Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

12.2K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
12.2K

You might also read

Related Articles

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

Sort by
Same author

Blood-based kinase activity profiling to predict response to immune checkpoint inhibitors in patients with advanced stage NSCLC: the prospective IOpener study.

Journal for immunotherapy of cancer·2026
Same author

CARGO: A Cytometry Analysis framework via Regularized Graph Optimal-transport.

PLoS computational biology·2026
Same author

Marker-Agnostic Tumor Anchoring Chimeras Enable pH-Gated Immune Engagement.

Angewandte Chemie (International ed. in English)·2026
Same author

Automated Computational Flow Cytometry Correlates Decreasing Neutrophil-to-Lymphocyte Ratio to Improved Survival in NSCLC After Immune Checkpoint Blockade.

Cancer immunology research·2026
Same author

Evolving treatment strategies and the impact of centralization on outcomes in thymic epithelial tumors: A nationwide population-based study from the Netherlands.

European journal of cancer (Oxford, England : 1990)·2026
Same author

Regional variations in molecular testing and treatment of patients with metastatic NSCLC in the Netherlands.

Lung cancer (Amsterdam, Netherlands)·2026
Same journal

The 1st Mediterranean Meeting on Flow Cytometry: Forging New Collaborations Across the Mediterranean and Beyond.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Publication Guidelines for Optimized Multiparameter Immunolabeling Panels (OMIPs).

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

A Modular High-Parameter Flow Cytometry Framework: Pre-Analytical Optimization and Validation for Clinical Research.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Comparison of Tissue Preparations to Identify and Phenotype T Cells in Human Colorectal Tumor Tissue.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same journal

Refractive Index-Correlated Pseudocoloring for Adaptive Color Fusion in Holotomographic Cytology.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
See all related articles

Related Experiment Video

Updated: May 30, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.3K

CytoNorm 2.0: A flexible normalization framework for cytometry data without requiring dedicated controls.

Katrien L A Quintelier1,2,3, Marcella Willemsen3, Victor Bosteels4,5,6

  • 1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|January 28, 2025
PubMed
Summary
This summary is machine-generated.

CytoNorm 2.0 enhances cytometry data analysis by providing robust batch effect removal without technical replicates. New features improve quality control and tailor normalization to experimental designs for broader applicability.

Keywords:
CytoNormbatch effectsdata integrationnormalizationquality control

More Related Videos

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

11.9K
Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells
08:09

Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells

Published on: June 19, 2019

9.2K

Related Experiment Videos

Last Updated: May 30, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.3K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

11.9K
Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells
08:09

Preparation of Whole Bone Marrow for Mass Cytometry Analysis of Neutrophil-lineage Cells

Published on: June 19, 2019

9.2K

Area of Science:

  • Single-cell analysis
  • High-dimensional data analysis
  • Biotechnology

Background:

  • Cytometry is a high-throughput technique for single-cell analysis.
  • Technical variations during data acquisition can introduce batch effects.
  • Batch effects complicate the interpretation of cytometry datasets.

Purpose of the Study:

  • To introduce CytoNorm 2.0, an updated algorithm for normalizing cytometry data.
  • To demonstrate new use cases and visualizations for improved quality control.
  • To enhance the applicability and understanding of batch effect removal in cytometry.

Main Methods:

  • CytoNorm 2.0 algorithm for batch effect correction.
  • FlowSOM clustering for marker selection.
  • Tailoring goal distribution for experimental design.
  • New visualization tools for quality control.

Main Results:

  • CytoNorm 2.0 effectively removes batch effects in cytometry data.
  • The algorithm can be applied without technical replicates or controls.
  • New visualizations aid in understanding the normalization process.
  • Customizable goal distributions enhance experimental design alignment.

Conclusions:

  • CytoNorm 2.0 offers a flexible and powerful solution for batch effect removal in cytometry.
  • The updated version improves data quality control and user understanding.
  • Expanded use cases increase the algorithm's utility across diverse research areas.