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 11 Reference Ions Near m/z 193.9808
NovoCell ID m/z Mass Window Metabolite Ranking Anatomy Context
MSI_000054130 Reliable 193.9738 193.9737 ~ 193.9739
MzDiff: 1.0 ppm
2-Amino-4,4-dichlorobutanoic acid (BioDeep_00000238580)
Formula: C4H7Cl2NO2 (170.9854)
3.37 (100%) MALDI - CHCA
[NOVOCELL:BACKGROUND] blank
MSI_000040619 Unreliable 193.9802 193.9802 ~ 193.9803
MzDiff: 0.1 ppm
1,2-dihydro-1λ³-iodinine (BioDeep_00002275001)
Formula: C5H7I (193.9592)
3.45 (100%) Posidonia oceanica
[PO:0006036] root epidermis
MSI_000012991 Unavailable 193.9808 193.9808 ~ 193.9808
MzDiff: none
Picric acid (BioDeep_00000182423)
Formula: C6H3N3O7 (228.9971)
-0.59 (100%) Plant
[PO:0005020] vascular bundle
MSI_000014564 Unreliable 193.9808 193.9808 ~ 193.9808
MzDiff: none
Picric acid (BioDeep_00000182423)
Formula: C6H3N3O7 (228.9971)
0.56 (100%) Plant
[PO:0006036] root epidermis
MSI_000018761 Unreliable 193.9808 193.9808 ~ 193.9808
MzDiff: none
Picric acid (BioDeep_00000182423)
Formula: C6H3N3O7 (228.9971)
1.49 (100%) Plant
[PO:0020124] root stele
MSI_000020126 Unavailable 193.9808 193.9808 ~ 193.9808
MzDiff: none
Picric acid (BioDeep_00000182423)
Formula: C6H3N3O7 (228.9971)
-0.53 (100%) Plant
[PO:0025197] stele
MSI_000032888 Unreliable 193.9737 193.9737 ~ 193.9737
MzDiff: none
2-Amino-4,4-dichlorobutanoic acid (BioDeep_00000238580)
Formula: C4H7Cl2NO2 (170.9854)
0.02 (100%) Posidonia oceanica
[PO:0005020] vascular bundle
MSI_000033615 Unreliable 193.9799 193.9799 ~ 193.9799
MzDiff: none
1,2-dihydro-1λ³-iodinine (BioDeep_00002275001)
Formula: C5H7I (193.9592)
1.39 (100%) Posidonia oceanica
[PO:0005352] xylem
MSI_000035127 Unreliable 193.9799 193.9799 ~ 193.9799
MzDiff: none
1,2-dihydro-1λ³-iodinine (BioDeep_00002275001)
Formula: C5H7I (193.9592)
1.7 (100%) Posidonia oceanica
[PO:0006203] pericycle
MSI_000037746 Unreliable 193.9741 193.9741 ~ 193.9741
MzDiff: none
2-Amino-4,4-dichlorobutanoic acid (BioDeep_00000238580)
Formula: C4H7Cl2NO2 (170.9854)
0.24 (100%) Posidonia oceanica
[UBERON:0000329] hair root
MSI_000040447 Unavailable 193.9739 193.9739 ~ 193.9739
MzDiff: none
2-Amino-4,4-dichlorobutanoic acid (BioDeep_00000238580)
Formula: C4H7Cl2NO2 (170.9854)
-0.21 (100%) Posidonia oceanica
[PO:0005417] phloem

Found 5 Sample Hits
Metabolite Species Sample
Picric acid

Formula: C6H3N3O7 (228.9971)
Adducts: [M+H-2H2O]+ (Ppm: 12.6)
Plant (Root)
MPIMM_035_QE_P_PO_6pm
Resolution: 30μm, 165x170

Description

1,2-dihydro-1λ³-iodinine

Formula: C5H7I (193.9592)
Adducts: [M-H2O+NH4]+ (Ppm: 13.5)
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.

1,2-dihydro-1λ³-iodinine

Formula: C5H7I (193.9592)
Adducts: [M-H2O+NH4]+ (Ppm: 11.9)
Posidonia oceanica (root)
20190613_MS1_A19r-18
Resolution: 17μm, 246x264

Description

1,2-dihydro-1λ³-iodinine

Formula: C5H7I (193.9592)
Adducts: [M-H2O+NH4]+ (Ppm: 11.4)
Posidonia oceanica (root)
MS1_20180404_PO_1200
Resolution: 17μm, 193x208

Description

Dimethoate

Formula: C5H12NO3PS2 (228.9996)
Adducts: [M+H-2H2O]+ (Ppm: 12.7)
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.