lc ms analysis biocrust soil water samples  (Millipore)


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    Millipore lc ms analysis biocrust soil water samples
    Metabolite patterns detected in <t>biocrust</t> soil water. Metabolite dynamics (85 metabolites displayed as the average peak area, normalized across each row) were observed in biocrust soil water across wetting and successional stages. Unique patterns are indicated by cluster 1 (early metabolites including fatty acids), cluster 2 (early-to-mid time point metabolites) and cluster 3 (late metabolites). Putative metabolites are indicated by parentheses. n = 2–5 for each group
    Lc Ms Analysis Biocrust Soil Water Samples, supplied by Millipore, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/lc ms analysis biocrust soil water samples/product/Millipore
    Average 92 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    lc ms analysis biocrust soil water samples - by Bioz Stars, 2020-08
    92/100 stars

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    96-well millipore filter plates

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    1) Product Images from "Linking soil biology and chemistry in biological soil crust using isolate exometabolomics"

    Article Title: Linking soil biology and chemistry in biological soil crust using isolate exometabolomics

    Journal: Nature Communications

    doi: 10.1038/s41467-017-02356-9

    Metabolite patterns detected in biocrust soil water. Metabolite dynamics (85 metabolites displayed as the average peak area, normalized across each row) were observed in biocrust soil water across wetting and successional stages. Unique patterns are indicated by cluster 1 (early metabolites including fatty acids), cluster 2 (early-to-mid time point metabolites) and cluster 3 (late metabolites). Putative metabolites are indicated by parentheses. n = 2–5 for each group
    Figure Legend Snippet: Metabolite patterns detected in biocrust soil water. Metabolite dynamics (85 metabolites displayed as the average peak area, normalized across each row) were observed in biocrust soil water across wetting and successional stages. Unique patterns are indicated by cluster 1 (early metabolites including fatty acids), cluster 2 (early-to-mid time point metabolites) and cluster 3 (late metabolites). Putative metabolites are indicated by parentheses. n = 2–5 for each group

    Techniques Used:

    Simplified biocrust foodweb for three dominant biocrust bacteria based on combining isolate exometabolomics data with in situ microbe–metabolite relationships. This network displays the relationships between metabolites and three dominant bacteria (or metabolically similar organisms) as they increase and decrease in relative abundance across wetting and successional stages in biocrust. The lower line plot corresponds to real relative abundance measurements for the three bacteria in level C successional stage biocrust. As Microcoleus sp. increases in relative abundance immediately after wetting, many metabolites released by the closest-related isolate are positively correlated with Microcoleus sp. in biocrust (solid red arrows) and as the two Bacilli increase in relative abundance (first Bacillus sp. 1 then Bacillus sp. 2), most metabolites consumed by the closest-related isolate decrease and are negatively correlated with these bacteria in biocrust (solid blue arrows) and most released metabolites are positively correlated (solid red arrows). Dotted arrows indicate metabolites that are released (red) or consumed (blue) by isolates, but did not display the expected relationship with that microorganism in situ. The thickness of the line corresponds to the absolute value of the Spearman’s rho correlation coefficient. The overall expected directionality (solid lines vs. dotted lines) was significant as determined by the exact binomial test (two-tailed p -value = 0.01). *FDR-adjusted p
    Figure Legend Snippet: Simplified biocrust foodweb for three dominant biocrust bacteria based on combining isolate exometabolomics data with in situ microbe–metabolite relationships. This network displays the relationships between metabolites and three dominant bacteria (or metabolically similar organisms) as they increase and decrease in relative abundance across wetting and successional stages in biocrust. The lower line plot corresponds to real relative abundance measurements for the three bacteria in level C successional stage biocrust. As Microcoleus sp. increases in relative abundance immediately after wetting, many metabolites released by the closest-related isolate are positively correlated with Microcoleus sp. in biocrust (solid red arrows) and as the two Bacilli increase in relative abundance (first Bacillus sp. 1 then Bacillus sp. 2), most metabolites consumed by the closest-related isolate decrease and are negatively correlated with these bacteria in biocrust (solid blue arrows) and most released metabolites are positively correlated (solid red arrows). Dotted arrows indicate metabolites that are released (red) or consumed (blue) by isolates, but did not display the expected relationship with that microorganism in situ. The thickness of the line corresponds to the absolute value of the Spearman’s rho correlation coefficient. The overall expected directionality (solid lines vs. dotted lines) was significant as determined by the exact binomial test (two-tailed p -value = 0.01). *FDR-adjusted p

    Techniques Used: In Situ, Metabolic Labelling, Two Tailed Test

    Experimental workflow and biocrust microbe–metabolite relationship predictions. a Biocrust wetup metabolomics and metagenomics experimental setup and analysis. To study microbe–metabolite relationships in situ, biocrusts from four successional stages were wetup and sampled at five time points (total n = 100). Biocrust soil water was removed and analyzed by liquid chromatography/ mass spectrometry ( n = 5 for each group) and biocrust DNA was extracted for shotgun sequencing ( n = 1 for each group). Metagenome-estimated genome and metabolite abundances were analyzed through Spearman rank correlations to determine microbe–metabolite relationships and compared to the expected relationships based on isolate exometabolomic studies. b Exometabolomics-based in situ microbe–metabolite relationship prediction. The hypothesis is that isolate exometabolomics can be used to predict microbe–metabolite patterns in situ based on microbial abundance: Across wetting and successional stages, microbes change in abundance and negatively correlate with metabolites that they consume and positively correlate with metabolites that they release (metabolites are indicated by dotted lines)
    Figure Legend Snippet: Experimental workflow and biocrust microbe–metabolite relationship predictions. a Biocrust wetup metabolomics and metagenomics experimental setup and analysis. To study microbe–metabolite relationships in situ, biocrusts from four successional stages were wetup and sampled at five time points (total n = 100). Biocrust soil water was removed and analyzed by liquid chromatography/ mass spectrometry ( n = 5 for each group) and biocrust DNA was extracted for shotgun sequencing ( n = 1 for each group). Metagenome-estimated genome and metabolite abundances were analyzed through Spearman rank correlations to determine microbe–metabolite relationships and compared to the expected relationships based on isolate exometabolomic studies. b Exometabolomics-based in situ microbe–metabolite relationship prediction. The hypothesis is that isolate exometabolomics can be used to predict microbe–metabolite patterns in situ based on microbial abundance: Across wetting and successional stages, microbes change in abundance and negatively correlate with metabolites that they consume and positively correlate with metabolites that they release (metabolites are indicated by dotted lines)

    Techniques Used: In Situ, Liquid Chromatography, Mass Spectrometry, Shotgun Sequencing

    Related Articles

    Liquid Chromatography with Mass Spectroscopy:

    Article Title: Linking soil biology and chemistry in biological soil crust using isolate exometabolomics
    Article Snippet: .. Metabolite extraction and LC/MS analysis Biocrust soil water samples (1.5 mL) were lyophilized and resuspended in methanol (200 μL) containing internal standards (2–10 μg/mL) and filtered through 96-well Millipore filter plates (0.2 μm PVDF) by centrifuging at 1500 × g for 2 min. .. Samples were analyzed using normal-phase LC/MS with a ZIC-pHILIC column (150 × 2.1 mm, 3.5 μm 200 Å, Merck Sequant, Darmstadt, Germany) using an Agilent 1290 series UHPLC (Agilent Technologies, Santa Clara, California, USA).

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    Millipore lc ms analysis biocrust soil water samples
    Metabolite patterns detected in <t>biocrust</t> soil water. Metabolite dynamics (85 metabolites displayed as the average peak area, normalized across each row) were observed in biocrust soil water across wetting and successional stages. Unique patterns are indicated by cluster 1 (early metabolites including fatty acids), cluster 2 (early-to-mid time point metabolites) and cluster 3 (late metabolites). Putative metabolites are indicated by parentheses. n = 2–5 for each group
    Lc Ms Analysis Biocrust Soil Water Samples, supplied by Millipore, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/lc ms analysis biocrust soil water samples/product/Millipore
    Average 92 stars, based on 1 article reviews
    Price from $9.99 to $1999.99
    lc ms analysis biocrust soil water samples - by Bioz Stars, 2020-08
    92/100 stars
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    Metabolite patterns detected in biocrust soil water. Metabolite dynamics (85 metabolites displayed as the average peak area, normalized across each row) were observed in biocrust soil water across wetting and successional stages. Unique patterns are indicated by cluster 1 (early metabolites including fatty acids), cluster 2 (early-to-mid time point metabolites) and cluster 3 (late metabolites). Putative metabolites are indicated by parentheses. n = 2–5 for each group

    Journal: Nature Communications

    Article Title: Linking soil biology and chemistry in biological soil crust using isolate exometabolomics

    doi: 10.1038/s41467-017-02356-9

    Figure Lengend Snippet: Metabolite patterns detected in biocrust soil water. Metabolite dynamics (85 metabolites displayed as the average peak area, normalized across each row) were observed in biocrust soil water across wetting and successional stages. Unique patterns are indicated by cluster 1 (early metabolites including fatty acids), cluster 2 (early-to-mid time point metabolites) and cluster 3 (late metabolites). Putative metabolites are indicated by parentheses. n = 2–5 for each group

    Article Snippet: Metabolite extraction and LC/MS analysis Biocrust soil water samples (1.5 mL) were lyophilized and resuspended in methanol (200 μL) containing internal standards (2–10 μg/mL) and filtered through 96-well Millipore filter plates (0.2 μm PVDF) by centrifuging at 1500 × g for 2 min.

    Techniques:

    Simplified biocrust foodweb for three dominant biocrust bacteria based on combining isolate exometabolomics data with in situ microbe–metabolite relationships. This network displays the relationships between metabolites and three dominant bacteria (or metabolically similar organisms) as they increase and decrease in relative abundance across wetting and successional stages in biocrust. The lower line plot corresponds to real relative abundance measurements for the three bacteria in level C successional stage biocrust. As Microcoleus sp. increases in relative abundance immediately after wetting, many metabolites released by the closest-related isolate are positively correlated with Microcoleus sp. in biocrust (solid red arrows) and as the two Bacilli increase in relative abundance (first Bacillus sp. 1 then Bacillus sp. 2), most metabolites consumed by the closest-related isolate decrease and are negatively correlated with these bacteria in biocrust (solid blue arrows) and most released metabolites are positively correlated (solid red arrows). Dotted arrows indicate metabolites that are released (red) or consumed (blue) by isolates, but did not display the expected relationship with that microorganism in situ. The thickness of the line corresponds to the absolute value of the Spearman’s rho correlation coefficient. The overall expected directionality (solid lines vs. dotted lines) was significant as determined by the exact binomial test (two-tailed p -value = 0.01). *FDR-adjusted p

    Journal: Nature Communications

    Article Title: Linking soil biology and chemistry in biological soil crust using isolate exometabolomics

    doi: 10.1038/s41467-017-02356-9

    Figure Lengend Snippet: Simplified biocrust foodweb for three dominant biocrust bacteria based on combining isolate exometabolomics data with in situ microbe–metabolite relationships. This network displays the relationships between metabolites and three dominant bacteria (or metabolically similar organisms) as they increase and decrease in relative abundance across wetting and successional stages in biocrust. The lower line plot corresponds to real relative abundance measurements for the three bacteria in level C successional stage biocrust. As Microcoleus sp. increases in relative abundance immediately after wetting, many metabolites released by the closest-related isolate are positively correlated with Microcoleus sp. in biocrust (solid red arrows) and as the two Bacilli increase in relative abundance (first Bacillus sp. 1 then Bacillus sp. 2), most metabolites consumed by the closest-related isolate decrease and are negatively correlated with these bacteria in biocrust (solid blue arrows) and most released metabolites are positively correlated (solid red arrows). Dotted arrows indicate metabolites that are released (red) or consumed (blue) by isolates, but did not display the expected relationship with that microorganism in situ. The thickness of the line corresponds to the absolute value of the Spearman’s rho correlation coefficient. The overall expected directionality (solid lines vs. dotted lines) was significant as determined by the exact binomial test (two-tailed p -value = 0.01). *FDR-adjusted p

    Article Snippet: Metabolite extraction and LC/MS analysis Biocrust soil water samples (1.5 mL) were lyophilized and resuspended in methanol (200 μL) containing internal standards (2–10 μg/mL) and filtered through 96-well Millipore filter plates (0.2 μm PVDF) by centrifuging at 1500 × g for 2 min.

    Techniques: In Situ, Metabolic Labelling, Two Tailed Test

    Experimental workflow and biocrust microbe–metabolite relationship predictions. a Biocrust wetup metabolomics and metagenomics experimental setup and analysis. To study microbe–metabolite relationships in situ, biocrusts from four successional stages were wetup and sampled at five time points (total n = 100). Biocrust soil water was removed and analyzed by liquid chromatography/ mass spectrometry ( n = 5 for each group) and biocrust DNA was extracted for shotgun sequencing ( n = 1 for each group). Metagenome-estimated genome and metabolite abundances were analyzed through Spearman rank correlations to determine microbe–metabolite relationships and compared to the expected relationships based on isolate exometabolomic studies. b Exometabolomics-based in situ microbe–metabolite relationship prediction. The hypothesis is that isolate exometabolomics can be used to predict microbe–metabolite patterns in situ based on microbial abundance: Across wetting and successional stages, microbes change in abundance and negatively correlate with metabolites that they consume and positively correlate with metabolites that they release (metabolites are indicated by dotted lines)

    Journal: Nature Communications

    Article Title: Linking soil biology and chemistry in biological soil crust using isolate exometabolomics

    doi: 10.1038/s41467-017-02356-9

    Figure Lengend Snippet: Experimental workflow and biocrust microbe–metabolite relationship predictions. a Biocrust wetup metabolomics and metagenomics experimental setup and analysis. To study microbe–metabolite relationships in situ, biocrusts from four successional stages were wetup and sampled at five time points (total n = 100). Biocrust soil water was removed and analyzed by liquid chromatography/ mass spectrometry ( n = 5 for each group) and biocrust DNA was extracted for shotgun sequencing ( n = 1 for each group). Metagenome-estimated genome and metabolite abundances were analyzed through Spearman rank correlations to determine microbe–metabolite relationships and compared to the expected relationships based on isolate exometabolomic studies. b Exometabolomics-based in situ microbe–metabolite relationship prediction. The hypothesis is that isolate exometabolomics can be used to predict microbe–metabolite patterns in situ based on microbial abundance: Across wetting and successional stages, microbes change in abundance and negatively correlate with metabolites that they consume and positively correlate with metabolites that they release (metabolites are indicated by dotted lines)

    Article Snippet: Metabolite extraction and LC/MS analysis Biocrust soil water samples (1.5 mL) were lyophilized and resuspended in methanol (200 μL) containing internal standards (2–10 μg/mL) and filtered through 96-well Millipore filter plates (0.2 μm PVDF) by centrifuging at 1500 × g for 2 min.

    Techniques: In Situ, Liquid Chromatography, Mass Spectrometry, Shotgun Sequencing