M/Z: 607.3984

Hit 1 annotations: PG(a-13:0/i-12:0)_[M+H-H2O]+

在BioDeep NovoCell知识数据库中，参考离子总共被划分为4个级别。

Confirmed: 这个参考离子已经通过手动审计得到确认和验证。
Reliable: 这个参考离子可能在特定的解剖组织环境中高度保守。
Unreliable: 这个参考离子具有较高的排名价值，但缺乏可重复性。
Unavailable: 由于排名价值低且缺乏可重复性，这个参考离子不应用于注释。

Found 2 Reference Ions Near m/z 607.3984

NovoCell ID	m/z	Mass Window	Metabolite	Ranking	Anatomy Context
MSI_000018066 Unreliable	607.3911	607.3911 ~ 607.3911 MzDiff: `none`	Canesceol (BioDeep_00000007031) Formula: C29H44O11 (568.2883)	0.56 (100%)	Vitis vinifera [PO:0009087] mesocarp
MSI_000024706 Unreliable	607.4029	607.4029 ~ 607.4029 MzDiff: `none`	PG(a-13:0/i-12:0) (BioDeep_00000108992) Formula: C31H61O10P (624.4002)	1.77 (100%)	Mus musculus [UBERON:0004269] upper arm connective tissue

Found 2 Sample Hits

Metabolite	Species	Sample
PG(a-13:0/i-12:0) Formula: C31H61O10P (624.4002) Adducts: [M+H-H2O]+ (Ppm: 9.8)	Mus musculus (Left upper arm)	357_l_total ion count Resolution: 50μm, 97x131 Description Diseased
PG(a-13:0/i-12:0) Formula: C31H61O10P (624.4002) Adducts: [M+H-H2O]+ (Ppm: 2.4)	Mus musculus (Liver)	Salmonella_final_pos_recal Resolution: 17μm, 691x430 Description A more complete and holistic view on host–microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium. [dataset] Nicole Strittmatter. Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging, metabolights_dataset, V1; 2022. https://www.ebi.ac.uk/metabolights/MTBLS2671.

Metabolite

Species

Sample

PG(a-13:0/i-12:0)

Formula: C31H61O10P (624.4002)
Adducts: [M+H-H2O]+ (Ppm: 9.8)

Mus musculus (Left upper arm)

357_l_total ion count

Resolution: 50μm, 97x131

Description

Diseased

PG(a-13:0/i-12:0)

Formula: C31H61O10P (624.4002)
Adducts: [M+H-H2O]+ (Ppm: 2.4)

Mus musculus (Liver)

Salmonella_final_pos_recal

Resolution: 17μm, 691x430

Description

A more complete and holistic view on host–microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium. [dataset] Nicole Strittmatter. Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging, metabolights_dataset, V1; 2022. https://www.ebi.ac.uk/metabolights/MTBLS2671.