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Cell Lines

Cell Lines

A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...

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Updated: Jul 21, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Single-cell reference mapping to construct and extend cell-type hierarchies.

Lieke Michielsen^1,2,3, Mohammad Lotfollahi^4,5, Daniel Strobl^4,6

¹Department of Human Genetics, Leiden University Medical Center, 2333ZC Leiden, The Netherlands.

NAR Genomics and Bioinformatics

|July 28, 2023

Summary

This summary is machine-generated.

Integrating single-cell genomics datasets requires harmonizing cell type annotations. Our treeArches framework builds dynamic reference atlases with hierarchical cell annotations, enabling consensus and discovery of novel cell states.

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell genomics generates vast datasets for health and disease atlases.
Integrating these datasets is challenged by diverse cell annotation terminology and depth.
Existing reference mapping methods lack systematic harmonization of cell-type annotations.

Purpose of the Study:

To develop a framework for automatically building and extending reference atlases.
To enrich atlases with updatable, hierarchical cell-type annotations across datasets.
To enable consensus-based cell-type annotation and discovery of novel cell states.

Main Methods:

Introducing 'treeArches', a novel computational framework.
Automated construction and extension of reference atlases.
Hierarchical organization of cell-type annotations derived from multiple datasets.

Main Results:

Demonstrated use cases for resolving cell-type relations between reference and query datasets.
Successfully identified novel cell types, including disease-associated cell states, absent in the reference.
Established a method for data-driven construction of consensus cell-type hierarchies.

Conclusions:

treeArches facilitates the creation of comprehensive, harmonized single-cell reference atlases.
The framework supports the identification of new cell types and states.
Enables efficient usage and expansion of reference atlases for biological discovery.