Cells

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15 pages, 3344 KiB

Open AccessArticle

Circadian Volume Changes in Hippocampal Glia Studied by Label-Free Interferometric Imaging

by Ghazal Naseri Kouzehgarani, Mikhail E. Kandel, Masayoshi Sakakura, Joshua S. Dupaty, Gabriel Popescu and Martha U. Gillette

Cells 2022, 11(13), 2073; https://doi.org/10.3390/cells11132073 - 30 Jun 2022

Cited by 2 | Viewed by 1902

Abstract

Complex brain functions, including learning and memory, arise in part from the modulatory role of astrocytes on neuronal circuits. Functionally, the dentate gyrus (DG) exhibits differences in the acquisition of long-term potentiation (LTP) between day and night. We hypothesize that the dynamic nature [...] Read more.

Complex brain functions, including learning and memory, arise in part from the modulatory role of astrocytes on neuronal circuits. Functionally, the dentate gyrus (DG) exhibits differences in the acquisition of long-term potentiation (LTP) between day and night. We hypothesize that the dynamic nature of astrocyte morphology plays an important role in the functional circuitry of hippocampal learning and memory, specifically in the DG. Standard microscopy techniques, such as differential interference contrast (DIC), present insufficient contrast for detecting changes in astrocyte structure and function and are unable to inform on the intrinsic structure of the sample in a quantitative manner. Recently, gradient light interference microscopy (GLIM) has been developed to upgrade a DIC microscope with quantitative capabilities such as single-cell dry mass and volume characterization. Here, we present a methodology for combining GLIM and electrophysiology to quantify the astrocyte morphological behavior over the day-night cycle. Colocalized measurements of GLIM and fluorescence allowed us to quantify the dry masses and volumes of hundreds of astrocytes. Our results indicate that, on average, there is a 25% cell volume reduction during the nocturnal cycle. Remarkably, this cell volume change takes place at constant dry mass, which suggests that the volume regulation occurs primarily through aqueous medium exchange with the environment. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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22 pages, 2965 KiB

Open AccessArticle

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

by David Rene Steike, Michael Hessler, Eberhard Korsching, Florian Lehmann, Christina Schmidt, Christian Ertmer, Jürgen Schnekenburger, Hans Theodor Eich, Björn Kemper and Burkhard Greve

Cells 2022, 11(4), 755; https://doi.org/10.3390/cells11040755 - 21 Feb 2022

Cited by 11 | Viewed by 3122

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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13 pages, 2993 KiB

Open AccessFeature PaperArticle

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data

by Jingfang K. Zhang, Michael Fanous, Nahil Sobh, Andre Kajdacsy-Balla and Gabriel Popescu

Cells 2022, 11(4), 716; https://doi.org/10.3390/cells11040716 - 17 Feb 2022

Cited by 3 | Viewed by 2535

Abstract

The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empirical assessment, a certain amount of subjectivity [...] Read more.

The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empirical assessment, a certain amount of subjectivity is unavoidable. Furthermore, the reliance on external contrast agents such as hematoxylin and eosin (H&E), albeit being well-established methods, makes it difficult to standardize color balance, staining strength, and imaging conditions, hindering automated computational analysis. In response to these challenges, we applied spatial light interference microscopy (SLIM), a label-free method that generates contrast based on intrinsic tissue refractive index signatures. Thus, we reduce human bias and make imaging data comparable across instruments and clinics. We applied a mask R-CNN deep learning algorithm to the SLIM data to achieve an automated colorectal cancer screening procedure, i.e., classifying normal vs. cancerous specimens. Our results, obtained on a tissue microarray consisting of specimens from 132 patients, resulted in 91% accuracy for gland detection, 99.71% accuracy in gland-level classification, and 97% accuracy in core-level classification. A SLIM tissue scanner accompanied by an application-specific deep learning algorithm may become a valuable clinical tool, enabling faster and more accurate assessments by pathologists. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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21 pages, 4286 KiB

Open AccessArticle

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

by Kai Moritz Eder, Anne Marzi, Álvaro Barroso, Steffi Ketelhut, Björn Kemper and Jürgen Schnekenburger

Cells 2022, 11(4), 644; https://doi.org/10.3390/cells11040644 - 12 Feb 2022

Cited by 22 | Viewed by 3094

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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12 pages, 2423 KiB

Open AccessArticle

Stability of Intracellular Protein Concentration under Extreme Osmotic Challenge

by Jordan E. Hollembeak and Michael A. Model

Cells 2021, 10(12), 3532; https://doi.org/10.3390/cells10123532 - 14 Dec 2021

Viewed by 2061

Abstract

Cell volume (CV) regulation is typically studied in short-term experiments to avoid complications resulting from cell growth and division. By combining quantitative phase imaging (by transport-of-intensity equation) with CV measurements (by the exclusion of an external absorbing dye), we were able to monitor [...] Read more.

Cell volume (CV) regulation is typically studied in short-term experiments to avoid complications resulting from cell growth and division. By combining quantitative phase imaging (by transport-of-intensity equation) with CV measurements (by the exclusion of an external absorbing dye), we were able to monitor the intracellular protein concentration (PC) in HeLa and 3T3 cells for up to 48 h. Long-term PC remained stable in solutions with osmolarities ranging from one-third to almost twice the normal. When cells were subjected to extreme hypoosmolarity (one-quarter of normal), their PC did not decrease as one might expect, but increased; a similar dehydration response was observed at high concentrations of ionophore gramicidin. Highly dilute media, or even moderately dilute in the presence of cytochalasin, caused segregation of water into large protein-free vacuoles, while the surrounding cytoplasm remained at normal density. These results suggest that: (1) dehydration is a standard cellular response to severe stress; (2) the cytoplasm resists prolonged dilution. In an attempt to investigate the mechanism behind the homeostasis of PC, we tested the inhibitors of the protein kinase complex mTOR and the volume-regulated anion channels (VRAC). The initial results did not fully elucidate whether these elements are directly involved in PC maintenance. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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9 pages, 2533 KiB

Open AccessArticle

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations

by Shani Ben Baruch, Noa Rotman-Nativ, Alon Baram, Hayit Greenspan and Natan T. Shaked

Cells 2021, 10(12), 3353; https://doi.org/10.3390/cells10123353 - 29 Nov 2021

Cited by 6 | Viewed by 2208

Abstract

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach [...] Read more.

We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processed with a deep-learning framework. We demonstrate this approach by classifying between two types of cancer cell lines of different metastatic potential originating from the same patient. It is based on the fact that both the cancer-cell morphology and its mechanical properties, as indicated by the cell temporal and spatial fluctuations, change over the disease progression. We tested different fusion methods for inputting both the morphological optical thickness maps and the coinciding spatio-temporal fluctuation maps of the cells to the classifying network framework. We show that the proposed integrated triple-path deep-learning architecture improves over deep-learning classification that is based only on the cell morphological evaluation via its quantitative optical thickness map, demonstrating the benefit in the acquisition of the cells over time and in extracting their spatio-temporal fluctuation maps, to be used as an input to the classifying deep neural network. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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13 pages, 4788 KiB

Open AccessArticle

Sperm Inspection for In Vitro Fertilization via Self-Assembled Microdroplet Formation and Quantitative Phase Microscopy

by Yuval Atzitz, Matan Dudaie, Itay Barnea and Natan T. Shaked

Cells 2021, 10(12), 3317; https://doi.org/10.3390/cells10123317 - 26 Nov 2021

Cited by 2 | Viewed by 2012

Abstract

We present a new method for the selection of individual sperm cells using a microfluidic device that automatically traps each cell in a separate microdroplet that then individually self-assembles with other microdroplets, permitting the controlled measurement of the cells using quantitative phase microscopy. [...] Read more.

We present a new method for the selection of individual sperm cells using a microfluidic device that automatically traps each cell in a separate microdroplet that then individually self-assembles with other microdroplets, permitting the controlled measurement of the cells using quantitative phase microscopy. Following cell trapping and droplet formation, we utilize quantitative phase microscopy integrated with bright-field imaging for individual sperm morphology and motility inspection. We then perform individual sperm selection using a single-cell micromanipulator, which is enhanced by the microdroplet-trapping procedure described above. This method can improve sperm selection for intracytoplasmic sperm injection, a common type of in vitro fertilization procedure. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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14 pages, 17482 KiB

Open AccessArticle

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

by Andrey V. Belashov, Anna A. Zhikhoreva, Tatiana N. Belyaeva, Anna V. Salova, Elena S. Kornilova, Irina V. Semenova and Oleg S. Vasyutinskii

Cells 2021, 10(10), 2587; https://doi.org/10.3390/cells10102587 - 29 Sep 2021

Cited by 18 | Viewed by 3044

Abstract

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier [...] Read more.

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses. Full article

(This article belongs to the Special Issue Current Trends in Quantitative Phase Imaging of Cells and Tissues)

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