Q-PHASE／イメージングサイトメトリー用
ホログラフィック顕微鏡文献

Q-PHASE／イメージングサイトメトリー用 ホログラフィック顕微鏡   文献

Cancer Cells Viscoelasticity Measurement by Quantitative Phase and Flow Stress Induction
Reference:bioRχiv August 6, 2021 (https://doi.org/10.1101/2021.08.05.455201)
Authors: Tomas Vicar, Jaromir Gumulec, Jiri Chmelik, Jiri Navratil, Radim Kolar, Larisa Chmelikova, Vratislav Cmiel, Ivo Provaznik, Michal Masarik

＜Abstract＞
Cell viscoelastic properties are affected by the cell cycle, differentiation, pathological processes such as malignant transformation. Therefore, evaluation of the mechanical properties of the cells proved to be an approach to obtaining information on the functional state of the cells. Most of the currently used methods for cell mechanophenotypisation are limited by low robustness or the need for highly expert operation. In this paper, the system and method for viscoelasticity measurement using shear stress induction by fluid flow is described and tested. Quantitative Phase Imaging (QPI) is used for image acquisition because this technique enables to quantify optical path length delays introduced by the sample, thus providing a label-free objective measure of morphology and dynamics. Viscosity and elasticity determination were refined using a new approach based on the linear system model and parametric deconvolution. The proposed method allows high-throughput measurements during live cell experiments and even through a time-lapse, where we demonstrated the possibility of simultaneous extraction of shear modulus, viscosity, cell morphology, and QPI-derived cell parameters like circularity or cell mass. Additionally, the proposed method provides a simple approach to measure cell refractive index with the same setup, which is required for reliable cell height measurement with QPI, an essential parameter for viscoelasticity calculation. Reliability of the proposed viscoelasticity measurement system was tested in several experiments including cell types of different Young/shear modulus and treatment with cytochalasin D or docetaxel, and an agreement with atomic force microscopy was observed. The applicability of the proposed approach was also confirmed by a time-lapse experiment with cytochalasin D washout, where an increase of stiffness corresponded to actin repolymerisation in time.

Silicane Derivative Increases Doxorubicin Efficacy in an Ovarian Carcinoma Mouse Model: Fighting Drug Resistance
Reference: ACS Appl. Mater. Interfaces. 2021, 13, 27, 31355–31370(https://pubs.acs.org/doi/10.1021/acsami.0c20458)
Authors: Michaela Fojtů, Jan Balvan, Tomáš Vičar, Hana Holcová Polanská, Barbora Peltanová, Stanislava Matějková, Martina Raudenská, Jiří Šturala, Paula Mayorga-Burrezo, Michal Masařík, and Martin Pumera*

＜Abstract＞
The development of cancer resistance continues to represent a bottleneck of cancer therapy. It is one of the leading factors preventing drugs to exhibit their full therapeutic potential. Consequently, it reduces the efficacy of anticancer therapy and causes the survival rate of therapy-resistant patients to be far from satisfactory. Here, an emerging strategy for overcoming drug resistance is proposed employing a novel two-dimensional (2D) nanomaterial polysiloxane (PSX). We have reported on the synthesis of PSX nanosheets (PSX NSs) and proved that they have favorable properties for biomedical applications. PSX NSs evinced unprecedented cytocompatibility up to the concentration of 300 μg/mL, while inducing very low level of red blood cell hemolysis and were found to be highly effective for anticancer drug binding. PSX NSs enhanced the efficacy of the anticancer drug doxorubicin (DOX) by around 27.8–43.4% on average and, interestingly, were found to be especially effective in the therapy of drug-resistant tumors, improving the effectiveness of up to 52%. Fluorescence microscopy revealed improved retention of DOX within the drug-resistant cells when bound on PSX NSs. DOX bound on the surface of PSX NSs, i.e., PSX@DOX, improved, in general, the DOX cytotoxicity in vitro. More importantly, PSX@DOX reduced the growth of DOX-resistant tumors in vivo with 3.5 times better average efficiency than the free drug. Altogether, this paper represents an introduction of a new 2D nanomaterial derived from silicane and pioneers its biomedical application. As advances in the field of material synthesis are rapidly progressing, novel 2D nanomaterials with improved properties are being synthesized and await thorough exploration. Our findings further provide a better understanding of the mechanisms involved in the cancer resistance and can promote the development of a precise cancer therapy.

Self-Supervised Pretraining for Transferable Quantitative Phase Image Cell Segmentation
Reference: Biomedical Optics Express Vol. 12, Issue 10, pp. 6514-6528. 1 Oct 2021, (https://doi.org/10.1364/BOE.433212)
Authors: Tomas Vicar, Jiri Chmelik, Roman Jakubicek, Larisa Chmelikova, Jaromir Gumulec, Jan Balvan, Ivo Provaznik, and Radim Kolar

＜Abstract＞
In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.

Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition
Reference: J. Biomed. Opt. 25 (8),086502, 18 August 2020 ( https://doi.org/10.1117/1.JBO.25.8.086502)
Authors: Lenka Strbkova, Brittany B. Carson, Theresa Vincent, Pavel Vesely, Radim Chmelik

＜Abstract＞
Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.
Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.
Approach: The methodology was demonstrated by studying epithelial–mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.
Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.
Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.