomposition.

METHODS

The four methods (NMF, KL-NMF, PLSA, and LDA) are compared on seven different samples: three originated from mice pancreas and four from human-lymph-node tissues, all obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).

RESULTS

Where matrix factorization methods are often used for the analysis of MSI data, we find that each method has different implications on the exactness and interpretability of the results. We have discovered promising results using KL-NMF, which has only rarely been used for MSI so far, improving both NMF and PLSA, and have shown that the hitherto stated equivalent KL-NMF and PLSA algorithms do differ in the case of MSI data analysis. LDA, assumed to be the better method in the field of text mining, is shown to be outperformed by PLSA in the setting of MALDI-MSI. Additionally, the molecular results of the human-lymph-node data have been thoroughly analyzed for better assessment of the methods under investigation.

CONCLUSIONS

We present an in-depth comparison of multiple NMF-related factorization methods for MSI. We aim to provide fellow researchers in the field of MSI a clear understanding of the mathematical implications using each of these analytical techniques, which might affect the exactness and interpretation of the results.

A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging

MOTIVATION

Probabilistic latent semantic analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how linear Poisson modelling advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond MS, using magnetic resonance imaging (MRI) data from colorectal xenograft models.

RESULTS

Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumours further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence.

AVAILABILITY AND IMPLEMENTATION

Open-source image analysis software available from TINA Vision, www.tina-vision.net.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ToF-SIMS 3D Analysis of Thin Films Deposited in High Aspect Ratio Structures via Atomic Layer Deposition and Chemical Vapor Deposition

For the analysis of thin films, with high aspect ratio (HAR) structures, time-of-flight secondary ion mass spectrometry (ToF-SIMS) overcomes several challenges in comparison to other frequently used techniques such as electron microscopy. The research presented herein focuses on two different kinds of HAR structures that represent different semiconductor technologies. In the first study, ToF-SIMS is used to illustrate cobalt seed layer corrosion by the copper electrolyte within the large through-silicon-vias (TSVs) before and after copper electroplating. However, due to the sample’s surface topography, ToF-SIMS analysis proved to be difficult due to the geometrical shadowing effects. Henceforth, in the second study, we introduce a new test platform to eliminate the difficulties with the HAR structures, and again, use ToF-SIMS for elemental analysis. We use data image slicing of 3D ToF-SIMS analysis combined with lateral HAR test chips (PillarHall™) to study the uniformity of silicon dopant concentration in atomic layer deposited (ALD) HfO₂ thin films.

Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra

Motivation

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian.

Results

We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth.

Availability and implementation

Software is available via the open source image analysis system TINA Vision, www.tina-vision.net.

Contact

paul.tar@manchester.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Correlating microscopy techniques and ToF-SIMS analysis of fully grown mammalian oocytes

An innovative protocol for the 2D-molecular thin film analysis applying ToF-SIMS, SEM, AFM and optical microscopy imaging of fully grown mice oocytes is described.

Visualization of acetaminophen-induced liver injury by time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (MS) provides secondary ion images that reflect distributions of substances with sub-micrometer spatial resolution. To evaluate the use of time-of-flight secondary ion MS to capture subcellular chemical changes in a tissue specimen, we visualized cellular damage showing a three-zone distribution in mouse liver tissue injured by acetaminophen overdose. First, we selected two types of ion peaks related to the hepatocyte nucleus and cytoplasm using control mouse liver. Acetaminophen-overdosed mouse liver was then classified into three areas using the time-of-flight secondary ion MS image of the two types of peaks, which roughly corresponded to established histopathological features. The ion peaks related to the cytoplasm decreased as the injury became more severe, and their origin was assumed to be mostly glycogen based on comparison with periodic acid-Schiff staining images and reference compound spectra. This indicated that the time-of-flight secondary ion MS image of the acetaminophen-overdosed mouse liver represented the chemical changes mainly corresponding to glycogen depletion on a subcellular scale. In addition, this technique also provided information on lipid species related to the injury. These results suggest that time-of-flight secondary ion MS has potential utility in histopathological applications.

Improving secondary ion mass spectrometry image quality with image fusion

The spatial resolution of chemical images acquired with cluster secondary ion mass spectrometry (SIMS) is limited not only by the size of the probe utilized to create the images but also by detection sensitivity. As the probe size is reduced to below 1 μm, for example, a low signal in each pixel limits lateral resolution because of counting statistics considerations. Although it can be useful to implement numerical methods to mitigate this problem, here we investigate the use of image fusion to combine information from scanning electron microscope (SEM) data with chemically resolved SIMS images. The advantage of this approach is that the higher intensity and, hence, spatial resolution of the electron images can help to improve the quality of the SIMS images without sacrificing chemical specificity. Using a pan-sharpening algorithm, the method is illustrated using synthetic data, experimental data acquired from a metallic grid sample, and experimental data acquired from a lawn of algae cells. The results show that up to an order of magnitude increase in spatial resolution is possible to achieve. A cross-correlation metric is utilized for evaluating the reliability of the procedure.

Imaging lipids with secondary ion mass spectrometry

This review discusses the application of time-of-flight secondary ion mass spectrometry (TOF-SIMS) and magnetic sector SIMS with high lateral resolution performed on a Cameca NanoSIMS 50(L) to imaging lipids. The similarities between the two SIMS approaches and the differences that impart them with complementary strengths are described, and various strategies for sample preparation and to optimize the quality of the SIMS data are presented. Recent reports that demonstrate the new insight into lipid biochemistry that can be acquired with SIMS are also highlighted. This article is part of a Special Issue entitled Tools to study lipid functions.

SIMS instrumentation and methodology for mapping of co-localized molecules

We describe an innovative mode for localizing surface molecules. In this methodology, individual C60 impacts at 50 keV are localized using an electron emission microscope, EEM, synchronized with a time-of-flight mass spectrometer for the detection of the concurrently emitted secondary ions. The instrumentation and methodologies for generating ion maps are presented. The performance of the localization scheme depends on the characteristics of the electron emission, those of the EEM and of the software solutions for image analysis. Using 50 keV C60 projectiles, analyte specific maps and maps of co-emitted species have been obtained. The individual impact sites were localized within 1-2 μm. A distinctive feature of recording individual impacts is the ability to identify co-emitted ions which originate from molecules co-located within ~10 nm.

Image and Spectral Processing for ToF-SIMS Analysis of Biological Materials

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) instruments can rapidly produce large complex data sets. Within each spectrum, there can be hundreds of peaks. A typical 256×256 pixel image contains 65,536 spectra. If this is extended to a 3D image, the number of spectra in a given data set can reach the millions. The challenge becomes how to process these large data sets while taking into account the changes and differences between all the peaks in the spectra. This is particularly challenging for biological materials that all contain the same types of proteins and lipids, just in varying concentrations and spatial distributions. This data analysis challenge is further complicated by the limitations in the ion yield of higher mass, more chemically specific species, and potentially by the processing power of typical computers. Herein we briefly discuss analysis methodologies including univariate analysis, multivariate analysis (MVA) methods, and some of the limitations of ToF-SIMS analysis of biological materials.

Correlated imaging--a grand challenge in chemical analysis

Correlated chemical imaging is an emerging strategy for acquisition of images by combining information from multiplexed measurement platforms to track, visualize, and interpret in situ changes in the structure, organization, and activities of interesting chemical systems, frequently spanning multiple decades in space and time. Acquiring and correlating information from complementary imaging experiments has the potential to expose complex chemical behavior in ways that are simply not available from single methods applied in isolation, thereby greatly amplifying the information gathering power of imaging experiments. However, in order to correlate image information across platforms, a number of issues must be addressed. First, signals are obtained from disparate experiments with fundamentally different figures of merit, including pixel size, spatial resolution, dynamic range, and acquisition rates. In addition, images are often acquired on different instruments in different locations, so the sample must be registered spatially so that the same area of the sample landscape is addressed. The signals acquired must be correlated in both spatial and temporal domains, and the resulting information has to be presented in a way that is readily understood. These requirements pose special challenges for image cross-correlation that go well beyond those posed in single technique imaging approaches. The special opportunities and challenges that attend correlated imaging are explored by specific reference to correlated mass spectrometric and Raman imaging, a topic of substantial and growing interest.

Secondary ion mass spectrometry imaging of biological membranes at high spatial resolution

Characterization of the distributions of specific proteins and lipids within cellular membranes is currently a major challenge. Advances in secondary ion mass spectrometry (SIMS) now enable the distributions of isotopically labeled lipids within cellular or model membranes to be imaged with chemical specificity and high (≥50 nm) lateral resolution. Here, methods to image the distributions of sphingolipids within the membranes of intact cells with a Cameca NanoSIMS are described. For NanoSIMS detection, the incorporation of distinct stable isotopes into the lipid species of interest is essential. Metabolic labeling, cell preservation, imaging conditions, and data analysis are critical factors. The methods and principles described here can be extended to studying other membrane lipids or cholesterol.

Use of mass spectrometry for imaging metabolites in plants

We discuss and illustrate recent advances that have been made to image the distribution of metabolites among cells and tissues of plants using different mass spectrometry technologies. These technologies include matrix-assisted laser desorption ionization, desorption electrospray ionization, and secondary ion mass spectrometry. These are relatively new technological applications of mass spectrometry and they are providing highly spatially resolved data concerning the cellular distribution of metabolites. We discuss the advantages and limitations of each of these mass spectrometric methods, and provide a description of the technical barriers that are currently limiting the technology to the level of single-cell resolution. However, we anticipate that advances in the next few years will increase the resolving power of the technology to provide unprecedented data on the distribution of metabolites at the subcellular level, which will increase our ability to decipher new knowledge concerning the spatial organization of metabolic processes in plants.

Fluorinated colloidal gold immunolabels for imaging select proteins in parallel with lipids using high-resolution secondary ion mass spectrometry

The local abundance of specific lipid species near a membrane protein is hypothesized to influence the protein's activity. The ability to simultaneously image the distributions of specific protein and lipid species in the cell membrane would facilitate testing these hypotheses. Recent advances in imaging the distribution of cell membrane lipids with mass spectrometry have created the desire for membrane protein probes that can be simultaneously imaged with isotope labeled lipids. Such probes would enable conclusive tests to determine whether specific proteins colocalize with particular lipid species. Here, we describe the development of fluorine-functionalized colloidal gold immunolabels that facilitate the detection and imaging of specific proteins in parallel with lipids in the plasma membrane using high-resolution SIMS performed with a NanoSIMS. First, we developed a method to functionalize colloidal gold nanoparticles with a partially fluorinated mixed monolayer that permitted NanoSIMS detection and rendered the functionalized nanoparticles dispersible in aqueous buffer. Then, to allow for selective protein labeling, we attached the fluorinated colloidal gold nanoparticles to the nonbinding portion of antibodies. By combining these functionalized immunolabels with metabolic incorporation of stable isotopes, we demonstrate that influenza hemagglutinin and cellular lipids can be imaged in parallel using NanoSIMS. These labels enable a general approach to simultaneously imaging specific proteins and lipids with high sensitivity and lateral resolution, which may be used to evaluate predictions of protein colocalization with specific lipid species.

Identifying individual cell types in heterogeneous cultures using secondary ion mass spectrometry imaging with C60 etching and multivariate analysis

Tissue engineering approaches fabricate and subsequently implant cell-seeded and unseeded scaffold biomaterials. Once in the body, these biomaterials are repopulated with somatic cells of various phenotypes whose identification upon explantation can be expensive and time-consuming. We show that imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS) can be used to distinguish mammalian cell types in heterogeneous cultures. Primary rat esophageal epithelial cells (REEC) were cultured with NIH 3T3 mouse fibroblasts on tissue culture polystyrene and freeze-dried before TOF-SIMS imaging. Results show that a short etching sequence with C(60)(+) ions can be used to clean the sample surface and improve the TOF-SIMS image quality. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used to identify peaks whose contributions to the total variance in the multivariate model were due to either the two cell types or the substrate. Using PLS-DA, unknown regions of cellularity that were otherwise unidentifiable by SIMS could be classified. From the loadings in the PLS-DA model, peaks were selected that were indicative of the two cell types and TOF-SIMS images were created and overlaid that showed the ability of this method to distinguish features visually.

From pixel to voxel: a deeper view of biological tissue by 3D mass spectral imaging

Three dimensional mass spectral imaging (3D MSI) is an exciting field that grants the ability to study a broad mass range of molecular species ranging from small molecules to large proteins by creating lateral and vertical distribution maps of select compounds. Although the general premise behind 3D MSI is simple, factors such as choice of ionization method, sample handling, software considerations and many others must be taken into account for the successful design of a 3D MSI experiment. This review provides a brief overview of ionization methods, sample preparation, software types and technological advancements driving 3D MSI research of a wide range of low- to high-mass analytes. Future perspectives in this field are also provided to conclude that the outlook for 3D MSI is positive and promises ever-growing applications in the biomedical field with continuous developments of this powerful analytical tool.

Compositional mapping of the surface and interior of mammalian cells at submicrometer resolution

We present progress toward imaging of chemical species within intact mammalian cells using secondary ion mass spectrometry, including the simultaneous mapping of subcellular elemental and molecular species along with intrinsic membrane-specific cellular markers. Results from imaging both the cell surface and cell interior exposed by site-specific focused ion beam milling demonstrate that in-plane resolutions of approximately 400-500 nm can be achieved. The results from mapping cell surface phosphatidylcholine and several other molecular ions present in the cells establish that spatially resolved chemical signatures of individual cells can be derived from novel multivariate analysis and classification of the molecular images obtained at different m/z ratios. The methods we present here for specimen preparation and chemical imaging of cell interiors provide the foundation for obtaining 3D molecular maps of unstained mammalian cells, with particular relevance for probing the subcellular distributions of small molecules, such as drugs and metabolites.

Correlated AFM and NanoSIMS imaging to probe cholesterol-induced changes in phase behavior and non-ideal mixing in ternary lipid membranes

Cholesterol is believed to be an important component in compositionally distinct lipid domains in the cellular plasma membrane, which are referred to as lipid rafts. Insight into how cholesterol influences the interactions that contribute to plasma membrane organization can be acquired from model lipid membranes. Here we characterize the lipid mixing and phase behavior exhibited by (15)N-dilaurolyphosphatidycholine ((15)N-DLPC)/deuterated distearoylphosphatiylcholine (D(70)-DSPC) membranes with various amounts of cholesterol (0, 3, 7, 15 or 19mol%) at room temperature. The microstructures and compositions of individual membrane domains were determined by imaging the same membrane locations with both atomic force microscopy (AFM) and high-resolution secondary ion mass spectrometry (SIMS) performed with a Cameca NanoSIMS 50. As the cholesterol composition increased from 0 to 19mol%, the circular ordered domains became more elongated, and the amount of (15)N-DLPC in the gel-phase domains remained constant at 6-7mol%. Individual and micron-sized clusters of nanoscopic domains enriched in D(70)-DSPC were abundant in the 19mol% cholesterol membrane. AFM imaging showed that these lipid domains had irregular borders, indicating that they were gel-phase domains, and not non-ideally mixed lipid clusters or nanoscopic liquid-ordered domains.

Mass spectrometry imaging of pharmacological compounds in tissue sections

The use of MS imaging (MSI) to resolve the spatial and pharmacodynamic distributions of compounds in tissues is emerging as a powerful tool for pharmacological research. Unlike established imaging techniques, only limited a priori knowledge is required and no extensive manipulation (e.g., radiolabeling) of drugs is necessary prior to dosing. MS provides highly multiplexed detection, making it possible to identify compounds, their metabolites and other changes in biomolecular abundances directly off tissue sections in a single pass. This can be employed to obtain near cellular, or potentially subcellular, resolution images. Consideration of technical limitations that affect the process is required, from sample preparation through to analyte ionization and detection. The techniques have only recently been adapted for imaging and novel variations to the established MSI methodologies will further enhance the application of MSI for pharmacological research.

Advances in mass spectrometry for lipidomics

Recent expansion in research in the field of lipidomics has been driven by the development of new mass spectrometric tools and protocols for the identification and quantification of molecular lipids in complex matrices. Although there are similarities between the field of lipidomics and the allied field of mass spectrometry (e.g., proteomics), lipids present some unique advantages and challenges for mass spectrometric analysis. The application of electrospray ionization to crude lipid extracts without prior fractionation-the so-called shotgun approach-is one such example, as it has perhaps been more successfully applied in lipidomics than in any other discipline. Conversely, the diverse molecular structure of lipids means that collision-induced dissociation alone may be limited in providing unique descriptions of complex lipid structures, and the development of additional, complementary tools for ion activation and analysis is required to overcome these challenges. In this article, we discuss the state of the art in lipid mass spectrometry and highlight several areas in which current approaches are deficient and further innovation is required.