Dataset Information

Spatial metabolomics of in situ, host-microbe interactions (UPLC-MS/MS assay)

ABSTRACT: Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defense molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric pressure mass spectrometry imaging (AP-MALDI-MSI) to image host-microbe symbioses and their metabolic interactions. metaFISH aligns and integrates metabolite and fluorescent images at the micrometer-scale for a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts, a variation in metabolic phenotypes in one symbiont type, and novel symbiosis metabolites. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ - a powerful tool for microbiologists across fields.

UPLC-MS/MS assay protocols and data are reported in the current study MTBLS746.

MALDI imaging MS assay protocols and data associated to this study are reported in MTBLS744.
On-tissue MALDI imaging MS/MS assay protocols and data associated to this study are reported in MTBLS805.
MRMS assay protocols and data associated to this study are reported in MTBLS811.

OTHER RELATED OMICS DATASETS IN: PXD018322PXD006154

INSTRUMENT(S): Liquid Chromatography Tandem MS (LC-MS/MS)

SUBMITTER: Manuel Liebeke

PROVIDER: MTBLS746 | MetaboLights | 2019-11-15

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	MTBLS746	Other
	FILES	Other
	a_MTBLS746_maldi-fish_lc-msms_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS746_maldi-fish_lc-msms_metabolite_profiling_mass_spectrometry_v2_maf.tsv	Tabular

Items per page:

1 - 5 of 7

Similar Datasets

Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.

Dataset Information

Spatial metabolomics of in situ, host-microbe interactions (UPLC-MS/MS assay)

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets