Single-frame label-free cell tomography at speed of more than 10,000 volumes per second

Ge, Baoliang; He, Ying; Deng, Minghua; Rahman, Md. Habibur; Wang, Yijin; Wu, Ziling; Wong, Chung Hong N.; Chan, Michael K.; Ho, Yi‐Ping; Duan, Liting; Yaqoob, Zahid; So, Peter T. C.; Barbastathis, George; Zhou, Renjie

doi:10.48550/arxiv.2202.03627

Cited by 12 publications

(15 citation statements)

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“…Moreover, the trend of changes in phase intensity was observed in line with the stiffness of cells characterized by AFM (Figure 7D); that is, softer cells exhibited relatively low phase values, corroborated by previous studies, 44 and plausibly rationalized by the phase being proportional to the sample thickness and inversely proportional to the refractive index. Furthermore, the refractive index may reflect how cells deformed along the axial direction, 45,46 and the biophysical parameters of cells, such as dry mass, wet mass, and protein concentration. Previous studies have observed a correlation between the cell refractive index and the protein concentration, 47,48 as well as the DNA content, for differentiating the G2/M phases from the G1/S phases.…”

Section: Phase Information Extracted By Qpdcmentioning

confidence: 99%

Quantitative phase deformability cytometry for noninvasive high‐throughput characterization of cells

Xiao

Rahman

et al. 2023

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Currently available deformability cytometry, combining microfluidics with the bright‐field high‐speed imaging system, has allowed label‐free mechanical characterizations of cells at a rate of hundreds of cells per second. However, the poor contrast between the signal and background under bright‐field imaging, especially for abnormal cells or cells treated with drugs, may produce artifacts during the recognition of cellular contours and subsequently bias the analysis of cellular deformability. In this study, a quantitative phase deformability cytometry (QPDC), constructed from a quantitative phase imaging system, is demonstrated with an improved signal‐to‐noise ratio of the acquired images and mitigated image artifacts, allowing a truthful illustration of cellular contours, especially for cells subjected to actin depolymerization. We have also demonstrated that the phase value extracted from captured images may provide an additional biophysical characteristic of cells. The presented QPDC platform is therefore expected to complement the existing deformability cytometry and expand the capacity of label‐free characterization of cells for biological and clinical applications.

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Section: Phase Information Extracted By Qpdcmentioning

confidence: 99%

Quantitative phase deformability cytometry for noninvasive high‐throughput characterization of cells

Xiao

Rahman

et al. 2023

VIEW

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“…In this framework, tomographic phase microscopy (TPM) probes the sample from different directions to obtain the cell's 3D refractive index (RI) tomogram from the sequence of quantitative phase maps (QPMs) 5,[7][8][9][10] . Tomographic phase microscopy in flow cytometry (TPM-FC) is one of the latest achievements in the QPI field 4,[11][12][13][14][15][16][17] . Instead of scanning the system or steering the probing beam, the cell is put in controlled rotation in a microfluidic circuit, so that a conventional holographic interferometer in transmission microscopy can access the full sample rotation and collect its different views in the form of QPMs.…”

Section: Introductionmentioning

confidence: 99%

Toward the specificity in QPI 3D tomographic cell flow cytometry holography: recent achievements and perspectives in biomedical sciences

Pirone

Lim

Merola

et al. 2023

Quantitative Phase Imaging IX

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The actual gap of the label-free quantitative phase microscopy in respect to fluorescence microscopy, that allows the subcellular characterization by using exogenous markers, is the lack of intracellular specificity. Recently, computational methods based on artificial intelligence have been demonstrated, which allow a virtual staining of single cells in both 2D and 3D, but they require co-registration systems able to collect simultaneously both fluorescence and quantitative phase information. However, a real limitation exists, i.e. these approaches cannot be used in flow cytometry condition. In this paper, we discuss a new methodology for adding the intracellular specificity analysis to tomographic phase microscopy in flow cytometry. The proposed strategy is based on the statistical clustering of tomograms voxels, thus allowing the segmentation of cell's organelles. Here we report the results of nuclear region identification for cancer cells.

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“…The conventional TPI systems probe the sample from different directions, experimentally fixed a priori, and the tomographic reconstruction process is performed by wellestablished techniques. In the last decade, the demonstration of such technology in flow cytometry (FC) condition, namely TPI-FC, enables the 3D RI mapping of flowing and rotating cells in a microfluidic channel [10][11][12] and allows highthroughput analysis comparable to the current imaging flow cytometry system 13 . However, in the TPI-FC solution reported in refs.…”

Section: Introductionmentioning

confidence: 99%

Computational methodologies for reliable tomographic phase imaging flow cytometry

Memmolo,

Pirone,

Valentino

et al. 2023

Modeling Aspects in Optical Metrology IX

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In recent years, tomographic phase microscopy has gained credits as one of the most powerful imaging modality for label-free single-cell analysis in 3D. The conventional tomographic imaging systems probe the sample from different directions, experimentally fixed a priori, and the tomographic reconstruction is performed by well-known techniques. The recent demonstration of such technology in flow cytometry condition, in which cells rotate in a microfluidic channel, permits to achieve high-throughput analysis, but it is needed of a reliable and robust computational processing pipeline. In fact, no a priori information about the rotation angles of cells are available, hence suitable algorithms are designed to recover them. Moreover, the number of such rotating angles is low if compared to the number of orientation directions explored in conventional systems, thus requiring more sophisticated algorithm to reconstruct cells tomograms. Finally, due to the high-throughput modality, the huge amount of data to manage becomes one of the main computational problems to face with. Here we show an efficient computational processing pipeline to achieve reliable tomographic reconstructions based on (i) a fast quantitative phase maps recovery method based on deep learning end-to-end reconstruction, (ii) the modelling of the cells' 3D pose in microfluidic flow through holographic tracking, (iii) the use of a suitable tomographic reconstruction algorithm, (iv) a new strategy to encode single-cell phase contrast tomograms by using the 3D version of Zernike polynomials, thus allowing an efficient data storage.

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Single-frame label-free cell tomography at speed of more than 10,000 volumes per second

Cited by 12 publications

References 0 publications

Quantitative phase deformability cytometry for noninvasive high‐throughput characterization of cells

Quantitative phase deformability cytometry for noninvasive high‐throughput characterization of cells

Toward the specificity in QPI 3D tomographic cell flow cytometry holography: recent achievements and perspectives in biomedical sciences

Computational methodologies for reliable tomographic phase imaging flow cytometry

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