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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: Jun 23, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Integrating multiple references for single-cell assignment.

Bin Duan¹, Shaoqi Chen¹, Xiaohan Chen¹

¹Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.

Nucleic Acids Research

|May 26, 2021

Summary

This summary is machine-generated.

This study introduces mtSC, a new framework for single-cell assignment that integrates multiple data sources. It effectively overcomes data heterogeneity challenges to improve cell type identification in complex tissues.

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Last Updated: Jun 23, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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Published on: April 30, 2019

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Reusable Single Cell for Iterative Epigenomic Analyses

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Published on: February 11, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell assignment is crucial for analyzing single-cell sequencing data.
Integrating multiple data sources can enhance assignment accuracy but faces challenges due to data heterogeneity.

Purpose of the Study:

To develop an efficient framework for single-cell assignment using multiple reference datasets.
To address the data heterogeneity challenge in multi-reference single-cell sequencing data integration.

Main Methods:

Developed mtSC, a flexible framework for single-cell assignment.
Employed multitask deep metric learning for integrating multiple references.
Designed for cell type identification in tissues with diverse single-cell sequencing data.

Main Results:

Evaluated mtSC on multiple public benchmark datasets.
Demonstrated state-of-the-art effectiveness of mtSC.
Showcased successful integrative single-cell assignment with multiple references.

Conclusions:

mtSC provides an effective solution for single-cell assignment with multi-reference integration.
The framework successfully handles data heterogeneity.
mtSC advances cell type identification in complex biological samples.