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Article

Developmental scRNAseq Trajectories in Gene- and Cell-State Space—The Flatworm Example

IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany
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Genes 2020, 11(10), 1214; https://doi.org/10.3390/genes11101214
Received: 3 September 2020 / Revised: 13 October 2020 / Accepted: 14 October 2020 / Published: 16 October 2020
Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data. View Full-Text
Keywords: pseudotime trajectories; transcriptomic landscapes; differentiation of tissues; planarian; machine learning; self-organizing maps; single cell RNA sequencing pseudotime trajectories; transcriptomic landscapes; differentiation of tissues; planarian; machine learning; self-organizing maps; single cell RNA sequencing
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MDPI and ACS Style

Schmidt, M.; Loeffler-Wirth, H.; Binder, H. Developmental scRNAseq Trajectories in Gene- and Cell-State Space—The Flatworm Example. Genes 2020, 11, 1214. https://doi.org/10.3390/genes11101214

AMA Style

Schmidt M, Loeffler-Wirth H, Binder H. Developmental scRNAseq Trajectories in Gene- and Cell-State Space—The Flatworm Example. Genes. 2020; 11(10):1214. https://doi.org/10.3390/genes11101214

Chicago/Turabian Style

Schmidt, Maria, Henry Loeffler-Wirth, and Hans Binder. 2020. "Developmental scRNAseq Trajectories in Gene- and Cell-State Space—The Flatworm Example" Genes 11, no. 10: 1214. https://doi.org/10.3390/genes11101214

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