Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Machine learning approach to analyzing complex care coordination patterns for medically complex children.

BMC medical informatics and decision making·2026

Same author

Temporal orchestration of transcriptional and epigenomic programming underlying maternal embryonic diapause in a cricket model.

Communications biology·2026

Same author

Residue interaction chains facilitating the structural change of ND6 subunit in respiratory complex I.

Biochemical and biophysical research communications·2026

Same author

Sustained efficacy and long-term outcomes of autologous oral mucosal epithelial cell sheet transplantation for pediatric esophageal anastomotic restenosis.

Scientific reports·2026

Same author

The Expression of Placental 17β-Hydroxysteroid Dehydrogenase Genes Is Associated with the Elevation of Active Androgens and Estrogens in Pregnant Women, but Does Not Affect 11-Oxygenated C19 Steroids.

International journal of molecular sciences·2026

Same author

In-utero cell transplantation for hypophosphatasia with gene-edited hESC-derived MSCs in a murine model.

Molecular therapy. Advances·2026

Search research articles

Related Experiment Video

Updated: Nov 22, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

High-precision multiclass cell classification by supervised machine learning on lectin microarray data.

Mayu Shibata^1,2, Kohji Okamura³, Kei Yura^2,4

¹Department of Reproductive Biology, National Center for Child Health and Development, Tokyo, 157-8535, Japan.

Regenerative Therapy

|January 11, 2021

Summary

This summary is machine-generated.

A new cell classification platform using machine learning accurately identifies human pluripotent stem cells (hPSCs). This glycome-based system enhances cell therapy safety and efficacy.

Keywords:

Artificial intelligence Lectin microarray Linear classification Neural network Pluripotent stem cells

More Related Videos

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Related Experiment Videos

Last Updated: Nov 22, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Area of Science:

Biotechnology
Cell Biology
Machine Learning

Background:

Cell classification is crucial for ensuring the safety and efficacy of cell-based therapies.
Human pluripotent stem cells (hPSCs) require reliable evaluation and selection methods.
Previous glycome-based discriminant functions for pluripotency assessment were not broadly applicable.

Purpose of the Study:

To develop a high-precision cell classification platform utilizing supervised machine learning.
To validate the platform's performance using glycome analysis as a proof-of-concept.
To establish a robust system for identifying and categorizing cell types, including hPSCs.

Main Methods:

Applied supervised machine learning, specifically linear classification and neural networks.
Utilized lectin microarray data from 1577 individual human cells.
Categorized cells into five distinct classes, including hPSCs.

Main Results:

The linear classification model achieved 89% accuracy in predicting sample types.
The neural network model demonstrated a higher accuracy of 97% in sample type prediction.
Both models proved effective in classifying cells based on glycome data.

Conclusions:

The developed platform offers high-precision cell classification capabilities for hPSCs.
The system requires minimal computational resources, making it efficient.
This platform can serve as a reliable conventional cell classification system for advancing cell therapies.