TEM ExosomeAnalyzer: a computer‐assisted software tool for quantitative evaluation of extracellular vesicles in transmission electron microscopy images

Kotrbová, Anna Vyhlídalová; Štěpka, Karel; Maška, Martin; Pálenik, Juraj; Ilkovics, Ladislav; Klemová, Dobromila; Kravec, Marek; Hubatka, František; Dave, Zankruti; Hampl, Aleš; Bryja, Vı́tězslav; Matula, Pavel; Pospíchalová, Vendula

doi:10.1080/20013078.2018.1560808

Cited by 46 publications

(46 citation statements)

References 27 publications

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“…3 It is increasingly realized that the classification of EVs into these three categories is oversimplified and will gain in complexity over time as new studies identify multiple subsets among these, both in vivo, depending upon physiological conditions, and in vitro, contingent on culture conditions. 5,14 Development Of EV Products: Process Qualification, Validation, And Consistency Are Key Parameters…”

Section: Defining Evsmentioning

confidence: 99%

<p>Extracellular Vesicles As Nanomedicine: Hopes And Hurdles In Clinical Translation</p>

Burnouf

Agrahari

2019

IJN

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The clinical development of cell therapies is revealing that extracellular vesicles (EVs) may become very instrumental as subcellular therapeutic adjuncts in human medicine. EVs are released by various types of cells, grown in culture, such as mesenchymal stromal cells, or obtained from patients or allogeneic donors. Some EV populations (especially species of exosomes and shed microvesicles) exhibit inherent roles in cell-cell communication, thanks to their ca. 30~1000-nm nanosize and the physiological expression of cellspecific markers on their lipid bilayer membranes. Biomedical engineers are now attempting to exploit this cellular crosstalk capacity to use EVs as smart drug delivery systems that display substantial benefits in targeting, safety, and pharmacokinetics compared to synthetic nanocarriers. In parallel, the development of a set of nano-instrumentation, biochemical tools, and preclinical assays needed for optimal characterization of both naïve and drugloaded EVs is ongoing. Although many hurdles remain, owing to the complexity of EV populations, translation of this "subcellular therapy" platform into reality is at hand and may soon change the landscape of the therapeutic arsenal in place to treat human degenerative and metabolic pathologies as well as diseases like cancer. This article provides objective opinions, balanced between unrealistic hopes of the capacity of EVs to resolve multiple clinical issues and concrete hurdles that have to be overcome to ensure that EVs are not lost in the translation phase, so that EVs can fulfill their promise by becoming a reliable therapeutic modality.

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Section: Defining Evsmentioning

confidence: 99%

<p>Extracellular Vesicles As Nanomedicine: Hopes And Hurdles In Clinical Translation</p>

Burnouf

Agrahari

2019

IJN

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“…Since the last decade, thanks to new and more powerful characterization techniques in the nanoscale, it has been possible to study in depth the biophysical composition of sEVs and their role in cellular processes 12–14 . Specifically, sEV morphology studies are currently performed by a battery of techniques: Nanoparticle-tracking analysis (NTA) 15 , tunable-resistive pulse sensing (TRPS) 16 , flow cytometry (FC) 17 and transmission electron microscopy (TEM) imaging 12,18 . All but TEM allow high-throughput, although accurate estimation of sEV size relies on the homogeneity of sEV populations and on the sphericity of sEV shapes.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, classifications of sEVs in sub-populations were proposed, being based on the biophysical characteristics and molecular compositions of sEVs 20 or on the shape of sEVs 21 . Additionally, TEM provides real images of the vesicles formed by electron beams transmitted through a specimen instead of performing indirect measurements, which makes it a very suitable technique for the characterization of sEV morphology 18 .…”

Section: Introductionmentioning

confidence: 99%

“…Regarding cell detection and segmentation in optical microscopy images, convolutional neural networks (CNNs) have produced the most accurate results 23,24,36 . To the best of our knowledge, there is only one published method devoted to the automatic segmentation of sEVs in TEM images: TEM ExosomeAnalyzer 18,37 . It applies a pipeline of classical image processing routines to obtain a labeled mask of sEVs under the assumption that they are almost perfect spherical objects.…”

Section: Introductionmentioning

confidence: 99%

“…It applies a pipeline of classical image processing routines to obtain a labeled mask of sEVs under the assumption that they are almost perfect spherical objects. TEM ExosomeAnalyzer requires manual curation of the detected sEV candidates to obtain biologically relevant measurements 18 .…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Gómez-de-Mariscal

Maška

Kotrbová

et al. 2019

Sci Rep

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Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.

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Lung‐specific exosomes for doxorubicin delivery in lung adenocarcinoma therapy

Yu,

Chen,

et al. 2024

Biotechnology Journal

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Doxorubicin (DOX) could be utilized to treat lung adenocarcinoma (LUAD), while dose‐limiting cardiotoxicity limits its clinical utilization. MDA‐MB‐231 cell‐derived exosomes show lung‐specific organotropism features. In this study, we aimed to explore the potential of MDA‐MB‐231 cell‐derived exosomes in DOX specific delivery to the lung. MDA‐MB‐231 cell‐derived exosomes were coincubated with to construct for the doxorubicin delivery system (D‐EXO). Exosomes labeled with fluorescein isothiocyanate were incubated with A549 cells or 293T cells, and the engulf and the mean intensity of the fluorescence were detected with immunofluorescence and flow cytometry assay. Cell viability was detected with cell counting kit‐8 (CCK‐8), and cell migration was determined by scratch test. The protein expression was detected by Western blot assay. A549 cell line‐derived xenograft mouse model was constructed to examine the treatment effect of D‐EXO. MDA‐MB‐231 cell‐derived exosomes could be specially taken up by A549 cells with diminished cell viability but not engulfed by 293T cells. D‐EXO inhibited A549 cell migration, and upregulated the protein expression of caspase 3 and cleaved caspase 3 expression, while did not show any inhibition on 293T cells. In vivo orthotopic xenotransplantation model indicated that D‐EXO inhibited tumor growth characterized by diminished tumor weight and improved survival rate. No significant change in body weight was observed after the D‐EXO treatment. In conclusion, D‐EXO proposed in this study could be utilized to treat LUAD with lung‐specific delivery effects to improve the survival rate.

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TEM ExosomeAnalyzer: a computer‐assisted software tool for quantitative evaluation of extracellular vesicles in transmission electron microscopy images

Cited by 46 publications

References 27 publications

<p>Extracellular Vesicles As Nanomedicine: Hopes And Hurdles In Clinical Translation</p>

<p>Extracellular Vesicles As Nanomedicine: Hopes And Hurdles In Clinical Translation</p>

Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Lung‐specific exosomes for doxorubicin delivery in lung adenocarcinoma therapy

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