Using single‐plant‐omics in the field to link maize genes to functions and phenotypes
Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laborato...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cruz, Daniel Felipe [verfasserIn] De Meyer, Sam [verfasserIn] Ampe, Joke [verfasserIn] Sprenger, Heike [verfasserIn] Herman, Dorota [verfasserIn] Van Hautegem, Tom [verfasserIn] De Block, Jolien [verfasserIn] Inzé, Dirk [verfasserIn] Nelissen, Hilde [verfasserIn] Maere, Steven [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Molecular Systems Biology - Nature Publishing Group UK, 2023, 16(2020), 12 vom: 21. Dez. |
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| Übergeordnetes Werk: |
volume:16 ; year:2020 ; number:12 ; day:21 ; month:12 |
| Links: |
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| DOI / URN: |
10.15252/msb.20209667 |
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| Katalog-ID: |
SPR058093869 |
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| 520 | |a Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. | ||
| 520 | |a SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. | ||
| 520 | |a Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. | ||
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| 700 | 1 | |a Nelissen, Hilde |e verfasserin |4 aut | |
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10.15252/msb.20209667 doi (DE-627)SPR058093869 (SPR)msb.20209667-e DE-627 ger DE-627 rakwb eng Cruz, Daniel Felipe verfasserin aut Using single‐plant‐omics in the field to link maize genes to functions and phenotypes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. field trial (dpeaa)DE-He213 lab‐field gap (dpeaa)DE-He213 predictive modeling (dpeaa)DE-He213 single‐plant ‐omics (dpeaa)DE-He213 De Meyer, Sam verfasserin aut Ampe, Joke verfasserin aut Sprenger, Heike verfasserin aut Herman, Dorota verfasserin aut Van Hautegem, Tom verfasserin aut De Block, Jolien verfasserin aut Inzé, Dirk verfasserin aut Nelissen, Hilde verfasserin aut Maere, Steven verfasserin (orcid)0000-0002-5341-136X aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 12 vom: 21. Dez. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:12 day:21 month:12 https://dx.doi.org/10.15252/msb.20209667 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 12 21 12 |
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10.15252/msb.20209667 doi (DE-627)SPR058093869 (SPR)msb.20209667-e DE-627 ger DE-627 rakwb eng Cruz, Daniel Felipe verfasserin aut Using single‐plant‐omics in the field to link maize genes to functions and phenotypes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. field trial (dpeaa)DE-He213 lab‐field gap (dpeaa)DE-He213 predictive modeling (dpeaa)DE-He213 single‐plant ‐omics (dpeaa)DE-He213 De Meyer, Sam verfasserin aut Ampe, Joke verfasserin aut Sprenger, Heike verfasserin aut Herman, Dorota verfasserin aut Van Hautegem, Tom verfasserin aut De Block, Jolien verfasserin aut Inzé, Dirk verfasserin aut Nelissen, Hilde verfasserin aut Maere, Steven verfasserin (orcid)0000-0002-5341-136X aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 12 vom: 21. Dez. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:12 day:21 month:12 https://dx.doi.org/10.15252/msb.20209667 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 12 21 12 |
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10.15252/msb.20209667 doi (DE-627)SPR058093869 (SPR)msb.20209667-e DE-627 ger DE-627 rakwb eng Cruz, Daniel Felipe verfasserin aut Using single‐plant‐omics in the field to link maize genes to functions and phenotypes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. field trial (dpeaa)DE-He213 lab‐field gap (dpeaa)DE-He213 predictive modeling (dpeaa)DE-He213 single‐plant ‐omics (dpeaa)DE-He213 De Meyer, Sam verfasserin aut Ampe, Joke verfasserin aut Sprenger, Heike verfasserin aut Herman, Dorota verfasserin aut Van Hautegem, Tom verfasserin aut De Block, Jolien verfasserin aut Inzé, Dirk verfasserin aut Nelissen, Hilde verfasserin aut Maere, Steven verfasserin (orcid)0000-0002-5341-136X aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 12 vom: 21. Dez. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:12 day:21 month:12 https://dx.doi.org/10.15252/msb.20209667 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 12 21 12 |
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10.15252/msb.20209667 doi (DE-627)SPR058093869 (SPR)msb.20209667-e DE-627 ger DE-627 rakwb eng Cruz, Daniel Felipe verfasserin aut Using single‐plant‐omics in the field to link maize genes to functions and phenotypes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. field trial (dpeaa)DE-He213 lab‐field gap (dpeaa)DE-He213 predictive modeling (dpeaa)DE-He213 single‐plant ‐omics (dpeaa)DE-He213 De Meyer, Sam verfasserin aut Ampe, Joke verfasserin aut Sprenger, Heike verfasserin aut Herman, Dorota verfasserin aut Van Hautegem, Tom verfasserin aut De Block, Jolien verfasserin aut Inzé, Dirk verfasserin aut Nelissen, Hilde verfasserin aut Maere, Steven verfasserin (orcid)0000-0002-5341-136X aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 12 vom: 21. Dez. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:12 day:21 month:12 https://dx.doi.org/10.15252/msb.20209667 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 12 21 12 |
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10.15252/msb.20209667 doi (DE-627)SPR058093869 (SPR)msb.20209667-e DE-627 ger DE-627 rakwb eng Cruz, Daniel Felipe verfasserin aut Using single‐plant‐omics in the field to link maize genes to functions and phenotypes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. field trial (dpeaa)DE-He213 lab‐field gap (dpeaa)DE-He213 predictive modeling (dpeaa)DE-He213 single‐plant ‐omics (dpeaa)DE-He213 De Meyer, Sam verfasserin aut Ampe, Joke verfasserin aut Sprenger, Heike verfasserin aut Herman, Dorota verfasserin aut Van Hautegem, Tom verfasserin aut De Block, Jolien verfasserin aut Inzé, Dirk verfasserin aut Nelissen, Hilde verfasserin aut Maere, Steven verfasserin (orcid)0000-0002-5341-136X aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 12 vom: 21. Dez. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:12 day:21 month:12 https://dx.doi.org/10.15252/msb.20209667 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 12 21 12 |
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Cruz, Daniel Felipe @@aut@@ De Meyer, Sam @@aut@@ Ampe, Joke @@aut@@ Sprenger, Heike @@aut@@ Herman, Dorota @@aut@@ Van Hautegem, Tom @@aut@@ De Block, Jolien @@aut@@ Inzé, Dirk @@aut@@ Nelissen, Hilde @@aut@@ Maere, Steven @@aut@@ |
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However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. 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Cruz, Daniel Felipe |
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Using single‐plant‐omics in the field to link maize genes to functions and phenotypes |
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Cruz, Daniel Felipe De Meyer, Sam Ampe, Joke Sprenger, Heike Herman, Dorota Van Hautegem, Tom De Block, Jolien Inzé, Dirk Nelissen, Hilde Maere, Steven |
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using single‐plant‐omics in the field to link maize genes to functions and phenotypes |
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Using single‐plant‐omics in the field to link maize genes to functions and phenotypes |
| abstract |
Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. © The Author(s) 2020 |
| abstractGer |
Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. © The Author(s) 2020 |
| abstract_unstemmed |
Abstract Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap. SYNOPSIS A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. There is substantial variability in the transcriptomes, metabolomes and phenotypes of individual plants of the same genetic background grown in the same field.This variability can be used to predict gene functions and phenotypes of individual plants.Profiling individual field‐grown plants enables mapping of gene networks and phenotypes, and may help close the lab‐field gap. Graphical Abstract A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments. © The Author(s) 2020 |
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Using single‐plant‐omics in the field to link maize genes to functions and phenotypes |
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De Meyer, Sam Ampe, Joke Sprenger, Heike Herman, Dorota Van Hautegem, Tom De Block, Jolien Inzé, Dirk Nelissen, Hilde Maere, Steven |
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De Meyer, Sam Ampe, Joke Sprenger, Heike Herman, Dorota Van Hautegem, Tom De Block, Jolien Inzé, Dirk Nelissen, Hilde Maere, Steven |
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10.15252/msb.20209667 |
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2024-10-25T04:56:52.831Z |
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| score |
7.4005537 |