processing the single-cell shear assay imaging data

Enhancing Targeted Cancer Diagnosis by Quantifying Single-Cell Mechanics

Studying the biophysical differences between cancerous and non-cancerous cells allows for novel diagnostic and therapeutic opportunities1. Understanding how differences in biomechanics/mechanobiology contribute to tumor progression and treatment resistance will reveal new avenues for targeted therapy and early diagnosis2.
While it is known that cancer cell mechanical properties differ from normal cells (e.g., viscoelasticity of the plasma membrane and nuclear envelope)3,4,5, robust and reproducible methods for measuring these properties in live cells are lacking6. The shear assay method is used to quantify the mechanical properties of cells by subjecting single cells to fluid shear stress and analyzing their individual responses and resistance to the applied stress3,4,5,7,8,9. Although several methods and techniques have been used to characterize the mechanical properties of single cells, these tend to affect cell material properties by i) perforating/damaging the cell membrane due to the indentation depth, complex tip geometries, or substrate stiffening associated with atomic force microscopy (AFM)10,11, ii) inducing cellular photodamage during optical trapping12,13, or iii) inducing complex stress states associated with micropipette aspiration14,15. These external effects are associated with significant uncertainties in the accuracy of cell viscoelasticity measurements6,16,17.
To address these limitations, the shear assay method described here provides a highly controllable and simple approach to simulate physiological flow in the body without affecting cellular material properties in the process. Fluid shear stresses in this assay represent mechanical stresses experienced by cells in the body either by fluids within the tumor interstitium or in the blood during circulation18,19,20. Further, these fluid stresses promote various malignant behaviors in cancer cells, including progression, migration, metastasis, and cell death19,21,22,23&#160;which vary between tumorigenic and non-tumorigenic cells. Moreover, the altered mechanical features of cancer cells (i.e., they are often &#34;softer&#34; than normal cells found within the same organ) allow them to persist in hostile tumor microenvironments, invade surrounding normal tissues, and metastasize to distant sites24,25,26. By creating a pseudo-biological environment where cells experience physiological levels of fluid shear stress, a process that is physiologically relevant and not destructive to the cell is achieved. The cellular responses to these applied fluid shear stresses allow us to characterize cell mechanical properties.
This paper provides a shear assay protocol for the extensive study of the mechanical properties and behavior of cancerous and non-cancerous cells under applied shear stress. Cells respond to external forces in an elastic and viscous manner and can therefore be idealized as a viscoelastic material3. This technique is categorized into: (i) cell culture of dispersed single cells, (ii) controlled application of fluid shear stress, (iii) in situ imaging and observation of cellular behavior (including resistance to stress and deformation), (iv) strain analysis of cells to determine the extent of deformation, and (v) characterization of the viscoelastic properties of single cells. By interrogating these mechanical properties and behaviors, complex cellular mechanobiology can be distilled to quantifiable data. A protocol outlining this method allows for the cataloging of and comparison between various malignant and non-malignant cell types. Quantifying these differences has the potential to establish diagnostic and therapeutic biomarkers.

Author Spotlight: Shear Assay Protocol for the Determination of Single-Cell Material Properties  

Shear Assay Protocol for the Determination of Single-Cell Material Properties

The physical hallmarks of cancer are relatively new and exciting topics in cancer research. Our research seeks to specifically characterize the mechanical properties of both cancers and non-cancer cells to better understand the physical mechanisms by which cancer cells survive shear forces in the body and invade the immune system. Recent development in my period of research is the use of microfluidic devices and high-throughput techniques to understudy in a larger scale, cellular deformability and the viscoelastic properties of cells, and also in the identification of novel biomarkers such as the regulation of cytoskeletal proteins and extracellular matrix proteins for early cancer diagnosis.Current experimental challenges involved in this field are the complexity of the tumor microenvironment, scaling throughput, clinical translation, standardization and reproducibility, and integration of multimodal data. So, some of the significant findings I've established in this field from my past research and from my current research is that cancer cells can be distinguished from normal cells based on their responses to mechanical stressors. And the extent to which resist mechanical stressors is dependent on structural integrity, which is dictated to a reasonable extent by the presence of cytoskeletal proteins like the filamentous actin and other structural proteins within the cell.Our protocol offers a simple, repeatable, and non-destructive approach to characterize cultured cells mechanically. This is in contrast to other techniques like AFM and optical tweezers, which require a certain level of technical expertise in order to perform an experiment in a relatively low throughput. Within the use of the shear acid technique, will greatly advance the single cell mechanical characterization, and will also aid in many different cross-sectional areas such as rare disease diagnosis, personalized diagnosis and care, and monitoring single-cell drug interactions.

Watch this Scientific Journal Video about Processing the Single-Cell Shear Assay Imaging Data at JoVE.com

Processing the Single-Cell Shear Assay Imaging Data  

To perform digital image correlation, or DIC, using the DIC software, import the images derived from the sheer assay recording to the DIC software, preferably in the TIF file format. For an optimized correlation, select the sum of differential option which tracks the deformation of a new image with respect to the last image. Select a subset size of 31 by 31 pixels with a step size of 20 pixels.Map out the region of interest for a chosen single cell. For an irregular shape, such as a cell, use a polygon mask to map out the cell's geometry. Select arbitrary points within the mapped cell to track deformation.After mapping, choose specific points on the cell, like the nucleus or cytoplasm, to be analyzed by clicking Add a Strain Gauge and drawing out individual strain gauges at points within the defined cellular boundary. Click on Start to begin the strain processing and obtain strain versus time data. The strain time plot generated as a result of this correlation can be exported as a CSV file for further analysis in MATLAB.Double click or right click on the generated strain time plot and select Export Data. DIC of image frames extracted from sheer assay recordings successfully produced strain time data compatible with the creep stress response. The results of studies using the sheer assay technique showed that cancer cells were generally more mechanically compliant and less viscous than normal cells.The stiffness and viscosities of the cells were observed to decrease with increased cancer progression from a normal cell state to a slightly metastatic state and finally to a highly metastatic state. However, the relaxation time for these cell types showed no significant variations.

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Cancer Research

98 Views.  University of Notre Dame. Per eseguire la correlazione digitale delle immagini, o DIC, utilizzando il software DIC, importare le immagini derivate dalla registrazione del saggio puro nel software DIC, preferibilmente nel formato di file TIF. Per una correlazione ottimizzata, selezionare l'opzione somma differenziale che traccia la deformazione di una nuova immagine rispetto all'ultima immagine. Selezionare una dimensione del sottoinsieme di 31 x 31 pixel con una dimensione del passo di 20 pixel.Mappa la regione...