- 标题
- 摘要
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- 实验方案
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Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods
摘要： Measurement techniques that allow the global analysis of cellular responses while retaining single-cell sensitivity are increasingly needed in order to understand complex and dynamic biological processes. in this context, compromises between sensitivity, degree of multiplexing, throughput, and invasiveness are often unavoidable. We present here a noninvasive optical approach that can retrieve quantitative biomarkers of both morphological and molecular phenotypes of individual cells, based on a combination of quantitative phase imaging and Raman spectroscopy measurements. We then develop generalized statistical tools to assess the influence of both controlled (cell sub-populations, immune stimulation) and uncontrolled (culturing conditions, animal variations, etc.) experimental parameters on the label-free biomarkers. These indicators can detect different macrophage cell sub-populations originating from different progenitors as well as their activation state, and how these changes are related to specific differences in morphology and molecular content. The molecular indicators also display further sensitivity that allow identification of other experimental conditions, such as differences between cells originating from different animals, allowing the detection of outlier behaviour from given cell sub-populations.
关键词： single cell heterogeneity,label-free optical methods,cell activation,immune cell type
更新于2025-09-11 14:15:04
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
摘要： We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.
关键词： lymphocyte identification,machine learning,holotomography,immune cell,immunology,Immunology and Infection,Quantitative phase imaging,optical diffraction tomography,holographic microscopy,label-free imaging
更新于2025-09-04 15:30:14