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. 2012 Nov 6;84(21):9259-67.
doi: 10.1021/ac302039u. Epub 2012 Oct 10.

Molecular analysis of model gut microbiotas by imaging mass spectrometry and nanodesorption electrospray ionization reveals dietary metabolite transformations

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Molecular analysis of model gut microbiotas by imaging mass spectrometry and nanodesorption electrospray ionization reveals dietary metabolite transformations

Christopher M Rath et al. Anal Chem. .

Abstract

The communities constituting our microbiotas are emerging as mediators of the health-disease continuum. However, deciphering the functional impact of microbial communities on host pathophysiology represents a formidable challenge, due to the heterogeneous distribution of chemical and microbial species within the gastrointestinal (GI) tract. Herein, we apply imaging mass spectrometry (IMS) to localize metabolites from the interaction between the host and colonizing microbiota. This approach complements other molecular imaging methodologies in that analytes need not be known a priori, offering the possibility of untargeted analysis. Localized molecules within the GI tract were then identified in situ by surface sampling with nanodesorption electrospray ionization Fourier transform ion cyclotron resonance-mass spectrometry (nanoDESI FTICR-MS). Products from diverse structural classes were identified including cholesterol-derived lipids, glycans, and polar metabolites. Specific chemical transformations performed by the microbiota were validated with bacteria in culture. This study illustrates how untargeted spatial characterization of metabolites can be applied to the molecular dissection of complex biology in situ.

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Figures

Figure 1
Figure 1
Experimental design. (A) GF: Germ-free, Bt: mono-associated Bacteroides thetaiotaomicron, and BtBl: bi-associated associated Bacteroides thetaiotaomicron plus Bifidobacterium longum mice. Color-coding is noted and conserved in Figures 2–3. (B) Gut tissue sections were imaged via mass spectrometry to reveal metabolite distributions in false color. A scale bar is noted for all images. (C) NanoDESI FT-MS was applied for in situ compound identification. The capillaries are drawn to scale with the tissue section, however the actual droplet contact area is substantially smaller. HV: high voltage, MS: mass spectrometer. (D) Regions of interest from a mouse gut tissue section: 1. host connective tissue, 2. large intestine lumen, 3. large intestine/epithelial border, and 4. small intestine. (E) Selective compounds (1–9) identified by IMS and nanoDESI FT-MS. Stereochemistry is not illustrated as MS-based methods cannot readily distinguish contributions from different isomers. The exact contributions from specific regioisomers (C3, C7, C12) for (7–8) are not known—only the C7 modified isomer is illustrated. Compounds are with conserved color-coding as Figure 3. Observed m/z values are provided in Figure 3.
Figure 2
Figure 2
Identification of metabolites detected from IMS experiments. Germ-free, mono-associated Bacteroides thetaiotaomicron, and BtBl: bi-associated associated Bacteroides thetaiotaomicron plus Bifidobacterium longum mice are noted by color, with overlap in black. Compounds (1–4, 8–9) are displayed with conserved color coding as in Figures 1, 3. (A) MALDI-TOF MS average spectra displayed as intensity versus m/z. (B) NanoDESI-FTICR MS displayed as intensity versus m/z. (C) Data-independent iontrap (IT)-MS/MS data from the large intestine/epithelial border (Figure 1D–3.) displayed as intensity in Z, MS/MS spectra in m/z in Y, versus precursor mass window in X in m/z. (D) Zoomed nanoDESI FT-MS spectra from (B) for (1–4) displayed as intensity versus MS spectra in m/z. Accurate mass assignments are noted, correlating with IMS data. See Figure S1 for zoomed nanoDESI-MS/MS spectra from (C). (E) Spectral network clustering of data-independent MS/MS data. Nodes are linked based on MS/MS spectral similarity as a proxy for chemical similarity. This figure is an alternative view of (C), illustrating some of the complexity of the dataset. (F) Spectral network zoomed, illustrating a node at m/z −512 (8) connected to a node at m/z −514 (9). This data illustrates how spectral networking can be used to link known compounds to unknowns—forming visual data hypotheses for manual validation.
Figure 3
Figure 3
Imaging mass spectrometry of the gut of three mouse microbiota models. A series of panels are provided for GF, Bt, and BtBl associated mice. The panels represent the ventral view of the mouse with the anterior facing left and the posterior facing right (Figure 1A). Selected anatomical features are noted (Figure 1D).Orientation and color of IMS data match (Figure 1E). Optical images were taken at the block face with a universal scale provided. Each false-colored IMS image represents the normalized signal intensity for the m/z value illustrated corresponding to (1–9) as a particular ion or charged adduct. Identities of m/z values were assigned by nanoDESI MS/MS and LC-MS (Figures 2 and S1–4). This figure thus depicts differential signal and localization patterns of (1–9) across three sample conditions.
Figure 4
Figure 4
Imaging mass spectrometry to explore microbial bile acid transformations. A series of panels are provided for two colonies of Bt and two colonies of Bl on TYG agar, TYG agar + taurocholate (9), and TYG agar + deoxytaurocholate (7). The panels represent the portion of the Petri dish agar relocated to a MALDI target plate. Each false-colored image represents the normalized signal intensity for the m/z value illustrated. Identities of m/z values were assigned by nanoDESI MS/MS and LC-MS (Figures 2 and S1–4). For compounds (7–9) present in Figures 1–3, identical color coding is utilized.

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