Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning

Cited 16 time in webofscience Cited 0 time in scopus
  • Hit : 366
  • Download : 215
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.
Publisher
Nature Publishing Group
Issue Date
2017-07
Language
English
Article Type
Article
Keywords

OPTICAL DIFFRACTION TOMOGRAPHY; AUTOIMMUNE-DISEASE; T-LYMPHOCYTES; LIVE CELLS; EXPRESSION; QUANTIFICATION; CLASSIFICATION; MICROSCOPY; INFECTION; TRACKING

Citation

SCIENTIFIC REPORTS, v.7, pp.6654

ISSN
2045-2322
DOI
10.1038/s41598-017-06311-y
URI
http://hdl.handle.net/10203/225485
Appears in Collection
BS-Journal Papers(저널논문)PH-Journal Papers(저널논문)
Files in This Item
s41598-017-06311-y.pdf(2.47 MB)Download
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 16 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0