Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

Белашов, А. В.; Жихорева, А.А.; Belyaeva, T. N.; Салова, А.В.; Корнилова, Е. С.; Семенова, И. В.; Vasyutinskiǐ, O. S.

doi:10.3390/cells10102587

Cited by 20 publications

(10 citation statements)

References 60 publications

(72 reference statements)

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“…Moreover, investigated RAW 264.7 macrophages ( Figure 2 a), although causing high contrast in DHM QPI images, grew with tight cell-cell contacts in clusters, which hindered a reliable threshold-based image evaluation for single-cell data. Here, sophisticated algorithms that, e.g., rely on convolutional neural networks (CNNs) [ 57 ] prospect improved biophysical parameter extraction, suitable for subsequent advanced analysis with machine learning approaches [ 58 , 59 ]. This can be used for the detection of subpopulations or phenotypes.…”

Section: Discussionmentioning

confidence: 99%

Label-Free Digital Holographic Microscopy for In Vitro Cytotoxic Effect Quantification of Organic Nanoparticles

Eder¹,

Marzi²,

Barroso³

et al. 2022

Cells

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Cytotoxicity quantification of nanoparticles is commonly performed by biochemical assays to evaluate their biocompatibility and safety. We explored quantitative phase imaging (QPI) with digital holographic microscopy (DHM) as a time-resolved in vitro assay to quantify effects caused by three different types of organic nanoparticles in development for medical use. Label-free proliferation quantification of native cell populations facilitates cytotoxicity testing in biomedical nanotechnology. Therefore, DHM quantitative phase images from measurements on nanomaterial and control agent incubated cells were acquired over 24 h, from which the temporal course of the cellular dry mass was calculated within the observed field of view. The impact of LipImage™ 815 lipidots® nanoparticles, as well as empty and cabazitaxel-loaded poly(alkyl cyanoacrylate) nanoparticles on the dry mass development of four different cell lines (RAW 264.7, NIH-3T3, NRK-52E, and RLE-6TN), was observed vs. digitonin as cytotoxicity control and cells in culture medium. The acquired QPI data were compared to a colorimetric cell viability assay (WST-8) to explore the use of the DHM assay with standard biochemical analysis methods downstream. Our results show that QPI with DHM is highly suitable to identify harmful or low-toxic nanomaterials. The presented DHM assay can be implemented with commercial microscopes. The capability for imaging of native cells and the compatibility with common 96-well plates allows high-throughput systems and future embedding into existing experimental routines for in vitro cytotoxicity assessment.

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Section: Discussionmentioning

confidence: 99%

Label-Free Digital Holographic Microscopy for In Vitro Cytotoxic Effect Quantification of Organic Nanoparticles

Eder¹,

Marzi²,

Barroso³

et al. 2022

Cells

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“…Digital holography (DH) in microscopy is a label-free computational imaging technique able to provide a posteriori multiple refocusing capability and quantitative phase-contrast imaging. [1][2][3] Thanks to these features, DH has been successfully employed in a variety of biomedicine applications, 4 including cancer cell identification and characterization, [5][6][7] diagnostics of blood diseases, [8][9][10][11] inflammations 12 and infectious diseases, [13][14][15] study of cell motility and migration, 16 and marker-free detection of lipid droplets (LDs). 17 The possibility to probe biological samples from different directions leads to the full 3D label-free imaging achieved by holographic tomography technology, 18,19 which represents the leading edge of biological inspection at the single-cell level.…”

Section: Introductionmentioning

confidence: 99%

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

et al. 2022

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“…As already mentioned in the discussion of the results in Figure 4 , cells that were activated pro- or anti-inflammatory presumably leaded an increased heterogeneity of biophysical cell parameters. Here, analysis of the data available by QPI with sophisticated evaluation concepts, e.g., based on machine learning algorithms that allow considering of multiple parameters [ 35 , 76 , 81 , 82 ], promises further insights into our data sets and into the identification of additional cell subfractions.…”

Section: Discussionmentioning

confidence: 99%

Digital Holographic Microscopy for Label-Free Detection of Leukocyte Alternations Associated with Perioperative Inflammation after Cardiac Surgery

Steike

Heßler

Korsching

et al. 2022

Cells

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In a prospective observational pilot study on patients undergoing elective cardiac surgery with cardiopulmonary bypass, we evaluated label-free quantitative phase imaging (QPI) with digital holographic microscopy (DHM) to describe perioperative inflammation by changes in biophysical cell properties of lymphocytes and monocytes. Blood samples from 25 patients were investigated prior to cardiac surgery and postoperatively at day 1, 3 and 6. Biophysical and morphological cell parameters accessible with DHM, such as cell volume, refractive index, dry mass, and cell shape related form factor, were acquired and compared to common flow cytometric blood cell markers of inflammation and selected routine laboratory parameters. In all examined patients, cardiac surgery induced an acute inflammatory response as indicated by changes in routine laboratory parameters and flow cytometric cell markers. DHM results were associated with routine laboratory and flow cytometric data and correlated with complications in the postoperative course. In a subgroup analysis, patients were classified according to the inflammation related C-reactive protein (CRP) level, treatment with epinephrine and the occurrence of postoperative complications. Patients with regular courses, without epinephrine treatment and with low CRP values showed a postoperative lymphocyte volume increase. In contrast, the group of patients with increased CRP levels indicated an even further enlarged lymphocyte volume, while for the groups of epinephrine treated patients and patients with complicative courses, no postoperative lymphocyte volume changes were detected. In summary, the study demonstrates the capability of DHM to describe biophysical cell parameters of perioperative lymphocytes and monocytes changes in cardiac surgery patients. The pattern of correlations between biophysical DHM data and laboratory parameters, flow cytometric cell markers, and the postoperative course exemplify DHM as a promising diagnostic tool for a characterization of inflammatory processes and course of disease.

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Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images

Cited by 20 publications

References 60 publications

Label-Free Digital Holographic Microscopy for In Vitro Cytotoxic Effect Quantification of Organic Nanoparticles

Label-Free Digital Holographic Microscopy for In Vitro Cytotoxic Effect Quantification of Organic Nanoparticles

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning

Digital Holographic Microscopy for Label-Free Detection of Leukocyte Alternations Associated with Perioperative Inflammation after Cardiac Surgery

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