Microscale, scanning defocusing volumetric particle-tracking velocimetry

Guo, Tianqi; Ardekani, Arezoo M.; Vlachos, Pavlos P.

doi:10.1007/s00348-019-2731-4

Cited by 16 publications

(19 citation statements)

References 33 publications

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“…To better delineate the effects that phenotypic heterogeneity has on tumor progression, we employed a cell tracking algorithm to evaluate the cellular dynamics throughout a prolonged 3D culture. Our cell tracking algorithm uses a multi-parametric approach to identify and track cells over time and space accurately [ 19 , 20 ]. Additionally, we used immunoblotting to measure changes in EMT markers within Ca1a cells upon exposure to FN.…”

Section: Discussionmentioning

confidence: 99%

“…The images were down-sampled by a factor of 5 such that each pixel in the down-sampled image corresponded to the averaged intensity level in its 5 × 5 neighborhood from the original image, in order to avoid over-segmentation of the cells due to intracellular signal heterogeneity. A local Hessian matrix of the intensity was calculated for each pixel, and the cells (resembling bright tubes or blobs) were marked by negative λ 2 values in the Hessian eigenmaps [ 19 ]. A dynamic erosion procedure with an adaptive threshold was used to identify each intensity peak of all cells that were analyzed.…”

Section: Methodsmentioning

confidence: 99%

“…Our algorithm identifies the most probable correspondence between cells by taking into consideration the characteristics of each cell (brightness, morphology, etc.) in addition to the classic nearest-neighbor criterion as tracking parameters ( Figure S2 ) [ 19 , 20 , 34 , 35 ]. Hence, cells were parameterized based on their refined segmentation and the equivalent ellipse, as shown in Figure 7 .…”

Section: Methodsmentioning

confidence: 99%

“…Each cell was then represented in the higher dimensional space by its feature vector ( C x , C y , I max , R , θ, S , AR ). Cell correspondence between frames was then determined by multi-parametric tracking to form cell migration tracks [ 19 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

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Fibronectin-Expressing Mesenchymal Tumor Cells Promote Breast Cancer Metastasis

Jun

Guo

Libring

et al. 2020

Cancers

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Tumor metastasis is connected to epithelial-mesenchymal heterogeneity (EMH) and the extracellular matrix (ECM) within the tumor microenvironment. Mesenchymal-like fibronectin (FN) expressing tumor cells enhance metastasis within tumors that have EMH. However, the secondary tumors are primarily composed of the FN null population. Interestingly, during tumor cell dissemination, the invasive front has more mesenchymal-like characteristics, although the outgrowths of metastatic colonies consist of a more epithelial-like population of cells. We hypothesize that soluble FN provided by mesenchymal-like tumor cells plays a role in supporting the survival of the more epithelial-like tumor cells within the metastatic niche in a paracrine manner. Furthermore, due to a lower rate of proliferation, the mesenchymal-like tumor cells become a minority population within the metastatic niche. In this study, we utilized a multi-parametric cell-tracking algorithm and immunoblotting to evaluate the effect of EMH on the growth and invasion of an isogenic cell series within a 3D collagen network using a microfluidic platform. Using the MCF10A progression series, we demonstrated that co-culture with FN-expressing MCF10CA1h cells significantly enhanced the survival of the more epithelial MCF10CA1a cells, with a two-fold increase in the population after 5 days in co-culture, whereas the population of the MCF10CA1a cells began to decrease after 2.5 days when cultured alone (p < 0.001). However, co-culture did not significantly alter the rate of proliferation for the more mesenchymal MCF10CA1h cells. Epithelial tumor cells not only showed prolonged survival, but migrated significantly longer distances (350 µm compared with 150 µm, respectively, p < 0.01) and with greater velocity magnitude (4.5 µm/h compared with 2.1 µm/h, respectively, p < 0.001) under co-culture conditions and in response to exogenously administered FN. Genetic depletion of FN from the MCF10CA1h cells resulted in a loss of survival and migration capacity of the epithelial and mesenchymal populations. These data suggest that mesenchymal tumor cells may function to support the survival and outgrowth of more epithelial tumor cells within the metastatic niche and that inhibition of FN production may provide a valuable target for treating metastatic disease.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Fibronectin-Expressing Mesenchymal Tumor Cells Promote Breast Cancer Metastasis

Jun

Guo

Libring

et al. 2020

Cancers

Self Cite

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“…Due to the difficulties and limitations of the measurement techniques, experimental observations of the flow field at the subpore scale are scarce in the literature. Volumetric velocimetry techniques, which have been proposed in the literature, are still suffering from complexity with regard to equipment, experimental procedures and data processing [29]. Single-camera systems used in three-dimensional three-component (3D3C) velocimetry are typically involved in a rotating pinhole or multiple apertures [30,31].…”

Section: Introductionmentioning

confidence: 99%

Micro- and Macro-Scale Measurement of Flow Velocity in Porous Media: A Shadow Imaging Approach for 2D and 3D

Sabbagh

Kazemi

Soltani

et al. 2020

Optics

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Flow measurement in porous media is a challenging subject, especially when it comes to performing a three-dimensional (3D) velocimetry at the micro scale. Volumetric flow measurement techniques such as defocusing and tomographic imaging generally involve rigorous procedures, complex experimental setups, and multi-part data processing procedures. However, detailed knowledge of the flow pattern at the pore and subpore scales is important in interpreting the phenomena that occur inside the porous media and understanding the macro-scale behaviors. In this work, the flow of an oil inside a porous medium is measured at the pore and subpore scales using refractive index matching (RIM) and shadowgraph imaging techniques. At the macro scale, flow is measured using the particle image velocimetry (PIV) method in two dimensions (2D) to confirm the volumetric nature of the flow and obtain the overall flow pattern in the vicinity of the flow entrance and at the far field. At the micro scale, the three-dimensional (3D) flow within an arbitrary volume of the porous medium was quantified using 2D particle-tracking velocimetry (PTV) utilizing the law of conservation of mass. Using the shadowgraphy method and a single camera makes the flow measurement much less complex than the approaches using laser light sheets or multiple cameras with multiple viewing angles.

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A velocity program using the Kanade–Lucas–Tomasi feature‐tracking algorithm with demonstration for pressure and electroosmosis conditions

2022

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Investigating microfluidic flow profiles is of interest in the microfluidics field for the determination of various characteristics of a lab-on-a-chip system. Microparticle tracking velocimetry uses computational methods upon recording video footage of microfluidic flow to ultimately visualize motion within a microfluidic system across all frames of a video. Current methods are computationally expensive or require extensive instrumentation. A computational method suited to microparticle tracking applications is the robust Kanade-Lucas-Tomasi (KLT) feature-tracking algorithm. This work explores a microparticle tracking velocimetry program using the KLT feature-tracking algorithm. The developed program is demonstrated using pressure-driven and EOF and compared with the respective mathematical fluid flow models. An electrostatics analysis of EOF conditions is performed in the development of the mathematical using a Poisson's Equation solver. This analysis is used to quantify the zeta potential of the electroosmotic system. Overall, the KLT featuretracking algorithm presented in this work proved to be highly reliable and computationally efficient for investigations of pressure-driven and EOF in a microfluidic system.

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Microscale, scanning defocusing volumetric particle-tracking velocimetry

Cited by 16 publications

References 33 publications

Fibronectin-Expressing Mesenchymal Tumor Cells Promote Breast Cancer Metastasis

Fibronectin-Expressing Mesenchymal Tumor Cells Promote Breast Cancer Metastasis

Micro- and Macro-Scale Measurement of Flow Velocity in Porous Media: A Shadow Imaging Approach for 2D and 3D

A velocity program using the Kanade–Lucas–Tomasi feature‐tracking algorithm with demonstration for pressure and electroosmosis conditions

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