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You searched for: EV210197 (EV-TRACK ID)

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Results list Study Tree
Experiment number
  • If needed, multiple experiments were identified in a single publication based on differing sample types, separation protocols and/or vesicle types of interest.
Species
  • Species of origin of the EVs.
Separation protocol
  • Gives a short, non-chronological overview of the different steps of the separation protocol.
    • (d)(U)C = (differential) (ultra)centrifugation
    • DG = density gradient
    • UF = ultrafiltration
    • SEC = size-exclusion chromatography
    • IAF = immuno-affinity capture
Details EV-TRACK ID Experiment nr. Species Sample type Separation protocol First author Year EV-METRIC
EV210197 1/4 Homo sapiens Platelets (d)(U)C Puhka, Maija 2017 56%

Study summary

Full title
Metabolomic Profiling of Extracellular Vesicles and Alternative Normalization Methods Reveal Enriched Metabolites and Strategies to Study Prostate Cancer-Related Changes
All authors
Maija Puhka, Maarit Takatalo, Maria-Elisa Nordberg, Sami Valkonen, Jatin Nandania, Maria Aatonen, Marjo Yliperttula, Saara Laitinen, Vidya Velagapudi, Tuomas Mirtti, Olli Kallioniemi, Antti Rannikko, Pia R-M Siljander, Taija Maria Af Hällström
Journal
Theranostics
Abstract
Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the se (show more...)Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the secreting cells. So far, biomarkers for pathological conditions have been mainly searched from their protein, (mi)RNA, DNA and lipid cargo. Here, we explored the small molecule metabolites from urinary and platelet EVs relative to their matched source samples. As a proof-of-concept study of intra-EV metabolites, we compared alternative normalization methods to profile urinary EVs from prostate cancer patients before and after prostatectomy and from healthy controls. Methods: We employed targeted ultra-performance liquid chromatography-tandem mass spectrometry to profile over 100 metabolites in the isolated EVs, original urine samples and platelets. We determined the enrichment of the metabolites in the EVs and analyzed their subcellular origin, pathways and relevant enzymes or transporters through data base searches. EV- and urine-derived factors and ratios between metabolites were tested for normalization of the metabolomics data. Results: Approximately 1 x 1010 EVs were sufficient for detection of metabolite profiles from EVs. The profiles of the urinary and platelet EVs overlapped with each other and with those of the source materials, but they also contained unique metabolites. The EVs enriched a selection of cytosolic metabolites including members from the nucleotide and spermidine pathways, which linked to a number of EV-resident enzymes or transporters. Analysis of the urinary EVs from the patients indicated that the levels of glucuronate, D-ribose 5-phosphate and isobutyryl-L-carnitine were 2-26-fold lower in all pre-prostatectomy samples compared to the healthy control and post-prostatectomy samples (p < 0.05). These changes were only detected from EVs by normalization to EV-derived factors or with metabolite ratios, and not from the original urine samples. Conclusions: Our results suggest that metabolite analysis of EVs from different samples is feasible using a high-throughput platform and relatively small amount of sample material. With the knowledge about the specific enrichment of metabolites and normalization methods, EV metabolomics could be used to gain novel biomarker data not revealed by the analysis of the original EV source materials. (hide)
EV-METRIC
56% (90th percentile of all experiments on the same sample type)
 Reported
 Not reported
 Not applicable
EV-enriched proteins
Protein analysis: analysis of three or more EV-enriched proteins
non EV-enriched protein
Protein analysis: assessment of a non-EV-enriched protein
qualitative and quantitative analysis
Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected.
electron microscopy images
Particle analysis: inclusion of a widefield and close-up electron microscopy image
density gradient
Separation method: density gradient, at least as validation of results attributed to EVs
EV density
Separation method: reporting of obtained EV density
ultracentrifugation specifics
Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps
antibody specifics
Protein analysis: antibody clone/reference number and dilution
lysate preparation
Protein analysis: lysis buffer composition
Study data
Sample type
Cell culture supernatant
Sample origin
Control condition
Focus vesicles
extracellular vesicle
Separation protocol
Separation protocol
  • Gives a short, non-chronological overview of the
    different steps of the separation protocol.
    • dUC = (Differential) (ultra)centrifugation
    • DG = density gradient
    • UF = ultrafiltration
    • SEC = size-exclusion chromatography
    • IAF = immuno-affinity capture
(d)(U)C
Protein markers
EV: TSG101/ CD63/ CD9
non-EV: TOMM20
Proteomics
no
Show all info
Study aim
Biomarker
Sample
Species
Homo sapiens
Sample Type
Cell culture supernatant
EV-producing cells
Platelets
EV-harvesting Medium
Serum free medium
Separation Method
(Differential) (ultra)centrifugation
dUC: centrifugation steps
Between 800 g and 10,000 g
Between 100,000 g and 150,000 g
Pelleting performed
Yes
Pelleting: time(min)
75
Pelleting: rotor type
Type 50.2 Ti
Pelleting: speed (g)
110000
Wash: volume per pellet (ml)
16
Wash: time (min)
75
Wash: Rotor Type
Not specified
Wash: speed (g)
110000
Characterization: Protein analysis
Protein Concentration Method
BCA
Western Blot
Detected EV-associated proteins
CD9/ CD63/ TSG101
Not detected contaminants
TOMM20
Characterization: Lipid analysis
No
Characterization: Particle analysis
NTA
Report type
Size range/distribution
Reported size (nm)
1-500
EV concentration
Yes
EM
EM-type
Transmission-EM
Image type
Wide-field
Report size (nm)
1-1000
EV210197 3/4 Homo sapiens Urine (d)(U)C
Filtration 		Puhka, Maija 2017 56%

Study summary

Full title
Metabolomic Profiling of Extracellular Vesicles and Alternative Normalization Methods Reveal Enriched Metabolites and Strategies to Study Prostate Cancer-Related Changes
All authors
Maija Puhka, Maarit Takatalo, Maria-Elisa Nordberg, Sami Valkonen, Jatin Nandania, Maria Aatonen, Marjo Yliperttula, Saara Laitinen, Vidya Velagapudi, Tuomas Mirtti, Olli Kallioniemi, Antti Rannikko, Pia R-M Siljander, Taija Maria Af Hällström
Journal
Theranostics
Abstract
Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the se (show more...)Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the secreting cells. So far, biomarkers for pathological conditions have been mainly searched from their protein, (mi)RNA, DNA and lipid cargo. Here, we explored the small molecule metabolites from urinary and platelet EVs relative to their matched source samples. As a proof-of-concept study of intra-EV metabolites, we compared alternative normalization methods to profile urinary EVs from prostate cancer patients before and after prostatectomy and from healthy controls. Methods: We employed targeted ultra-performance liquid chromatography-tandem mass spectrometry to profile over 100 metabolites in the isolated EVs, original urine samples and platelets. We determined the enrichment of the metabolites in the EVs and analyzed their subcellular origin, pathways and relevant enzymes or transporters through data base searches. EV- and urine-derived factors and ratios between metabolites were tested for normalization of the metabolomics data. Results: Approximately 1 x 1010 EVs were sufficient for detection of metabolite profiles from EVs. The profiles of the urinary and platelet EVs overlapped with each other and with those of the source materials, but they also contained unique metabolites. The EVs enriched a selection of cytosolic metabolites including members from the nucleotide and spermidine pathways, which linked to a number of EV-resident enzymes or transporters. Analysis of the urinary EVs from the patients indicated that the levels of glucuronate, D-ribose 5-phosphate and isobutyryl-L-carnitine were 2-26-fold lower in all pre-prostatectomy samples compared to the healthy control and post-prostatectomy samples (p < 0.05). These changes were only detected from EVs by normalization to EV-derived factors or with metabolite ratios, and not from the original urine samples. Conclusions: Our results suggest that metabolite analysis of EVs from different samples is feasible using a high-throughput platform and relatively small amount of sample material. With the knowledge about the specific enrichment of metabolites and normalization methods, EV metabolomics could be used to gain novel biomarker data not revealed by the analysis of the original EV source materials. (hide)
EV-METRIC
56% (90th percentile of all experiments on the same sample type)
 Reported
 Not reported
 Not applicable
EV-enriched proteins
Protein analysis: analysis of three or more EV-enriched proteins
non EV-enriched protein
Protein analysis: assessment of a non-EV-enriched protein
qualitative and quantitative analysis
Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected.
electron microscopy images
Particle analysis: inclusion of a widefield and close-up electron microscopy image
density gradient
Separation method: density gradient, at least as validation of results attributed to EVs
EV density
Separation method: reporting of obtained EV density
ultracentrifugation specifics
Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps
antibody specifics
Protein analysis: antibody clone/reference number and dilution
lysate preparation
Protein analysis: lysis buffer composition
Study data
Sample type
Urine
Sample origin
Control condition
Focus vesicles
extracellular vesicle
Separation protocol
Separation protocol
  • Gives a short, non-chronological overview of the
    different steps of the separation protocol.
    • dUC = (Differential) (ultra)centrifugation
    • DG = density gradient
    • UF = ultrafiltration
    • SEC = size-exclusion chromatography
    • IAF = immuno-affinity capture
(d)(U)C
Filtration
Protein markers
EV: TSG101/ CD59/ CD63/ CD9
non-EV: Calnexin/ TOMM20/ GM130
Proteomics
no
Show all info
Study aim
Biomarker
Sample
Species
Homo sapiens
Sample Type
Urine
Separation Method
(Differential) (ultra)centrifugation
dUC: centrifugation steps
Between 800 g and 10,000 g
Between 100,000 g and 150,000 g
Pelleting performed
Yes
Pelleting: time(min)
90
Pelleting: rotor type
Not specified
Pelleting: speed (g)
100000
Wash: volume per pellet (ml)
30
Wash: time (min)
90
Wash: Rotor Type
Not specified
Wash: speed (g)
100000
Filtration steps
> 0.45 µm,
Characterization: Protein analysis
Protein Concentration Method
BCA
Western Blot
Detected EV-associated proteins
CD9/ CD63/ CD59/ TSG101
Not detected contaminants
Calnexin/ TOMM20/ GM130
Characterization: Lipid analysis
No
Characterization: Particle analysis
NTA
Report type
Size range/distribution
Reported size (nm)
1-500
EV concentration
Yes
EM
EM-type
Immuno-EM/ Transmission-EM
EM protein
Other;CD63;CD59
Image type
Close-up, Wide-field
Report size (nm)
1-1000
EV210197 4/4 Homo sapiens Urine (d)(U)C
Filtration 		Puhka, Maija 2017 22%

Study summary

Full title
Metabolomic Profiling of Extracellular Vesicles and Alternative Normalization Methods Reveal Enriched Metabolites and Strategies to Study Prostate Cancer-Related Changes
All authors
Maija Puhka, Maarit Takatalo, Maria-Elisa Nordberg, Sami Valkonen, Jatin Nandania, Maria Aatonen, Marjo Yliperttula, Saara Laitinen, Vidya Velagapudi, Tuomas Mirtti, Olli Kallioniemi, Antti Rannikko, Pia R-M Siljander, Taija Maria Af Hällström
Journal
Theranostics
Abstract
Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the se (show more...)Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the secreting cells. So far, biomarkers for pathological conditions have been mainly searched from their protein, (mi)RNA, DNA and lipid cargo. Here, we explored the small molecule metabolites from urinary and platelet EVs relative to their matched source samples. As a proof-of-concept study of intra-EV metabolites, we compared alternative normalization methods to profile urinary EVs from prostate cancer patients before and after prostatectomy and from healthy controls. Methods: We employed targeted ultra-performance liquid chromatography-tandem mass spectrometry to profile over 100 metabolites in the isolated EVs, original urine samples and platelets. We determined the enrichment of the metabolites in the EVs and analyzed their subcellular origin, pathways and relevant enzymes or transporters through data base searches. EV- and urine-derived factors and ratios between metabolites were tested for normalization of the metabolomics data. Results: Approximately 1 x 1010 EVs were sufficient for detection of metabolite profiles from EVs. The profiles of the urinary and platelet EVs overlapped with each other and with those of the source materials, but they also contained unique metabolites. The EVs enriched a selection of cytosolic metabolites including members from the nucleotide and spermidine pathways, which linked to a number of EV-resident enzymes or transporters. Analysis of the urinary EVs from the patients indicated that the levels of glucuronate, D-ribose 5-phosphate and isobutyryl-L-carnitine were 2-26-fold lower in all pre-prostatectomy samples compared to the healthy control and post-prostatectomy samples (p < 0.05). These changes were only detected from EVs by normalization to EV-derived factors or with metabolite ratios, and not from the original urine samples. Conclusions: Our results suggest that metabolite analysis of EVs from different samples is feasible using a high-throughput platform and relatively small amount of sample material. With the knowledge about the specific enrichment of metabolites and normalization methods, EV metabolomics could be used to gain novel biomarker data not revealed by the analysis of the original EV source materials. (hide)
EV-METRIC
22% (49th percentile of all experiments on the same sample type)
 Reported
 Not reported
 Not applicable
EV-enriched proteins
Protein analysis: analysis of three or more EV-enriched proteins
non EV-enriched protein
Protein analysis: assessment of a non-EV-enriched protein
qualitative and quantitative analysis
Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected.
electron microscopy images
Particle analysis: inclusion of a widefield and close-up electron microscopy image
density gradient
Separation method: density gradient, at least as validation of results attributed to EVs
EV density
Separation method: reporting of obtained EV density
ultracentrifugation specifics
Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps
antibody specifics
Protein analysis: antibody clone/reference number and dilution
lysate preparation
Protein analysis: lysis buffer composition
Study data
Sample type
Urine
Sample origin
Prostate cancer
Focus vesicles
extracellular vesicle
Separation protocol
Separation protocol
  • Gives a short, non-chronological overview of the
    different steps of the separation protocol.
    • dUC = (Differential) (ultra)centrifugation
    • DG = density gradient
    • UF = ultrafiltration
    • SEC = size-exclusion chromatography
    • IAF = immuno-affinity capture
(d)(U)C
Filtration
Protein markers
EV: TSG101/ CD59/ CD63/ CD9
non-EV: Calnexin/ TOMM20/ GM130
Proteomics
no
Show all info
Study aim
Biomarker
Sample
Species
Homo sapiens
Sample Type
Urine
Separation Method
(Differential) (ultra)centrifugation
dUC: centrifugation steps
Between 800 g and 10,000 g
Between 100,000 g and 150,000 g
Pelleting performed
Yes
Pelleting: time(min)
90
Pelleting: rotor type
Not specified
Pelleting: speed (g)
100000
Wash: volume per pellet (ml)
30
Wash: time (min)
90
Wash: Rotor Type
Not specified
Wash: speed (g)
100000
Filtration steps
> 0.45 µm,
Characterization: Protein analysis
Protein Concentration Method
BCA
Western Blot
Detected EV-associated proteins
CD9/ CD63/ CD59
Characterization: Lipid analysis
No
Characterization: Particle analysis
NTA
Report type
Size range/distribution
Reported size (nm)
1-500
EV concentration
Yes
EV210197 2/4 Homo sapiens Blood plasma (d)(U)C Puhka, Maija 2017 14%

Study summary

Full title
Metabolomic Profiling of Extracellular Vesicles and Alternative Normalization Methods Reveal Enriched Metabolites and Strategies to Study Prostate Cancer-Related Changes
All authors
Maija Puhka, Maarit Takatalo, Maria-Elisa Nordberg, Sami Valkonen, Jatin Nandania, Maria Aatonen, Marjo Yliperttula, Saara Laitinen, Vidya Velagapudi, Tuomas Mirtti, Olli Kallioniemi, Antti Rannikko, Pia R-M Siljander, Taija Maria Af Hällström
Journal
Theranostics
Abstract
Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the se (show more...)Body fluids are a rich source of extracellular vesicles (EVs), which carry cargo derived from the secreting cells. So far, biomarkers for pathological conditions have been mainly searched from their protein, (mi)RNA, DNA and lipid cargo. Here, we explored the small molecule metabolites from urinary and platelet EVs relative to their matched source samples. As a proof-of-concept study of intra-EV metabolites, we compared alternative normalization methods to profile urinary EVs from prostate cancer patients before and after prostatectomy and from healthy controls. Methods: We employed targeted ultra-performance liquid chromatography-tandem mass spectrometry to profile over 100 metabolites in the isolated EVs, original urine samples and platelets. We determined the enrichment of the metabolites in the EVs and analyzed their subcellular origin, pathways and relevant enzymes or transporters through data base searches. EV- and urine-derived factors and ratios between metabolites were tested for normalization of the metabolomics data. Results: Approximately 1 x 1010 EVs were sufficient for detection of metabolite profiles from EVs. The profiles of the urinary and platelet EVs overlapped with each other and with those of the source materials, but they also contained unique metabolites. The EVs enriched a selection of cytosolic metabolites including members from the nucleotide and spermidine pathways, which linked to a number of EV-resident enzymes or transporters. Analysis of the urinary EVs from the patients indicated that the levels of glucuronate, D-ribose 5-phosphate and isobutyryl-L-carnitine were 2-26-fold lower in all pre-prostatectomy samples compared to the healthy control and post-prostatectomy samples (p < 0.05). These changes were only detected from EVs by normalization to EV-derived factors or with metabolite ratios, and not from the original urine samples. Conclusions: Our results suggest that metabolite analysis of EVs from different samples is feasible using a high-throughput platform and relatively small amount of sample material. With the knowledge about the specific enrichment of metabolites and normalization methods, EV metabolomics could be used to gain novel biomarker data not revealed by the analysis of the original EV source materials. (hide)
EV-METRIC
14% (37th percentile of all experiments on the same sample type)
 Reported
 Not reported
 Not applicable
EV-enriched proteins
Protein analysis: analysis of three or more EV-enriched proteins
non EV-enriched protein
Protein analysis: assessment of a non-EV-enriched protein
qualitative and quantitative analysis
Particle analysis: implementation of both qualitative and quantitative methods. For the quantitative method, the reporting of measured EV concentration is expected.
electron microscopy images
Particle analysis: inclusion of a widefield and close-up electron microscopy image
density gradient
Separation method: density gradient, at least as validation of results attributed to EVs
EV density
Separation method: reporting of obtained EV density
ultracentrifugation specifics
Separation method: reporting of g-forces, duration and rotor type of ultracentrifugation steps
antibody specifics
Protein analysis: antibody clone/reference number and dilution
lysate preparation
Protein analysis: lysis buffer composition
Study data
Sample type
Blood plasma
Sample origin
Prostate cancer
Focus vesicles
extracellular vesicle
Separation protocol
Separation protocol
  • Gives a short, non-chronological overview of the
    different steps of the separation protocol.
    • dUC = (Differential) (ultra)centrifugation
    • DG = density gradient
    • UF = ultrafiltration
    • SEC = size-exclusion chromatography
    • IAF = immuno-affinity capture
(d)(U)C
Protein markers
EV: TSG101/ CD63/ CD9
non-EV: TOMM20
Proteomics
no
Show all info
Study aim
Biomarker
Sample
Species
Homo sapiens
Sample Type
Blood plasma
EV-producing cells
Platelets
EV-harvesting Medium
Serum free medium
Separation Method
(Differential) (ultra)centrifugation
dUC: centrifugation steps
Between 800 g and 10,000 g
Between 100,000 g and 150,000 g
Pelleting performed
Yes
Pelleting: time(min)
75
Pelleting: rotor type
Type 50.2 Ti
Pelleting: speed (g)
110000
Wash: volume per pellet (ml)
16
Wash: time (min)
75
Wash: Rotor Type
Not specified
Wash: speed (g)
110000
Protein Concentration Method
BCA
1 - 4 of 4
  • CM = Commercial method
  • dUC = differential ultracentrifugation
  • DG = density gradient
  • UF = ultrafiltration
  • SEC = size-exclusion chromatography
EV-TRACK ID
EV210197
species
Homo sapiens
sample type
Cell culture
Urine
Urine
Blood plasma
cell type
Platelets
NA
NA
Platelets
medium
Serum free medium
NA
NA
Serum free medium
condition
Control condition
Control condition
Prostate cancer
Prostate cancer
separation protocol
(d)(U)C
(d)(U)C
Filtration
(d)(U)C
Filtration
(d)(U)C
Exp. nr.
1
3
4
2
EV-METRIC %
56
56
22
14