Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-03

AUTHORS

Ching Yu Lin, Huifeng Wu, Ronald S. Tjeerdema, Mark R. Viant

ABSTRACT

Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method. More... »

PAGES

55-67

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-006-0043-1

DOI

http://dx.doi.org/10.1007/s11306-006-0043-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043049251


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0301", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Analytical Chemistry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of California, Davis", 
          "id": "https://www.grid.ac/institutes/grid.27860.3b", 
          "name": [
            "Department of Environmental Toxicology, University of California, 95616-8588, Davis, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lin", 
        "givenName": "Ching Yu", 
        "id": "sg:person.0772171646.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772171646.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Birmingham", 
          "id": "https://www.grid.ac/institutes/grid.6572.6", 
          "name": [
            "School of Biosciences, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Huifeng", 
        "id": "sg:person.0644734366.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644734366.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California, Davis", 
          "id": "https://www.grid.ac/institutes/grid.27860.3b", 
          "name": [
            "Department of Environmental Toxicology, University of California, 95616-8588, Davis, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tjeerdema", 
        "givenName": "Ronald S.", 
        "id": "sg:person.01273346045.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01273346045.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Birmingham", 
          "id": "https://www.grid.ac/institutes/grid.6572.6", 
          "name": [
            "School of Biosciences, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Viant", 
        "givenName": "Mark R.", 
        "id": "sg:person.01140655554.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01140655554.38"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1139/o59-099", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001206449"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-005-4429-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002168056", 
          "https://doi.org/10.1007/s11306-005-4429-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-005-4429-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002168056", 
          "https://doi.org/10.1007/s11306-005-4429-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0031-9422(02)00704-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006633212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ab.2004.04.037", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012615476"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0038-0717(01)00021-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012892998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10646-003-4477-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016435293", 
          "https://doi.org/10.1007/s10646-003-4477-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/mrm.1106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020043838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbrc.2003.09.092", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022502205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bbrc.2003.09.092", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022502205"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nbm.740", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023612837"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0378-4347(00)00286-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025776797"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es034281x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026818092"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/es034281x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026818092"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0079-6565(95)01017-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030113218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m507380200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031576895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1074/jbc.m507380200", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031576895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/b:fish.0000035938.92027.81", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033711516", 
          "https://doi.org/10.1023/b:fish.0000035938.92027.81"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pca.776", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035383195"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0031-9422(02)00713-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041962365"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1104/pp.104.041012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049061265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-005-4428-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051455798", 
          "https://doi.org/10.1007/s11306-005-4428-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11306-005-4428-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051455798", 
          "https://doi.org/10.1007/s11306-005-4428-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nbm.980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052429175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/nbm.980", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052429175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac048803i", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054996267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac048803i", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054996267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051312t", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997367"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac051312t", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054997367"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/tx0256127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056297619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/tx0256127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056297619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/tx990210t", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056301064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/tx990210t", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056301064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01926230590958146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058308258"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01926230590958146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058308258"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/1536231041388348", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059215071"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/omi.2005.9.281", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059303616"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-03", 
    "datePublishedReg": "2007-03-01", 
    "description": "Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11306-006-0043-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4041703", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1036887", 
        "issn": [
          "1573-3882", 
          "1573-3890"
        ], 
        "name": "Metabolomics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "3"
      }
    ], 
    "name": "Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics", 
    "pagination": "55-67", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cd0ee71c97e95bb730ee8cc58b74765c62832a380e5bb9ee27b11cadd4b04759"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11306-006-0043-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043049251"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11306-006-0043-1", 
      "https://app.dimensions.ai/details/publication/pub.1043049251"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T23:27", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8693_00000523.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11306-006-0043-1"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11306-006-0043-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11306-006-0043-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11306-006-0043-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11306-006-0043-1'


 

This table displays all metadata directly associated to this object as RDF triples.

169 TRIPLES      21 PREDICATES      53 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11306-006-0043-1 schema:about anzsrc-for:03
2 anzsrc-for:0301
3 schema:author Ndced5602b58b4240a2a6612761bf861a
4 schema:citation sg:pub.10.1007/s10646-003-4477-1
5 sg:pub.10.1007/s11306-005-4428-3
6 sg:pub.10.1007/s11306-005-4429-2
7 sg:pub.10.1023/b:fish.0000035938.92027.81
8 https://doi.org/10.1002/mrm.1106
9 https://doi.org/10.1002/nbm.740
10 https://doi.org/10.1002/nbm.980
11 https://doi.org/10.1002/pca.776
12 https://doi.org/10.1016/0079-6565(95)01017-3
13 https://doi.org/10.1016/j.ab.2004.04.037
14 https://doi.org/10.1016/j.bbrc.2003.09.092
15 https://doi.org/10.1016/s0031-9422(02)00704-5
16 https://doi.org/10.1016/s0031-9422(02)00713-6
17 https://doi.org/10.1016/s0038-0717(01)00021-9
18 https://doi.org/10.1016/s0378-4347(00)00286-3
19 https://doi.org/10.1021/ac048803i
20 https://doi.org/10.1021/ac051312t
21 https://doi.org/10.1021/es034281x
22 https://doi.org/10.1021/tx0256127
23 https://doi.org/10.1021/tx990210t
24 https://doi.org/10.1074/jbc.m507380200
25 https://doi.org/10.1080/01926230590958146
26 https://doi.org/10.1089/1536231041388348
27 https://doi.org/10.1089/omi.2005.9.281
28 https://doi.org/10.1104/pp.104.041012
29 https://doi.org/10.1139/o59-099
30 schema:datePublished 2007-03
31 schema:datePublishedReg 2007-03-01
32 schema:description Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method.
33 schema:genre research_article
34 schema:inLanguage en
35 schema:isAccessibleForFree true
36 schema:isPartOf N248f33cabc42412ba762a46efa01b180
37 Nbbbc6b40c99c490e87cbb942da958f98
38 sg:journal.1036887
39 schema:name Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics
40 schema:pagination 55-67
41 schema:productId N08961ef7ecc34df3ae8095a3af7491d5
42 N845387fe9e654912b6d784bedad1110c
43 Nb19b3c3efa4c439f9eaf78fbae5cadba
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043049251
45 https://doi.org/10.1007/s11306-006-0043-1
46 schema:sdDatePublished 2019-04-10T23:27
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher Na73c948c27bc449a88a4afff44e79b80
49 schema:url http://link.springer.com/10.1007%2Fs11306-006-0043-1
50 sgo:license sg:explorer/license/
51 sgo:sdDataset articles
52 rdf:type schema:ScholarlyArticle
53 N037c2afaa90e49b4b7e0157dda42edf7 rdf:first sg:person.0644734366.50
54 rdf:rest N529b278963ca49b59c957bc8caba5ced
55 N08961ef7ecc34df3ae8095a3af7491d5 schema:name doi
56 schema:value 10.1007/s11306-006-0043-1
57 rdf:type schema:PropertyValue
58 N248f33cabc42412ba762a46efa01b180 schema:volumeNumber 3
59 rdf:type schema:PublicationVolume
60 N438fb47f1f854acb9682193958ecc6e9 rdf:first sg:person.01140655554.38
61 rdf:rest rdf:nil
62 N529b278963ca49b59c957bc8caba5ced rdf:first sg:person.01273346045.13
63 rdf:rest N438fb47f1f854acb9682193958ecc6e9
64 N845387fe9e654912b6d784bedad1110c schema:name dimensions_id
65 schema:value pub.1043049251
66 rdf:type schema:PropertyValue
67 Na73c948c27bc449a88a4afff44e79b80 schema:name Springer Nature - SN SciGraph project
68 rdf:type schema:Organization
69 Nb19b3c3efa4c439f9eaf78fbae5cadba schema:name readcube_id
70 schema:value cd0ee71c97e95bb730ee8cc58b74765c62832a380e5bb9ee27b11cadd4b04759
71 rdf:type schema:PropertyValue
72 Nbbbc6b40c99c490e87cbb942da958f98 schema:issueNumber 1
73 rdf:type schema:PublicationIssue
74 Ndced5602b58b4240a2a6612761bf861a rdf:first sg:person.0772171646.75
75 rdf:rest N037c2afaa90e49b4b7e0157dda42edf7
76 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
77 schema:name Chemical Sciences
78 rdf:type schema:DefinedTerm
79 anzsrc-for:0301 schema:inDefinedTermSet anzsrc-for:
80 schema:name Analytical Chemistry
81 rdf:type schema:DefinedTerm
82 sg:grant.4041703 http://pending.schema.org/fundedItem sg:pub.10.1007/s11306-006-0043-1
83 rdf:type schema:MonetaryGrant
84 sg:journal.1036887 schema:issn 1573-3882
85 1573-3890
86 schema:name Metabolomics
87 rdf:type schema:Periodical
88 sg:person.01140655554.38 schema:affiliation https://www.grid.ac/institutes/grid.6572.6
89 schema:familyName Viant
90 schema:givenName Mark R.
91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01140655554.38
92 rdf:type schema:Person
93 sg:person.01273346045.13 schema:affiliation https://www.grid.ac/institutes/grid.27860.3b
94 schema:familyName Tjeerdema
95 schema:givenName Ronald S.
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01273346045.13
97 rdf:type schema:Person
98 sg:person.0644734366.50 schema:affiliation https://www.grid.ac/institutes/grid.6572.6
99 schema:familyName Wu
100 schema:givenName Huifeng
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0644734366.50
102 rdf:type schema:Person
103 sg:person.0772171646.75 schema:affiliation https://www.grid.ac/institutes/grid.27860.3b
104 schema:familyName Lin
105 schema:givenName Ching Yu
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0772171646.75
107 rdf:type schema:Person
108 sg:pub.10.1007/s10646-003-4477-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016435293
109 https://doi.org/10.1007/s10646-003-4477-1
110 rdf:type schema:CreativeWork
111 sg:pub.10.1007/s11306-005-4428-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051455798
112 https://doi.org/10.1007/s11306-005-4428-3
113 rdf:type schema:CreativeWork
114 sg:pub.10.1007/s11306-005-4429-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002168056
115 https://doi.org/10.1007/s11306-005-4429-2
116 rdf:type schema:CreativeWork
117 sg:pub.10.1023/b:fish.0000035938.92027.81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033711516
118 https://doi.org/10.1023/b:fish.0000035938.92027.81
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1002/mrm.1106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020043838
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1002/nbm.740 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023612837
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1002/nbm.980 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052429175
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1002/pca.776 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035383195
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/0079-6565(95)01017-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030113218
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.ab.2004.04.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012615476
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.bbrc.2003.09.092 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022502205
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/s0031-9422(02)00704-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006633212
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/s0031-9422(02)00713-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041962365
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/s0038-0717(01)00021-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012892998
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/s0378-4347(00)00286-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025776797
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1021/ac048803i schema:sameAs https://app.dimensions.ai/details/publication/pub.1054996267
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1021/ac051312t schema:sameAs https://app.dimensions.ai/details/publication/pub.1054997367
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1021/es034281x schema:sameAs https://app.dimensions.ai/details/publication/pub.1026818092
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1021/tx0256127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056297619
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1021/tx990210t schema:sameAs https://app.dimensions.ai/details/publication/pub.1056301064
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1074/jbc.m507380200 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031576895
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1080/01926230590958146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058308258
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1089/1536231041388348 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059215071
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1089/omi.2005.9.281 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059303616
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1104/pp.104.041012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049061265
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1139/o59-099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001206449
163 rdf:type schema:CreativeWork
164 https://www.grid.ac/institutes/grid.27860.3b schema:alternateName University of California, Davis
165 schema:name Department of Environmental Toxicology, University of California, 95616-8588, Davis, CA, USA
166 rdf:type schema:Organization
167 https://www.grid.ac/institutes/grid.6572.6 schema:alternateName University of Birmingham
168 schema:name School of Biosciences, The University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK
169 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...