Approaches for targeted proteomics and its potential applications in neuroscience

Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench t...
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Autor*in:	Sethi, Sumit [verfasserIn] Chourasia, Dipti [verfasserIn] Parhar, Ishwar S [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Brain differential proteomics ICAT LCM neuron tandem mass spectrometry

Übergeordnetes Werk:	Enthalten in: Journal of biosciences - Bangalore : Indian Acad. of Sciences, 1979, 40(2015), 3 vom: 08. Juli, Seite 607-627
Übergeordnetes Werk:	volume:40 ; year:2015 ; number:3 ; day:08 ; month:07 ; pages:607-627

Links:	Volltext

DOI / URN:	10.1007/s12038-015-9537-1

Katalog-ID:	SPR024019461

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912			\|a GBV_ILN_2088
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allfields	10.1007/s12038-015-9537-1 doi (DE-627)SPR024019461 (SPR)s12038-015-9537-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Sethi, Sumit verfasserin aut Approaches for targeted proteomics and its potential applications in neuroscience 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction. Brain (dpeaa)DE-He213 differential proteomics (dpeaa)DE-He213 ICAT (dpeaa)DE-He213 LCM (dpeaa)DE-He213 neuron (dpeaa)DE-He213 tandem mass spectrometry (dpeaa)DE-He213 Chourasia, Dipti verfasserin aut Parhar, Ishwar S verfasserin aut Enthalten in Journal of biosciences Bangalore : Indian Acad. of Sciences, 1979 40(2015), 3 vom: 08. Juli, Seite 607-627 (DE-627)342317474 (DE-600)2071290-X 0973-7138 nnns volume:40 year:2015 number:3 day:08 month:07 pages:607-627 https://dx.doi.org/10.1007/s12038-015-9537-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 40 2015 3 08 07 607-627
spelling	10.1007/s12038-015-9537-1 doi (DE-627)SPR024019461 (SPR)s12038-015-9537-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Sethi, Sumit verfasserin aut Approaches for targeted proteomics and its potential applications in neuroscience 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction. Brain (dpeaa)DE-He213 differential proteomics (dpeaa)DE-He213 ICAT (dpeaa)DE-He213 LCM (dpeaa)DE-He213 neuron (dpeaa)DE-He213 tandem mass spectrometry (dpeaa)DE-He213 Chourasia, Dipti verfasserin aut Parhar, Ishwar S verfasserin aut Enthalten in Journal of biosciences Bangalore : Indian Acad. of Sciences, 1979 40(2015), 3 vom: 08. Juli, Seite 607-627 (DE-627)342317474 (DE-600)2071290-X 0973-7138 nnns volume:40 year:2015 number:3 day:08 month:07 pages:607-627 https://dx.doi.org/10.1007/s12038-015-9537-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 40 2015 3 08 07 607-627
allfields_unstemmed	10.1007/s12038-015-9537-1 doi (DE-627)SPR024019461 (SPR)s12038-015-9537-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Sethi, Sumit verfasserin aut Approaches for targeted proteomics and its potential applications in neuroscience 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction. Brain (dpeaa)DE-He213 differential proteomics (dpeaa)DE-He213 ICAT (dpeaa)DE-He213 LCM (dpeaa)DE-He213 neuron (dpeaa)DE-He213 tandem mass spectrometry (dpeaa)DE-He213 Chourasia, Dipti verfasserin aut Parhar, Ishwar S verfasserin aut Enthalten in Journal of biosciences Bangalore : Indian Acad. of Sciences, 1979 40(2015), 3 vom: 08. Juli, Seite 607-627 (DE-627)342317474 (DE-600)2071290-X 0973-7138 nnns volume:40 year:2015 number:3 day:08 month:07 pages:607-627 https://dx.doi.org/10.1007/s12038-015-9537-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 40 2015 3 08 07 607-627
allfieldsGer	10.1007/s12038-015-9537-1 doi (DE-627)SPR024019461 (SPR)s12038-015-9537-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Sethi, Sumit verfasserin aut Approaches for targeted proteomics and its potential applications in neuroscience 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction. Brain (dpeaa)DE-He213 differential proteomics (dpeaa)DE-He213 ICAT (dpeaa)DE-He213 LCM (dpeaa)DE-He213 neuron (dpeaa)DE-He213 tandem mass spectrometry (dpeaa)DE-He213 Chourasia, Dipti verfasserin aut Parhar, Ishwar S verfasserin aut Enthalten in Journal of biosciences Bangalore : Indian Acad. of Sciences, 1979 40(2015), 3 vom: 08. Juli, Seite 607-627 (DE-627)342317474 (DE-600)2071290-X 0973-7138 nnns volume:40 year:2015 number:3 day:08 month:07 pages:607-627 https://dx.doi.org/10.1007/s12038-015-9537-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 40 2015 3 08 07 607-627
allfieldsSound	10.1007/s12038-015-9537-1 doi (DE-627)SPR024019461 (SPR)s12038-015-9537-1-e DE-627 ger DE-627 rakwb eng 570 ASE 42.00 bkl Sethi, Sumit verfasserin aut Approaches for targeted proteomics and its potential applications in neuroscience 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction. Brain (dpeaa)DE-He213 differential proteomics (dpeaa)DE-He213 ICAT (dpeaa)DE-He213 LCM (dpeaa)DE-He213 neuron (dpeaa)DE-He213 tandem mass spectrometry (dpeaa)DE-He213 Chourasia, Dipti verfasserin aut Parhar, Ishwar S verfasserin aut Enthalten in Journal of biosciences Bangalore : Indian Acad. of Sciences, 1979 40(2015), 3 vom: 08. Juli, Seite 607-627 (DE-627)342317474 (DE-600)2071290-X 0973-7138 nnns volume:40 year:2015 number:3 day:08 month:07 pages:607-627 https://dx.doi.org/10.1007/s12038-015-9537-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 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_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 42.00 ASE AR 40 2015 3 08 07 607-627
language	English
source	Enthalten in Journal of biosciences 40(2015), 3 vom: 08. Juli, Seite 607-627 volume:40 year:2015 number:3 day:08 month:07 pages:607-627
sourceStr	Enthalten in Journal of biosciences 40(2015), 3 vom: 08. Juli, Seite 607-627 volume:40 year:2015 number:3 day:08 month:07 pages:607-627
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Brain differential proteomics ICAT LCM neuron tandem mass spectrometry
dewey-raw	570
isfreeaccess_bool	false
container_title	Journal of biosciences
authorswithroles_txt_mv	Sethi, Sumit @@aut@@ Chourasia, Dipti @@aut@@ Parhar, Ishwar S @@aut@@
publishDateDaySort_date	2015-07-08T00:00:00Z
hierarchy_top_id	342317474
dewey-sort	3570
id	SPR024019461
language_de	englisch
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author	Sethi, Sumit
spellingShingle	Sethi, Sumit ddc 570 bkl 42.00 misc Brain misc differential proteomics misc ICAT misc LCM misc neuron misc tandem mass spectrometry Approaches for targeted proteomics and its potential applications in neuroscience
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topic_title	570 ASE 42.00 bkl Approaches for targeted proteomics and its potential applications in neuroscience Brain (dpeaa)DE-He213 differential proteomics (dpeaa)DE-He213 ICAT (dpeaa)DE-He213 LCM (dpeaa)DE-He213 neuron (dpeaa)DE-He213 tandem mass spectrometry (dpeaa)DE-He213
topic	ddc 570 bkl 42.00 misc Brain misc differential proteomics misc ICAT misc LCM misc neuron misc tandem mass spectrometry
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title	Approaches for targeted proteomics and its potential applications in neuroscience
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title_full	Approaches for targeted proteomics and its potential applications in neuroscience
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author_browse	Sethi, Sumit Chourasia, Dipti Parhar, Ishwar S
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title_sort	approaches for targeted proteomics and its potential applications in neuroscience
title_auth	Approaches for targeted proteomics and its potential applications in neuroscience
abstract	Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction.
abstractGer	Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction.
abstract_unstemmed	Abstract An extensive guide on practicable and significant quantitative proteomic approaches in neuroscience research is important not only because of the existing overwhelming limitations but also for gaining valuable understanding into brain function and deciphering proteomics from the workbench to the bedside. Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. This approach may lead to further investigation in basic biology, disease analysis and surveillance, as well as drug discovery. Although numerous challenges still exist, we are confident that this approach will increase the understanding of pathological mechanisms involved in neuroendocrinology, neuropsychiatric and neurodegenerative disorders by delivering protein biomarker signatures for brain dysfunction.
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title_short	Approaches for targeted proteomics and its potential applications in neuroscience
url	https://dx.doi.org/10.1007/s12038-015-9537-1
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up_date	2024-07-03T22:59:37.734Z
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Early methodologies to understand the functioning of biological systems are now improving with high-throughput technologies, which allow analysis of various samples concurrently, or of thousand of analytes in a particular sample. Quantitative proteomic approaches include both gel-based and non-gel-based methods that can be further divided into different labelling approaches. This review will emphasize the role of existing technologies, their advantages and disadvantages, as well as their applications in neuroscience. This review will also discuss advanced approaches for targeted proteomics using isotope-coded affinity tag (ICAT) coupled with laser capture microdissection (LCM) followed by liquid chromatography tandem mass spectrometric (LC-MS/MS) analysis. This technology can further be extended to single cell proteomics in other areas of biological sciences and can be combined with other ‘omics’ approaches to reveal the mechanism of a cellular alterations. 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Approaches for targeted proteomics and its potential applications in neuroscience

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