Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.

Exosomes are small vesicles of endosomal origin that are released by almost all cell types, even those that are pathologically altered. Exosomes widely participate in cell-to-cell communication via transferring cargo, including nucleic acids, proteins, and other metabolites, into recipient cells. Tumour-derived exosomes (TDEs) participate in many important molecular pathways and affect various hallmarks of cancer, including fibroblasts activation, modification of the tumour microenvironment (TME), modulation of immune responses, angiogenesis promotion, setting the pre-metastatic niche, enhancing metastatic potential, and affecting therapy sensitivity and resistance. The unique exosome biogenesis, composition, nontoxicity, and ability to target specific tumour cells bring up their use as promising drug carriers and cancer biomarkers. In this review, we focus on the role of exosomes, with an emphasis on their protein cargo, in the key mechanisms promoting cancer progression. We also briefly summarise the mechanism of exosome biogenesis, its structure, protein composition, and potential as a signalling hub in both normal and pathological conditions.

Cisplatin is a widely used chemotherapeutic agent that is clinically approved to fight both carcinomas and sarcomas. It has relatively high efficiency in treating ovarian cancers and metastatic testicular cancers. It is generally accepted that the major mechanism of cisplatin anti-cancer action is DNA damage. However, cisplatin is also effective in metastatic cancers and should, therefore, affect slow-cycling cancer stem cells in some way. In this review, we focused on the alternative effects of cisplatin that can support a good therapeutic response. First, attention was paid to the effects of cisplatin at the cellular level such as changes in intracellular pH and cellular mechanical properties. Alternative cellular targets of cisplatin, and the effects of cisplatin on cancer cell metabolism and ER stress were also discussed. Furthermore, the impacts of cisplatin on the tumor microenvironment and in the whole organism context were reviewed. In this review, we try to reveal possible causes of the unexpected effectiveness of this anti-cancer drug.

We focused on the biomechanical and morphological characteristics of prostate cancer cells and their changes resulting from the effect of docetaxel, cisplatin, and long-term zinc supplementation. Cell population surviving the treatment was characterized as follows: cell stiffness was assessed by atomic force microscopy, cell motility and invasion capacity were determined by colony forming assay, wound healing assay, coherence-controlled holographic microscopy, and real-time cell analysis. Cells of metastatic origin exhibited lower height than cells derived from the primary tumour. Cell dry mass and CAV1 gene expression followed similar trends as cell stiffness. Docetaxel- and cisplatin-surviving cells had higher stiffness, and decreased motility and invasive potential as compared to non-treated cells. This effect was not observed in zinc(II)-treated cells. We presume that cell stiffness changes may represent an important overlooked effect of cisplatin-based anti-cancer drugs. Atomic force microscopy and confocal microscopy data images used in our study are available for download in the Zenodo repository (https://zenodo.org/, Digital Object Identifiers:10.5281/zenodo.1494935).

Cell viability and cytotoxicity assays are highly important for drug screening and cytotoxicity tests of antineoplastic or other therapeutic drugs. Even though biochemical-based tests are very helpful to obtain preliminary preview, their results should be confirmed by methods based on direct cell death assessment. In this study, time-dependent changes in quantitative phase-based parameters during cell death were determined and methodology useable for rapid and label-free assessment of direct cell death was introduced. The goal of our study was distinction between apoptosis and primary lytic cell death based on morphologic features. We have distinguished the lytic and non-lytic type of cell death according to their end-point features (Dance of Death typical for apoptosis versus swelling and membrane rupture typical for all kinds of necrosis common for necroptosis, pyroptosis, ferroptosis and accidental cell death). Our method utilizes Quantitative Phase Imaging (QPI) which enables the time-lapse observation of subtle changes in cell mass distribution. According to our results, morphological and dynamical features extracted from QPI micrographs are suitable for cell death detection (76% accuracy in comparison with manual annotation). Furthermore, based on QPI data alone and machine learning, we were able to classify typical dynamical changes of cell morphology during both caspase 3,7-dependent and -independent cell death subroutines. The main parameters used for label-free detection of these cell death modalities were cell density (pg/pixel) and average intensity change of cell pixels further designated as Cell Dynamic Score (CDS). To the best of our knowledge, this is the first study introducing CDS and cell density as a parameter typical for individual cell death subroutines with prediction accuracy 75.4% for caspase 3,7-dependent and -independent cell death.