Quantitative phase imaging for cell culture quality control

Kastl, Lena; Isbach, Michael; Dirksen, Dieter; Schnekenburger, Jürgen; Kemper, Björn

doi:10.1002/cyto.a.23082

Cited by 82 publications

(63 citation statements)

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“…Estimating a spherical appearance of the specimens, which is typical for many detached cell types in suspension, the binarized images were used to measure the projected cell surface area S i = π R i 2 from which the effective cell radius R i was calculated. Utilizing the parameters R i and

normalΔ {\overset{true¯}{φ}}_{i}

, the cellular dry mass DM i of each single segmented cell can be determined :

{DM}_{i} = \frac{10 λ}{2 italicπβ} Δ {\overset{false¯}{φ}}_{i} S_{i}

The parameter β represents the refractive index increment of the intracellular content. For the determination of the dry mass of the investigated pancreatic tumor cell lines PaTu8988T and PaTu 8988S, the parameter β is estimated to be 0.002 m 3 /kg .…”

Section: Numerical Reconstruction and Evaluation Of Digital Off‐axis mentioning

confidence: 99%

Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer‐based off‐axis digital holographic microscopy

Min

Trendafilova²,

Ketelhut³

et al. 2019

Journal of Biophotonics

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We combined Michelson‐interferometer‐based off‐axis digital holographic microscopy (DHM) with a common flow cytometry (FCM) arrangement. Utilizing object recognition procedures and holographic autofocusing during the numerical reconstruction of the acquired off‐axis holograms, sharply focused quantitative phase images of suspended cells in flow were retrieved without labeling, from which biophysical cellular features of distinct cells, such as cell radius, refractive index and dry mass, can be subsequently retrieved in an automated manner. The performance of the proposed concept was first characterized by investigations on microspheres that were utilized as test standards. Then, we analyzed two types of pancreatic tumor cells with different morphology to further verify the applicability of the proposed method for quantitative live cell imaging. The retrieved biophysical datasets from cells in flow are found in good agreement with results from comparative investigations with previously developed DHM methods under static conditions, which demonstrates the effectiveness and reliability of our approach. Our results contribute to the establishment of DHM in imaging FCM and prospect to broaden the application spectrum of FCM by providing complementary quantitative imaging as well as additional biophysical cell parameters which are not accessible in current high‐throughput FCM measurements.

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normalΔ {\overset{true¯}{φ}}_{i}

, the cellular dry mass DM i of each single segmented cell can be determined :

{DM}_{i} = \frac{10 λ}{2 italicπβ} Δ {\overset{false¯}{φ}}_{i} S_{i}

Section: Numerical Reconstruction and Evaluation Of Digital Off‐axis mentioning

confidence: 99%

Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer‐based off‐axis digital holographic microscopy

Min

Trendafilova²,

Ketelhut³

et al. 2019

Journal of Biophotonics

Self Cite

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“…Recently, several approaches for quantitative phase imaging (QPI) have been developed for live-imaging of cancer cells in culture (8)(9)(10)(11)(12)(13)(14)(15)(16)(17). As a label-free imaging technology that is non-cytotoxic even with prolonged exposure, QPI is a noninvasive method for single cell analysis of adherent cell behavior.…”

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confidence: 99%

Evaluation of holographic imaging cytometer holomonitor M4® motility applications

Zhang

Judson

2018

Cytometry Pt A

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Digital holographic cytometry (DHC) and other methods of quantitative phase imaging permit extended time-lapse imaging of mammalian cells in the absence of induced cellular toxicity. Manufactured DHC platforms equipped with semi-automated image acquisition, segmentation, and analysis software packages (or modules) for assessing cell behavior are now commercially available. When housed in mammalian cell incubators these cytometers offer the potential to monitor and quantify a range of cellular behaviors without disrupting routine culture. Realization of this potential requires validation against established standards. Two proprietary software modules for assessing cellular motility available using the HoloMonitor M4 DHC platform were evaluated on human melanoma cells lines with known relative motility and metastatic potential. One such software package, the Track Cells module, was run during routine culture. In addition, the Wound Healing module was conducted in parallel with established transwell migration and invasion assays. Each module was evaluated for reproducibility and correlation to established assays. Both software modules reliably recorded increased cellular motility in the metastatic 1205Lu line as compared with the non-metastatic WM793 line. In a direct comparison of the two propriety DHC software modules and two established transwell assays, the relative cell motilities were well correlated. The granularity of data provided by the Track Cells module permitted the additional identification of rare hyper-motile cells in the metastatic population and the distinction of motility from division associated displacement. The two HoloMonitor M4 DHC proprietary software modules for assessing cellular motility yielded reproducible results that were well-correlated with established standards.

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“…Similar to the study by Roitshtain et al (2), a previous report distinguished between two phenotypes of tumor cells with similar genetic background, using cell refractive index, radius, volume, and dry mass to distinguish pancreatic tumor PaTu 8988 T cells, with high migration capacity, from PaTu 8988 S cells with lower migration capacity (8). Similar to the study by Roitshtain et al (2), a previous report distinguished between two phenotypes of tumor cells with similar genetic background, using cell refractive index, radius, volume, and dry mass to distinguish pancreatic tumor PaTu 8988 T cells, with high migration capacity, from PaTu 8988 S cells with lower migration capacity (8).…”

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confidence: 58%

Holography, machine learning, and cancer cells

Raub

Nehmetallah

2017

Cytometry Pt A

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COMMENTARY LIQUID biopsy to detect circulating solid tumor cells, cellfree DNA or tumor exosomes in peripheral blood holds great promise as a minimally invasive method to detect cancer at an early stage, plan treatments, and monitor disease response to therapy (1). Two great challenges to detecting cancer cells in whole blood are the low relative abundance of tumor cells in proportion to erythrocytes and leukocytes, and the need to introduce highly-specific exogenous labels to identify tumor biomarkers. A recently published article in Cytometry Part A (2) detects cancer cells using a rapid, label-free technique based on machine learning from quantitative phase/digital holographic microscopy datasets. The results demonstrate automated discrimination of normal and cancer cell types flowing in a microfluidic channel, pointing to the potential for high-throughput processing of circulating tumor cells in an imaging flow cytometer. The method is compatible with microfluidic approaches to sequester or enrich circulating tumor cells from normal blood cells and whole blood, respectively.Without staining, biological cells are transparent under bright field microscopy resulting in low contrast intensity images. In that article, Shaked and coauthors (2) utilize an internal contrast mechanism based on the index of refraction. Contrary to traditional qualitative phase contrast (PC) techniques, such as Zernike's PC and differential interference contrast microscopy, this off-axis interferometric label-free approach can distinguish normal cells from cancer cell lines of different disease stages using quantitative textural and morphological parameters derived from the segmented optical thickness or optical path delay (OPD) of the cell on all spatial points. This OPD is the integral of the index of refraction at each pixel of a projection across the cells' thickness. To use OPD parameters for sensitive cell screening, the phase signal must possess high fidelity and consistency across images and during the imaging session.The optical setup used in this study is a low-coherence off-axis interferometer with a Mach-Zehnder configuration. The source used is a supercontinuum laser coupled to an acousto-optical tunable filter with a 7 nm bandwidth. The low-coherence source is used to reduce speckle noise. Retroreflectors are used to adjust the sample and reference beam paths to enhance fringe visibility. Stationary aberrations and field curvature aberrations are compensated for using empty sample measurements. Phase unwrapping was performed using a standard unweighted least square algorithm to obtain the OPD maps of the cells. A normalized cut-edge detector algorithm, which is based on graph formulation, was used to separate cells from their background. This algorithm was followed by morphological image operations (opening, dilation, and thresholding) to connect the gradient lines and to remove background pixels erroneously detected. The cells from cultured cell lines, flowed through a microfluidic channel and imaged at high frame rates, ...

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Quantitative phase imaging for cell culture quality control

Cited by 82 publications

References 50 publications

Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer‐based off‐axis digital holographic microscopy

Quantitative phase imaging of cells in a flow cytometry arrangement utilizing Michelson interferometer‐based off‐axis digital holographic microscopy

Evaluation of holographic imaging cytometer holomonitor M4® motility applications

Holography, machine learning, and cancer cells

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