scholarly journals Cancer Cells Viscoelasticity Measurement by Quantitative Phase and Flow Stress Induction

2021 ◽  
Author(s):  
Tomas Vicar ◽  
Jaromir Gumulec ◽  
Jiri Chmelik ◽  
Jiri Navratil ◽  
Radim Kolar ◽  
...  
Keyword(s):  
Shear Modulus ◽  
Cell Mass ◽  
Time Lapse ◽  
Cytochalasin D ◽  
Phase Imaging ◽  
Label Free ◽  
Height Measurement ◽  
Stress Induction ◽  
Cell Parameters ◽  
Quantitative Phase

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.

scholarly journals Detection and characterization of apoptotic and necrotic cell death by time-lapse quantitative phase image analysis

2019 ◽  
Author(s):  
Tomas Vicar ◽  
Martina Raudenska ◽  
Jaromir Gumulec ◽  
Michal Masarik ◽  
Jan Balvan
Keyword(s):  
Cell Death ◽  
Cell Density ◽  
Cell Mass ◽  
Time Lapse ◽  
Intensity Change ◽  
Average Intensity ◽  
Label Free ◽  
Quantitative Phase ◽  
Label Free Detection ◽  
Direct Cell

AbstractCell 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. 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.

scholarly journals Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

2021 ◽  
Author(s):  
DongHun Ryu ◽  
Hyeono Nam ◽  
Jessie Sungyun Jeon ◽  
YongKeun Park
Keyword(s):  
Blood Cells ◽  
Histopathological Examination ◽  
Point Of Care ◽  
Hemoglobin Concentration ◽  
Biochemical Properties ◽  
Phase Imaging ◽  
Sample Treatment ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

Histopathological examination of blood cells plays a crucial role in the diagnosis of various diseases. However, it involves time-consuming and laborious staining procedures required for microscopic review by medical experts and is not directly applicable for point-of-care diagnosis in resource-limited locations. This study reports a dilution-, actuation- and label-free method for the analysis of individual red blood cells (RBCs) using a capillary microfluidic device and quantitative phase imaging. Blood, without any sample treatment, is directly loaded into a micrometer-thick channel such that it forms a quasi-monolayer inside the channel. The morphological and biochemical properties of RBCs, including hemoglobin concentration, hemoglobin content, and corpuscular volume, were retrieved using the refractive index tomograms of individual RBCs measured using 3D quantitative phase imaging. The deformability of individual RBCs was also obtained by measuring the dynamic membrane fluctuations. The proposed framework applies to other imaging modalities and biomedical applications, facilitating rapid and cost-effective diagnosis and prognosis of diseases.

scholarly journals Patterned substrates modulate growth and dynamics of 3D cellular systems

2019 ◽  
Author(s):  
Michael J. Fanous ◽  
Yanfen Li ◽  
Mikhail E. Kandel ◽  
Kristopher A. Kilian ◽  
Gabriel Popescu
Keyword(s):  
Cell Mass ◽  
Mouse Embryonic Fibroblast ◽  
Growth Patterns ◽  
Cellular Systems ◽  
Polar Coordinates ◽  
Interference Microscopy ◽  
Phase Imaging ◽  
Label Free ◽  
Patterned Substrates ◽  
Independent Manner

AbstractThe development of 3D cellular architectures during development and pathological processes involves intricate migratory patterns that are modulated by genetics and the surrounding microenvironment. The substrate composition of cell cultures has been demonstrated to influence growth, proliferation, and migration in 2D. Here we study the growth and dynamics of mouse embryonic fibroblast (MEF) cultures patterned in a tissue sheet which then exhibits 3D growth. Using gradient light interference microscopy (GLIM), a label-free quantitative phase imaging approach, we explored the influence of geometry on cell growth patterns and rotational dynamics. We apply, for the first time to our knowledge, dispersion-relation phase spectroscopy (DPS) in polar coordinates to generate the radial and rotational cell mass-transport. Our data show that cells cultured on engineered substrates undergo rotational transport in a radially independent manner and exhibit faster vertical growth than the control, unpatterned cells. The use of GLIM and polar DPS provides a novel quantitative approach to studying the effects of spatially patterned substrates on cell motility and growth.

scholarly journals Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Diane N. H. Kim ◽  
Alexander A. Lim ◽  
Michael A. Teitell
Keyword(s):  
Machine Learning ◽  
T Cell ◽  
Time Lapse ◽  
Cell Receptor ◽  
Model System ◽  
Cell Killing ◽  
Label Free ◽  
Receptor Model ◽  
Quantitative Phase ◽  
Phase Microscopy

AbstractQuantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.

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