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Related Experiment Video

Updated: Dec 24, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

19.0K

Inclusion of temporal information in single cell transcriptomics.

Pedro Olivares-Chauvet1, Jan Philipp Junker1

  • 1Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.

The International Journal of Biochemistry & Cell Biology
|April 14, 2020
PubMed
Summary

Single cell transcriptomics can now include temporal data using computational inference and RNA metabolic labeling for short-term dynamics. Emerging lineage tracing methods record long-term cell fate information in the genome.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Computational Biology

Background:

  • Single cell transcriptomics reveals cell diversity and fate decisions.
  • Incorporating temporal dynamics into single cell studies is challenging due to the one-time measurement of each cell.

Purpose of the Study:

  • To review recent advancements and ongoing efforts in adding temporal information to single cell transcriptomics.
  • To highlight methods for inferring and directly measuring cellular dynamics.

Main Methods:

  • Pseudo-temporal ordering of single cell transcriptomes from snapshot data.
  • Analysis of intronic reads and RNA metabolic labeling for short-term dynamics.
  • High-throughput lineage tracing using CRISPR/Cas9 for long-term information storage.
Keywords:
High-throughput lineage tracingPseudo-temporal orderingRNA metabolic labelingSingle cell transcriptomics

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Transcriptome Analysis of Single Cells
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Related Experiment Videos

Last Updated: Dec 24, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

19.0K
Transcriptome Analysis of Single Cells
07:27

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

30.4K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

559

Main Results:

  • Computational inference can reconstruct temporal trajectories from static transcriptomic data.
  • Intronic reads and metabolic labeling provide evidence for pseudo-time and gene regulatory interactions.
  • CRISPR-based lineage tracing enables recording of cellular history over extended periods.

Conclusions:

  • Integrating temporal information significantly enhances the power of single cell transcriptomics.
  • A combination of computational and experimental methods is advancing the study of cellular dynamics.
  • Future research can leverage these techniques to understand complex biological processes across different timescales.