Jove
Visualize
Contact Us

Related Concept Videos

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

5.6K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
5.6K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K

You might also read

Related Articles

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

Sort by
Same author

Author Correction: Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint.

Nature communications·2024
Same author

Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint.

Nature communications·2024
Same author

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

eLife·2024
Same author

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

bioRxiv : the preprint server for biology·2023
Same author

Cellular anatomy of the mouse primary motor cortex.

Nature·2021
Same author

An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo.

PLoS biology·2021
Same journal

Corrigendum: Neurodegenerative diseases and immune system: From pathogenic mechanism to therapy.

Neural regeneration research·2026
Same journal

Injury and repair in limb deformities associated with peripheral neuropathy: Visualization analyses of research trends and hotspots.

Neural regeneration research·2026
Same journal

Circulating exosomes convey the cognitive benefits of Tai Chi: The role of miR-625-5p in prefrontal remodeling and therapeutic potential.

Neural regeneration research·2026
Same journal

Induced neural stem cells in neuroregeneration: Progress and clinical prospects.

Neural regeneration research·2026
Same journal

Locus coeruleus-norepinephrine system dysfunction: A new concept in cognitive aging and neurodegenerative diseases.

Neural regeneration research·2026
Same journal

The casual explanations of non-coding risk variants in Alzheimer's disease: From single mutation to lipid dysregulation.

Neural regeneration research·2026
See all related articles
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 Experiment Video

Updated: Jun 12, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.4K

A novel method for clustering cellular data to improve classification.

Diek W Wheeler1, Giorgio A Ascoli

  • 1Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study; and Bioengineering Department, Volgenau School of Engineering; George Mason University, Fairfax, VA, USA.

Neural Regeneration Research
|September 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new protocol for classifying large cellular datasets using hierarchical clustering and statistical testing. It provides an objective method to determine optimal data granularity for neuroscience and other fields.

More Related Videos

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array
07:19

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

8.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

Related Experiment Videos

Last Updated: Jun 12, 2025

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
09:48

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

7.4K
Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array
07:19

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

8.5K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

Area of Science:

  • Neuroscience
  • Bioinformatics
  • Computational Biology

Background:

  • The rapid growth of cellular data necessitates effective organization and interpretation methods.
  • Hierarchical clustering is common for data partitioning, but lacks objective criteria for determining classification granularity.

Purpose of the Study:

  • To present a protocol for classifying cellular datasets by combining unsupervised hierarchical clustering with statistical testing.
  • To provide an objective method for determining the appropriate granularity of cluster subdivision.

Main Methods:

  • Developed a protocol integrating data-driven unsupervised hierarchical clustering with statistical testing.
  • Applied the method to cellular data from the Janelia MouseLight project for neuron morphology characterization.

Main Results:

  • The protocol systematically identifies optimal cluster subdivision points based on inter-cluster versus intra-cluster cell differences.
  • Demonstrated the protocol's applicability to diverse 2D numerical datasets, including molecular, physiological, and anatomical data.

Conclusions:

  • The presented protocol offers a general-purpose, objective approach to classifying large cellular datasets.
  • This method enhances the interpretation of complex biological data, particularly in fields like neuroscience.