Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

Abstract

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.

Holography Cytometry: Imaging of Cells in Flow

“…The measured phase deformation, change in localized phase from before applying shear stress to when the cell reaches a steady state under shear, informs how cell height changes. ∆ϕ x, y, t ðÞ ¼ ϕ x, y, t ðÞ À ϕ x, y,0 ðÞ ∝∆hx , y, t ðÞ [17].…”

“…Figure 9.Performance of CNN classification trained with three cell lines. (A) Stack box plot of classification results; and (B) Confusion matrix for CNN predictions (%)[17].…”

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

“…18,19 Therefore, the capability of a Holographic-IFC (HIFC) to provide a quantitative biophysical cell fingerprint suggests the enormous potential in combining this technology with artificial intelligence (AI) for label-free single-cell phenotyping. While in the 3D PCT case, the use of machine learning 20,21 and deep learning 22 is starting to be tested, in the 2D QPI case the learning approaches have been widely investigated, [23][24][25][26][27] also in a flow cytometry environment. [28][29][30][31][32][33][34][35] The standard workflow consists of creating a training dataset, usually obtained by acquiring digital holograms of single cells in the HIFC system under investigation.…”

Accurate holographic cytometry using three-dimensional hydrodynamic focusing

“…Moreover, if an individual object is far from the nominal plane that is in focus while recording the digital hologram, the corresponding object field may be highly blurred, and there is a possibility of missing such objects completely. To reduce the number of steps in numerical refocusing algorithms, Chen et al [21] and Park et al [22] performed DHM-based IFC in a shallow microchannel. Although the use of a shallow microchannel, which requires an expensive microfabrication process, reduced the numerical refocusing steps, the experiments of Park et al [22] showed that it did not prevent the cells from clustering.…”