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Details	EV-TRACK ID	Experiment nr.	Species	Sample type	Separation protocol	First author	Year	EV-METRIC
	EV210506	3/6	Mus musculus	Mixed cortical neurons/astrocytes	IAF	Ko J	2018	38%
Study summary Full title Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. All authors Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D Journal Lab Chip Abstract The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited (show more...)The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries. (hide) EV-METRIC 38% (79th 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 Immunoaffinity capture (non-commercial) Protein markers EV: Alix/ TSG101/ CD9/ GluR2 non-EV: None Proteomics no Show all info Study aim Technical analysis comparing/optimizing EV-related methods Sample Species Mus musculus Sample Type Cell culture supernatant EV-producing cells Mixed cortical neurons/astrocytes Separation Method Immunoaffinity capture Selected surface protein(s) GluR1/GluR2 Characterization: Protein analysis Protein Concentration Method Not determined Western Blot Antibody details provided? Yes Antibody dilution provided? Yes Detected EV-associated proteins Alix/ CD9/ TSG101/ GluR2 Characterization: RNA analysis RNA analysis Type RNAsequencing Database No Proteinase treatment No RNAse treatment No Characterization: Lipid analysis No Characterization: Particle analysis DLS Report type Modus EM EM-type Scanning-EM Image type Close-up Report size (nm) 150-200

	EV210506	1/6	Homo sapiens	Blood plasma	IAF	Ko J	2018	0%
Study summary Full title Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. All authors Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D Journal Lab Chip Abstract The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited (show more...)The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries. (hide) EV-METRIC 0% (median: 22% 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 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 Immunoaffinity capture (non-commercial) Protein markers EV: None non-EV: None Proteomics no Show all info Study aim Technical analysis comparing/optimizing EV-related methods Sample Species Homo sapiens Sample Type Blood plasma Separation Method Immunoaffinity capture Selected surface protein(s) GluR1/GluR2 Characterization: Protein analysis None Protein Concentration Method Not determined Characterization: RNA analysis RNA analysis Type RNA sequencing Database No Proteinase treatment No RNAse treatment No Characterization: Lipid analysis No Characterization: Particle analysis None

	EV210506	2/6	Homo sapiens	Blood plasma	Total Exosome Isolation	Ko J	2018	0%
Study summary Full title Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. All authors Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D Journal Lab Chip Abstract The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited (show more...)The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries. (hide) EV-METRIC 0% (median: 22% 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 Traumatic brain injury 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 Commercial method Protein markers EV: None non-EV: None Proteomics no Show all info Study aim Technical analysis comparing/optimizing EV-related methods Sample Species Homo sapiens Sample Type Blood plasma Separation Method Commercial kit Total Exosome Isolation Characterization: Protein analysis None Protein Concentration Method Not determined Characterization: RNA analysis RNA analysis Type RNAsequencing Database No Proteinase treatment No RNAse treatment No Characterization: Lipid analysis No Characterization: Particle analysis None

	EV210506	4/6	Mus musculus	Mixed cortical neurons/astrocytes	IAF	Ko J	2018	0%
Study summary Full title Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. All authors Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D Journal Lab Chip Abstract The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited (show more...)The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries. (hide) EV-METRIC 0% (median: 14% 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 Immunoaffinity capture (non-commercial) Protein markers EV: None non-EV: None Proteomics no Show all info Study aim Technical analysis comparing/optimizing EV-related methods Sample Species Mus musculus Sample Type Cell culture supernatant EV-producing cells Mixed cortical neurons/astrocytes Separation Method Immunoaffinity capture Selected surface protein(s) GluR1/GluR2 Characterization: Protein analysis None Protein Concentration Method Not determined Characterization: RNA analysis RNA analysis Type RNAsequencing Database No Proteinase treatment No RNAse treatment No Characterization: Lipid analysis No Characterization: Particle analysis None

	EV210506	5/6	Mus musculus	Blood plasma	IAF	Ko J	2018	0%
Study summary Full title Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. All authors Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D Journal Lab Chip Abstract The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited (show more...)The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries. (hide) EV-METRIC 0% (median: 22% 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 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 Immunoaffinity capture (non-commercial) Protein markers EV: None non-EV: None Proteomics no Show all info Study aim Technical analysis comparing/optimizing EV-related methods Sample Species Mus musculus Sample Type Blood plasma Separation Method Immunoaffinity capture Selected surface protein(s) GluR1/GluR2 Characterization: Protein analysis None Protein Concentration Method Not determined Characterization: RNA analysis RNA analysis Type RNAsequencing Database No Proteinase treatment No RNAse treatment No Characterization: Lipid analysis No Characterization: Particle analysis None

	EV210506	6/6	Mus musculus	Blood plasma	IAF	Ko J	2018	0%
Study summary Full title Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. All authors Ko J, Hemphill M, Yang Z, Sewell E, Na YJ, Sandsmark DK, Haber M, Fisher SA, Torre EA, Svane KC, Omelchenko A, Firestein BL, Diaz-Arrastia R, Kim J, Meaney DF, Issadore D Journal Lab Chip Abstract The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited (show more...)The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries. (hide) EV-METRIC 0% (median: 22% 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 Blast injury 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 Immunoaffinity capture (non-commercial) Protein markers EV: None non-EV: None Proteomics no Show all info Study aim Technical analysis comparing/optimizing EV-related methods Sample Species Mus musculus Sample Type Blood plasma Separation Method Immunoaffinity capture Selected surface protein(s) GluR1/GluR2 Characterization: Protein analysis None Protein Concentration Method Not determined Characterization: RNA analysis RNA analysis Type RNAsequencing Database No Proteinase treatment No RNAse treatment No Characterization: Lipid analysis No Characterization: Particle analysis None

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EV-TRACK ID	EV210506
species	Mus musculus	Homo sapiens	Homo sapiens	Mus musculus	Mus musculus	Mus musculus
sample type	Cell culture	Blood plasma	Blood plasma	Cell culture	Blood plasma	Blood plasma
cell type	Mixed cortical neurons/astrocytes	NA	NA	Mixed cortical neurons/astrocytes	NA	NA
condition	Control condition	Control condition	Traumatic brain injury	Control condition	Control condition	Blast injury
separation protocol	IAF capture (non-commercial)	IAF capture (non-commercial)	Total Exosome Isolation	IAF capture (non-commercial)	IAF capture (non-commercial)	IAF capture (non-commercial)
Exp. nr.	3	1	2	4	5	6
EV-METRIC %	38	0	0	0	0	0