Flow cytometric analysis, confocal laser scanning microscopic, and holotomographic imaging demonstrate potentials of levofloxacin dry powder aerosols for TB treatment

Srichana, Teerapol; Thawithong, Ekawat; Nakpheng, Titpawan; Paul, Pijush

doi:10.1016/j.jddst.2023.104464

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…HTM lacks the molecular specificity of fluorescence microscopy but can distinguish different organelles or exogenous agents (NMs) because of their differences in RI values. A recent study investigates the efficiency of levofloxacin dry powder aerosols for tuberculosis treatment [ 30 ]. The authors used confocal laser scanning microscopy (CLSM) and HTM to look into the cellular uptake of the levofloxacin formulations.…”

Section: Holotomographic Microscopy (Htm) Principlementioning

confidence: 99%

Holotomography and atomic force microscopy: a powerful combination to enhance cancer, microbiology and nanotoxicology research

Medina-Ramirez,

Macias-Diaz,

Masuoka-Ito

et al. 2024

Discover Nano

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Modern imaging strategies are paramount to studying living systems such as cells, bacteria, and fungi and their response to pathogens, toxicants, and nanomaterials (NMs) as modulated by exposure and environmental factors. The need to understand the processes and mechanisms of damage, healing, and cell survivability of living systems continues to motivate the development of alternative imaging strategies. Of particular interest is the use of label-free techniques (microscopy procedures that do not require sample staining) that minimize interference of biological processes by foreign marking substances and reduce intense light exposure and potential photo-toxicity effects. This review focuses on the synergic capabilities of atomic force microscopy (AFM) as a well-developed and robust imaging strategy with demonstrated applications to unravel intimate details in biomedical applications, with the label-free, fast, and enduring Holotomographic Microscopy (HTM) strategy. HTM is a technique that combines holography and tomography using a low intensity continuous illumination laser to investigate (quantitatively and non-invasively) cells, microorganisms, and thin tissue by generating three-dimensional (3D) images and monitoring in real-time inner morphological changes. We first review the operating principles that form the basis for the complementary details provided by these techniques regarding the surface and internal information provided by HTM and AFM, which are essential and complimentary for the development of several biomedical areas studying the interaction mechanisms of NMs with living organisms. First, AFM can provide superb resolution on surface morphology and biomechanical characterization. Second, the quantitative phase capabilities of HTM enable superb modeling and quantification of the volume, surface area, protein content, and mass density of the main components of cells and microorganisms, including the morphology of cells in microbiological systems. These capabilities result from directly quantifying refractive index changes without requiring fluorescent markers or chemicals. As such, HTM is ideal for long-term monitoring of living organisms in conditions close to their natural settings. We present a case-based review of the principal uses of both techniques and their essential contributions to nanomedicine and nanotoxicology (study of the harmful effects of NMs in living organisms), emphasizing cancer and infectious disease control. The synergic impact of the sequential use of these complementary strategies provides a clear drive for adopting these techniques as interdependent fundamental tools. Graphical abstract

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Section: Holotomographic Microscopy (Htm) Principlementioning

confidence: 99%

Holotomography and atomic force microscopy: a powerful combination to enhance cancer, microbiology and nanotoxicology research

Medina-Ramirez,

Macias-Diaz,

Masuoka-Ito

et al. 2024

Discover Nano

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“…Over the past two decades, QPI has provided important insights into diverse biological phenomena, ranging from membrane dynamics of red blood cells 6 to neuronal activity 7 and cell-nanoparticle interactions, 8 , 9 and cell-drug interactions. 10 Recently, it has also been shown that QPI images can be mapped to fluorescence images using deep learning techniques, a concept coined as image-to-image translation. Prediction of stains (i.e., where specific fluorophores/stains would bind in an unlabeled specimen) using a combination of QPI and machine learning has been successfully demonstrated, 11 – 13 and gradually, more stains are being added to the library.…”

Section: Introductionmentioning

confidence: 99%

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

Raj,

Paidi,

Conway

et al. 2024

J. Biomed. Opt.

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. Significance Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. Aim We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. Approach The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the plane. Leveraging cell continuity in consecutive -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. Results The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Conclusion Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

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“…Over the past two decades, QPI has provided important insights into diverse biological phenomena, ranging from membrane dynamics of red blood cells [6] to neuronal activity [7] and cell-nanoparticle [8] [9], and cell-drug interactions [10]. Recently, it has also been shown that QPI images can be mapped to fluorescence images using deep learning techniques, a concept coined as image-to-image translation.…”

Section: Introductionmentioning

confidence: 99%

CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

Raj

Paidi

Conway

et al. 2023

Preprint

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Quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. In particular, three-dimensional (3D) tomographic imaging of live cells has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw 3D tomograms is not well-developed. This work focuses on a critical, yet often underappreciated, step of the analysis pipeline, that of 3D cell segmentation from the acquired tomograms. The current method employed for such tasks is the Otsu-based 3D watershed algorithm, which works well for isolated cells; however, it is very challenging to draw boundaries when the cells are clumped. This process is also memory intensive since the processing requires computation on a 3D stack of images. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the segmentation of QPI images, which outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 seconds per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused segmentation tools. We envision our work will lead to the broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

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Flow cytometric analysis, confocal laser scanning microscopic, and holotomographic imaging demonstrate potentials of levofloxacin dry powder aerosols for TB treatment

Cited by 4 publications

References 30 publications

Holotomography and atomic force microscopy: a powerful combination to enhance cancer, microbiology and nanotoxicology research

Holotomography and atomic force microscopy: a powerful combination to enhance cancer, microbiology and nanotoxicology research

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

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