Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells

Lin, Meiai; Xu, Qiao; Li, Qiao; Shao, Changshun; Su, Xuantao

doi:10.1364/boe.7.005170

Cited by 28 publications

(14 citation statements)

References 38 publications

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“…More advanced optical techniques have also been reported to be capable of identifying senescent cells. These include light-sheet [125,126] and digital holographic cytometry [127], methods which probe the biophysical aspects of cells to determine senescence. In particular, light-sheet cytometry uses the different scattering patterns of cells to determine their respective sizes and in turn senescence levels.…”

Section: Imaging and Other Label-free Methodsmentioning

confidence: 99%

Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions

et al. 2019

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Section: Imaging and Other Label-free Methodsmentioning

confidence: 99%

Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions

et al. 2019

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“…The deconvolution layer up‐samples the feature map obtained by the last convolution layer, restores it to the same size as the input image, and performs pixel‐by‐pixel classification on the up‐sampling map to complete the image segmentation. Figure shows a schematic diagram for the segmentation of single‐cell 2D light scattering images using FCN. Deep learning has achieved good results in many cell optical image segmentation problems, such as the segmentation of overlapped cells and the segmentation of cellular organelles.…”

Section: Applications Of Deep Learning In Single‐cell Optical Image Smentioning

confidence: 99%

“…The deconvolution layer upsamples the feature map obtained by the last convolution layer, restores it to the same size as the input image, and performs pixel-by-pixel classification on the up-sampling map to complete the image segmentation. Figure 7 shows a schematic diagram for the segmentation of single-cell 2D light scattering images (72,73)…”

Section: Image Segmentationmentioning

confidence: 99%

Deep Learning‐Based Single‐Cell Optical Image Studies

Sun

Tárnok

2020

Cytometry Pt A

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Optical imaging technology that has the advantages of high sensitivity and cost‐effectiveness greatly promotes the progress of nondestructive single‐cell studies. Complex cellular image analysis tasks such as three‐dimensional reconstruction call for machine‐learning technology in cell optical image research. With the rapid developments of high‐throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large‐scale image processing and analysis, which brings a new dawn for single‐cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single‐cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single‐cell optical image studies. We explore the feasibility of applying deep learning techniques to single‐cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end‐to‐end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single‐cell optical image studies are reviewed, which include image segmentation, super‐resolution image reconstruction, cell tracking, cell counting, cross‐modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single‐cell optical imaging techniques such as label‐free cell optical imaging, high‐content screening, and high‐throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single‐cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry

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“…For the automatic classification of NHFs and SHFs, a machine learning method (SVM) was adopted for the analysis of the 2D light scattering patterns [36]. Unlike the previously reported analysis for feature extraction from 2D patterns [37], a gray level co-occurrence matrices (GLCM) method that is based on the spatial distribution of pixels was employed in this report. The various GLCM parameters can be calculated as texture descriptors [38].…”

Section: Automatic Identification Of Senescent Cells Using a Label-frmentioning

confidence: 99%

Label-free light-sheet microfluidic cytometry for the automatic identification of senescent cells

Lin

Liu

et al. 2018

Biomed. Opt. Express

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Label-free microfluidic cytometry is of increasing interest for single cell analysis due to its advantages of high-throughput, miniaturization, as well as noninvasive detection. Here we develop a next generation label-free light-sheet microfluidic cytometer for single cell analysis by two-dimensional (2D) light scattering measurements. Our cytometer integrates light sheet illumination with a disposable hydrodynamic focusing unit, which can achieve 3D hydrodynamic focusing of a sample fluid to a diameter of 19 micrometer without microfabrication. This integration also improves the signal to noise ratio (SNR) for the acquisition of 2D light scattering patterns from label-free cells. Particle sizing with submicron resolution is achieved by our light-sheet flow cytometer, where Euclidean distance-based similarity measures are performed. Label-free, automatic classification of senescent and normal cells is achieved with a high accuracy rate by incorporating our light-sheet flow cytometry with support vector machine (SVM) algorithms. Our light-sheet microfluidic cytometry with a microfabrication-free hydrodynamic focusing unit may find wide applications for automatic and label-free clinical diagnosis.

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Light-sheet-based 2D light scattering cytometry for label-free characterization of senescent cells

Cited by 28 publications

References 38 publications

Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions

Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions

Deep Learning‐Based Single‐Cell Optical Image Studies

Label-free light-sheet microfluidic cytometry for the automatic identification of senescent cells

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