ACS Publications. Most Trusted. Most Cited. Most Read
Ultra-High Mass Resolving Power, Mass Accuracy, and Dynamic Range MALDI Mass Spectrometry Imaging by 21-T FT-ICR MS
My Activity
  • Open Access
Article

Ultra-High Mass Resolving Power, Mass Accuracy, and Dynamic Range MALDI Mass Spectrometry Imaging by 21-T FT-ICR MS
Click to copy article linkArticle link copied!

  • Andrew P. Bowman
    Andrew P. Bowman
    Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The Netherlands
  • Greg T. Blakney
    Greg T. Blakney
    Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The Netherlands
  • Christopher L. Hendrickson
    Christopher L. Hendrickson
    National High Magnetic Field Laboratory, Florida State University, 1800 East Paul Dirac Drive, Tallahassee, Florida 32310-4005, United States
    Department of Chemistry and Biochemistry, Florida State University, 95 Chieftain Way, Tallahassee, Florida 32306, United States
  • Shane R. Ellis
    Shane R. Ellis
    Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The Netherlands
  • Ron M. A. Heeren*
    Ron M. A. Heeren
    Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The Netherlands
    *(R.M.A.H.) Email: [email protected]
  • Donald F. Smith*
    Donald F. Smith
    National High Magnetic Field Laboratory, Florida State University, 1800 East Paul Dirac Drive, Tallahassee, Florida 32310-4005, United States
    *(D.F.S.) Email: [email protected]
Open PDFSupporting Information (1)

Analytical Chemistry

Cite this: Anal. Chem. 2020, 92, 4, 3133–3142
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.analchem.9b04768
Published January 19, 2020

Copyright © 2020 American Chemical Society. This publication is licensed under CC-BY-NC-ND.

Abstract

Click to copy section linkSection link copied!

Detailed characterization of complex biological surfaces by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) requires instrumentation that is capable of high mass resolving power, mass accuracy, and dynamic range. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the highest mass spectral performance for MALDI MSI experiments, and often reveals molecular features that are unresolved on lower performance instrumentation. Higher magnetic field strength improves all performance characteristics of FT-ICR; mass resolving power improves linearly, while mass accuracy and dynamic range improve quadratically with magnetic field strength. Here, MALDI MSI at 21T is demonstrated for the first time: mass resolving power in excess of 1 600 000 (at m/z 400), root-mean-square mass measurement accuracy below 100 ppb, and dynamic range per pixel over 500:1 were obtained from the direct analysis of biological tissue sections. Molecular features with m/z differences as small as 1.79 mDa were resolved and identified with high mass accuracy. These features allow for the separation and identification of lipids to the underlying structures of tissues. The unique molecular detail, accuracy, sensitivity, and dynamic range combined in a 21T MALDI FT-ICR MSI experiment enable researchers to visualize molecular structures in complex tissues that have remained hidden until now. The instrument described allows for future innovative, such as high-end studies to unravel the complexity of biological, geological, and engineered organic material surfaces with an unsurpassed detail.

Copyright © 2020 American Chemical Society
Mass spectrometry imaging (MSI) has proven to be a versatile tool, finding applications in a variety of fields including diseased tissue classification, (1−4) bacterial infections and resistance, (5,6) and drug metabolism. (7,8) The main strength of MSI is the ability to simultaneously reveal the spatial distributions of multiple molecules in a single experiment from complex biological materials, typically tissue sections. (9) However, the chemically complex samples typically analyzed bring challenges associated with the mass resolution and unambiguous assignment of the many different molecules detected. Due to this complexity, many signals are often unresolved from isobaric ions, such that generated ion images are not reflective of one unique molecule. This is a major hindrance to the study of the biochemical changes within tissues.
The most popular approach to begin addressing this complexity is the coupling of high mass resolving power and high mass accuracy analyzers with MSI ion sources, most commonly matrix-assisted laser desorption/ionization (MALDI). (10) This combination allows mass resolution of many isobaric ion species and direct assignment of elemental composition, thereby providing insight into the specific identities of the detected molecules. For lipids that are arguably the most widespread analyte class studies with MSI, (11−14) high mass resolving power and accuracy can facilitate true identification of sum-composition formula (i.e., lipid class and the combined number of carbons and double bonds across both fatty acyl chains), when sufficient to allow separation from isobaric interferences. In comparison to other biological molecules, resolving lipid complexity is further complicated by their relatively narrow mass range, with the majority of signals observed between m/z 700–900. (15) Lipids can further be observed as multiple adducts (e.g., addition of H+, Na+, K+, OAc, Cl, or loss of H+) that are entangled with isotopes and other isobaric species. This results in a highly complex mass spectra that cannot be resolved with conventional high mass resolving power (e.g., ≤ 150 000 @ m/z 750). (15) Improvements in the achievable mass resolving of MSI technology is needed to unravel the spatial distributions of unique sum-composition lipid species that can have dramatically different biological functions
Fourier transform mass spectrometers; Fourier transform ion cyclotron resonance (FT-ICR) or orbital trapping (i.e., Orbitrap) offer higher mass resolving power and mass accuracy than other types of mass spectrometers (e.g., time-of-flight and ion trap). FT-ICR mass spectrometers provide the highest mass resolving power and mass accuracy of any mass analyzer, with up to parts-per-billion (ppb) mass accuracy, high dynamic range, and mass resolving power values greater than 1 000 000 in routine analyses. (16−18) Mass resolution and sensitivity in FT-ICR instrumentation can also be improved by the use of absorption mode processing, (18,19) although this has not yet been widely exploited for MSI applications. (17) In a proof-of-principle study, absorption mode MALDI FT-ICR MSI on a 9.4 T system provided mass resolving powers in excess of 250 000 for lipid species observed from mouse brain tissue. (18,20) Several studies have shown similar high mass resolution on Orbitrap systems, (21−23) though additional difficulties introduced in imaging systems typically report lower overall mass resolution. (24−26) High mass resolution is necessary to distinguish both nominally isobaric lipids, where common mass differences of less than 10 mDa (15) occur, as well as isotopic interferences, where mass differences less than 3 mDa occur. While many lower field FT-ICR and Orbitrap instruments can distinguish the more common isobaric interferences, they are typically incapable of resolving mass differences less than 3 mDa. (27−29) More recently, desorption-electrospray ionization-MSI using a 7T FT-ICR system combined with absorption mode processing and external acquisition electronics demonstrated resolving powers up to 1 000 000 for lipid species. (17) However, the number of ions had to be reduced to avoid space-charge and peak coalescence effects, which reduced the dynamic range by 2 orders of magnitude, and the m/z range was truncated (m/z 765–832). Higher magnetic field strength mitigates these problems, and enables larger ion populations to be analyzed, for high dynamic range broadband spectra at high mass resolution. The method described for DESI at 7 T helps overcome a key challenge in FT-ICR MSI by increasing the transient length while minimizing acquisition overhead, helping to balance the desired mass resolution with practical acquisition times for experiments that typically involve acquisitions of tens of thousands of spectra. These practical acquisition times are paramount within MALDI imaging, where the use of volatile matrices limits how long any single experiment can be performed before the matrix sublimes from the sample.
Outside of the improvements offered by absorption mode data processing, analysis times can be reduced by increasing the strength of the magnet used for FT-ICR. Mass resolution increases linearly with magnetic field strength, (30) allowing for decreases in transient length without sacrificing resolving power. In the context of typically long MSI acquisition times, this improvement can reduce experimental times by several hours, a significant increase in throughput. Multiple frequency detection promises an increase in mass resolving power that scales linearly with the frequency order multiple. (31) However, to date this technique has not been applied to mass spectrometry imaging, though significant progress has been shown in ESI-based methods, which have reported mass resolving power of more than 10 000 000 in the lipid range. (16,21,32)
The key parameters of FT-ICR that vary with magnetic field strength (dynamic range, mass accuracy, and ion-number induced frequency fluctuations) are especially important in MSI, due to the changes in ion yield depending on tissue type, (33) as well as a lack of control (e.g., via automatic gain control) over the number of ions entering the analyzer cell at each pixel. Further, the rich information available within the lipid range sees an enormous benefit from higher magnetic fields, in part from the biological dynamic range of lipids, but also from the number of nominally isobaric peaks possible in biological tissues. The advantage of increased mass resolution is obvious, but the improvement to mass accuracy and dynamic range can be crucial. High mass accuracy over long analysis times is important to generate highly accurate MSI images, as any drift in mass across an experiment would necessitate either pixel-to-pixel correction for this drift, or wider mass selection windows for image generation to encapsulate the ion as its apparent m/z shifts over time. High-field FT-ICR mass spectrometers offer external mass calibrations of less than 0.2 ppm (ppm), (34) and internal calibration less than 0.1 ppm. (35) High dynamic range is a key performance metric for MSI, given the wide dynamic range of lipid concentrations, (36) and differences in the ionization efficiencies of these biomolecules. Increased dynamic range is important to distinguish low abundance species while still detecting highly abundant lipids without distortion in relative ion abundances. The higher the magnetic field of an FT-ICR, the less susceptible it is to ion-number induced frequency shifts, which can hinder identification of peaks and complicate calibration of data sets, as has been described previously. (37−39)
Within the field of lipidomics, both shotgun and LC-MS based methodologies have achieved mass resolution in the lipid range greater than 100 000 along with sub-ppm mass accuracy, enabling assignment of 200–500 lipids in a single experiment. (40−42) Due to the increased fluctuations in signal intensity inherent to MSI, progress toward such endeavors is slower, success has been shown in a variety of FT based instruments with numerous ionization techniques, including Liquid Extraction Surface Analysis, (43) MALDI, (44) DESI, (17) and LAESI. (20) LAESI was performed on a 21 T FT-ICR mass spectrometer which separated the isotopic fine structure of nominally overlapping metabolites of plant leaves, which improved identification by utilizing multiple peaks per metabolite in the identification process. Additionally, the experimental time frame for the 21 T is significantly reduced compared to other instruments with similar mass resolution, without sacrificing either signal magnitude or mass range, as has been attempted with lower-field instruments. (17,20,35)
In this work, we evaluate for the first time the performance of MALDI MSI combined with 21 T FT-ICR MS for biological tissue imaging, as well as the use of automated annotation to begin exploring the highly complex information available from such experiments. In particular, we demonstrate (i) the combined higher mass resolving power and mass accuracy with the stability of these parameters across long MSI experiments; (ii) increased biochemical information obtained during MALDI MSI facilitated by the high mass resolving power and mass accuracy; (iii) single-pixel dynamic range exceeding 500:1, which enables imaging and identification of very low abundance ions; (iv) automated analytical tools to identify potentially hundreds of lipids utilizing thousands of peaks. Combined, this work demonstrates the high potential of MALDI MSI and 21 T FT-ICR for studying localized biomolecular processes within tissues and their disease-induced alterations.

Methods

Click to copy section linkSection link copied!

Materials

Methanol (LC-MS grade), ethanol (LC-MS grade), xylene (LC-MS grade), water (LC-MS grade), anhydrous chloroform (≥99.9% purity), and crystalline norharmane (9H-β-carboline) were purchased from Sigma-Aldrich (Zwijndrecht, The Netherlands) and used without further purification. Indium tin oxide (ITO)-coated glass slides were purchased from Delta Technologies (Loveland, CO).

Biological Samples

Healthy rat brain was obtained from Maastricht University in accordance with protocols approved by the Animal Care and Use Committee under Animal Experiment Committee (DEC) number 2016–006 AVD107002016720. Four transverse rat brain segments (12 μm thick) were sectioned with a cryo-microtome at −20 °C and thaw-mounted on ITO-coated glass slides. Some distortion of the tissue sections occurred during the mounting process.

Sample Preparation

Norharmane matrix (7 mg/mL) in CHCl3:MeOH (2:1 v/v) was applied to the tissue with a TM-Sprayer (HTX Technologies, Chapel Hill, NC). Spray conditions were as follows: flow rate, 0.12 mL/min; N2 pressure, 10 psi; N2 temperature, 30 °C; spray-head velocity, 1200 mm/min; track spacing, 3 mm; number of layers, 15; drying time between layers, 30 s.

Instrumentation

All MSI experiments were performed on a hybrid linear ion trap 21 T FT-ICR mass spectrometer at the National High Magnetic Field Laboratory (NHMFL) at Florida State University (Tallahassee, FL). A Velos Pro linear ion trap (Thermo Scientific, San Jose, CA) was combined with NHMFL-designed external linear quadrupole ion trap, quadrupole ion transfer optics, and a novel dynamically harmonized ICR cell, which is operated at 7.5-V trapping potential. (34) Briefly, the cell uses 120° cell segments for ion excitation and detection, for improved excitation electric field, detection sensitivity, and reduced third harmonic signals. (45,46)
The commercial ion source and stacked ring ion guide were replaced with an elevated-pressure MALDI ion source incorporating a dual-ion funnel interface (Spectroglyph LLC, Kennewick, WA) as has been described previously. (47) Voltages within the funnels were 625 kHz, 150 V peak-to-peak (first, high-pressure ion funnel), and 1.2 MHz, 90 V peak-to-peak (second, low-pressure ion funnel). An electric field gradient of ∼10 Vcm–1 was maintained within the dual-funnel system, with a gradient of 100 Vcm–1 between the sample and the funnel inlet. The system was equipped with a Q-switched, frequency-tripled Nd:YLF laser emitting 349 nm light (Explorer One, Spectra Physics, Mountain View, CA). The laser was operated at a repetition rate of 1 kHz and pulse energy of ∼1.2 μJ. Pressure within the ion source was set to 10 mbar in the first ion funnel, and 2 mbar in the second ion funnel. MALDI stage motion was synchronized with ion accumulation using the Velos trigger signal indicating commencement of the ion trap injection event, as previously described. (47) The mass spectrometer was operated with an ion injection time of 250 ms and automatic gain control (AGC) was turned off. A transient duration of 3.1 s was used for ultrahigh mass resolving power analyses, resulting in a total time of 4s per pixel. Spectra were obtained in both positive and negative mode, at 100 μm spatial resolution. Total number of pixels per brain section were approximately 22 000, and 24 h of experimental time. A Predator data station was used for ion excitation and detection. (48)

Data Processing and analysis

Absorption mode mass spectra were generated by phase correction of the time domain transients, (49) and peaks with a signal magnitude greater than 6 times the standard deviation of the baseline root-mean-square (RMS) noise were exported to peak lists. Mass calibration was performed on known lipid species, with a wide range of spectral intensities ([PC 34:1 + K]+, [SM 34:1;2 + H]+, [PE 36:4 13C + H]+, [PC 32:0 + Na]+, [PC 34:1 + H]+, [PC 38:4 + Na]+, and [PC 38:4 + K]+) and the data were converted to imzML format using in-house Matlab routines, msconvert from the ProteoWizard software suite (version 3.0.11537), (50) and imzMLConverter version 1.3. (51) The ALEX123 software package was used for sum-composition lipid identification at a search tolerance of 1 mDa. (41,52) All phospholipid classes were chosen, as were sphingolipids and glycerolipids, with chain-lengths of 14 carbons or greater. Adducts were limited to H+, Na+, and K+, and negative mode was restricted here to simple loss of H+. Images generated are normalized to the total ion current per pixel (TIC).

Results and Discussion

Click to copy section linkSection link copied!

High Mass Resolving Power

To assess the benefits of performing 21 T MALDI MSI in terms of mass resolving power, we analyzed rat brain sections in both positive and negative ion mode using different transient acquisition times. Figure 1 shows the achieved mass resolving power in the positive-ion mode using 0.76, 1.55, and 3.1 s transients within the m/z range 785.52–785.6. Increasing mass resolution shows increasing spectral complexity, as five peaks are resolved from what first appears to be only two, with two additional peaks within 100 mDa which were sufficiently distinct to be identified at all transient lengths. We annotated the seven peaks within this region as belonging to six different species of lipids: [PE(36:1)+K]+, [PC-O(34:1)+K]+, [PC(34:1)+Na]+, [PC(36:4)+H]+, [PC(34:0)+Na]+, and [PC(36:3)+H]+. Of these, four are the 13C1 isotope: ([PE(36:1)+13C+K]+, [PC-O(34:1)+13C+K]+, [PC(34:0)+13C+Na]+, and [PC(36:3)+13C+H]+), two are the 13C3 isotope ([PC(34:1)+ 13C3+Na]+ and [PC(36:4)+ 13C3+H]+), and the final peak is the 13C18O isotope ([PC(34:1)+ 13C18O+Na]+). These peaks show mass accuracy errors between −50 and 13 parts-per-billion (ppb). Additionally, isotope ratios in the summed average spectra deviate <15% from theoretical in these seven peaks (SI Figure 1), offering additional certainty in that correct sum-composition identification has been made, as well that there are no convoluted peaks being presented as a single peak. Deviation from the expected 2-fold improvement in mass resolving power upon doubling of the transient duration is due to known collisional damping during the detection event. (34) Current work focuses on a solution to limit transmission of the neutral buffer gas in the external accumulation multipole to the ultrahigh vacuum region. Recently, a mass resolving power of ∼600 000 (at m/z 760) for MALDI MSI on a 15 T FT-ICR MS (the highest commercially magnetic field available for FT-ICR) was reported. This value also deviates from the theoretical mass resolving power for a 5.2 s transient (magnitude mode), which is ∼788 000. (53)

Figure 1

Figure 1. Mass resolution and sensitivity improve with longer transient length. Within a 100 mDa mass range, seven different peaks are detected, which belong to six different lipid species. Of these, five are unresolved at 0.77 s. While distinguishable at 1.55 s, all seven peaks are fully resolved only at 3.1 s transient. These seven peaks correspond to the isotopologues of the monoisotopic species, typically the 13C ion, as in (a), (b), (f), and (g). Other species are also present, corresponding to the 13C3 isotopologue, as in (d) and (e). The 18O13C isotopologue of [PC(34:1)+Na]+ is also resolved (c) from the 13C3 isotopologue of the same parent species.

To further assess the utility of the 21 T, we analyzed the data set for peaks with close neighbors (here defined as <10 mDa). We extracted the 3.1 s transient from a single pixel (number 10 000) as a representative spectrum from each data set. In positive-ion mode, a difference of 0.0024 Da (2.4 mDa) at m/z 810 was present (Figure 2a), representing the difference between Na1H1 versus C2 (the addition of two carbon atoms and three double bonds to the lipid fatty acid chains) which requires a mass resolving power (m/Δm50%) of 337 000 at m/z 810 to resolve. These two ions were well resolved, and lipid identities were assigned [PC(36:1)+Na]+ and [PC(38:4+H)]+ with high confidence (100 ppb, see discussion below). Each species had very different spatial distributions, with the former ([PC(36:1)+Na]+) being relatively evenly distributed (Figure 2b), while the latter ([PC(38:4+H)]+) had higher abundance in the lateral ventricle (Figure 2c). The higher abundance of [PC(38:4)+Na]+ in the ventricles matches with its role as a pro-inflammatory cytokine. (54) Interestingly, such a small mass difference was not uncommon, with a mass difference of 2.4 mDa observed over 190 times in any single pixel spectrum, and more than 1 000 000 times over a single MSI experiment (Figure 2d). Without sufficient mass resolving power, any one of the images of these ∼190 pairs of closely spaced ions could yield incorrect assignments and yield a summed spatial distribution reflective of neither individual species. A variety of other recurrent mass differences can be detected in the single spectra, ranging from 1 to 10 mDa, including isotopic patterns (e.g., 13C2 vs H2 is a difference of 8.94 mDa). The change in 13C2 vs H2 is an important one, as this denotes the possible overlap for species that differ by a single double bond (i.e., as PC(34:1) to PC(34:0)). Single unsaturation changes have been shown to be important in various types of disease states, including cancers (55,56) and multiple sclerosis, (57) and so the ability to resolve such fine mass differences opens the door to studying the precise roles of the subtle changes in lipid structure throughout tissues.

Figure 2

Figure 2. Representative images of close mass differences in negative and positive mode, from a single, scan. Images are total ion current normalized. Positive mode lipid spectra have a significant number of mass differences of 2.4 mDa (a), representing the difference between 12C2 and 23Na1H. 2.4 mDa differences are baseline resolved, and show significantly different distributions within brain tissue (b and c). There are nearly 200 such differences in the averaged spectra, shown in 0.5 mDa bins (d). Similarly, negative mode spectra have 1.79 mDa mass differences (e). These 1.79 mDa differences are resolved to better than full-width half-maximum, differentiated well enough to distinguish them in brain tissue (f and g). The of 1.79 mDa mass difference is relatively uncommon in negative mode, but mass differences of 10 mDa or less occur approximately 500 times in the averaged spectra, shown in 0.25 mDa bins (h).

Using the same experimental design in the negative-ion mode, additional small mass differences could be resolved. For example, a mass difference of 0.00179 Da (1.79 mDa) at m/z 757.52 was observed at 31 different masses. This corresponds the mass difference of C2N113C1 versus H3O3 (Figure 2e). While less common than the NaH vs C2 mass difference in positive mode, ether-linked phosphatidylethanolamine (PE) and PC lipids can have this difference from the phosphatidylglycerol (PG) class. These peaks were thus identified as phosphatidylethanolamine [PE(O-38:7)+13C–H] and [PG(34:1)-H]. This is the smallest mass difference observed in any MSI data set to date. The PE is a 13C-containing nuclide of the monoisotopic PE lipid at m/z 746.51300. PE and PG lipids are synthesized by different biological pathways and have different physiological function. PE lipids are ∼20% of all phospholipids, and are especially abundant in white matter of the cerebellum (Figure 2f). (58) By contrast, PG lipids are associated with ATP-Binding Cassette 3, though what transport function is utilized is unknown. (59) The 1.79 mDa mass difference occurred over 100 000 times in our MSI experiment, with 33 unique pairs detected in the total mass spectrum. As in the positive mode, the 13C2 vs H2 difference occurs regularly, and has many of the same ramifications as discussed above.

Dynamic Range

One of the most problematic complications in MSI is the low relative ionization efficiency from the surface, which combined with the wide range of analyte concentrations, places significant demands on the single scan dynamic range achievable in an MSI experiment. High sensitivity and dynamic range are thus necessary to detect low abundance and/or poorly ionized species without distorting the peak abundances obtained from high intensity signals. Figure 3a shows a single pixel mass spectrum of the lipid m/z range from the positive-ion mode data set (scan no. 10 000), which has a dynamic range of 536:1 (expanded mass range spectrum shown in SI Figure 2). The dynamic range was calculated by dividing the signal magnitude of the base peak by the peak detection threshold of six standard deviations (6σ) above the baseline noise. As typically observed from brain tissue, the [M+K]+ ion of PC(34:1) generated the highest signal magnitude, with a signal-to-noise = 2370:1 (Figure 3b; side lobe artifacts are a result of the absorption mode processing, and current work is focused on their removal). By contrast, rat brain tissue sections prepared from the same original organ and under the same conditions showed a signal-to-noise = 336:1 on a Thermo Orbitrap Elite set at 240 000 resolving power (@ m/z 400) at the Maastricht MultiModal Molecular Imaging Institute (data not shown). Using a peak detection threshold of 6σ above the baseline noise, the lowest intensity signal was observed with a signal-to-noise = 6 and corresponded to the (PE(46:5)+13C2+H)+ (Figure 3c). SI Figure 3 shows the isotopic distribution for PE 46:5, where the 13C2 containing nuclide can be identified at M+2. In addition, SI Figure 4 shows ppm error distributions for the monoisotopic peak, M+1 (13C1), and M+2 (13C2) which show good mass accuracy, despite the low S/N of the M+2 peak. Per pixel, the average dynamic range in positive ion mode was 438:1, with a maximum dynamic range of 2090:1 and a minimum of 60:1 (SI Figure 5). Negative-ion mode spectra had lower signal magnitude than positive mode, limiting the average dynamic range to 214:1, with a maximum of 849:1 and minimum of 30:1 (SI Figure 6).

Figure 3

Figure 3. Single on-tissue mass spectrum illustrates high dynamic range per pixel. Peaks were picked at a threshold of six standard deviations above the baseline noise. Dynamic range in a single average pixel of at 536:1 is demonstrated here at pixel number 10 000, (a). Mass scale expanded segment around most abundant peak [PC 34:1 + K]+ (b). Further, peak at 798.5410 generates a bright image (b). One of the lowest S/N peaks, the 13C2 isotope of [PE 46:5 + H]+ (c) while less clear, still yields informative molecular images, being highlighted especially in the ventricles (images are TIC normalized).

High Mass Accuracy

FT-ICR MSI at 21 T showed a root-mean-square (rms) mass measurement accuracy of 62.12 ppb (Figure 4a), over 2-fold lower rms mass accuracy achieved on a 9 T instrument, which was limited to an rms of 158 ppb. (60) The center of the distribution is centered near zero, and the low standard deviation indicates low m/z fluctuation during the imaging experiment. SI Figure 7 shows the measured m/z variation for [PC(36:1)+H]+ (m/z = 788.61638, dotted red line indicates the exact m/z) over the imaging experiment, where the maximum m/z deviation is 0.00018, with a standard deviation of 0.00078. Internal calibration was performed using seven tentatively identified lipid masses ([PC(34:1)+K]+, [SM(34:1;2)+H]+, [PE(36:4)+13C+H]+, [PC(32:0)+Na]+, [PC(34:1)+H]+, [PC(38:4)+Na]+, and [PC(38:4)+K]+). After this internal calibration, all scans were summed (in the mass domain), which generated an initial peak list of 2,643 above the 6σ noise limit. This list was then submitted to ALEX123 for identification. We tentatively identify 702 monoistopic lipid peaks in positive-ion mode, which all have mass accuracy values of ±150 ppb (Figure 4b). These 702 lipid peaks correspond to 388 unique lipid IDs, after accounting for three possible cations types, which accounts for 26.9% of the initial peak list. SI Table 1 contains a full list of these lipids. An additional 1400 spectral peaks are as isotopologues (typically 13C and 13C2) of the 702 lipids, which accounts for ∼80% of all peaks. Negative ion mode yielded similar results, where 662 potential monoisotopic lipid peaks (34%) were identified out of an initial peak list of 1927. Due to the lower S/N of the negative mode spectra, only 738 further peaks were identified as isotopes, for a total of ∼72.6% of all peaks identified.

Figure 4

Figure 4. Error histogram and average mass error of tentatively identified lipids after internal calibration. Measured mass error histogram of 139 phosphatidylcholine lipids; the rms error is 61.12 ppb. Bin size = 10 ppb. (a), Lipid identifications by class. A tolerance of ±250 ppb results in 702 potential lipids identified within 150 ppb of their expected mass (b).

These lipid IDs are supported both by the high mass accuracy (<150 ppb, most <100 ppb) and in the positive mode by the intensity of multiple cations for the same species, relative to one another (Figure 5a). As protonation, sodiation, and potassiation are all potentially available in brain MSI, we examined the potential to confirm our lipid identifications by comparing all three cations. For the most abundant lipid (PC(34:1)), the [M+K] ion has an average ppb error of 16.7, [M + Na] −43.5, and [M + H] 2.2. While these mass errors are low enough individually to be highly confident in their assignment, having all three ions within 60 ppb of one another provides another layer of certainty. Additionally, we can examine the normalized peak intensities of all three ions to one another, in this case showing 100%, 47.9%, and 21.4%, simplified to a ratio of 4.7:2.2:1. While this insight is not necessarily informative on its own, we can compare this ratio to other PCs, with all the PCs above 3% of the base peak showing the same ratio (Figure 5a). Further, PCs that vary in relative intensity down to 0.2% of the base peak have generally similar ratios to PC(34:1), although as the intensities begin to approach the 6σ limit, the ratios begin to deviate and be less similar (Figure 5b). One likely scenario for this discrepancy at low S/N is that as peaks for any given scan drop below the 6σ threshold, the least abundant ions are ignored, leading to sum signal magnitudes in the averaged spectrum that are slightly erroneous. However, as the relative ratios of the three cations are invariable across three orders of magnitude, it improves our certainty that each identification is correct for all lipids within that class. While we observe no alterations to this ratio in the abundant lipid classes, theoretically alterations to this standard ratio could indicate greater abundance of a given lipid within different brain structures (i.e., within the ventricle space rather than within gray or white matter). It is worthwhile to further explore the potentials here, and whether there are observable changes to this ratio between other brain tissues. Additionally, we observe that other lipid classes show similar, though slightly different ratios (SI Figure 8), potentially related to the changes in brain tissue. Negative ionization does not typically have multiple ions of the same species (with deprotonation being the only common method of generating lipid anions unless dopants are added (61,62)); however, between the most abundant phosphatidylserine (PS(40:6)) and the least abundant (PS(34:4)) there is only a change in ppb error of 17.3 despite a change in intensity of more than an order of magnitude (Figure 5c).

Figure 5

Figure 5. Relative abundance of identified lipids by cation and anion for selected classes. In the positive mode, the three major cations (proton, sodium, and potassium) are aligned next to one another, showing the same relative percentages between species, from the most abundant species (PC 34:1) and the other PCs above 3% (a), as well as for the lower abundant species down to the least abundant species with all three cations represented, PC 44:12 (b). The relative ionization rate between K+, H+, and Na+ hold strictly true down to 1.5%, and generally true down to 0.05%. While the dynamic range is lower for negative mode, we see many potential identifications for many lipid classes (c). We further observe a similar ability to identify potential lipids as low as 0.15% of the most abundant peak (PI 38:4), for a range of nearly three orders of magnitude from the summed spectra (d).

Conclusion

Click to copy section linkSection link copied!

We have demonstrated the utility of combining MSI workflows with a 21 T FT-ICR mass spectrometer. The high magnetic field, combined with a state-of-the-art ICR cell design provides ultrahigh mass resolving power, ppb mass measurement accuracy, and high sensitivity for molecular imaging studies. This advanced instrumentation will pave the way for better understanding of the molecular structure of many tissue types, as well as clarifying current ambiguities in MSI. The unique capabilities of this instrument have not yet been fully utilized: online tandem mass spectrometry is possible via collision induced dissociation in the linear ion trap, or in the ICR cell via infrared multiphoton dissociation or ultraviolet photo dissociation. Further, the use of harmonic detection cells would further increase the speed of acquisition in these experiments or allow for even higher mass resolving power in a similar time frame. Combined with data-driven MSI acquisition techniques (such as Data-Dependent Acquisition), this instrument promises the most information per unit time of any MSI platform. The estimated number of charges sent to the ICR cell in these experiments is ∼4 × 105, based on the mass spectral calibration parameters. The 21T FT-ICR routinely operates with ion targets of 1–3 × 106, so additional improvement in dynamic range per pixel is expected. Further, we aim to leverage the unique capabilities of this instrument for other biomolecule imaging experiments, such as metabolites, tryptic peptides, and intact proteins.. This instrument will provide valuable insight into the molecular complexity of tissues at an unprecedented mass spectral resolution, allowing for greater insight into the true distribution of all molecules, as well as accelerating the identification of subtle changes hidden within tissues.

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.9b04768.

  • Eight additional figures as described in the text and table with a complete list of all tentatively identified lipid species (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!

  • Corresponding Authors
  • Authors
    • Andrew P. Bowman - Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The Netherlands
    • Greg T. Blakney - Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The NetherlandsOrcidhttp://orcid.org/0000-0002-4205-9866
    • Christopher L. Hendrickson - National High Magnetic Field Laboratory, Florida State University, 1800 East Paul Dirac Drive, Tallahassee, Florida 32310-4005, United StatesDepartment of Chemistry and Biochemistry, Florida State University, 95 Chieftain Way, Tallahassee, Florida 32306, United States
    • Shane R. Ellis - Maastricht MultiModal Molecular Imaging (M4I) Institute, Division of Imaging Mass Spectrometry (IMS), Maastricht University, Universiteitssingel 50, Maastricht 6629ER, The NetherlandsOrcidhttp://orcid.org/0000-0002-3326-5991
  • Author Contributions

    R.M.A.H. and D.F.S. conceived and coordinated the experiments. A.P.B., C.L.H., S.R.E., R.M.A.H., and D.F.S. designed the experiments. A.P.B., S.R.E., R.M.A.H., and D.F.S. carried out the MSI experiments. A.P.B., G.T.B., S.R.E., and D.F.S., conducted data processing and analysis. G.T.B., C.L.H., and R.M.A.H. provided feedback and quality control of MSI data. A.P.B., S.R.E., and D.F.S. wrote the manuscript, which was edited by all coauthors.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

Click to copy section linkSection link copied!

A portion of this work was performed at the National High Magnetic Field Laboratory ICR User Facility, which is supported by the National Science Foundation Division of Chemistry through DMR-1644779 and the State of Florida. The 21T FT-ICR is available free of charge to all qualified users as part of the NSF High Field FT-ICR Mass Spectrometry User Facility. Part of this work was financially supported through the LINK program of the Dutch province of Limburg. Part of this work was financially supported through the EURLIPIDS program of Euregio.

References

Click to copy section linkSection link copied!

This article references 62 other publications.

  1. 1
    Vaysse, P. M.; Heeren, R. M. A.; Porta, T.; Balluff, B. Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations. Analyst 2017, 142 (15), 26902712,  DOI: 10.1039/C7AN00565B
  2. 2
    Lazar, A. N.; Bich, C.; Panchal, M.; Desbenoit, N.; Petit, V. W.; Touboul, D.; Dauphinot, L.; Marquer, C.; Laprevote, O.; Brunelle, A.; Duyckaerts, C. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging reveals cholesterol overload in the cerebral cortex of Alzheimer disease patients. Acta Neuropathol. 2013, 125 (1), 13344,  DOI: 10.1007/s00401-012-1041-1
  3. 3
    Chaurand, P.; Schwartz, S. A.; Caprioli, R. M. Assessing Protein Patterns in Disease Using Imaging Mass Spectrometry. J. Proteome Res. 2004, 3 (2), 245252,  DOI: 10.1021/pr0341282
  4. 4
    Chen, Y.; Allegood, J.; Liu, Y.; Wang, E.; Cachon-Gonzalez, B.; Cox, T. M.; Merrill, A. H., Jr.; Sullards, M. C. Imaging MALDI mass spectrometry using an oscillating capillary nebulizer matrix coating system and its application to analysis of lipids in brain from a mouse model of Tay-Sachs/Sandhoff disease. Anal. Chem. 2008, 80 (8), 27808,  DOI: 10.1021/ac702350g
  5. 5
    Scott, A. J.; Post, J. M.; Lerner, R.; Ellis, S. R.; Lieberman, J.; Shirey, K. A.; Heeren, R. M. A.; Bindila, L.; Ernst, R. K. Host-based lipid inflammation drives pathogenesis in Francisella infection. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (47), 1259612601,  DOI: 10.1073/pnas.1712887114
  6. 6
    Hoefler, B. C.; Gorzelnik, K. V.; Yang, J. Y.; Hendricks, N.; Dorrestein, P. C.; Straight, P. D. Enzymatic resistance to the lipopeptide surfactin as identified through imaging mass spectrometry of bacterial competition. Proc. Natl. Acad. Sci. U. S. A. 2012, 109 (32), 130827,  DOI: 10.1073/pnas.1205586109
  7. 7
    Schulz, S.; Becker, M.; Groseclose, M. R.; Schadt, S.; Hopf, C. Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development. Curr. Opin. Biotechnol. 2019, 55, 5159,  DOI: 10.1016/j.copbio.2018.08.003
  8. 8
    Castellino, S.; Groseclose, M. R.; Wagner, D. MALDI imaging mass spectrometry: bridging biology and chemistry in drug development. Bioanalysis 2011, 3 (21), 242741,  DOI: 10.4155/bio.11.232
  9. 9
    Ellis, S. R.; Bruinen, A. L.; Heeren, R. M. A critical evaluation of the current state-of-the-art in quantitative imaging mass spectrometry. Anal. Bioanal. Chem. 2014, 406 (5), 127589,  DOI: 10.1007/s00216-013-7478-9
  10. 10
    Taban, I. M.; Altelaar, A. F. M.; Van der Burgt, Y. E. M.; McDonnell, L. A.; Heeren, R. M. A.; Fuchser, J.; Baykut, G. Imaging of peptides in the rat brain using MALDI-FTICR mass spectrometry. J. Am. Soc. Mass Spectrom. 2007, 18 (1), 145151,  DOI: 10.1016/j.jasms.2006.09.017
  11. 11
    Bowman, A. P.; Heeren, R. M. A.; Ellis, S. R., Advances in mass spectrometry imaging enabling observation of localised lipid biochemistry within tissues. TrAC, Trends Anal. Chem. 2019. 120 115197 DOI: 10.1016/j.trac.2018.07.012
  12. 12
    Zhao, C.; Xie, P. S.; Yang, T.; Wang, H. L.; Chung, A. C. K.; Cai, Z. W. Identification of glycerophospholipid fatty acid remodeling by using mass spectrometry imaging in bisphenol S induced mouse liver. Chin. Chem. Lett. 2018, 29 (8), 12811283,  DOI: 10.1016/j.cclet.2018.01.034
  13. 13
    Sugiyama, E.; Yao, I.; Setou, M. Visualization of local phosphatidylcholine synthesis within hippocampal neurons using a compartmentalized culture system and imaging mass spectrometry. Biochem. Biophys. Res. Commun. 2018, 495 (1), 10481054,  DOI: 10.1016/j.bbrc.2017.11.108
  14. 14
    Sans, M.; Feider, C. L.; Eberlin, L. S. Advances in mass spectrometry imaging coupled to ion mobility spectrometry for enhanced imaging of biological tissues. Curr. Opin. Chem. Biol. 2018, 42, 138146,  DOI: 10.1016/j.cbpa.2017.12.005
  15. 15
    Bielow, C.; Mastrobuoni, G.; Orioli, M.; Kempa, S. On Mass Ambiguities in High-Resolution Shotgun Lipidomics. Anal. Chem. 2017, 89 (5), 29862994,  DOI: 10.1021/acs.analchem.6b04456
  16. 16
    Shaw, J. B.; Lin, T. Y.; Leach, F. E., 3rd; Tolmachev, A. V.; Tolic, N.; Robinson, E. W.; Koppenaal, D. W.; Pasa-Tolic, L. 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometer Greatly Expands Mass Spectrometry Toolbox. J. Am. Soc. Mass Spectrom. 2016, 27 (12), 19291936,  DOI: 10.1007/s13361-016-1507-9
  17. 17
    Kooijman, P. C.; Nagornov, K. O.; Kozhinov, A. N.; Kilgour, D. P. A.; Tsybin, Y. O.; Heeren, R. M. A.; Ellis, S. R. Increased throughput and ultra-high mass resolution in DESI FT-ICR MS imaging through new-generation external data acquisition system and advanced data processing approaches. Sci. Rep. 2019, 9 (1), 8,  DOI: 10.1038/s41598-018-36957-1
  18. 18
    Smith, D. F.; Kilgour, D. P.; Konijnenburg, M.; O’Connor, P. B.; Heeren, R. M. Absorption mode FTICR mass spectrometry imaging. Anal. Chem. 2013, 85 (23), 111804,  DOI: 10.1021/ac403039t
  19. 19
    Qi, Y.; Barrow, M. P.; Li, H.; Meier, J. E.; Van Orden, S. L.; Thompson, C. J.; O’Connor, P. B. Absorption-mode: the next generation of Fourier transform mass spectra. Anal. Chem. 2012, 84 (6), 29239,  DOI: 10.1021/ac3000122
  20. 20
    Stopka, S. A.; Samarah, L. Z.; Shaw, J. B.; Liyu, A. V.; Velickovic, D.; Agtuca, B. J.; Kukolj, C.; Koppenaal, D. W.; Stacey, G.; Pasa-Tolic, L.; Anderton, C. R.; Vertes, A. Ambient Metabolic Profiling and Imaging of Biological Samples with Ultrahigh Molecular Resolution Using Laser Ablation Electrospray Ionization 21 T FTICR Mass Spectrometry. Anal. Chem. 2019, 91 (8), 50285035,  DOI: 10.1021/acs.analchem.8b05084
  21. 21
    Schwudke, D.; Schuhmann, K.; Herzog, R.; Bornstein, S. R.; Shevchenko, A. Shotgun lipidomics on high resolution mass spectrometers. Cold Spring Harbor Perspect. Biol. 2011, 3 (9), a004614  DOI: 10.1101/cshperspect.a004614
  22. 22
    Hu, C.; Duan, Q.; Han, X. Strategies to Improve/Eliminate the Limitations in Shotgun Lipidomics. Proteomics 2019, e1900070  DOI: 10.1002/pmic.201900070
  23. 23
    Ryan, E.; Reid, G. E. Chemical Derivatization and Ultrahigh Resolution and Accurate Mass Spectrometry Strategies for “Shotgun” Lipidome Analysis. Acc. Chem. Res. 2016, 49 (9), 1596604,  DOI: 10.1021/acs.accounts.6b00030
  24. 24
    Wildburger, N. C.; Wood, P. L.; Gumin, J.; Lichti, C. F.; Emmett, M. R.; Lang, F. F.; Nilsson, C. L. ESI-MS/MS and MALDI-IMS Localization Reveal Alterations in Phosphatidic Acid, Diacylglycerol, and DHA in Glioma Stem Cell Xenografts. J. Proteome Res. 2015, 14 (6), 25119,  DOI: 10.1021/acs.jproteome.5b00076
  25. 25
    Holcapek, M.; Cervena, B.; Cifkova, E.; Lisa, M.; Chagovets, V.; Vostalova, J.; Bancirova, M.; Galuszka, J.; Hill, M. Lipidomic analysis of plasma, erythrocytes and lipoprotein fractions of cardiovascular disease patients using UHPLC/MS, MALDI-MS and multivariate data analysis. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2015, 990, 5263,  DOI: 10.1016/j.jchromb.2015.03.010
  26. 26
    Korte, A. R.; Yandeau-Nelson, M. D.; Nikolau, B. J.; Lee, Y. J. Subcellular-level resolution MALDI-MS imaging of maize leaf metabolites by MALDI-linear ion trap-Orbitrap mass spectrometer. Anal. Bioanal. Chem. 2015, 407 (8), 23019,  DOI: 10.1007/s00216-015-8460-5
  27. 27
    Olsen, J. V.; de Godoy, L. M.; Li, G.; Macek, B.; Mortensen, P.; Pesch, R.; Makarov, A.; Lange, O.; Horning, S.; Mann, M. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 2005, 4 (12), 201021,  DOI: 10.1074/mcp.T500030-MCP200
  28. 28
    Makarov, A.; Denisov, E.; Lange, O.; Horning, S. Dynamic range of mass accuracy in LTQ Orbitrap hybrid mass spectrometer. J. Am. Soc. Mass Spectrom. 2006, 17 (7), 977982,  DOI: 10.1016/j.jasms.2006.03.006
  29. 29
    Scigelova, M.; Makarov, A. Orbitrap mass analyzer--overview and applications in proteomics. Proteomics 2006, 6 (Suppl 2), 1621,  DOI: 10.1002/pmic.200600528
  30. 30
    Marshall, A. G.; Hendrickson, C. L.; Jackson, G. S. Fourier transform ion cyclotron resonance mass spectrometry: A primer. Mass Spectrom. Rev. 1998, 17 (1), 135,  DOI: 10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K
  31. 31
    Nikolaev, E. N.; Gorshkov, M. V.; Mordehai, A. V.; Talrose, V. L. Ion cyclotron resonance signal-detection at multiples of the cyclotron frequency. Rapid Commun. Mass Spectrom. 1990, 4 (5), 144146,  DOI: 10.1002/rcm.1290040503
  32. 32
    Walker, L. R.; Tfaily, M. M.; Shaw, J. B.; Hess, N. J.; Pasa-Tolic, L.; Koppenaal, D. W. Unambiguous identification and discovery of bacterial siderophores by direct injection 21 T Fourier transform ion cyclotron resonance mass spectrometry. Metallomics 2017, 9 (1), 8292,  DOI: 10.1039/C6MT00201C
  33. 33
    Zimmerman, T. A.; Monroe, E. B.; Tucker, K. R.; Rubakhin, S. S.; Sweedler, J. V. Chapter 13: Imaging of cells and tissues with mass spectrometry: adding chemical information to imaging. Methods Cell Biol. 2008, 89, 36190,  DOI: 10.1016/S0091-679X(08)00613-4
  34. 34
    Hendrickson, C. L.; Quinn, J. P.; Kaiser, N. K.; Smith, D. F.; Blakney, G. T.; Chen, T.; Marshall, A. G.; Weisbrod, C. R.; Beu, S. C. 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometer: A National Resource for Ultrahigh Resolution Mass Analysis. J. Am. Soc. Mass Spectrom. 2015, 26 (9), 162632,  DOI: 10.1007/s13361-015-1182-2
  35. 35
    Smith, D. F.; Podgorski, D. C.; Rodgers, R. P.; Blakney, G. T.; Hendrickson, C. L. 21 T FT-ICR Mass Spectrometer for Ultrahigh-Resolution Analysis of Complex Organic Mixtures. Anal. Chem. 2018, 90 (3), 20412047,  DOI: 10.1021/acs.analchem.7b04159
  36. 36
    Burla, B.; Arita, M.; Arita, M.; Bendt, A. K.; Cazenave-Gassiot, A.; Dennis, E. A.; Ekroos, K.; Han, X.; Ikeda, K.; Liebisch, G.; Lin, M. K.; Loh, T. P.; Meikle, P. J.; Oresic, M.; Quehenberger, O.; Shevchenko, A.; Torta, F.; Wakelam, M. J. O.; Wheelock, C. E.; Wenk, M. R. MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines. J. Lipid Res. 2018, 59 (10), 20012017,  DOI: 10.1194/jlr.S087163
  37. 37
    Xiang, X.; Grosshans, P. B.; Marshall, A. G. Image charge-induced ion cyclotron orbital frequency shift for orthorhombic and cylindrical FT-ICR ion traps. Int. J. Mass Spectrom. Ion Processes 1993, 125 (1), 3343,  DOI: 10.1016/0168-1176(93)80014-6
  38. 38
    Wong, R. L.; Amster, I. J. Experimental Evidence for Space-Charge Effects between Ions of the Same Mass-to-Charge in Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry. Int. J. Mass Spectrom. 2007, 265 (2–3), 99105,  DOI: 10.1016/j.ijms.2007.01.014
  39. 39
    Savory, J. J.; Kaiser, N. K.; McKenna, A. M.; Xian, F.; Blakney, G. T.; Rodgers, R. P.; Hendrickson, C. L.; Marshall, A. G. Parts-per-billion Fourier transform ion cyclotron resonance mass measurement accuracy with a “walking” calibration equation. Anal. Chem. 2011, 83 (5), 17326,  DOI: 10.1021/ac102943z
  40. 40
    Schuhmann, K.; Almeida, R.; Baumert, M.; Herzog, R.; Bornstein, S. R.; Shevchenko, A. Shotgun lipidomics on a LTQ Orbitrap mass spectrometer by successive switching between acquisition polarity modes. J. Mass Spectrom. 2012, 47 (1), 96104,  DOI: 10.1002/jms.2031
  41. 41
    Almeida, R.; Pauling, J. K.; Sokol, E.; Hannibal-Bach, H. K.; Ejsing, C. S. Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer. J. Am. Soc. Mass Spectrom. 2015, 26 (1), 13348,  DOI: 10.1007/s13361-014-1013-x
  42. 42
    Simons, B.; Kauhanen, D.; Sylvanne, T.; Tarasov, K.; Duchoslav, E.; Ekroos, K. Shotgun Lipidomics by Sequential Precursor Ion Fragmentation on a Hybrid Quadrupole Time-of-Flight Mass Spectrometer. Metabolites 2012, 2 (1), 195213,  DOI: 10.3390/metabo2010195
  43. 43
    Velickovic, D.; Chu, R. K.; Carrell, A. A.; Thomas, M.; Pasa-Tolic, L.; Weston, D. J.; Anderton, C. R. Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS(2) Increases Confidence in Both Molecular Identification and Localization. Anal. Chem. 2018, 90 (1), 702707,  DOI: 10.1021/acs.analchem.7b04319
  44. 44
    Spraggins, J. M.; Rizzo, D. G.; Moore, J. L.; Rose, K. L.; Hammer, N. D.; Skaar, E. P.; Caprioli, R. M. MALDI FTICR IMS of Intact Proteins: Using Mass Accuracy to Link Protein Images with Proteomics Data. J. Am. Soc. Mass Spectrom. 2015, 26 (6), 97485,  DOI: 10.1007/s13361-015-1147-5
  45. 45
    Hendrickson, C. L.; Beu, S. C.; Blakney, G. T.; Kaiser, N. K.; McIntosh, D. G.; Quinn, J. P.; Marshall, A. G. In Optimized cell geometry for Fourier transform ion cyclotron resonance mass spectrometry, Proceedings of the 57th ASMS Conference on Mass Spectrometry and Allied Topics, Philadelphia, PA, May 31 to June 4; Philadelphia, PA, 2009.
  46. 46
    Chen, T.; Beu, S. C.; Kaiser, N. K.; Hendrickson, C. L. Note: Optimized circuit for excitation and detection with one pair of electrodes for improved Fourier transform ion cyclotron resonance mass spectrometry. Rev. Sci. Instrum. 2014, 85 (6), 066107,  DOI: 10.1063/1.4883179
  47. 47
    Belov, M. E.; Ellis, S. R.; Dilillo, M.; Paine, M. R. L.; Danielson, W. F.; Anderson, G. A.; de Graaf, E. L.; Eijkel, G. B.; Heeren, R. M. A.; McDonnell, L. A. Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry. Anal. Chem. 2017, 89 (14), 74937501,  DOI: 10.1021/acs.analchem.7b01168
  48. 48
    Blakney, G. T.; Hendrickson, C. L.; Marshall, A. G. Predator data station: A fast data acquisition system for advanced FT-ICR MS experiments. Int. J. Mass Spectrom. 2011, 306 (2–3), 246252,  DOI: 10.1016/j.ijms.2011.03.009
  49. 49
    Xian, F.; Hendrickson, C. L.; Blakney, G. T.; Beu, S. C.; Marshall, A. G. Automated broadband phase correction of Fourier transform ion cyclotron resonance mass spectra. Anal. Chem. 2010, 82 (21), 880712,  DOI: 10.1021/ac101091w
  50. 50
    Chambers, M. C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D. L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J.; Hoff, K.; Kessner, D.; Tasman, N.; Shulman, N.; Frewen, B.; Baker, T. A.; Brusniak, M. Y.; Paulse, C.; Creasy, D.; Flashner, L.; Kani, K.; Moulding, C.; Seymour, S. L.; Nuwaysir, L. M.; Lefebvre, B.; Kuhlmann, F.; Roark, J.; Rainer, P.; Detlev, S.; Hemenway, T.; Huhmer, A.; Langridge, J.; Connolly, B.; Chadick, T.; Holly, K.; Eckels, J.; Deutsch, E. W.; Moritz, R. L.; Katz, J. E.; Agus, D. B.; MacCoss, M.; Tabb, D. L.; Mallick, P. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012, 30 (10), 91820,  DOI: 10.1038/nbt.2377
  51. 51
    Race, A. M.; Styles, I. B.; Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteomics 2012, 75 (16), 51112,  DOI: 10.1016/j.jprot.2012.05.035
  52. 52
    Husen, P.; Tarasov, K.; Katafiasz, M.; Sokol, E.; Vogt, J.; Baumgart, J.; Nitsch, R.; Ekroos, K.; Ejsing, C. S. Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data. PLoS One 2013, 8 (11), e79736  DOI: 10.1371/journal.pone.0079736
  53. 53
    Spraggins, J. M.; Djambazova, K. V.; Rivera, E. S.; Migas, L. G.; Neumann, E. K.; Fuetterer, A.; Suetering, J.; Goedecke, N.; Ly, A.; Van de Plas, R.; Caprioli, R. M. High-Performance Molecular Imaging with MALDI Trapped Ion-Mobility Time-of-Flight (timsTOF) Mass Spectrometry. Anal. Chem. 2019, 91 (22), 1455214560,  DOI: 10.1021/acs.analchem.9b03612
  54. 54
    Campos, A. M.; Maciel, E.; Moreira, A. S.; Sousa, B.; Melo, T.; Domingues, P.; Curado, L.; Antunes, B.; Domingues, M. R.; Santos, F. Lipidomics of Mesenchymal Stromal Cells: Understanding the Adaptation of Phospholipid Profile in Response to Pro-Inflammatory Cytokines. J. Cell. Physiol. 2016, 231 (5), 102432,  DOI: 10.1002/jcp.25191
  55. 55
    Guo, S.; Wang, Y.; Zhou, D.; Li, Z. Significantly increased monounsaturated lipids relative to polyunsaturated lipids in six types of cancer microenvironment are observed by mass spectrometry imaging. Sci. Rep. 2015, 4, 5959,  DOI: 10.1038/srep05959
  56. 56
    Ide, Y.; Waki, M.; Hayasaka, T.; Nishio, T.; Morita, Y.; Tanaka, H.; Sasaki, T.; Koizumi, K.; Matsunuma, R.; Hosokawa, Y.; Ogura, H.; Shiiya, N.; Setou, M. Human breast cancer tissues contain abundant phosphatidylcholine(36ratio1) with high stearoyl-CoA desaturase-1 expression. PLoS One 2013, 8 (4), e61204  DOI: 10.1371/journal.pone.0061204
  57. 57
    Harbige, L. S.; Sharief, M. K. Polyunsaturated fatty acids in the pathogenesis and treatment of multiple sclerosis. Br. J. Nutr. 2007, 98 (Suppl 1), S4653,  DOI: 10.1017/S0007114507833010
  58. 58
    Vance, J. E.; Tasseva, G. Formation and function of phosphatidylserine and phosphatidylethanolamine in mammalian cells. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2013, 1831 (3), 54354,  DOI: 10.1016/j.bbalip.2012.08.016
  59. 59
    Kim, W. S.; Weickert, C. S.; Garner, B. Role of ATP-binding cassette transporters in brain lipid transport and neurological disease. J. Neurochem. 2008, 104 (5), 114566,  DOI: 10.1111/j.1471-4159.2007.05099.x
  60. 60
    Smith, D. F.; Kharchenko, A.; Konijnenburg, M.; Klinkert, I.; Pasa-Tolic, L.; Heeren, R. M. Advanced mass calibration and visualization for FT-ICR mass spectrometry imaging. J. Am. Soc. Mass Spectrom. 2012, 23 (11), 186572,  DOI: 10.1007/s13361-012-0464-1
  61. 61
    Estrada, R.; Yappert, M. C. Alternative approaches for the detection of various phospholipid classes by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Mass Spectrom. 2004, 39 (4), 41222,  DOI: 10.1002/jms.603
  62. 62
    Fuchs, B.; Schiller, J.; Suss, R.; Schurenberg, M.; Suckau, D. A direct and simple method of coupling matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) to thin-layer chromatography (TLC) for the analysis of phospholipids from egg yolk. Anal. Bioanal. Chem. 2007, 389 (3), 82734,  DOI: 10.1007/s00216-007-1488-4

Cited By

Click to copy section linkSection link copied!

This article is cited by 82 publications.

  1. Walter Wißdorf, Marco Thinius, Thorsten Benter. Simulation of Space Charge Effects in Fourier Transform Quadrupole Ion Traps (FT-QITs). Journal of the American Society for Mass Spectrometry 2024, 35 (12) , 2969-2983. https://doi.org/10.1021/jasms.4c00296
  2. Kasper Krijnen, Paul Blenkinsopp, Ron M. A. Heeren, Ian G. M. Anthony. Processing Next-Generation Mass Spectrometry Imaging Data: Principal Component Analysis at Scale. Journal of the American Society for Mass Spectrometry 2024, 35 (12) , 3063-3069. https://doi.org/10.1021/jasms.4c00314
  3. Fang-Hsu Chen, Chun-Yen Cheng, Szu-Wei Chou, Cheng-Han Yang, I-Chung Lu, Ming-Long Yeh. High-Resolution Intact Protein Analysis via Phase-Modulated, Stepwise Frequency Scan Ion Trap Mass Spectrometry. Analytical Chemistry 2024, 96 (37) , 14867-14876. https://doi.org/10.1021/acs.analchem.4c02775
  4. Zhongling Liang, Yingchan Guo, Xizheng Diao, Boone M. Prentice. Enhancing Spatial Resolution in Tandem Mass Spectrometry Ion/Ion Reaction Imaging Experiments through Image Fusion. Journal of the American Society for Mass Spectrometry 2024, 35 (8) , 1797-1805. https://doi.org/10.1021/jasms.4c00144
  5. Jens Soltwisch, Andrew Palmer, Hyundae Hong, Jan Majer, Klaus Dreisewerd, Peter Marshall. Large-Scale Screening of Pharmaceutical Compounds to Explore the Application Space of On-Tissue MALDI and MALDI-2 Mass Spectrometry. Analytical Chemistry 2024, 96 (25) , 10294-10301. https://doi.org/10.1021/acs.analchem.4c01088
  6. Wojciech Michno, Andrew Bowman, Durga Jha, Karolina Minta, Junyue Ge, Srinivas Koutarapu, Henrik Zetterberg, Kaj Blennow, Tammaryn Lashley, Ron M. A. Heeren, Jörg Hanrieder. Spatial Neurolipidomics at the Single Amyloid-β Plaque Level in Postmortem Human Alzheimer’s Disease Brain. ACS Chemical Neuroscience 2024, 15 (4) , 877-888. https://doi.org/10.1021/acschemneuro.4c00006
  7. Miranda R. Weigand, Daisy M. Unsihuay Vila, Manxi Yang, Hang Hu, Emerson Hernly, Matthew Muhoberac, Shane Tichy, Julia Laskin. Lipid Isobar and Isomer Imaging Using Nanospray Desorption Electrospray Ionization Combined with Triple Quadrupole Mass Spectrometry. Analytical Chemistry 2024, 96 (7) , 2975-2982. https://doi.org/10.1021/acs.analchem.3c04705
  8. Andrej Grgic, Konstantin O. Nagornov, Anton N. Kozhinov, Jesse A. Michael, Ian G.M. Anthony, Yury O. Tsybin, Ron M.A. Heeren, Shane R. Ellis. Ultrahigh-Mass Resolution Mass Spectrometry Imaging with an Orbitrap Externally Coupled to a High-Performance Data Acquisition System. Analytical Chemistry 2024, 96 (2) , 794-801. https://doi.org/10.1021/acs.analchem.3c04146
  9. Julia R. Bonney, Woo-Young Kang, Jonathan T. Specker, Zhongling Liang, Troy R. Scoggins, IV, Boone M. Prentice. Relative Quantification of Lipid Isomers in Imaging Mass Spectrometry Using Gas-Phase Charge Inversion Ion/Ion Reactions and Infrared Multiphoton Dissociation. Analytical Chemistry 2023, 95 (48) , 17766-17775. https://doi.org/10.1021/acs.analchem.3c03804
  10. Jessica L. Moore, Georgia Charkoftaki. A Guide to MALDI Imaging Mass Spectrometry for Tissues. Journal of Proteome Research 2023, 22 (11) , 3401-3417. https://doi.org/10.1021/acs.jproteome.3c00167
  11. Tingting Yan, Zhongling Liang, Boone M. Prentice. Imaging and Structural Characterization of Phosphatidylcholine Isomers from Rat Brain Tissue Using Sequential Collision-Induced Dissociation/Electron-Induced Dissociation. Analytical Chemistry 2023, 95 (42) , 15707-15715. https://doi.org/10.1021/acs.analchem.3c03077
  12. Britt S. R. Claes, Andrew P. Bowman, Berwyck L. J. Poad, Ron M. A. Heeren, Stephen J. Blanksby, Shane R. Ellis. Isomer-Resolved Mass Spectrometry Imaging of Acidic Phospholipids. Journal of the American Society for Mass Spectrometry 2023, 34 (10) , 2269-2277. https://doi.org/10.1021/jasms.3c00192
  13. Jonathan T. Specker, Boone M. Prentice. Separation of Isobaric Lipids in Imaging Mass Spectrometry Using Gas-Phase Charge Inversion Ion/Ion Reactions. Journal of the American Society for Mass Spectrometry 2023, 34 (9) , 1868-1878. https://doi.org/10.1021/jasms.3c00081
  14. Gregory W. Vandergrift, Kevin J. Zemaitis, Dušan Veličković, Jessica K. Lukowski, Ljiljana Paša-Tolić, Christopher R. Anderton, William Kew. Experimental Assessment of Mammalian Lipidome Complexity Using Multimodal 21 T FTICR Mass Spectrometry Imaging. Analytical Chemistry 2023, 95 (29) , 10921-10929. https://doi.org/10.1021/acs.analchem.3c00518
  15. Anjusha Mathew, Frans Giskes, Alexandros Lekkas, Jean-François Greisch, Gert B. Eijkel, Ian G. M. Anthony, Kyle Fort, Albert J. R. Heck, Dimitris Papanastasiou, Alexander A. Makarov, Shane R. Ellis, Ron M. A. Heeren. An Orbitrap/Time-of-Flight Mass Spectrometer for Photofragment Ion Imaging and High-Resolution Mass Analysis of Native Macromolecular Assemblies. Journal of the American Society for Mass Spectrometry 2023, 34 (7) , 1359-1371. https://doi.org/10.1021/jasms.3c00053
  16. Daniel C. Castro, Karl W. Smith, Miles D. Norsworthy, Stanislav S. Rubakhin, Chad R. Weisbrod, Christopher L. Hendrickson, Jonathan V. Sweedler. Single-Cell and Subcellular Analysis Using Ultrahigh Resolution 21 T MALDI FTICR Mass Spectrometry. Analytical Chemistry 2023, 95 (17) , 6980-6988. https://doi.org/10.1021/acs.analchem.3c00393
  17. Yonghui Dong, Nir Shachaf, Liron Feldberg, Ilana Rogachev, Uwe Heinig, Asaph Aharoni. PICA: Pixel Intensity Correlation Analysis for Deconvolution and Metabolite Identification in Mass Spectrometry Imaging. Analytical Chemistry 2023, 95 (2) , 1652-1662. https://doi.org/10.1021/acs.analchem.2c04778
  18. William Bahureksa, Thomas Borch, Robert B. Young, Chad. R. Weisbrod, Greg T. Blakney, Amy M. McKenna. Improved Dynamic Range, Resolving Power, and Sensitivity Achievable with FT-ICR Mass Spectrometry at 21 T Reveals the Hidden Complexity of Natural Organic Matter. Analytical Chemistry 2022, 94 (32) , 11382-11389. https://doi.org/10.1021/acs.analchem.2c02377
  19. Mathieu Tiquet, Raphaël La Rocca, Stefan Kirnbauer, Samuele Zoratto, Daan Van Kruining, Loïc Quinton, Gauthier Eppe, Pilar Martinez-Martinez, Martina Marchetti-Deschmann, Edwin De Pauw, Johann Far. FT-ICR Mass Spectrometry Imaging at Extreme Mass Resolving Power Using a Dynamically Harmonized ICR Cell with 1ω or 2ω Detection. Analytical Chemistry 2022, 94 (26) , 9316-9326. https://doi.org/10.1021/acs.analchem.2c00754
  20. Sem Tamara, Maurits A. den Boer, Albert J. R. Heck. High-Resolution Native Mass Spectrometry. Chemical Reviews 2022, 122 (8) , 7269-7326. https://doi.org/10.1021/acs.chemrev.1c00212
  21. Luke T. Richardson, Elizabeth K. Neumann, Richard M. Caprioli, Jeffrey M. Spraggins, Touradj Solouki. Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments. Analytical Chemistry 2022, 94 (14) , 5504-5513. https://doi.org/10.1021/acs.analchem.1c03715
  22. Yuxuan Richard Xie, Daniel C. Castro, Stanislav S. Rubakhin, Jonathan V. Sweedler, Fan Lam. Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling. Analytical Chemistry 2022, 94 (13) , 5335-5343. https://doi.org/10.1021/acs.analchem.1c05279
  23. Gregory W. Vandergrift, William Kew, Jessica K. Lukowski, Arunima Bhattacharjee, Andrey V. Liyu, Elizabeth A. Shank, Ljiljana Paša-Tolić, Venkateshkumar Prabhakaran, Christopher R. Anderton. Imaging and Direct Sampling Capabilities of Nanospray Desorption Electrospray Ionization with Absorption-Mode 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Analytical Chemistry 2022, 94 (8) , 3629-3636. https://doi.org/10.1021/acs.analchem.1c05216
  24. Ri Wu, Jonas B. Metternich, Prince Tiwari, Renato Zenobi. Adapting a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer for Gas-Phase Fluorescence Spectroscopy Measurement of Trapped Biomolecular Ions. Analytical Chemistry 2021, 93 (47) , 15626-15632. https://doi.org/10.1021/acs.analchem.1c02858
  25. Steven M. Rowland, Donald F. Smith, Gregory T. Blakney, Yuri E. Corilo, Christopher L. Hendrickson, Ryan P. Rodgers. Online Coupling of Liquid Chromatography with Fourier Transform Ion Cyclotron Resonance Mass Spectrometry at 21 T Provides Fast and Unique Insight into Crude Oil Composition. Analytical Chemistry 2021, 93 (41) , 13749-13754. https://doi.org/10.1021/acs.analchem.1c01169
  26. Julia R. Bonney, Boone M. Prentice. Perspective on Emerging Mass Spectrometry Technologies for Comprehensive Lipid Structural Elucidation. Analytical Chemistry 2021, 93 (16) , 6311-6322. https://doi.org/10.1021/acs.analchem.1c00061
  27. Michael J. Taylor, Jessica K. Lukowski, Christopher R. Anderton. Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics. Journal of the American Society for Mass Spectrometry 2021, 32 (4) , 872-894. https://doi.org/10.1021/jasms.0c00439
  28. Konstantin O. Nagornov, Anton N. Kozhinov, Edith Nicol, Oleg Yu. Tsybin, David Touboul, Alain Brunelle, Yury O. Tsybin. Narrow Aperture Detection Electrodes ICR Cell with Quadrupolar Ion Detection for FT-ICR MS at the Cyclotron Frequency. Journal of the American Society for Mass Spectrometry 2020, 31 (11) , 2258-2269. https://doi.org/10.1021/jasms.0c00221
  29. Yuxuan Richard Xie, Daniel C. Castro, Fan Lam, Jonathan V. Sweedler. Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach. Journal of the American Society for Mass Spectrometry 2020, 31 (11) , 2338-2347. https://doi.org/10.1021/jasms.0c00276
  30. Jonathan T. Specker, Steven L. Van Orden, Mark E. Ridgeway, Boone M. Prentice. Identification of Phosphatidylcholine Isomers in Imaging Mass Spectrometry Using Gas-Phase Charge Inversion Ion/Ion Reactions. Analytical Chemistry 2020, 92 (19) , 13192-13201. https://doi.org/10.1021/acs.analchem.0c02350
  31. Jens Soltwisch, Bram Heijs, Annika Koch, Simeon Vens-Cappell, Jens Höhndorf, Klaus Dreisewerd. MALDI-2 on a Trapped Ion Mobility Quadrupole Time-of-Flight Instrument for Rapid Mass Spectrometry Imaging and Ion Mobility Separation of Complex Lipid Profiles. Analytical Chemistry 2020, 92 (13) , 8697-8703. https://doi.org/10.1021/acs.analchem.0c01747
  32. Eric Schneider, Christopher P. Rüger, Martha L. Chacón-Patiño, Markus Somero, Meri M. Ruppel, Mika Ihalainen, Kajar Köster, Olli Sippula, Hendryk Czech, Ralf Zimmermann. The complex composition of organic aerosols emitted during burning varies between Arctic and boreal peat. Communications Earth & Environment 2024, 5 (1) https://doi.org/10.1038/s43247-024-01304-y
  33. Abhik Mojumdar, Hee-Jin Yoo, Duck-Hyun Kim, Jiwon Park, Su-Jin Park, Eunji Jeon, Sunhee Choi, Jung Hoon Choi, Moonhee Park, Geul Bang, Kun Cho. Advances in mass spectrometry-based approaches for characterizing monoclonal antibodies: resolving structural complexity and analytical challenges. Journal of Analytical Science and Technology 2024, 15 (1) https://doi.org/10.1186/s40543-024-00437-1
  34. Silvia Dudášová, Johann Wurz, Urs Berger, Thorsten Reemtsma, Qiuguo Fu, Oliver J. Lechtenfeld. An automated and high-throughput data processing workflow for PFAS identification in biota by direct infusion ultra-high resolution mass spectrometry. Analytical and Bioanalytical Chemistry 2024, 416 (22) , 4833-4848. https://doi.org/10.1007/s00216-024-05426-2
  35. Boone M. Prentice. An analytical evaluation of tools for lipid isomer differentiation in imaging mass spectrometry. International Journal of Mass Spectrometry 2024, 502 , 117268. https://doi.org/10.1016/j.ijms.2024.117268
  36. Darcy Cochran, Robert Powers. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Applications for Metabolomics. Biomedicines 2024, 12 (8) , 1786. https://doi.org/10.3390/biomedicines12081786
  37. Zihao Wu, Yun Yang, Li Ling. Detecting disinfection byproducts and understanding their formation mechanisms using isotopic analysis. TrAC Trends in Analytical Chemistry 2024, 175 , 117717. https://doi.org/10.1016/j.trac.2024.117717
  38. Nicolas Joly-Tonetti, Raphael Legouffe, Aurore Tomezyk, Clémence Gumez, Mathieu Gaudin, David Bonnel, Martin Schaller. Penetration Profiles of Four Topical Antifungals in Mycotic Human Toenails Quantified by Matrix-Assisted Laser Desorption Ionization–Fourier Transform Ion Cyclotron Resonance Imaging. Infectious Diseases and Therapy 2024, 13 (6) , 1269-1279. https://doi.org/10.1007/s40121-024-00978-3
  39. Nicholas R. Ellin, Yingchan Guo, Ramón Alain Miranda-Quintana, Boone M. Prentice. Extended similarity methods for efficient data mining in imaging mass spectrometry. Digital Discovery 2024, 3 (4) , 805-817. https://doi.org/10.1039/D3DD00165B
  40. Madeline E. Colley, Allison B. Esselman, Claire F. Scott, Jeffrey M. Spraggins. High-Specificity Imaging Mass Spectrometry. Annual Review of Analytical Chemistry 2024, https://doi.org/10.1146/annurev-anchem-083023-024546
  41. Xin Ma, Facundo M. Fernández. Advances in mass spectrometry imaging for spatial cancer metabolomics. Mass Spectrometry Reviews 2024, 43 (2) , 235-268. https://doi.org/10.1002/mas.21804
  42. Seth W. Croslow, Timothy J. Trinklein, Jonathan V. Sweedler. Advances in multimodal mass spectrometry for single‐cell analysis and imaging enhancement. FEBS Letters 2024, 598 (6) , 591-601. https://doi.org/10.1002/1873-3468.14798
  43. Bharath S. Kumar. Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview. Mass Spectrometry 2024, 13 (1) , A0142-A0142. https://doi.org/10.5702/massspectrometry.A0142
  44. Arjun Pitchai, Kimberly Buhman, Jonathan H. Shannahan. Lipid mediators of inhalation exposure-induced pulmonary toxicity and inflammation. Inhalation Toxicology 2024, 36 (2) , 57-74. https://doi.org/10.1080/08958378.2024.2318389
  45. Diego F. Cobice, Karl W. Smith. Vitamin D tissue distribution by mass spectrometry imaging. 2024, 1115-1129. https://doi.org/10.1016/B978-0-323-91386-7.00019-2
  46. Bibiana Juan, Ahmed A.K. Salama, Suha Serhan, Xavier Such, Gerardo Caja, Laura Pont, Fernando Benavente, Buenaventura Guamis, Antonio-José Trujillo. β-Casein: type A1 and A2. 2024, 99-121. https://doi.org/10.1016/B978-0-443-15836-0.00010-X
  47. Ibrahim Kaya, Anna Nilsson, Dominika Luptáková, Yachao He, Theodosia Vallianatou, Patrik Bjärterot, Per Svenningsson, Erwan Bezard, Per E. Andrén. Spatial lipidomics reveals brain region-specific changes of sulfatides in an experimental MPTP Parkinson’s disease primate model. npj Parkinson's Disease 2023, 9 (1) https://doi.org/10.1038/s41531-023-00558-1
  48. Xing Guo, Xin Wang, Caiyan Tian, Jianxiong Dai, Zhongjun Zhao, Yixiang Duan. Development of mass spectrometry imaging techniques and its latest applications. Talanta 2023, 264 , 124721. https://doi.org/10.1016/j.talanta.2023.124721
  49. Behnaz Akbari, Bertrand Russell Huber, Janet Hope Sherman. Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques. Critical Reviews in Analytical Chemistry 2023, 17 , 1-30. https://doi.org/10.1080/10408347.2023.2266838
  50. Marisa Maia, Andreia Figueiredo, Carlos Cordeiro, Marta Sousa Silva. FT‐ICR‐MS‐based metabolomics: A deep dive into plant metabolism. Mass Spectrometry Reviews 2023, 42 (5) , 1535-1556. https://doi.org/10.1002/mas.21731
  51. Gerard Baquer, Lluc Sementé, Toufik Mahamdi, Xavier Correig, Pere Ràfols, María García‐Altares. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. Mass Spectrometry Reviews 2023, 42 (5) , 1927-1964. https://doi.org/10.1002/mas.21794
  52. Xizheng Diao, Nicholas R. Ellin, Boone M. Prentice. Selective Schiff base formation via gas-phase ion/ion reactions to enable differentiation of isobaric lipids in imaging mass spectrometry. Analytical and Bioanalytical Chemistry 2023, 415 (18) , 4319-4331. https://doi.org/10.1007/s00216-023-04523-y
  53. Li-Xue Jiang, Manxi Yang, Syeda Nazifa Wali, Julia Laskin. High-throughput mass spectrometry imaging of biological systems: Current approaches and future directions. TrAC Trends in Analytical Chemistry 2023, 163 , 117055. https://doi.org/10.1016/j.trac.2023.117055
  54. Shane R. Ellis, Jens Soltwisch. Mass Spectrometry Imaging of Lipids. 2023, 117-150. https://doi.org/10.1002/9783527836512.ch5
  55. Colin T. McDowell, Xiaowei Lu, Anand S. Mehta, Peggi M. Angel, Richard R. Drake. Applications and continued evolution of glycan imaging mass spectrometry. Mass Spectrometry Reviews 2023, 42 (2) , 674-705. https://doi.org/10.1002/mas.21725
  56. Shahid Aziz, Faisal Rasheed, Rabaab Zahra, Simone König. Mass Spectrometry-Based Proteomics of Minor Species in the Bulk: Questions to Raise with Respect to the Untargeted Analysis of Viral Proteins in Human Tissue. Life 2023, 13 (2) , 544. https://doi.org/10.3390/life13020544
  57. Kanchustambham Vijaya Lakshmi. Spatial Metabolomics Using Imaging Mass Spectrometry. 2023, 423-477. https://doi.org/10.1007/978-3-031-39094-4_13
  58. Weimin Wang, Fuxing Xu, Fangling Wu, Huanmin Wu, Chuan-Fan Ding, Li Ding. Genetic algorithm parallel optimization of a new high mass resolution planar electrostatic ion trap mass analyzer. The Analyst 2022, 147 (24) , 5764-5774. https://doi.org/10.1039/D2AN01568D
  59. Jin-jun Hou, Zi-jia Zhang, Wen-yong Wu, Qing-qing He, Teng-qian Zhang, Ya-wen Liu, Zhao-jun Wang, Lei Gao, Hua-li Long, Min Lei, Wan-ying Wu, De-an Guo. Mass spectrometry imaging: new eyes on natural products for drug research and development. Acta Pharmacologica Sinica 2022, 43 (12) , 3096-3111. https://doi.org/10.1038/s41401-022-00990-8
  60. Juliane Hermann, Leon Schurgers, Vera Jankowski. Identification and characterization of post-translational modifications: Clinical implications. Molecular Aspects of Medicine 2022, 86 , 101066. https://doi.org/10.1016/j.mam.2022.101066
  61. Megan I. Mitchell, Junfeng Ma, Claire L. Carter, Olivier Loudig. Circulating Exosome Cargoes Contain Functionally Diverse Cancer Biomarkers: From Biogenesis and Function to Purification and Potential Translational Utility. Cancers 2022, 14 (14) , 3350. https://doi.org/10.3390/cancers14143350
  62. Eduardo A. De La Toba, Sara E. Bell, Elena V. Romanova, Jonathan V. Sweedler. Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation. Annual Review of Analytical Chemistry 2022, 15 (1) , 83-106. https://doi.org/10.1146/annurev-anchem-061020-022048
  63. Maykel Hernández-Mesa, David Moreno-González. Current Role of Mass Spectrometry in the Determination of Pesticide Residues in Food. Separations 2022, 9 (6) , 148. https://doi.org/10.3390/separations9060148
  64. Michael Tuck, Florent Grélard, Landry Blanc, Nicolas Desbenoit. MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives. Frontiers in Chemistry 2022, 10 https://doi.org/10.3389/fchem.2022.904688
  65. Walid M Abdelmoula, Sylwia A Stopka, Elizabeth C Randall, Michael Regan, Jeffrey N Agar, Jann N Sarkaria, William M Wells, Tina Kapur, Nathalie Y R Agar, . massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation. Bioinformatics 2022, 38 (7) , 2015-2021. https://doi.org/10.1093/bioinformatics/btac032
  66. Konstantin O. Nagornov, Oleg Y. Tsybin, Edith Nicol, Anton N. Kozhinov, Yury O. Tsybin. Fourier transform ion cyclotron resonance mass spectrometry at the true cyclotron frequency. Mass Spectrometry Reviews 2022, 41 (2) , 314-337. https://doi.org/10.1002/mas.21681
  67. Boone M. Prentice. Gas-Phase Ion–Ion Reactions for Lipid Identification in Biological Tissue Sections. 2022, 3-19. https://doi.org/10.1007/978-1-0716-2030-4_1
  68. Katherine E Manz, Kyle Yamada, Lukas Scheidl, Michele A La Merrill, Lars Lind, Kurt D Pennell. Targeted and Nontargeted Detection and Characterization of Trace Organic Chemicals in Human Serum and Plasma Using QuEChERS Extraction. Toxicological Sciences 2021, 185 (1) , 77-88. https://doi.org/10.1093/toxsci/kfab121
  69. Klára Ščupáková, Oluwatobi T. Adelaja, Benjamin Balluff, Vinay Ayyappan, Caitlin M. Tressler, Nicole M. Jenkinson, Britt S.R. Claes, Andrew P. Bowman, Ashley M. Cimino-Mathews, Marissa J. White, Pedram Argani, Ron M.A. Heeren, Kristine Glunde. Clinical importance of high-mannose, fucosylated, and complex N-glycans in breast cancer metastasis. JCI Insight 2021, 6 (24) https://doi.org/10.1172/jci.insight.146945
  70. Gennady A. Badun, Maria G. Chernysheva, Yury V. Zhernov, Alina S. Poroshina, Valery V. Smirnov, Sergey E. Pigarev, Tatiana A. Mikhnevich, Dmitry S. Volkov, Irina V. Perminova, Elena I. Fedoros. A Use of Tritium-Labeled Peat Fulvic Acids and Polyphenolic Derivatives for Designing Pharmacokinetic Experiments on Mice. Biomedicines 2021, 9 (12) , 1787. https://doi.org/10.3390/biomedicines9121787
  71. Mariaimmacolata Preianò, Serena Correnti, Corrado Pelaia, Rocco Savino, Rosa Terracciano. MALDI MS-Based Investigations for SARS-CoV-2 Detection. BioChem 2021, 1 (3) , 250-278. https://doi.org/10.3390/biochem1030018
  72. Hiroshi Tsugawa, Amit Rai, Kazuki Saito, Ryo Nakabayashi. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Natural Product Reports 2021, 38 (10) , 1729-1759. https://doi.org/10.1039/D1NP00014D
  73. Pey Yee Lee, Yeelon Yeoh, Nursyazwani Omar, Yuh-Fen Pung, Lay Cheng Lim, Teck Yew Low. Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research. Critical Reviews in Clinical Laboratory Sciences 2021, 58 (7) , 513-529. https://doi.org/10.1080/10408363.2021.1942781
  74. Reuben S. E. Young, Britt S. R. Claes, Andrew P. Bowman, Elizabeth D. Williams, Benjamin Shepherd, Aurel Perren, Berwyck L. J. Poad, Shane R. Ellis, Ron M. A. Heeren, Martin C. Sadowski, Stephen J. Blanksby. Isomer-Resolved Imaging of Prostate Cancer Tissues Reveals Specific Lipid Unsaturation Profiles Associated With Lymphocytes and Abnormal Prostate Epithelia. Frontiers in Endocrinology 2021, 12 https://doi.org/10.3389/fendo.2021.689600
  75. Md. Mahmudul Hasan, Mst. Afsana Mimi, Md. Al Mamun, Ariful Islam, A. S. M. Waliullah, Md. Mahamodun Nabi, Zinat Tamannaa, Tomoaki Kahyo, Mitsutoshi Setou. Mass Spectrometry Imaging for Glycome in the Brain. Frontiers in Neuroanatomy 2021, 15 https://doi.org/10.3389/fnana.2021.711955
  76. Reza Shariatgorji, Anna Nilsson, Elva Fridjonsdottir, Nicole Strittmatter, Andreas Dannhorn, Per Svenningsson, Richard J. A. Goodwin, Luke R. Odell, Per E. Andrén. Spatial visualization of comprehensive brain neurotransmitter systems and neuroactive substances by selective in situ chemical derivatization mass spectrometry imaging. Nature Protocols 2021, 16 (7) , 3298-3321. https://doi.org/10.1038/s41596-021-00538-w
  77. Peggi M. Angel, Denys Rujchanarong, Sarah Pippin, Laura Spruill, Richard Drake. Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge. Expert Review of Proteomics 2021, 18 (6) , 423-436. https://doi.org/10.1080/14789450.2021.1941893
  78. Md. Mahmudul Hasan, Fumihiro Eto, Md. Al Mamun, Shumpei Sato, Ariful Islam, A.S.M. Waliullah, Do Huu Chi, Yutaka Takahashi, Tomoaki Kahyo, Yasuhide Naito, Masahiro Kotani, Takayuki Ohmura, Mitsutoshi Setou. Desorption ionization using through‐hole alumina membrane offers higher reproducibility than 2,5‐dihydroxybenzoic acid, a widely used matrix in Fourier transform ion cyclotron resonance mass spectrometry imaging analysis. Rapid Communications in Mass Spectrometry 2021, 35 (10) https://doi.org/10.1002/rcm.9076
  79. Kevin J. Zemaitis, Alexandra M. Izydorczak, Alexis C. Thompson, Troy D. Wood. Streamlined Multimodal DESI and MALDI Mass Spectrometry Imaging on a Singular Dual-Source FT-ICR Mass Spectrometer. Metabolites 2021, 11 (4) , 253. https://doi.org/10.3390/metabo11040253
  80. Richard R. Drake, Danielle A. Scott, Peggi M. Angel. Imaging Mass Spectrometry. 2021, 303-323. https://doi.org/10.1016/B978-0-12-816386-3.00017-X
  81. Koralege C. Pathmasiri, Thu T.A. Nguyen, Nigina Khamidova, Stephanie M. Cologna. Mass spectrometry-based lipid analysis and imaging. 2021, 315-357. https://doi.org/10.1016/bs.ctm.2021.10.005
  82. Sylvia K. Neef, Stefan Winter, Ute Hofmann, Thomas E. Mürdter, Elke Schaeffeler, Heike Horn, Achim Buck, Axel Walch, Jörg Hennenlotter, German Ott, Falko Fend, Jens Bedke, Matthias Schwab, Mathias Haag. Optimized protocol for metabolomic and lipidomic profiling in formalin-fixed paraffin-embedded kidney tissue by LC-MS. Analytica Chimica Acta 2020, 1134 , 125-135. https://doi.org/10.1016/j.aca.2020.08.005

Analytical Chemistry

Cite this: Anal. Chem. 2020, 92, 4, 3133–3142
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.analchem.9b04768
Published January 19, 2020

Copyright © 2020 American Chemical Society. This publication is licensed under CC-BY-NC-ND.

Article Views

9685

Altmetric

-

Citations

Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

  • Abstract

    Figure 1

    Figure 1. Mass resolution and sensitivity improve with longer transient length. Within a 100 mDa mass range, seven different peaks are detected, which belong to six different lipid species. Of these, five are unresolved at 0.77 s. While distinguishable at 1.55 s, all seven peaks are fully resolved only at 3.1 s transient. These seven peaks correspond to the isotopologues of the monoisotopic species, typically the 13C ion, as in (a), (b), (f), and (g). Other species are also present, corresponding to the 13C3 isotopologue, as in (d) and (e). The 18O13C isotopologue of [PC(34:1)+Na]+ is also resolved (c) from the 13C3 isotopologue of the same parent species.

    Figure 2

    Figure 2. Representative images of close mass differences in negative and positive mode, from a single, scan. Images are total ion current normalized. Positive mode lipid spectra have a significant number of mass differences of 2.4 mDa (a), representing the difference between 12C2 and 23Na1H. 2.4 mDa differences are baseline resolved, and show significantly different distributions within brain tissue (b and c). There are nearly 200 such differences in the averaged spectra, shown in 0.5 mDa bins (d). Similarly, negative mode spectra have 1.79 mDa mass differences (e). These 1.79 mDa differences are resolved to better than full-width half-maximum, differentiated well enough to distinguish them in brain tissue (f and g). The of 1.79 mDa mass difference is relatively uncommon in negative mode, but mass differences of 10 mDa or less occur approximately 500 times in the averaged spectra, shown in 0.25 mDa bins (h).

    Figure 3

    Figure 3. Single on-tissue mass spectrum illustrates high dynamic range per pixel. Peaks were picked at a threshold of six standard deviations above the baseline noise. Dynamic range in a single average pixel of at 536:1 is demonstrated here at pixel number 10 000, (a). Mass scale expanded segment around most abundant peak [PC 34:1 + K]+ (b). Further, peak at 798.5410 generates a bright image (b). One of the lowest S/N peaks, the 13C2 isotope of [PE 46:5 + H]+ (c) while less clear, still yields informative molecular images, being highlighted especially in the ventricles (images are TIC normalized).

    Figure 4

    Figure 4. Error histogram and average mass error of tentatively identified lipids after internal calibration. Measured mass error histogram of 139 phosphatidylcholine lipids; the rms error is 61.12 ppb. Bin size = 10 ppb. (a), Lipid identifications by class. A tolerance of ±250 ppb results in 702 potential lipids identified within 150 ppb of their expected mass (b).

    Figure 5

    Figure 5. Relative abundance of identified lipids by cation and anion for selected classes. In the positive mode, the three major cations (proton, sodium, and potassium) are aligned next to one another, showing the same relative percentages between species, from the most abundant species (PC 34:1) and the other PCs above 3% (a), as well as for the lower abundant species down to the least abundant species with all three cations represented, PC 44:12 (b). The relative ionization rate between K+, H+, and Na+ hold strictly true down to 1.5%, and generally true down to 0.05%. While the dynamic range is lower for negative mode, we see many potential identifications for many lipid classes (c). We further observe a similar ability to identify potential lipids as low as 0.15% of the most abundant peak (PI 38:4), for a range of nearly three orders of magnitude from the summed spectra (d).

  • References


    This article references 62 other publications.

    1. 1
      Vaysse, P. M.; Heeren, R. M. A.; Porta, T.; Balluff, B. Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations. Analyst 2017, 142 (15), 26902712,  DOI: 10.1039/C7AN00565B
    2. 2
      Lazar, A. N.; Bich, C.; Panchal, M.; Desbenoit, N.; Petit, V. W.; Touboul, D.; Dauphinot, L.; Marquer, C.; Laprevote, O.; Brunelle, A.; Duyckaerts, C. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging reveals cholesterol overload in the cerebral cortex of Alzheimer disease patients. Acta Neuropathol. 2013, 125 (1), 13344,  DOI: 10.1007/s00401-012-1041-1
    3. 3
      Chaurand, P.; Schwartz, S. A.; Caprioli, R. M. Assessing Protein Patterns in Disease Using Imaging Mass Spectrometry. J. Proteome Res. 2004, 3 (2), 245252,  DOI: 10.1021/pr0341282
    4. 4
      Chen, Y.; Allegood, J.; Liu, Y.; Wang, E.; Cachon-Gonzalez, B.; Cox, T. M.; Merrill, A. H., Jr.; Sullards, M. C. Imaging MALDI mass spectrometry using an oscillating capillary nebulizer matrix coating system and its application to analysis of lipids in brain from a mouse model of Tay-Sachs/Sandhoff disease. Anal. Chem. 2008, 80 (8), 27808,  DOI: 10.1021/ac702350g
    5. 5
      Scott, A. J.; Post, J. M.; Lerner, R.; Ellis, S. R.; Lieberman, J.; Shirey, K. A.; Heeren, R. M. A.; Bindila, L.; Ernst, R. K. Host-based lipid inflammation drives pathogenesis in Francisella infection. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (47), 1259612601,  DOI: 10.1073/pnas.1712887114
    6. 6
      Hoefler, B. C.; Gorzelnik, K. V.; Yang, J. Y.; Hendricks, N.; Dorrestein, P. C.; Straight, P. D. Enzymatic resistance to the lipopeptide surfactin as identified through imaging mass spectrometry of bacterial competition. Proc. Natl. Acad. Sci. U. S. A. 2012, 109 (32), 130827,  DOI: 10.1073/pnas.1205586109
    7. 7
      Schulz, S.; Becker, M.; Groseclose, M. R.; Schadt, S.; Hopf, C. Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development. Curr. Opin. Biotechnol. 2019, 55, 5159,  DOI: 10.1016/j.copbio.2018.08.003
    8. 8
      Castellino, S.; Groseclose, M. R.; Wagner, D. MALDI imaging mass spectrometry: bridging biology and chemistry in drug development. Bioanalysis 2011, 3 (21), 242741,  DOI: 10.4155/bio.11.232
    9. 9
      Ellis, S. R.; Bruinen, A. L.; Heeren, R. M. A critical evaluation of the current state-of-the-art in quantitative imaging mass spectrometry. Anal. Bioanal. Chem. 2014, 406 (5), 127589,  DOI: 10.1007/s00216-013-7478-9
    10. 10
      Taban, I. M.; Altelaar, A. F. M.; Van der Burgt, Y. E. M.; McDonnell, L. A.; Heeren, R. M. A.; Fuchser, J.; Baykut, G. Imaging of peptides in the rat brain using MALDI-FTICR mass spectrometry. J. Am. Soc. Mass Spectrom. 2007, 18 (1), 145151,  DOI: 10.1016/j.jasms.2006.09.017
    11. 11
      Bowman, A. P.; Heeren, R. M. A.; Ellis, S. R., Advances in mass spectrometry imaging enabling observation of localised lipid biochemistry within tissues. TrAC, Trends Anal. Chem. 2019. 120 115197 DOI: 10.1016/j.trac.2018.07.012
    12. 12
      Zhao, C.; Xie, P. S.; Yang, T.; Wang, H. L.; Chung, A. C. K.; Cai, Z. W. Identification of glycerophospholipid fatty acid remodeling by using mass spectrometry imaging in bisphenol S induced mouse liver. Chin. Chem. Lett. 2018, 29 (8), 12811283,  DOI: 10.1016/j.cclet.2018.01.034
    13. 13
      Sugiyama, E.; Yao, I.; Setou, M. Visualization of local phosphatidylcholine synthesis within hippocampal neurons using a compartmentalized culture system and imaging mass spectrometry. Biochem. Biophys. Res. Commun. 2018, 495 (1), 10481054,  DOI: 10.1016/j.bbrc.2017.11.108
    14. 14
      Sans, M.; Feider, C. L.; Eberlin, L. S. Advances in mass spectrometry imaging coupled to ion mobility spectrometry for enhanced imaging of biological tissues. Curr. Opin. Chem. Biol. 2018, 42, 138146,  DOI: 10.1016/j.cbpa.2017.12.005
    15. 15
      Bielow, C.; Mastrobuoni, G.; Orioli, M.; Kempa, S. On Mass Ambiguities in High-Resolution Shotgun Lipidomics. Anal. Chem. 2017, 89 (5), 29862994,  DOI: 10.1021/acs.analchem.6b04456
    16. 16
      Shaw, J. B.; Lin, T. Y.; Leach, F. E., 3rd; Tolmachev, A. V.; Tolic, N.; Robinson, E. W.; Koppenaal, D. W.; Pasa-Tolic, L. 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometer Greatly Expands Mass Spectrometry Toolbox. J. Am. Soc. Mass Spectrom. 2016, 27 (12), 19291936,  DOI: 10.1007/s13361-016-1507-9
    17. 17
      Kooijman, P. C.; Nagornov, K. O.; Kozhinov, A. N.; Kilgour, D. P. A.; Tsybin, Y. O.; Heeren, R. M. A.; Ellis, S. R. Increased throughput and ultra-high mass resolution in DESI FT-ICR MS imaging through new-generation external data acquisition system and advanced data processing approaches. Sci. Rep. 2019, 9 (1), 8,  DOI: 10.1038/s41598-018-36957-1
    18. 18
      Smith, D. F.; Kilgour, D. P.; Konijnenburg, M.; O’Connor, P. B.; Heeren, R. M. Absorption mode FTICR mass spectrometry imaging. Anal. Chem. 2013, 85 (23), 111804,  DOI: 10.1021/ac403039t
    19. 19
      Qi, Y.; Barrow, M. P.; Li, H.; Meier, J. E.; Van Orden, S. L.; Thompson, C. J.; O’Connor, P. B. Absorption-mode: the next generation of Fourier transform mass spectra. Anal. Chem. 2012, 84 (6), 29239,  DOI: 10.1021/ac3000122
    20. 20
      Stopka, S. A.; Samarah, L. Z.; Shaw, J. B.; Liyu, A. V.; Velickovic, D.; Agtuca, B. J.; Kukolj, C.; Koppenaal, D. W.; Stacey, G.; Pasa-Tolic, L.; Anderton, C. R.; Vertes, A. Ambient Metabolic Profiling and Imaging of Biological Samples with Ultrahigh Molecular Resolution Using Laser Ablation Electrospray Ionization 21 T FTICR Mass Spectrometry. Anal. Chem. 2019, 91 (8), 50285035,  DOI: 10.1021/acs.analchem.8b05084
    21. 21
      Schwudke, D.; Schuhmann, K.; Herzog, R.; Bornstein, S. R.; Shevchenko, A. Shotgun lipidomics on high resolution mass spectrometers. Cold Spring Harbor Perspect. Biol. 2011, 3 (9), a004614  DOI: 10.1101/cshperspect.a004614
    22. 22
      Hu, C.; Duan, Q.; Han, X. Strategies to Improve/Eliminate the Limitations in Shotgun Lipidomics. Proteomics 2019, e1900070  DOI: 10.1002/pmic.201900070
    23. 23
      Ryan, E.; Reid, G. E. Chemical Derivatization and Ultrahigh Resolution and Accurate Mass Spectrometry Strategies for “Shotgun” Lipidome Analysis. Acc. Chem. Res. 2016, 49 (9), 1596604,  DOI: 10.1021/acs.accounts.6b00030
    24. 24
      Wildburger, N. C.; Wood, P. L.; Gumin, J.; Lichti, C. F.; Emmett, M. R.; Lang, F. F.; Nilsson, C. L. ESI-MS/MS and MALDI-IMS Localization Reveal Alterations in Phosphatidic Acid, Diacylglycerol, and DHA in Glioma Stem Cell Xenografts. J. Proteome Res. 2015, 14 (6), 25119,  DOI: 10.1021/acs.jproteome.5b00076
    25. 25
      Holcapek, M.; Cervena, B.; Cifkova, E.; Lisa, M.; Chagovets, V.; Vostalova, J.; Bancirova, M.; Galuszka, J.; Hill, M. Lipidomic analysis of plasma, erythrocytes and lipoprotein fractions of cardiovascular disease patients using UHPLC/MS, MALDI-MS and multivariate data analysis. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2015, 990, 5263,  DOI: 10.1016/j.jchromb.2015.03.010
    26. 26
      Korte, A. R.; Yandeau-Nelson, M. D.; Nikolau, B. J.; Lee, Y. J. Subcellular-level resolution MALDI-MS imaging of maize leaf metabolites by MALDI-linear ion trap-Orbitrap mass spectrometer. Anal. Bioanal. Chem. 2015, 407 (8), 23019,  DOI: 10.1007/s00216-015-8460-5
    27. 27
      Olsen, J. V.; de Godoy, L. M.; Li, G.; Macek, B.; Mortensen, P.; Pesch, R.; Makarov, A.; Lange, O.; Horning, S.; Mann, M. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 2005, 4 (12), 201021,  DOI: 10.1074/mcp.T500030-MCP200
    28. 28
      Makarov, A.; Denisov, E.; Lange, O.; Horning, S. Dynamic range of mass accuracy in LTQ Orbitrap hybrid mass spectrometer. J. Am. Soc. Mass Spectrom. 2006, 17 (7), 977982,  DOI: 10.1016/j.jasms.2006.03.006
    29. 29
      Scigelova, M.; Makarov, A. Orbitrap mass analyzer--overview and applications in proteomics. Proteomics 2006, 6 (Suppl 2), 1621,  DOI: 10.1002/pmic.200600528
    30. 30
      Marshall, A. G.; Hendrickson, C. L.; Jackson, G. S. Fourier transform ion cyclotron resonance mass spectrometry: A primer. Mass Spectrom. Rev. 1998, 17 (1), 135,  DOI: 10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K
    31. 31
      Nikolaev, E. N.; Gorshkov, M. V.; Mordehai, A. V.; Talrose, V. L. Ion cyclotron resonance signal-detection at multiples of the cyclotron frequency. Rapid Commun. Mass Spectrom. 1990, 4 (5), 144146,  DOI: 10.1002/rcm.1290040503
    32. 32
      Walker, L. R.; Tfaily, M. M.; Shaw, J. B.; Hess, N. J.; Pasa-Tolic, L.; Koppenaal, D. W. Unambiguous identification and discovery of bacterial siderophores by direct injection 21 T Fourier transform ion cyclotron resonance mass spectrometry. Metallomics 2017, 9 (1), 8292,  DOI: 10.1039/C6MT00201C
    33. 33
      Zimmerman, T. A.; Monroe, E. B.; Tucker, K. R.; Rubakhin, S. S.; Sweedler, J. V. Chapter 13: Imaging of cells and tissues with mass spectrometry: adding chemical information to imaging. Methods Cell Biol. 2008, 89, 36190,  DOI: 10.1016/S0091-679X(08)00613-4
    34. 34
      Hendrickson, C. L.; Quinn, J. P.; Kaiser, N. K.; Smith, D. F.; Blakney, G. T.; Chen, T.; Marshall, A. G.; Weisbrod, C. R.; Beu, S. C. 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometer: A National Resource for Ultrahigh Resolution Mass Analysis. J. Am. Soc. Mass Spectrom. 2015, 26 (9), 162632,  DOI: 10.1007/s13361-015-1182-2
    35. 35
      Smith, D. F.; Podgorski, D. C.; Rodgers, R. P.; Blakney, G. T.; Hendrickson, C. L. 21 T FT-ICR Mass Spectrometer for Ultrahigh-Resolution Analysis of Complex Organic Mixtures. Anal. Chem. 2018, 90 (3), 20412047,  DOI: 10.1021/acs.analchem.7b04159
    36. 36
      Burla, B.; Arita, M.; Arita, M.; Bendt, A. K.; Cazenave-Gassiot, A.; Dennis, E. A.; Ekroos, K.; Han, X.; Ikeda, K.; Liebisch, G.; Lin, M. K.; Loh, T. P.; Meikle, P. J.; Oresic, M.; Quehenberger, O.; Shevchenko, A.; Torta, F.; Wakelam, M. J. O.; Wheelock, C. E.; Wenk, M. R. MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines. J. Lipid Res. 2018, 59 (10), 20012017,  DOI: 10.1194/jlr.S087163
    37. 37
      Xiang, X.; Grosshans, P. B.; Marshall, A. G. Image charge-induced ion cyclotron orbital frequency shift for orthorhombic and cylindrical FT-ICR ion traps. Int. J. Mass Spectrom. Ion Processes 1993, 125 (1), 3343,  DOI: 10.1016/0168-1176(93)80014-6
    38. 38
      Wong, R. L.; Amster, I. J. Experimental Evidence for Space-Charge Effects between Ions of the Same Mass-to-Charge in Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry. Int. J. Mass Spectrom. 2007, 265 (2–3), 99105,  DOI: 10.1016/j.ijms.2007.01.014
    39. 39
      Savory, J. J.; Kaiser, N. K.; McKenna, A. M.; Xian, F.; Blakney, G. T.; Rodgers, R. P.; Hendrickson, C. L.; Marshall, A. G. Parts-per-billion Fourier transform ion cyclotron resonance mass measurement accuracy with a “walking” calibration equation. Anal. Chem. 2011, 83 (5), 17326,  DOI: 10.1021/ac102943z
    40. 40
      Schuhmann, K.; Almeida, R.; Baumert, M.; Herzog, R.; Bornstein, S. R.; Shevchenko, A. Shotgun lipidomics on a LTQ Orbitrap mass spectrometer by successive switching between acquisition polarity modes. J. Mass Spectrom. 2012, 47 (1), 96104,  DOI: 10.1002/jms.2031
    41. 41
      Almeida, R.; Pauling, J. K.; Sokol, E.; Hannibal-Bach, H. K.; Ejsing, C. S. Comprehensive lipidome analysis by shotgun lipidomics on a hybrid quadrupole-orbitrap-linear ion trap mass spectrometer. J. Am. Soc. Mass Spectrom. 2015, 26 (1), 13348,  DOI: 10.1007/s13361-014-1013-x
    42. 42
      Simons, B.; Kauhanen, D.; Sylvanne, T.; Tarasov, K.; Duchoslav, E.; Ekroos, K. Shotgun Lipidomics by Sequential Precursor Ion Fragmentation on a Hybrid Quadrupole Time-of-Flight Mass Spectrometer. Metabolites 2012, 2 (1), 195213,  DOI: 10.3390/metabo2010195
    43. 43
      Velickovic, D.; Chu, R. K.; Carrell, A. A.; Thomas, M.; Pasa-Tolic, L.; Weston, D. J.; Anderton, C. R. Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS(2) Increases Confidence in Both Molecular Identification and Localization. Anal. Chem. 2018, 90 (1), 702707,  DOI: 10.1021/acs.analchem.7b04319
    44. 44
      Spraggins, J. M.; Rizzo, D. G.; Moore, J. L.; Rose, K. L.; Hammer, N. D.; Skaar, E. P.; Caprioli, R. M. MALDI FTICR IMS of Intact Proteins: Using Mass Accuracy to Link Protein Images with Proteomics Data. J. Am. Soc. Mass Spectrom. 2015, 26 (6), 97485,  DOI: 10.1007/s13361-015-1147-5
    45. 45
      Hendrickson, C. L.; Beu, S. C.; Blakney, G. T.; Kaiser, N. K.; McIntosh, D. G.; Quinn, J. P.; Marshall, A. G. In Optimized cell geometry for Fourier transform ion cyclotron resonance mass spectrometry, Proceedings of the 57th ASMS Conference on Mass Spectrometry and Allied Topics, Philadelphia, PA, May 31 to June 4; Philadelphia, PA, 2009.
    46. 46
      Chen, T.; Beu, S. C.; Kaiser, N. K.; Hendrickson, C. L. Note: Optimized circuit for excitation and detection with one pair of electrodes for improved Fourier transform ion cyclotron resonance mass spectrometry. Rev. Sci. Instrum. 2014, 85 (6), 066107,  DOI: 10.1063/1.4883179
    47. 47
      Belov, M. E.; Ellis, S. R.; Dilillo, M.; Paine, M. R. L.; Danielson, W. F.; Anderson, G. A.; de Graaf, E. L.; Eijkel, G. B.; Heeren, R. M. A.; McDonnell, L. A. Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry. Anal. Chem. 2017, 89 (14), 74937501,  DOI: 10.1021/acs.analchem.7b01168
    48. 48
      Blakney, G. T.; Hendrickson, C. L.; Marshall, A. G. Predator data station: A fast data acquisition system for advanced FT-ICR MS experiments. Int. J. Mass Spectrom. 2011, 306 (2–3), 246252,  DOI: 10.1016/j.ijms.2011.03.009
    49. 49
      Xian, F.; Hendrickson, C. L.; Blakney, G. T.; Beu, S. C.; Marshall, A. G. Automated broadband phase correction of Fourier transform ion cyclotron resonance mass spectra. Anal. Chem. 2010, 82 (21), 880712,  DOI: 10.1021/ac101091w
    50. 50
      Chambers, M. C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D. L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J.; Hoff, K.; Kessner, D.; Tasman, N.; Shulman, N.; Frewen, B.; Baker, T. A.; Brusniak, M. Y.; Paulse, C.; Creasy, D.; Flashner, L.; Kani, K.; Moulding, C.; Seymour, S. L.; Nuwaysir, L. M.; Lefebvre, B.; Kuhlmann, F.; Roark, J.; Rainer, P.; Detlev, S.; Hemenway, T.; Huhmer, A.; Langridge, J.; Connolly, B.; Chadick, T.; Holly, K.; Eckels, J.; Deutsch, E. W.; Moritz, R. L.; Katz, J. E.; Agus, D. B.; MacCoss, M.; Tabb, D. L.; Mallick, P. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012, 30 (10), 91820,  DOI: 10.1038/nbt.2377
    51. 51
      Race, A. M.; Styles, I. B.; Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteomics 2012, 75 (16), 51112,  DOI: 10.1016/j.jprot.2012.05.035
    52. 52
      Husen, P.; Tarasov, K.; Katafiasz, M.; Sokol, E.; Vogt, J.; Baumgart, J.; Nitsch, R.; Ekroos, K.; Ejsing, C. S. Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data. PLoS One 2013, 8 (11), e79736  DOI: 10.1371/journal.pone.0079736
    53. 53
      Spraggins, J. M.; Djambazova, K. V.; Rivera, E. S.; Migas, L. G.; Neumann, E. K.; Fuetterer, A.; Suetering, J.; Goedecke, N.; Ly, A.; Van de Plas, R.; Caprioli, R. M. High-Performance Molecular Imaging with MALDI Trapped Ion-Mobility Time-of-Flight (timsTOF) Mass Spectrometry. Anal. Chem. 2019, 91 (22), 1455214560,  DOI: 10.1021/acs.analchem.9b03612
    54. 54
      Campos, A. M.; Maciel, E.; Moreira, A. S.; Sousa, B.; Melo, T.; Domingues, P.; Curado, L.; Antunes, B.; Domingues, M. R.; Santos, F. Lipidomics of Mesenchymal Stromal Cells: Understanding the Adaptation of Phospholipid Profile in Response to Pro-Inflammatory Cytokines. J. Cell. Physiol. 2016, 231 (5), 102432,  DOI: 10.1002/jcp.25191
    55. 55
      Guo, S.; Wang, Y.; Zhou, D.; Li, Z. Significantly increased monounsaturated lipids relative to polyunsaturated lipids in six types of cancer microenvironment are observed by mass spectrometry imaging. Sci. Rep. 2015, 4, 5959,  DOI: 10.1038/srep05959
    56. 56
      Ide, Y.; Waki, M.; Hayasaka, T.; Nishio, T.; Morita, Y.; Tanaka, H.; Sasaki, T.; Koizumi, K.; Matsunuma, R.; Hosokawa, Y.; Ogura, H.; Shiiya, N.; Setou, M. Human breast cancer tissues contain abundant phosphatidylcholine(36ratio1) with high stearoyl-CoA desaturase-1 expression. PLoS One 2013, 8 (4), e61204  DOI: 10.1371/journal.pone.0061204
    57. 57
      Harbige, L. S.; Sharief, M. K. Polyunsaturated fatty acids in the pathogenesis and treatment of multiple sclerosis. Br. J. Nutr. 2007, 98 (Suppl 1), S4653,  DOI: 10.1017/S0007114507833010
    58. 58
      Vance, J. E.; Tasseva, G. Formation and function of phosphatidylserine and phosphatidylethanolamine in mammalian cells. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2013, 1831 (3), 54354,  DOI: 10.1016/j.bbalip.2012.08.016
    59. 59
      Kim, W. S.; Weickert, C. S.; Garner, B. Role of ATP-binding cassette transporters in brain lipid transport and neurological disease. J. Neurochem. 2008, 104 (5), 114566,  DOI: 10.1111/j.1471-4159.2007.05099.x
    60. 60
      Smith, D. F.; Kharchenko, A.; Konijnenburg, M.; Klinkert, I.; Pasa-Tolic, L.; Heeren, R. M. Advanced mass calibration and visualization for FT-ICR mass spectrometry imaging. J. Am. Soc. Mass Spectrom. 2012, 23 (11), 186572,  DOI: 10.1007/s13361-012-0464-1
    61. 61
      Estrada, R.; Yappert, M. C. Alternative approaches for the detection of various phospholipid classes by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Mass Spectrom. 2004, 39 (4), 41222,  DOI: 10.1002/jms.603
    62. 62
      Fuchs, B.; Schiller, J.; Suss, R.; Schurenberg, M.; Suckau, D. A direct and simple method of coupling matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) to thin-layer chromatography (TLC) for the analysis of phospholipids from egg yolk. Anal. Bioanal. Chem. 2007, 389 (3), 82734,  DOI: 10.1007/s00216-007-1488-4
  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.9b04768.

    • Eight additional figures as described in the text and table with a complete list of all tentatively identified lipid species (PDF)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.