Description and optimization of a multiplex bead-based flow cytometry method (MBFCM) to characterize extracellular vesicles in serum samples from patients with hematological malignancies

Li, Lin; Görgens, André; Mussack, Veronika; Pepeldjiyska, Elena; Hartz, Anne Sophie; Rank, Andreas; Schmohl, Jörg; Krämer, Doris; Andaloussi, Samir El; Pfaffl, Michael W.; Schmetzer, Helga

doi:10.1038/s41417-022-00466-1

Cited by 10 publications

(19 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typical cup-shaped appearance of serum EVs were identied by TEM and EV concentrations and size distribution proles of puried serum EV samples from H and AML patients by fNTA, as shown before. 22 In this manuscript, we focused on the quantitative and qualitative assessment of EV surface characteristics via MBFCM and especially the characterization of the EV-derived miRNA cargo by RNA-Seq to evaluate their potential as biomarkers in AML samples compared to healthy volunteers.…”

Section: Resultsmentioning

confidence: 99%

“…Serum samples (each tube 0.5 ml) were thawed and subjected to MBFCM (MACSPlex Exosome Kit, human, Miltenyi Biotec). 13,22 EV-containing serum samples were centrifugated at 2500×g for 15 minutes before supernatants were processed. 30 mL of each sample was diluted with 30 mL MACSPlex buffer (MPB) and loaded onto wells of a pre-wet and drained MACSPlex 96-well 0.22 mm lter plate.…”

Section: Ev Characterizationmentioning

confidence: 99%

“…Next, 8 mL of MACSPlex Exosome Capture Beads (containing 39 different antibody-coated bead subsets) were added to each well as described elsewhere. 22 Surface proteins included in the MBFCM assay comprise the tetraspanins CD9, CD63, and CD81, and other surface proteins such as various leukocyte, T cell (CD4, CD8), B cell (CD19, CD20, CD24), monocyte (CD14), thrombocyte (CD41b, CD42a, CD62P, CD69), integrin (CD11c (integrin aX or CR4), CD29 (integrin b1), CD41b (integrin aIIb), CD49e (integrin a5)), endothelial (CD31, CD105, CD146 (Mel-CAM)), or MHC-associated (HLA-ABC (MHC-I), HLA-DRDPDQ (MHC-II)) antigens.…”

Section: Ev Characterizationmentioning

confidence: 99%

See 2 more Smart Citations

The potential role of serum extracellular vesicle derived small RNAs in AML research as non-invasive biomarker

Li¹,

Mussack

Goergens

et al. 2023

Nanoscale Adv.

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Ev Characterizationmentioning

confidence: 99%

Section: Ev Characterizationmentioning

confidence: 99%

See 1 more Smart Citation

The potential role of serum extracellular vesicle derived small RNAs in AML research as non-invasive biomarker

Li¹,

Mussack

Goergens

et al. 2023

Nanoscale Adv.

View full text Add to dashboard Cite

show abstract

“…A recent breakthrough was achieved by combining flow cytometry with imaging optics and CCD camera detection. In a systematic study, Gorgens and coworkers demonstrated the application of imaging flow cytometry (IMFC, commercialised by Luminex/Amnis Image Stream) for robust detection and quantification of EVs down to about 40 nm (Görgens et al., 2019 ), and meanwhile IMFC has seen more widespread use in the EV field (Li et al., 2022 ; Ricklefs et al., 2019 ; Tertel et al., 2022 ). Other approaches available are based on affinity‐capture of EVs on surfaces followed by light microscopy using scattering and/or fluorescence detection by proprietary technologies, such as from NanoView (‘ExoView’) or Oxford Nanoimaging amongst the most advanced implementations ( Nanoview ; Nanoimager ).…”

Section: Discussionmentioning

confidence: 99%

EVAnalyzer: High content imaging for rigorous characterisation of single extracellular vesicles using standard laboratory equipment and a new open‐source ImageJ/Fiji plugin

Schürz

Danmayr

Jaritsch

et al. 2022

J of Extracellular Vesicle

View full text Add to dashboard Cite

Extracellular vesicle (EV) research increasingly demands for quantitative characterisation at the single vesicle level to address heterogeneity and complexity of EV subpopulations. Emerging, commercialised technologies for single EV analysis based on, for example, imaging flow cytometry or imaging after capture on chips generally require dedicated instrumentation and proprietary software not readily accessible to every lab. This limits their implementation for routine EV characterisation in the rapidly growing EV field. We and others have shown that single vesicles can be detected as light diffraction limited fluorescent spots using standard confocal and widefield fluorescence microscopes. Advancing this simple strategy into a process for routine EV quantitation, we developed ‘EVAnalyzer’, an ImageJ/Fiji (Fiji is just ImageJ) plugin for automated, quantitative single vesicle analysis from imaging data. Using EVAnalyzer, we established a robust protocol for capture, (immuno‐)labelling and fluorescent imaging of EVs. To exemplify the application scope, the process was optimised and systematically tested for (i) quantification of EV subpopulations, (ii) validation of EV labelling reagents, (iii) in situ determination of antibody specificity, sensitivity and species cross‐reactivity for EV markers and (iv) optimisation of genetic EV engineering. Additionally, we show that the process can be applied to synthetic nanoparticles, allowing to determine siRNA encapsulation efficiencies of lipid‐based nanoparticles (LNPs) and protein loading of SiO 2 nanoparticles. EVAnalyzer further provides a pipeline for automated quantification of cell uptake at the single cell–single vesicle level, thereby enabling high content EV cell uptake assays and plate‐based screens. Notably, the entire procedure from sample preparation to the final data output is entirely based on standard reagents, materials, laboratory equipment and open access software. In summary, we show that EVAnalyzer enables rigorous characterisation of EVs with generally accessible tools. Since we further provide the plugin as open‐source code, we expect EVAnalyzer to not only be a resource of immediate impact, but an open innovation platform for the EV and nanoparticle research communities.

show abstract

“…[ 43 ] Multiplexed assays with up to 39 different antibody‐coated bead populations have also been used for an overall semi‐quantitative analysis of EV surface proteins. [ 44 ] However, this method is only applicable under the prerequisite that the EVs’ surface biomarkers have been identified. The current understanding of EVs populations remains largely unclear due to their heterogeneity and only a few surface markers can be reliably linked to origin cells.…”

Section: Detection Of Extracellular Vesicles Using Optical Read‐out S...mentioning

confidence: 99%

Nanostructured‐Based Optical Readouts Interfaced with Machine Learning for Identification of Extracellular Vesicles

Mata

Jeanne

Jalali

et al. 2022

Adv Healthcare Materials

View full text Add to dashboard Cite

Extracellular vesicles (EVs) are shed from cancer cells into body fluids, enclosing molecular information about the underlying disease with the potential for being the target cancer biomarker in emerging diagnosis approaches such as liquid biopsy. Still, the study of EVs presents major challenges due to their heterogeneity, complexity, and scarcity. Recently, liquid biopsy platforms have allowed the study of tumor‐derived materials, holding great promise for early‐stage diagnosis and monitoring of cancer when interfaced with novel adaptations of optical readouts and advanced machine learning analysis. Here, recent advances in labeled and label‐free optical techniques such as fluorescence, plasmonic, and chromogenic‐based systems interfaced with nanostructured sensors like nanoparticles, nanoholes, and nanowires, and diverse machine learning analyses are reviewed. The adaptability of the different optical methods discussed is compared and insights are provided into prospective avenues for the translation of the technological approaches for cancer diagnosis. It is discussed that the inherent augmented properties of nanostructures enhance the sensitivity of the detection of EVs. It is concluded by reviewing recent integrations of nanostructured‐based optical readouts with diverse machine learning models as novel analysis ventures that can potentially increase the capability of the methods to the point of translation into diagnostic applications.

show abstract

Description and optimization of a multiplex bead-based flow cytometry method (MBFCM) to characterize extracellular vesicles in serum samples from patients with hematological malignancies

Cited by 10 publications

References 60 publications

The potential role of serum extracellular vesicle derived small RNAs in AML research as non-invasive biomarker

The potential role of serum extracellular vesicle derived small RNAs in AML research as non-invasive biomarker

EVAnalyzer: High content imaging for rigorous characterisation of single extracellular vesicles using standard laboratory equipment and a new open‐source ImageJ/Fiji plugin

Nanostructured‐Based Optical Readouts Interfaced with Machine Learning for Identification of Extracellular Vesicles

Contact Info

Product

Resources

About