Extracellular Vesicle
Research Group
The Extracellular Vesicle Research Group, led by Dr Krisztina Buzás in the Department of Immunology at the University of Szeged, studies how cell-derived vesicles carry information in health and disease. By combining immunology, proteomics, advanced imaging and data analysis, we aim to translate basic vesicle biology into reliable biomarkers and therapeutic insights. Our interdisciplinary team collaborates with clinicians, engineers, and informatics experts to translate discoveries into practical benefits.
The Extracellular Vesicle Research Group, led by Dr Krisztina Buzás in the Department of Immunology at the University of Szeged, studies how cell-derived vesicles carry information in health and disease. By combining immunology, proteomics, advanced imaging and data analysis, we aim to translate basic vesicle biology into reliable biomarkers and therapeutic insights. Our interdisciplinary team collaborates with clinicians, engineers, and informatics experts to translate discoveries into practical benefits.
The EXTRACELLULAR VESICLE RESEARCH GROUP, led by Dr. Krisztina Buzás at the Department of Immunology University of Szeged, studies how cell-derived vesicles carry information in health and disease. Combining immunology, proteomics, advanced imaging and data analysis, we work to translate basic vesicle biology into dependable biomarkers and therapeutic insights. Our interdisciplinary team partners with clinicians and engineers and informatcians to move discoveries from the bench toward practical benefit.
15 years experience with extracellular vesicles
Our group's years of expertise covers a variety of isolation techniques, functional and molecular testing.
Expertise in
2D and 3D cell culture
We conduct diverse in vitro assays with primary and tumor cell lines, including wound healing, migration, spheroid models, and perfusion systems like the MIVO platform.
Proficiency in
animal models
We excel in animal models (mouse melanoma, disease induction, air pouch) and safely integrate them into extracellular vesicle experiments.
High-level competence in data science
We demonstrate outstanding expertise in advanced statistical methods and machine learning techniques.
A Team That Bridges Disciplines
Each member of our team brings a distinct area of expertise from molecular biology and clinical research to data analysis and bioinformatics. It is not only our individual strengths that make our group successful, but also how we combine different disciplines to provide truly integrated scientific solutions.
Krisztina Buzás
Edina Gyukity-Sebestyén
Gabriella Dobra
Mátyás Bukva
Tímea Böröczky
Lilla Pintér
Krisztina Buzás
Edina Gyukity-Sebestyén
Gabriella Dobra
Mátyás Bukva
Tímea Böröczky
Lilla Pintér
A Team That Bridges Disciplines
Each member of our team brings a distinct area of expertise from molecular biology and clinical research to data analysis and bioinformatics. What sets us apart is not only our individual strengths, but how we connect the dots across disciplines to deliver truly integrative scientific solutions.
Krisztina Buzás
Edina Gyukity-Sebestyén
Gabriella Dobra
Mátyás Bukva
Tímea Böröczky
Lilla Pintér
Krisztina Buzás
Edina Gyukity-Sebestyén
Gabriella Dobra
Mátyás Bukva
Tímea Böröczky
Lilla Pintér
Ongoing Projects
sEV dynamics
EVs in immunity
sEV communication network
Raman spectrocopy
sEV proteomics
Corona project
Describing the quantitative characteristics of sEV dynamics, and communication pathways mediated by different sEVs. We quantitatively characterize the dynamics of extracellular vesicle (sEV) trafficking in the tumor microenvironment by measuring both vesicle release and uptake across multiple cell types, including different tumor, and stromal cells. Using dual-color in-cell EV labeling combined with flow cytometry, we can simultaneously track sEV production and internalization without isolation, thus preserving physiologically relevant concentrations. This allows us to calculate the EV-dynamic profile, a robust metric expressing the balance between vesicle release and uptake for each cell type. By applying this method under baseline and stress conditions, we identify cell type–specific communication patterns and, importantly, can monitor shifts in vesicle exchange during treatments — capturing not only changes in release but the full dynamic balance of vesicle trafficking.
sEV dynamics
EVs in immunity
sEV communication network
Raman spectrocopy
sEV proteomics
Corona project
Describing the quantitative characteristics of sEV dynamics, and communication pathways mediated by different sEVs. We quantitatively characterize the dynamics of extracellular vesicle (sEV) trafficking in the tumor microenvironment by measuring both vesicle release and uptake across multiple cell types, including different tumor, and stromal cells. Using dual-color in-cell EV labeling combined with flow cytometry, we can simultaneously track sEV production and internalization without isolation, thus preserving physiologically relevant concentrations. This allows us to calculate the EV-dynamic profile, a robust metric expressing the balance between vesicle release and uptake for each cell type. By applying this method under baseline and stress conditions, we identify cell type–specific communication patterns and, importantly, can monitor shifts in vesicle exchange during treatments — capturing not only changes in release but the full dynamic balance of vesicle trafficking.
sEV dynamics
EVs in immunity
sEV communication network
Raman spectrocopy
sEV proteomics
Corona project
We quantitatively characterize the dynamics of extracellular vesicle (sEV) trafficking in the tumor microenvironment by measuring both vesicle release and uptake across multiple cell types, including different tumor, and stromal cells. Using dual-color in-cell EV labeling combined with flow cytometry, we can simultaneously track sEV production and internalization without isolation, thus preserving physiologically relevant concentrations. This allows us to calculate the EV-dynamic profile, a robust metric expressing the balance between vesicle release and uptake for each cell type. By applying this method under baseline and stress conditions, we identify cell type–specific communication patterns and, importantly, can monitor shifts in vesicle exchange during treatments — capturing not only changes in release but the full dynamic balance of vesicle trafficking.
sEV dynamics
EVs in immunity
sEV communication network
Raman spectrocopy
sEV proteomics
Corona project
We quantitatively characterize the dynamics of extracellular vesicle (sEV) trafficking in the tumor microenvironment by measuring both vesicle release and uptake across multiple cell types, including different tumor, and stromal cells. Using dual-color in-cell EV labeling combined with flow cytometry, we can simultaneously track sEV production and internalization without isolation, thus preserving physiologically relevant concentrations. This allows us to calculate the EV-dynamic profile, a robust metric expressing the balance between vesicle release and uptake for each cell type. By applying this method under baseline and stress conditions, we identify cell type–specific communication patterns and, importantly, can monitor shifts in vesicle exchange during treatments — capturing not only changes in release but the full dynamic balance of vesicle trafficking.
sEV dynamics
EVs in immunity
sEV communication network
Raman spectrocopy
sEV proteomics
Corona project
We quantitatively characterize the dynamics of extracellular vesicle (sEV) trafficking in the tumor microenvironment by measuring both vesicle release and uptake across multiple cell types, including different tumor, and stromal cells. Using dual-color in-cell EV labeling combined with flow cytometry, we can simultaneously track sEV production and internalization without isolation, thus preserving physiologically relevant concentrations. This allows us to calculate the EV-dynamic profile, a robust metric expressing the balance between vesicle release and uptake for each cell type. By applying this method under baseline and stress conditions, we identify cell type–specific communication patterns and, importantly, can monitor shifts in vesicle exchange during treatments — capturing not only changes in release but the full dynamic balance of vesicle trafficking.
Selected publications
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Selected publications
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Proteomic profiling of serum small extracellular vesicles predicts post-COVID syndrome development (Dobra & Gyukity-Sebestyén et al., 2025)
In this study, we profiled the protein cargo of serum-derived small extracellular vesicles (sEVs) collected during acute SARS-CoV-2 infection to learn whether these molecular fingerprints could forecast who would later develop long-COVID symptoms. Using mass-spectrometry on 59 stratified patients, we identified vesicular protein signatures that already separated future post-COVID cases from fully recovering individuals at hospital admission. Several innate-immune and coagulation-related proteins emerged as the strongest predictors, underscoring persistent inflammatory wiring as a risk factor. Our work provides a minimally invasive early-warning tool and suggests mechanistic targets for intervention.
Single-cell light-sheet fluorescence 3D images of tumour–stroma spheroid multicultures (Diosdi et al., 2025)
Here our team presents an open, high-resolution three-dimensional image atlas generated with light-sheet microscopy on spheroids that combine tumour cells with stromal partners. By segmenting every nucleus in millimetre-scale volumes, the resource captures spatial heterogeneity and cell-to-cell interactions at single-cell level, offering a realistic in-vitro proxy of the tumour micro-environment. The freely downloadable dataset will accelerate algorithm development for image analysis, support drug-penetration studies and improve the biological relevance of spheroid models.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
Minimal information for studies of
extracellular vesicles (MISEV2023):
From basic to advanced approaches (Welsh et al., 2024)The latest MISEV 2023 guidelines update minimum experimental and reporting standards for extracellular-vesicle research, reflecting rapid methodological advances since 2018. The international task force clarifies recommendations on EV isolation, characterisation, cargo analysis and functional assays, and introduces new sections on in-vivo tracking, single-EV technologies and clinical-grade preparations. Adhering to these rules will heighten reproducibility, enable meta-analyses and smooth the path toward diagnostic and therapeutic applications. We are very grateful to collaborate in this work.
Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumour-specificity and predictive potential (Bukva et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
MMP-9 as prognostic marker for brain tumours: A comparative study on serum-derived sEVs (Dobra et al., 2023)
Pooling NCI-60 datasets, this meta-analysis applied machine-learning to more than 3,700 extracellular-vesicle proteins and linked them to matched invasion and proliferation metrics. The algorithms uncovered vesicular signatures that distinguished tumour types with 90 % accuracy and pinpointed protein subsets whose presence forecast aggressive behaviour, validating EV proteomes as rich repositories of tumour-specific information and nominating practical biomarker panels for future liquid-biopsy development.
Impact of experimental conditions on extracellular-vesicle proteome:
A comparative study (Böröczky et al., 2023)This systematic comparison demonstrates that subtle changes in culture media, isolation kits or storage temperatures can dramatically reshape EV protein profiles. Variability affected pathway-enrichment analyses and downstream functional conclusions, highlighting the risk of artefacts when protocols differ. The study offers practical standardisation tips and a reference dataset to benchmark future experiments.
Raman spectral signatures of serum-derived EV-enriched isolates may support CNS-tumour diagnosis (Bukva et al., 2021)
Leveraging label-free Raman spectroscopy on 138 patient sera, our team identified distinct spectral fingerprints for glioblastoma, brain-metastatic lung cancer and meningioma. Machine-learning classifiers built on these spectra discriminated tumours from controls with high sensitivity, advocating Raman read-outs of sEV preparations as a rapid adjunct to neuro-oncological diagnostics.
Serum-isolated small extracellular vesicles may serve as signal-enhancers for monitoring CNS tumours (Dobra et al., 2020)
Proteomic comparison revealed that sEV fractions concentrate 65 tumour-related proteins while depleting 129 abundant serum components, yielding clearer molecular contrasts between central-nervous-system tumour entities than whole serum. Principal-component analysis confirmed superior patient-group separation, supporting routine sEV enrichment for biomarker discovery and longitudinal monitoring.
Melanoma-derived exosomes induce PD-1 over-expression and tumour progression via MSC oncogenic reprogramming (Gyukity-Sebestyén et al., 2019)
We demonstrate that melanoma exosomes can convert naïve mesenchymal stem cells into PD-1-high, melanoma-like cells that foster tumour growth. Transcriptomic and functional assays reveal a wholesale oncogenic rewiring of recipient cells, providing a new mechanistic layer to immune evasion and metastasis and suggesting that targeting exosome-mediated reprogramming may enhance PD-1 checkpoint blockade.
Small extracellular vesicles convey the stress-induced adaptive responses of melanoma cells (Harmati et al., 2019)
Exosomes released under oxidative, heat or cytostatic stress carried stress-specific cargo that boosted proliferation, migration and three-dimensional micro-tissue formation in recipient cells. These findings illustrate how tumour cells externalise and distribute adaptive programmes, enabling the micro-environment to synchronise with fluctuating stresses. Blocking stress-conditioned sEV release might curb melanoma plasticity.
The role of the metabolite cargo of extracellular vesicles in tumor progression (Harmati et al., 2021)
In this review, our team synthesised findings from 95 studies to catalogue over 100 metabolites enriched in tumour-derived EVs, including amino acids, lipids and TCA-cycle intermediates. We highlight proline, succinate, adenosine and phospholipids as key mediators of cancer cell fueling, micro-environment remodelling and immune suppression, framing EV metabolomics as a promising but still technically challenging avenue for biomarker and therapeutic development.
Our Methodological Toolbox
Trusted by leading research groups
Márta Széll - Department of Medical Genetics
Zoltán Kónya - Department of Applied and Environmental Chemistry
Zoltán Szabó - Department of Medical Chemistry
László Szivos - Department of Neurosurgery
Pál Barzó - Department of Neurosurgery
Katalin Hideghéthy - Department of Oncotherapy
Judit Oláh - Department of Oncotherapy
György Lázár - Department of Surgery
Krisztina Budai - Department of Surgery
Mihály Boros - Institute of Surgical Research
Attila Gácser - Department of Microbiology
Mónika Kiricsi - Department of Biochemistry and Molecular Biology
Csaba Berecky - Department of Pediatrics
Gabriella Terhes - Institute of Clinical Microbiology
Szabolcs Várbíró - Department of Obstetrics and Gynaecology
Collaborators from University of Szeged
Márta Széll - Department of Medical Genetics
Zoltán Kónya - Department of Applied and Environmental Chemistry
Zoltán Szabó - Department of Medical Chemistry
László Szivos - Department of Neurosurgery
Pál Barzó - Department of Neurosurgery
Katalin Hideghéthy - Department of Oncotherapy
Judit Oláh - Department of Oncotherapy
György Lázár - Department of Surgery
Krisztina Budai - Department of Surgery
Mihály Boros - Institute of Surgical Research
Attila Gácser - Department of Microbiology
Mónika Kiricsi - Department of Biochemistry and Molecular Biology
Csaba Berecky - Department of Pediatrics
Gabriella Terhes - Institute of Clinical Microbiology
Szabolcs Várbíró - Department of Obstetrics and Gynaecology
Collaborators from University of Szeged
Collaborators from University of Szeged
Márta Széll - Department of Medical Genetics
Zoltán Kónya - Department of Applied and Environmental Chemistry
Zoltán Szabó - Department of Medical Chemistry
László Szivos - Department of Neurosurgery
Pál Barzó - Department of Neurosurgery
Katalin Hideghéthy - Department of Oncotherapy
Judit Oláh - Department of Oncotherapy
György Lázár - Department of Surgery
Krisztina Budai - Department of Surgery
Mihály Boros - Institute of Surgical Research
Attila Gácser - Department of Microbiology
Mónika Kiricsi - Department of Biochemistry and Molecular Biology
Csaba Berecky - Department of Pediatrics
Gabriella Terhes - Institute of Clinical Microbiology
Szabolcs Várbíró - Department of Obstetrics and Gynaecology
Collaborators from University of Szeged
Márta Széll - Department of Medical Genetics
Zoltán Kónya - Department of Applied and Environmental Chemistry
Zoltán Szabó - Department of Medical Chemistry
László Szivos - Department of Neurosurgery
Pál Barzó - Department of Neurosurgery
Katalin Hideghéthy - Department of Oncotherapy
Judit Oláh - Department of Oncotherapy
György Lázár - Department of Surgery
Krisztina Budai - Department of Surgery
Mihály Boros - Institute of Surgical Research
Attila Gácser - Department of Microbiology
Mónika Kiricsi - Department of Biochemistry and Molecular Biology
Csaba Berecky - Department of Pediatrics
Gabriella Terhes - Institute of Clinical Microbiology
Szabolcs Várbíró - Department of Obstetrics and Gynaecology
Collaborators from University of Szeged
Márta Széll - Department of Medical Genetics
Zoltán Kónya - Department of Applied and Environmental Chemistry
Zoltán Szabó - Department of Medical Chemistry
László Szivos - Department of Neurosurgery
Pál Barzó - Department of Neurosurgery
Katalin Hideghéthy - Department of Oncotherapy
Judit Oláh - Department of Oncotherapy
György Lázár - Department of Surgery
Krisztina Budai - Department of Surgery
Mihály Boros - Institute of Surgical Research
Attila Gácser - Department of Microbiology
Mónika Kiricsi - Department of Biochemistry and Molecular Biology
Csaba Berecky - Department of Pediatrics
Gabriella Terhes - Institute of Clinical Microbiology
Szabolcs Várbíró - Department of Obstetrics and Gynaecology
Our Methodological Toolbox
Contact us!
Office:
University of Szeged, Faculty of Medicine, Department of Immunology
Head of Department: Krisztina Dr. Körmöndiné Buzás, PhD, DSc,
Address: H-6720 Szeged, Hungary 6 Szőkefalvi-Nagy Béla Street, 2nd floor, Room 30
E-mail: office.immun@med.u-szeged.hu
Phone: +36 (62) 342 826 ; +36 (70) 638 2413
Research Laboratory:
HUN-REN, Biological Research Centre, Institute of Biochemistry
Group Leader: Krisztina Buzás, PhD, DSc,
Address: H-6726 Szeged, Hungary 62 Temesvári Boulevard, 6th floor, Room 648
E-mail: krisztina.buzas@brc.hu
Phone: +36 (62) 599 600
Contact us!
Office:
University of Szeged, Faculty of Medicine, Department of Immunology
Head of Department: Krisztina Dr. Körmöndiné Buzás, PhD, DSc,
Address: H-6720 Szeged, Hungary 6 Szőkefalvi-Nagy Béla Street, 2nd floor, Room 30
E-mail: office.immun@med.u-szeged.hu
Phone: +36 (62) 342 826 ; +36 (70) 638 2413
Research Laboratory:
HUN-REN, Biological Research Centre, Institute of Biochemistry
Group Leader: Krisztina Buzás, PhD, DSc,
Address: H-6726 Szeged, Hungary 62 Temesvári Boulevard, 6th floor, Room 648
E-mail: krisztina.buzas@brc.hu
Phone: +36 (62) 599 600
Contact us!
Office:
University of Szeged, Faculty of Medicine, Department of Immunology
Head of Department: Krisztina Dr. Körmöndiné Buzás, PhD, DSc,
Address: H-6720 Szeged, Hungary 6 Szőkefalvi-Nagy Béla Street, 2nd floor, Room 30
E-mail: office.immun@med.u-szeged.hu
Phone: +36 (62) 342 826 ; +36 (70) 638 2413
Research Laboratory:
HUN-REN, Biological Research Centre, Institute of Biochemistry
Group Leader: Krisztina Buzás, PhD, DSc,
Address: H-6726 Szeged, Hungary 62 Temesvári Boulevard, 6th floor, Room 648
E-mail: krisztina.buzas@brc.hu
Phone: +36 (62) 599 600
Contact us!
Office:
University of Szeged, Faculty of Medicine, Department of Immunology
Head of Department: Krisztina Dr. Körmöndiné Buzás, PhD, DSc,
Address: H-6720 Szeged, Hungary 6 Szőkefalvi-Nagy Béla Street, 2nd floor, Room 30
E-mail: office.immun@med.u-szeged.hu
Phone: +36 (62) 342 826 ; +36 (70) 638 2413
Research Laboratory:
HUN-REN, Biological Research Centre, Institute of Biochemistry
Group Leader: Krisztina Buzás, PhD, DSc,
Address: H-6726 Szeged, Hungary 62 Temesvári Boulevard, 6th floor, Room 648
E-mail: krisztina.buzas@brc.hu
Phone: +36 (62) 599 600
Our Methodological Toolbox
15 years experience with extracellular vesicles
Our group's years of expertise covers a variety of isolation techniques, functional and molecular testing.
Expertise in
2D and 3D cell culture
We conduct diverse in vitro assays with primary and tumor cell lines, including wound healing, migration, spheroid models, and perfusion systems like the MIVO platform.
Proficiency in
animal models
We excel in animal models (mouse melanoma, disease induction, air pouch) and safely integrate them into extracellular vesicle experiments.
High-level competence in data science
We demonstrate outstanding expertise in advanced statistical methods and machine learning techniques.