M/Z: 130.0651


Hit 1 annotations:  L-Phenylalanine_[M+H-2H2O]+


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 5 Reference Ions Near m/z 130.0651
NovoCell ID m/z Mass Window Metabolite Ranking Anatomy Context
MSI_000033571 Unreliable 130.0651 130.0651 ~ 130.0651
MzDiff: none
L-Phenylalanine (BioDeep_00000000351)
Formula: C9H11NO2 (165.079)
1.64 (100%) Posidonia oceanica
[PO:0005352] xylem
MSI_000034668 Unreliable 130.0651 130.0651 ~ 130.0651
MzDiff: none
L-Phenylalanine (BioDeep_00000000351)
Formula: C9H11NO2 (165.079)
0.23 (100%) Posidonia oceanica
[PO:0006036] root epidermis
MSI_000035194 Unreliable 130.0651 130.0651 ~ 130.0651
MzDiff: none
L-Phenylalanine (BioDeep_00000000351)
Formula: C9H11NO2 (165.079)
1.23 (100%) Posidonia oceanica
[PO:0006203] pericycle
MSI_000038436 Unreliable 130.0668 130.0668 ~ 130.0668
MzDiff: none
L-Phenylalanine (BioDeep_00000000351)
Formula: C9H11NO2 (165.079)
2.09 (100%) Posidonia oceanica
[PO:0005059] root endodermis
MSI_000039942 Unreliable 130.0668 130.0668 ~ 130.0668
MzDiff: none
L-Phenylalanine (BioDeep_00000000351)
Formula: C9H11NO2 (165.079)
0.39 (100%) Posidonia oceanica
[PO:0005417] phloem

Found 4 Sample Hits
Metabolite Species Sample
L-Phenylalanine

Formula: C9H11NO2 (165.079)
Adducts: [M+H-2H2O]+ (Ppm: 1.4)
Marker Pen (NA)
3ul_0.8Mpa_RAW_20241016-PAPER PNMK
Resolution: 30μm, 315x42

Description

By writing the four English letters “PNMK” on white paper with a marker pen, and then scanning with a DESI ion source to obtain the scanning result. The signal of the chemical substances on the marker pen used appears on the channel with an m/z value of 322.1918, 323.1953, 546.4010, and etc, from the single cell deconvolution sampling layer class_4. This test data was tested by chuxiaoping from PANOMIX’s R&D laboratory.

L-Phenylalanine

Formula: C9H11NO2 (165.079)
Adducts: [M+H-2H2O]+ (Ppm: 0.2)
Posidonia oceanica (root)
20190614_MS1_A19r-20
Resolution: 17μm, 262x276

Description

Seagrasses are one of the most efficient natural sinks of carbon dioxide (CO2) on Earth. Despite covering less than 0.1% of coastal regions, they have the capacity to bury up to 10% of marine organic matter and can bury the same amount of carbon 35 times faster than tropical rainforests. On land, the soil’s ability to sequestrate carbon is intimately linked to microbial metabolism. Despite the growing attention to the link between plant production, microbial communities, and the carbon cycle in terrestrial ecosystems, these processes remain enigmatic in the sea. Here, we show that seagrasses excrete organic sugars, namely in the form of sucrose, into their rhizospheres. Surprisingly, the microbial communities living underneath meadows do not fully use this sugar stock in their metabolism. Instead, sucrose piles up in the sediments to mM concentrations underneath multiple types of seagrass meadows. Sediment incubation experiments show that microbial communities living underneath a meadow use sucrose at low metabolic rates. Our metagenomic analyses revealed that the distinct community of microorganisms occurring underneath meadows is limited in their ability to degrade simple sugars, which allows these compounds to persist in the environment over relatively long periods of time. Our findings reveal how seagrasses form blue carbon stocks despite the relatively small area they occupy. Unfortunately, anthropogenic disturbances are threatening the long-term persistence of seagrass meadows. Given that these sediments contain a large stock of sugars that heterotopic bacteria can degrade, it is even more important to protect these ecosystems from degradation.

L-Phenylalanine

Formula: C9H11NO2 (165.079)
Adducts: [M+H-2H2O]+ (Ppm: 12.9)
Posidonia oceanica (root)
MS1_20180404_PO_1200
Resolution: 17μm, 193x208

Description

L-Phenylalanine

Formula: C9H11NO2 (165.079)
Adducts: [M+H-2H2O]+ (Ppm: 7.5)
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.