Quantitative phase‐contrast imaging—A potential tool for future cancer diagnostics

Gjörloff-Wingren, Anette

doi:10.1002/cyto.a.23104

Cited by 5 publications

(4 citation statements)

References 18 publications

(22 reference statements)

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Section: Introductionmentioning

confidence: 99%

“…Digital holographic cytometry (DHC) is a novel imaging technique for observations of cell samples in their native monolayer settings [12]. DHC functions on the principle of quantitative phase imaging (QPI) providing high spatial resolution and quantification of cellular morphological parameters such as volume, area, and optical thickness from recorded holograms [12,13]. Since it does not require any fluorescent labels or staining, DHC allows for long-term time-lapse experiments with precise observations and with no phototoxic effects on the cells compared to differential interface contrast microscopy [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Circulating Tumor Cell Models Mimicking Metastasizing Cells In Vitro: Discrimination of Colorectal Cancer Cells and White Blood Cells Using Digital Holographic Cytometry

et al. 2022

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Colorectal cancer (CRC) is the second most metastatic disease with the majority of cases detected in Western countries. Metastases are formed by circulating altered phenotype tumor cells causing 20% of CRC related deaths. Metastatic cells may show higher expression of surface molecules such as CD44, and changes in morphological properties are associated with increased invasiveness and poor prognosis. In this study, we intended to mimic the environment for metastasizing cells. Here, we used digital holographic cytometry (DHC) analysis to determine cellular morphological properties of three metastatic and two non-metastatic colorectal cancer cell lines to show differences in morphology between the CRC cells and peripheral blood mononuclear cells (PBMCs). By establishing differences in cell area, cell thickness, cell volume, and cell irregularity even when the CRC cells were in minority (5% out of PBMCs), DHC does discriminate between CRC cells and the PBMCs in vitro. We also analyzed the epithelial marker EpCAM and migration marker CD44 using flow cytometry and demonstrate that the CRC cell lines and PBMC cells differ in EpCAM and CD44 expression. Here, we present DHC as a new powerful tool in discriminating cells of different sizes in suspension together with a combination of biomarkers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Circulating Tumor Cell Models Mimicking Metastasizing Cells In Vitro: Discrimination of Colorectal Cancer Cells and White Blood Cells Using Digital Holographic Cytometry

et al. 2022

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“…Quantitative-phase imaging (QPI) of biological systems has met with significant success in recent years, both in fundamental and biomedical investigations [1][2][3][4][5]. In QPI, an image is formed by quantifying the optical path length (or optical phase delay) difference between the specimen and its background without any fluorescent staining [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Deep learning classification of lipid droplets in quantitative phase images

Sheneman

Stephanopoulos

Vasdekis

2020

Preprint

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We report the application of supervised machine learning to the automated classification of lipid-droplets in label-free, quantitative-phase images. By comparing various machine learning methods that are publicly available and commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, the implementation of all machine learning methods that we compared, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent replacement candidate of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism at the single-cell level.

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“…By QPI, studies associated with both cellular thickness and refractive index fluctuations have been performed. The possibilities for utilizing QPI for cancer diagnostics have increased . Cellular behavior and phenotypes are other parameters that can be measured .…”

mentioning

confidence: 99%

Moving into a new dimension: Tracking migrating cells with digital holographic cytometry in 3D

Wingren

2018

Cytometry Pt A

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Quantitative phase‐contrast imaging—A potential tool for future cancer diagnostics

Cited by 5 publications

References 18 publications

Circulating Tumor Cell Models Mimicking Metastasizing Cells In Vitro: Discrimination of Colorectal Cancer Cells and White Blood Cells Using Digital Holographic Cytometry

Circulating Tumor Cell Models Mimicking Metastasizing Cells In Vitro: Discrimination of Colorectal Cancer Cells and White Blood Cells Using Digital Holographic Cytometry

Deep learning classification of lipid droplets in quantitative phase images

Moving into a new dimension: Tracking migrating cells with digital holographic cytometry in 3D

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