Using established biorepositories for emerging research questions: a feasibility study
Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biologic...
Ausführliche Beschreibung
Autor*in: |
Lerink, Lente J. S. [verfasserIn] Sutton, Christopher W. [verfasserIn] Otten, Henny G. [verfasserIn] Faro, Letizia Lo [verfasserIn] Ploeg, Rutger J. [verfasserIn] Lindeman, Jan H. N. [verfasserIn] Shaheed, Sadr [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2024 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s) 2024 |
---|
Übergeordnetes Werk: |
Enthalten in: Clinical proteomics - BioMed Central, 2004, 21(2024), 1 vom: 17. Aug. |
---|---|
Übergeordnetes Werk: |
volume:21 ; year:2024 ; number:1 ; day:17 ; month:08 |
Links: |
---|
DOI / URN: |
10.1186/s12014-024-09504-6 |
---|
Katalog-ID: |
SPR057004021 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | SPR057004021 | ||
003 | DE-627 | ||
005 | 20240818064654.0 | ||
007 | cr uuu---uuuuu | ||
008 | 240818s2024 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s12014-024-09504-6 |2 doi | |
035 | |a (DE-627)SPR057004021 | ||
035 | |a (SPR)s12014-024-09504-6-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 610 |q VZ |
084 | |a 35.29 |2 bkl | ||
084 | |a 35.76 |2 bkl | ||
100 | 1 | |a Lerink, Lente J. S. |e verfasserin |4 aut | |
245 | 1 | 0 | |a Using established biorepositories for emerging research questions: a feasibility study |
264 | 1 | |c 2024 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a © The Author(s) 2024 | ||
520 | |a Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. | ||
650 | 4 | |a Kidney transplantation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Proteomics |7 (dpeaa)DE-He213 | |
650 | 4 | |a Metabolomics |7 (dpeaa)DE-He213 | |
650 | 4 | |a Biobank |7 (dpeaa)DE-He213 | |
650 | 4 | |a Serum |7 (dpeaa)DE-He213 | |
650 | 4 | |a Plasma |7 (dpeaa)DE-He213 | |
700 | 1 | |a Sutton, Christopher W. |e verfasserin |4 aut | |
700 | 1 | |a Otten, Henny G. |e verfasserin |4 aut | |
700 | 1 | |a Faro, Letizia Lo |e verfasserin |4 aut | |
700 | 1 | |a Ploeg, Rutger J. |e verfasserin |4 aut | |
700 | 1 | |a Lindeman, Jan H. N. |e verfasserin |4 aut | |
700 | 1 | |a Shaheed, Sadr |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Clinical proteomics |d BioMed Central, 2004 |g 21(2024), 1 vom: 17. Aug. |w (DE-627)397618883 |w (DE-600)2163624-2 |x 1559-0275 |7 nnns |
773 | 1 | 8 | |g volume:21 |g year:2024 |g number:1 |g day:17 |g month:08 |
856 | 4 | 0 | |u https://dx.doi.org/10.1186/s12014-024-09504-6 |m X:SPRINGER |x Resolving-System |z kostenfrei |3 Volltext |
912 | |a SYSFLAG_0 | ||
912 | |a GBV_SPRINGER | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_74 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_206 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2003 | ||
912 | |a GBV_ILN_2005 | ||
912 | |a GBV_ILN_2009 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_2111 | ||
912 | |a GBV_ILN_2190 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
936 | b | k | |a 35.29 |q VZ |
936 | b | k | |a 35.76 |q VZ |
951 | |a AR | ||
952 | |d 21 |j 2024 |e 1 |b 17 |c 08 |
author_variant |
l j s l ljs ljsl c w s cw cws h g o hg hgo l l f ll llf r j p rj rjp j h n l jhn jhnl s s ss |
---|---|
matchkey_str |
article:15590275:2024----::snetbihdirpstrefrmrigeerhusi |
hierarchy_sort_str |
2024 |
bklnumber |
35.29 35.76 |
publishDate |
2024 |
allfields |
10.1186/s12014-024-09504-6 doi (DE-627)SPR057004021 (SPR)s12014-024-09504-6-e DE-627 ger DE-627 rakwb eng 610 VZ 35.29 bkl 35.76 bkl Lerink, Lente J. S. verfasserin aut Using established biorepositories for emerging research questions: a feasibility study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. Kidney transplantation (dpeaa)DE-He213 Proteomics (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Biobank (dpeaa)DE-He213 Serum (dpeaa)DE-He213 Plasma (dpeaa)DE-He213 Sutton, Christopher W. verfasserin aut Otten, Henny G. verfasserin aut Faro, Letizia Lo verfasserin aut Ploeg, Rutger J. verfasserin aut Lindeman, Jan H. N. verfasserin aut Shaheed, Sadr verfasserin aut Enthalten in Clinical proteomics BioMed Central, 2004 21(2024), 1 vom: 17. Aug. (DE-627)397618883 (DE-600)2163624-2 1559-0275 nnns volume:21 year:2024 number:1 day:17 month:08 https://dx.doi.org/10.1186/s12014-024-09504-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 35.29 VZ 35.76 VZ AR 21 2024 1 17 08 |
spelling |
10.1186/s12014-024-09504-6 doi (DE-627)SPR057004021 (SPR)s12014-024-09504-6-e DE-627 ger DE-627 rakwb eng 610 VZ 35.29 bkl 35.76 bkl Lerink, Lente J. S. verfasserin aut Using established biorepositories for emerging research questions: a feasibility study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. Kidney transplantation (dpeaa)DE-He213 Proteomics (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Biobank (dpeaa)DE-He213 Serum (dpeaa)DE-He213 Plasma (dpeaa)DE-He213 Sutton, Christopher W. verfasserin aut Otten, Henny G. verfasserin aut Faro, Letizia Lo verfasserin aut Ploeg, Rutger J. verfasserin aut Lindeman, Jan H. N. verfasserin aut Shaheed, Sadr verfasserin aut Enthalten in Clinical proteomics BioMed Central, 2004 21(2024), 1 vom: 17. Aug. (DE-627)397618883 (DE-600)2163624-2 1559-0275 nnns volume:21 year:2024 number:1 day:17 month:08 https://dx.doi.org/10.1186/s12014-024-09504-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 35.29 VZ 35.76 VZ AR 21 2024 1 17 08 |
allfields_unstemmed |
10.1186/s12014-024-09504-6 doi (DE-627)SPR057004021 (SPR)s12014-024-09504-6-e DE-627 ger DE-627 rakwb eng 610 VZ 35.29 bkl 35.76 bkl Lerink, Lente J. S. verfasserin aut Using established biorepositories for emerging research questions: a feasibility study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. Kidney transplantation (dpeaa)DE-He213 Proteomics (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Biobank (dpeaa)DE-He213 Serum (dpeaa)DE-He213 Plasma (dpeaa)DE-He213 Sutton, Christopher W. verfasserin aut Otten, Henny G. verfasserin aut Faro, Letizia Lo verfasserin aut Ploeg, Rutger J. verfasserin aut Lindeman, Jan H. N. verfasserin aut Shaheed, Sadr verfasserin aut Enthalten in Clinical proteomics BioMed Central, 2004 21(2024), 1 vom: 17. Aug. (DE-627)397618883 (DE-600)2163624-2 1559-0275 nnns volume:21 year:2024 number:1 day:17 month:08 https://dx.doi.org/10.1186/s12014-024-09504-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 35.29 VZ 35.76 VZ AR 21 2024 1 17 08 |
allfieldsGer |
10.1186/s12014-024-09504-6 doi (DE-627)SPR057004021 (SPR)s12014-024-09504-6-e DE-627 ger DE-627 rakwb eng 610 VZ 35.29 bkl 35.76 bkl Lerink, Lente J. S. verfasserin aut Using established biorepositories for emerging research questions: a feasibility study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. Kidney transplantation (dpeaa)DE-He213 Proteomics (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Biobank (dpeaa)DE-He213 Serum (dpeaa)DE-He213 Plasma (dpeaa)DE-He213 Sutton, Christopher W. verfasserin aut Otten, Henny G. verfasserin aut Faro, Letizia Lo verfasserin aut Ploeg, Rutger J. verfasserin aut Lindeman, Jan H. N. verfasserin aut Shaheed, Sadr verfasserin aut Enthalten in Clinical proteomics BioMed Central, 2004 21(2024), 1 vom: 17. Aug. (DE-627)397618883 (DE-600)2163624-2 1559-0275 nnns volume:21 year:2024 number:1 day:17 month:08 https://dx.doi.org/10.1186/s12014-024-09504-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 35.29 VZ 35.76 VZ AR 21 2024 1 17 08 |
allfieldsSound |
10.1186/s12014-024-09504-6 doi (DE-627)SPR057004021 (SPR)s12014-024-09504-6-e DE-627 ger DE-627 rakwb eng 610 VZ 35.29 bkl 35.76 bkl Lerink, Lente J. S. verfasserin aut Using established biorepositories for emerging research questions: a feasibility study 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. Kidney transplantation (dpeaa)DE-He213 Proteomics (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Biobank (dpeaa)DE-He213 Serum (dpeaa)DE-He213 Plasma (dpeaa)DE-He213 Sutton, Christopher W. verfasserin aut Otten, Henny G. verfasserin aut Faro, Letizia Lo verfasserin aut Ploeg, Rutger J. verfasserin aut Lindeman, Jan H. N. verfasserin aut Shaheed, Sadr verfasserin aut Enthalten in Clinical proteomics BioMed Central, 2004 21(2024), 1 vom: 17. Aug. (DE-627)397618883 (DE-600)2163624-2 1559-0275 nnns volume:21 year:2024 number:1 day:17 month:08 https://dx.doi.org/10.1186/s12014-024-09504-6 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 35.29 VZ 35.76 VZ AR 21 2024 1 17 08 |
language |
English |
source |
Enthalten in Clinical proteomics 21(2024), 1 vom: 17. Aug. volume:21 year:2024 number:1 day:17 month:08 |
sourceStr |
Enthalten in Clinical proteomics 21(2024), 1 vom: 17. Aug. volume:21 year:2024 number:1 day:17 month:08 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Kidney transplantation Proteomics Metabolomics Biobank Serum Plasma |
dewey-raw |
610 |
isfreeaccess_bool |
true |
container_title |
Clinical proteomics |
authorswithroles_txt_mv |
Lerink, Lente J. S. @@aut@@ Sutton, Christopher W. @@aut@@ Otten, Henny G. @@aut@@ Faro, Letizia Lo @@aut@@ Ploeg, Rutger J. @@aut@@ Lindeman, Jan H. N. @@aut@@ Shaheed, Sadr @@aut@@ |
publishDateDaySort_date |
2024-08-17T00:00:00Z |
hierarchy_top_id |
397618883 |
dewey-sort |
3610 |
id |
SPR057004021 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057004021</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240818064654.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240818s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12014-024-09504-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057004021</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12014-024-09504-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.29</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lerink, Lente J. S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using established biorepositories for emerging research questions: a feasibility study</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kidney transplantation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Proteomics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metabolomics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biobank</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Serum</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasma</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sutton, Christopher W.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Otten, Henny G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Faro, Letizia Lo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ploeg, Rutger J.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lindeman, Jan H. N.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shaheed, Sadr</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Clinical proteomics</subfield><subfield code="d">BioMed Central, 2004</subfield><subfield code="g">21(2024), 1 vom: 17. Aug.</subfield><subfield code="w">(DE-627)397618883</subfield><subfield code="w">(DE-600)2163624-2</subfield><subfield code="x">1559-0275</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:08</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12014-024-09504-6</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.29</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.76</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">08</subfield></datafield></record></collection>
|
author |
Lerink, Lente J. S. |
spellingShingle |
Lerink, Lente J. S. ddc 610 bkl 35.29 bkl 35.76 misc Kidney transplantation misc Proteomics misc Metabolomics misc Biobank misc Serum misc Plasma Using established biorepositories for emerging research questions: a feasibility study |
authorStr |
Lerink, Lente J. S. |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)397618883 |
format |
electronic Article |
dewey-ones |
610 - Medicine & health |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
issn |
1559-0275 |
topic_title |
610 VZ 35.29 bkl 35.76 bkl Using established biorepositories for emerging research questions: a feasibility study Kidney transplantation (dpeaa)DE-He213 Proteomics (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Biobank (dpeaa)DE-He213 Serum (dpeaa)DE-He213 Plasma (dpeaa)DE-He213 |
topic |
ddc 610 bkl 35.29 bkl 35.76 misc Kidney transplantation misc Proteomics misc Metabolomics misc Biobank misc Serum misc Plasma |
topic_unstemmed |
ddc 610 bkl 35.29 bkl 35.76 misc Kidney transplantation misc Proteomics misc Metabolomics misc Biobank misc Serum misc Plasma |
topic_browse |
ddc 610 bkl 35.29 bkl 35.76 misc Kidney transplantation misc Proteomics misc Metabolomics misc Biobank misc Serum misc Plasma |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Clinical proteomics |
hierarchy_parent_id |
397618883 |
dewey-tens |
610 - Medicine & health |
hierarchy_top_title |
Clinical proteomics |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)397618883 (DE-600)2163624-2 |
title |
Using established biorepositories for emerging research questions: a feasibility study |
ctrlnum |
(DE-627)SPR057004021 (SPR)s12014-024-09504-6-e |
title_full |
Using established biorepositories for emerging research questions: a feasibility study |
author_sort |
Lerink, Lente J. S. |
journal |
Clinical proteomics |
journalStr |
Clinical proteomics |
lang_code |
eng |
isOA_bool |
true |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2024 |
contenttype_str_mv |
txt |
author_browse |
Lerink, Lente J. S. Sutton, Christopher W. Otten, Henny G. Faro, Letizia Lo Ploeg, Rutger J. Lindeman, Jan H. N. Shaheed, Sadr |
container_volume |
21 |
class |
610 VZ 35.29 bkl 35.76 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Lerink, Lente J. S. |
doi_str_mv |
10.1186/s12014-024-09504-6 |
dewey-full |
610 |
author2-role |
verfasserin |
title_sort |
using established biorepositories for emerging research questions: a feasibility study |
title_auth |
Using established biorepositories for emerging research questions: a feasibility study |
abstract |
Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. © The Author(s) 2024 |
abstractGer |
Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. © The Author(s) 2024 |
abstract_unstemmed |
Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events. © The Author(s) 2024 |
collection_details |
SYSFLAG_0 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
Using established biorepositories for emerging research questions: a feasibility study |
url |
https://dx.doi.org/10.1186/s12014-024-09504-6 |
remote_bool |
true |
author2 |
Sutton, Christopher W. Otten, Henny G. Faro, Letizia Lo Ploeg, Rutger J. Lindeman, Jan H. N. Shaheed, Sadr |
author2Str |
Sutton, Christopher W. Otten, Henny G. Faro, Letizia Lo Ploeg, Rutger J. Lindeman, Jan H. N. Shaheed, Sadr |
ppnlink |
397618883 |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s12014-024-09504-6 |
up_date |
2024-08-18T04:48:15.867Z |
_version_ |
1807699406616526848 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057004021</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240818064654.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240818s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12014-024-09504-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057004021</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12014-024-09504-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.29</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lerink, Lente J. S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using established biorepositories for emerging research questions: a feasibility study</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2024</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze–thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics. Methods A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank. Results Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways. Conclusions This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kidney transplantation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Proteomics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metabolomics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biobank</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Serum</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasma</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sutton, Christopher W.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Otten, Henny G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Faro, Letizia Lo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ploeg, Rutger J.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lindeman, Jan H. N.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shaheed, Sadr</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Clinical proteomics</subfield><subfield code="d">BioMed Central, 2004</subfield><subfield code="g">21(2024), 1 vom: 17. Aug.</subfield><subfield code="w">(DE-627)397618883</subfield><subfield code="w">(DE-600)2163624-2</subfield><subfield code="x">1559-0275</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:21</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:08</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s12014-024-09504-6</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.29</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.76</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">21</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">08</subfield></datafield></record></collection>
|
score |
7.399743 |