ArticleScientific reports2017
Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Bidirectional in-silico clearing approach for deep refractive-index tomography using a sparsely sampled transmission matrix.Biomedical optics express · 2024Article
- Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit.Communications biology · 2024Article
- AI-driven projection tomography with multicore fibre-optic cell rotation.Nature communications · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Cell Granularity Reflects Immune Cell Function and Enables Selection of Lymphocytes with Superior Attributes for Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry.Scientific reports · 2023Article
- Diffractive interconnects: all-optical permutation operation using diffractive networks.Nanophotonics (Berlin, Germany) · 2023Article
- Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization.Scientific reports · 2023Article
- Real-time 3D imaging of ocean algae with crosstalk suppressed single-shot digital holographic microscopy.Biomedical optics express · 2022Article
- Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network.Light, science & applications · 2022Article
- Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow.Cells · 2022Article
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- Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis.BME frontiers · 2022Article
- In silico-labeled ghost cytometry.eLife · 2021Article
- Characterizing Organelles in Live Stem Cells Using Label-Free Optical Diffraction Tomography.Molecules and cells · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.
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