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

Abstract

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.

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

“…Indeed, advanced machine learning techniques, including deep learning, 29 have recently been applied to isolate cell subpopulations based on unique phase features 6 and other phenotypic differences, 66,37 including metastatic versus primary cancer 67 and different types of nonactivated lymphocytes. 68 The phase/morphology score concept described here could be applied to support decision-making in intelligent cell sorting systems, such as flow cytometry with QPI, 69,34 to partition cells from a heterogeneous population into distinct morphological groups. 70 The proposed technique to generate EM scores offers greater robustness, adaptability, and flexibility than qualitative or single-parameter morphological characterization, but requires some interpretation.…”

Cell and nucleus refractive‐index mapping by interferometric phase microscopy and rapid confocal fluorescence microscopy

“…Other methods include evaluating the sample thickness at each spatial location in addition to the IPM measurement, which allows the calculation of the integral RI at each location. Thickness evaluation can be achieved by assuming spherical [4,[16][17][18][19][20][21][22] or ellipsoidal [23] shape for cells in suspension. An alternative approach is physically measuring the cell thickness using atomic force microscopy [24,25], confocal fluorescence microscopy [26], confocal reflectance microscopy [1], or by constraining cells into a microstructure with known dimensions [19].…”

Microscopic phase reconstruction of cervical exfoliated cell under partially coherent illumination

“…Recently, noninvasive optical imaging methods have gained increasingly more attention [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Since in these methods the sample does not require preprocessing such as labeling or staining, the measurement outcomes give a more authentic impression of the sample.…”

“…Plenty of quantitative phase reconstruction methods have been developed during the past few decades. Coherent diffractive imaging (CDI) [10][11][12] is one of the most popular methods, which can reconstruct the whole transmission function of an object from one or series of diffracted far-field intensities. The phase information is closely related to the density, thickness, and refractive index, and is useful for distinguishing different components and states of cells.…”