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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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Related Experiment Video

Updated: Jul 7, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Automatic cell-type harmonization and integration across Human Cell Atlas datasets.

Chuan Xu¹, Martin Prete¹, Simone Webb²

¹Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

|December 22, 2023

Summary

This summary is machine-generated.

CellHint harmonizes cell types across single-cell datasets, creating a unified Human Cell Atlas. This tool resolves technical biases and reveals new cell relationships, aiding in disease and tissue research.

Keywords:

Human Cell Atlas cell hierarchy cell-type harmonization data integration harmonization graph machine learning organ atlas predictive clustering tree single cell

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Area of Science:

Genomics
Bioinformatics
Cell Biology

Background:

Standardizing cell type definitions is crucial for the Human Cell Atlas.
Technical biases and varying annotation resolutions hinder cross-dataset analysis in single-cell genomics.

Purpose of the Study:

To introduce CellHint, a novel computational tool for harmonizing cell types across diverse single-cell datasets.
To develop a framework for resolving technical biases and improving cell-type annotation accuracy.
To facilitate the construction of a standardized Human Cell Atlas.

Main Methods:

CellHint utilizes a predictive clustering tree-based approach to quantify cell-cell transcriptomic similarities.
A relationship graph is constructed to hierarchically define shared and unique cell subtypes.
A workflow for fast cross-dataset integration guided by harmonized cell types and hierarchy was developed.

Main Results:

CellHint accurately recapitulates expert-curated annotations in immune cell datasets.
New relationships between healthy and diseased lung cell states were identified across eight diseases.
Underappreciated cell types in the adult human hippocampus were uncovered.
A cross-tissue database of approximately 3.7 million cells from 12 tissues was curated.

Conclusions:

CellHint provides a robust method for harmonizing cell types and resolving technical biases in single-cell data.
The tool enables the discovery of novel cell type relationships and facilitates the creation of comprehensive atlases.
CellHint supports automated cell annotation across diverse human tissues, advancing single-cell research.