Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening

Jagannadh, Veerendra Kalyan; Gopakumar, G.; Subrahmanyam, Gorthi R. K. Sai; Gorthi, Sai Siva

doi:10.1007/s11517-016-1549-y

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…Whole-slide digital pathology (50) and a high-throughput well-plate culture imaging techniques (51,52) require robust, automated methods to identify cells of clinical interest. The classification accuracy from this study of 90-100% is high, similar to other reported values using machine-learning algorithms trained on optical phase map data (8,49,53). A leaveone-out classification analysis (Supporting Information Table S2) suggested predictive power derived from most of the 17 parameters, which could be reduced to five to six principal components, in accordance with other studies (2,8,49).…”

Section: Discussionsupporting

confidence: 89%

“…The classification accuracy from this study of 90–100% is high, similar to other reported values using machine‐learning algorithms trained on optical phase map data . A leave‐one‐out classification analysis (Supporting Information Table S2) suggested predictive power derived from most of the 17 parameters, which could be reduced to five to six principal components, in accordance with other studies . Taken together, these results indicate that phase parameters from the pixel histogram and gray‐level co‐occurrence matrix, as well as cell outline‐based morphology features , help to classify adherent cell lines.…”

Section: Discussionsupporting

confidence: 88%

“…Indeed, the expression of VE‐cadherin, which forms cell–cell connections, reverts MDA‐MB‐231 cells from mesenchymal to epithelial morphology and reduces migration ability, illustrating a molecular link between morphology and functional motility . Cell lines were distinguished using QPI and machine learning even on isolated cells in suspension , when cell–substrate attachment information is not present, indicating significant value to phase parameters relating to cell volume, dry mass, and subcellular texture. In contrast, optical phase imaging and classification of substrate‐adherent cells provides additional information about cell–cell and cell–substrate interactions.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

et al. 2019

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Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non‐cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

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Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

et al. 2019

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“…This label-free technique takes advantage of the diversity of plankton morphologies to sort target species from a mixture of cells. This approach differed from advanced image recognition systems such as snake, background subtraction-based algorithms, and the recent combination of forward and side scatters in fluorescence-activated cell sorting sorters that mainly detect objects1435363738. The major advantage of this technology compared with other methods is the possibility to recognise any object in a droplet without fine-tuning parameters or an extensive data set to calibrate the system37.…”

Section: Discussionmentioning

confidence: 99%

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

Girault¹,

Kim²,

Arakawa³

et al. 2017

Sci Rep

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A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

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“…Based on optical barcoding, such approaches can even be multiplexed for different drugs . 3) Microfluidics also enables more complex sorting mechanisms, including entirely label‐free approaches based on: imaging, cell deformability, size, and/or invasive features. A nice example for sorting on complex properties was shown by Liu et al, who developed a microfluidic chip in which cells are sorted based on the capacity of cells to travel through small constrictions (in between tilted microposts), as required for the formation of metastases.…”

Section: Phenotypic Testing Of Patient Cancer Cellsmentioning

confidence: 99%

Microfluidics as an Enabling Technology for Personalized Cancer Therapy

Mathur

Ballinger

Utharala

et al. 2019

Small

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Tailoring patient‐specific treatments for cancer is necessary in order to achieve optimal results but requires new diagnostic approaches at affordable prices. Microfluidics has immense potential to provide solutions for this, as it enables the processing of samples that are not available in large quantities (e.g., cells from patient biopsies), is cost efficient, provides a high level of automation, and allows the set‐up of complex models for cancer studies. In this review, individual solutions in the fields of genetics, circulating tumor cell monitoring, biomarker analysis, phenotypic drug sensitivity tests, and systems providing controlled environments for disease modeling are discussed. An overview on how these early stage achievements can be combined or developed further is showcased, and the required translational steps before microfluidics becomes a routine tool for clinical applications are critically discussed.

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Microfluidic microscopy-assisted label-free approach for cancer screening: automated microfluidic cytology for cancer screening

Cited by 12 publications

References 27 publications

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

Microfluidics as an Enabling Technology for Personalized Cancer Therapy

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