Label-free characterization of white blood cells using fluorescence lifetime imaging and flow-cytometry: molecular heterogeneity and erythrophagocytosis [Invited]

Yakimov, Boris P.; Gogoleva, M. A.; Semenov, Alexei; Rodionov, Sergei A.; Новоселова, М. В.; Gayer, Alexey V.; Kovalev, Alexey V.; Bernakevich, A I; Фадеев, В. В.; Armaganov, Artashes G.; Drachev, Vladimir P.; Gorin, Dmitry A.; Darvin, Maxim E.; Shcheslavskiy, Vladislav I.; Budylin, Gleb S.; Priezzhev, Alexander V.; Shirshin, Evgeny A.

doi:10.1364/boe.10.004220

Cited by 45 publications

(49 citation statements)

References 53 publications

(86 reference statements)

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“…The in vivo behavior of fluorescent SCy-MiExo was evaluated with optical imaging. The SCy dye was selected because it showed high hydrophilicity and high intensity emission in the NIR range, which is far from the autofluorescence range of natural biomolecules, such as hemoglobin [ 38 ]. We performed longitudinal in vivo imaging of the fluorescent nanovesicles in healthy mice to evaluate their biodistribution over time.…”

Section: Discussionmentioning

confidence: 99%

Covalently Labeled Fluorescent Exosomes for In Vitro and In Vivo Applications

et al. 2021

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The vertiginous increase in the use of extracellular vesicles and especially exosomes for therapeutic applications highlights the necessity of advanced techniques for gaining a deeper knowledge of their pharmacological properties. Herein, we report a novel chemical approach for the robust attachment of commercial fluorescent dyes to the exosome surface with covalent binding. The applicability of the methodology was tested on milk and cancer cell-derived exosomes (from U87 and B16F10 cancer cells). We demonstrated that fluorescent labeling did not modify the original physicochemical properties of exosomes. We tested this nanoprobe in cell cultures and healthy mice to validate its use for in vitro and in vivo applications. We confirmed that these fluorescently labeled exosomes could be successfully visualized with optical imaging.

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Section: Discussionmentioning

confidence: 99%

Covalently Labeled Fluorescent Exosomes for In Vitro and In Vivo Applications

et al. 2021

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“…Also, T cells in our study are isolated from the bulk blood cell population, and two subtypes of T cells are separated during data acquisition. It is possible to classify some blood cell types in a bulk population with only label‐free autofluorescence parameters , but future work is needed to assess the performance of our approach on image samples with additional immune cells and mixed activation states. Finally, quantitative fluorescence intensity imaging has technical limitations, requiring consistent imaging settings such as illumination power and detector gain.…”

Section: Discussionmentioning

confidence: 99%

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Wang

Walsh

Skala

et al. 2019

Journal of Biophotonics

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The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen‐induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non‐destructive manner by detecting endogenous changes in metabolic co‐enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single‐cell images from six donors, we evaluate classifiers ranging from traditional models that use previously‐extracted image features to convolutional neural networks (CNNs) pre‐trained on general non‐biological images. Adapting pre‐trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.

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“…In particular, laser aggregometry was successfully used to study microrheologic alterations of blood at various conditions accompanied with disturbances in protein content in plasma [ 22 , 43 ]. Flow cytometry is a well-known method for studying parameters of the interactions between different components of blood [ 44 ]. Optical trapping proved to be a very useful approach for studying peculiarities of RBC interaction, microrheology, and biomechanics [ 18 , 45 , 46 , 47 , 48 ] as well as for quantifications of a number of proaggregant macromolecules adsorbed on the surface of RBC [ 49 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Fibrinogen Macromolecules Interaction with Red Blood Cells Membrane by Means of Laser Aggregometry, Flow Cytometry, and Optical Tweezers Combined with Microfluidics

et al. 2020

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An elevated concentration of fibrinogen in blood is a significant risk factor during many pathological diseases, as it leads to an increase in red blood cells (RBC) aggregation, resulting in hemorheological disorders. Despite the biomedical importance, the mechanisms of fibrinogen-induced RBC aggregation are still debatable. One of the discussed models is the non-specific adsorption of fibrinogen macromolecules onto the RBC membrane, leading to the cells bridging in aggregates. However, recent works point to the specific character of the interaction between fibrinogen and the RBC membrane. Fibrinogen is the major physiological ligand of glycoproteins receptors IIbIIIa (GPIIbIIIa or αIIββ3 or CD41/CD61). Inhibitors of GPIIbIIIa are widely used in clinics for the treatment of various cardiovascular diseases as antiplatelets agents preventing the platelets’ aggregation. However, the effects of GPIIbIIIa inhibition on RBC aggregation are not sufficiently well studied. The objective of the present work was the complex multimodal in vitro study of the interaction between fibrinogen and the RBC membrane, revealing the role of GPIIbIIIa in the specificity of binding of fibrinogen by the RBC membrane and its involvement in the cells’ aggregation process. We demonstrate that GPIIbIIIa inhibition leads to a significant decrease in the adsorption of fibrinogen macromolecules onto the membrane, resulting in the reduction of RBC aggregation. We show that the mechanisms underlying these effects are governed by a decrease in the bridging components of RBC aggregation forces.

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Label-free characterization of white blood cells using fluorescence lifetime imaging and flow-cytometry: molecular heterogeneity and erythrophagocytosis [Invited]

Cited by 45 publications

References 53 publications

Covalently Labeled Fluorescent Exosomes for In Vitro and In Vivo Applications

Covalently Labeled Fluorescent Exosomes for In Vitro and In Vivo Applications

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

Assessment of Fibrinogen Macromolecules Interaction with Red Blood Cells Membrane by Means of Laser Aggregometry, Flow Cytometry, and Optical Tweezers Combined with Microfluidics

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