TY - JOUR
T1 - Single-Cell Transcriptomics Meets Lineage Tracing
AU - Kester, Lennart
AU - van Oudenaarden, Alexander
N1 - Funding Information:
Research in the authors’ lab is supported by a European Research Council Advanced grant (ERC-AdG 742225-IntScOmics), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) TOP award (NWO-CW 714.016.001), and a Dutch Cancer Society project grant (KWF project 10158/2016-1). This work is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society. We'd like to thank M. Sen and A. Alemany for the careful reading and correcting of the manuscript.
Funding Information:
Research in the authors’ lab is supported by a European Research Council Advanced grant ( ERC-AdG 742225-IntScOmics ), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) TOP award ( NWO-CW 714.016.001 ), and a Dutch Cancer Society project grant ( KWF project 10158 / 2016-1 ). This work is part of the Oncode Institute , which is partly financed by the Dutch Cancer Society . We’d like to thank M. Sen and A. Alemany for the careful reading and correcting of the manuscript.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/8/2
Y1 - 2018/8/2
N2 - Reconstructing lineage relationships between cells within a tissue or organism is a long-standing aim in biology. Traditionally, lineage tracing has been achieved through the (genetic) labeling of a cell followed by the tracking of its offspring. Currently, lineage trajectories can also be predicted using single-cell transcriptomics. Although single-cell transcriptomics provides detailed phenotypic information, the predicted lineage trajectories do not necessarily reflect genetic relationships. Recently, techniques have been developed that unite these strategies. In this Review, we discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research. In this Review, Kester and van Oudenaarden discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research.
AB - Reconstructing lineage relationships between cells within a tissue or organism is a long-standing aim in biology. Traditionally, lineage tracing has been achieved through the (genetic) labeling of a cell followed by the tracking of its offspring. Currently, lineage trajectories can also be predicted using single-cell transcriptomics. Although single-cell transcriptomics provides detailed phenotypic information, the predicted lineage trajectories do not necessarily reflect genetic relationships. Recently, techniques have been developed that unite these strategies. In this Review, we discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research. In this Review, Kester and van Oudenaarden discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research.
KW - lineage trajectory reconstruction
KW - single-cell lineage tracing
KW - single-cell mRNA sequencing
UR - https://www.scopus.com/pages/publications/85046818699
U2 - 10.1016/j.stem.2018.04.014
DO - 10.1016/j.stem.2018.04.014
M3 - Review article
AN - SCOPUS:85046818699
SN - 1934-5909
VL - 23
SP - 166
EP - 179
JO - Cell Stem Cell
JF - Cell Stem Cell
IS - 2
ER -