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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: Jun 22, 2026

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Mapping Genome-wide Accessible Chromatin in Primary Human T Lymphocytes by ATAC-Seq

Published on: November 13, 2017

Cellcano: supervised cell type identification for single cell ATAC-seq data.

Wenjing Ma¹, Jiaying Lu¹, Hao Wu^2,3

¹Department of Computer Science, Emory University, 400 Dowman Drive, Atlanta, GA, 30322, USA.

Nature Communications

|April 3, 2023

Summary

This summary is machine-generated.

Cellcano is a new computational method for identifying cell types using single-cell chromatin accessibility (scATAC-seq) data. It effectively addresses data distribution differences, improving accuracy and efficiency in cell type classification.

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Last Updated: Jun 22, 2026

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Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Single-cell omics analysis relies on accurate cell type identification.
Supervised methods excel in single-cell RNA sequencing (scRNA-seq) due to reference datasets.
Single-cell ATAC sequencing (scATAC-seq) offers epigenetic insights but lacks dedicated supervised tools.

Purpose of the Study:

To develop a novel computational method for supervised cell type identification specifically for scATAC-seq data.
To address the distributional shift challenge between reference and target scATAC-seq datasets.
To provide an accurate, robust, and efficient tool for scATAC-seq cell typing.

Main Methods:

Developed Cellcano, a computational method utilizing a two-round supervised learning algorithm.
Implemented strategies to alleviate distributional shifts between datasets.
Benchmarked performance across 50 diverse scATAC-seq cell typing tasks.

Main Results:

Cellcano demonstrates superior accuracy and robustness in cell type identification from scATAC-seq data.
The method effectively mitigates distributional discrepancies, enhancing prediction performance.
Cellcano proved computationally efficient across various datasets.

Conclusions:

Cellcano is a highly effective supervised learning tool for scATAC-seq data analysis.
The method advances cell type identification in epigenomic studies.
Cellcano is publicly available and well-documented for broader research application.