One common drawback of all these techniques is that spatial
information is lost when cells are dissociated into suspension, however, the robust characterization of spatial markers within a tissue
and developing embryo make it possible to reconstruct spatial
patterning in silico. To reconstruct spatial information from dissociated tissues or embryos, Seurat can be employed to estimate a
cell’s likely position within spatial domains of the original tissue or
embryo. As software matures and techniques improve in resolution,
spatial transcriptomic technologies like Spatial Transcriptomics,
Slide-Seq, and Seurat can provide more accurate spatial transcriptomic distributions [37, 38].
An outcome sought from this long list of computational
options is a list of genes to be used in follow-up mechanistic studies.
The question of how to reduce the size of that list varies with the
goals in the system. In the case of the EMT, one approach might be
to eliminate RNAs that are constitutively expressed since the EMT
is fundamentally a change. Then, the direction of change and its
timing can be considered using trajectories of RNAs and clustering
programs. To that, data on perturbations, either based on known
transcription factor control or perhaps on known drug effects can
provide differential expression data that helps narrow the candidate
list. Ultimately the goal is to identify candidates that are essential to
the EMT and will help the investigator understand how the process
works. To that end scRNA-seq provides an excellent tool.
Acknowledgements
The authors thank members of the McClay and the Wray labs for
their critical input. We also appreciate the help provided by the
Duke Core facility and the Benfey lab in the Biology Department.
Support for this project was provided by NIH to DRM (RO1 HD
14483 and PO1 HD037105), and by NSF to GAW
(IOS-1457305) and AJM for his NSF predoctoral fellowship
(DGE-1644868). GS is supported by a Career Award at the Scientific Interface from the Burroughs Welcome Fund, and by funds
from the Klarman Cell Observatory.
References
1. Schafer G, Narasimha M, Vogelsang E, Leptin
M (2014) Cadherin switching during the formation and differentiation of the Drosophila
mesoderm - implications for epithelial-to-mesenchymal transitions. J Cell Sci 127
(Pt
7):1511–1522.
https://doi.org/10.
1242/jcs.139485
2. Schindler AJ, Sherwood DR (2013) Morphogenesis of the Caenorhabditis elegans vulva.
Wiley Interdiscip Rev Dev Biol 2(1):75–95.
https://doi.org/10.1002/wdev.87
3. Saunders LR, McClay DR (2014) Sub-circuits
of a gene regulatory network control a developmental epithelial-mesenchymal transition.
Development 141(7):1503–1513. https://
doi.org/10.1242/dev.101436
4. Cao J, Packer JS, Ramani V, Cusanovich DA,
Huynh C, Daza R, Qiu X, Lee C, Furlan SN,
312
Abdull J. Massri et al.
information is lost when cells are dissociated into suspension, however, the robust characterization of spatial markers within a tissue
and developing embryo make it possible to reconstruct spatial
patterning in silico. To reconstruct spatial information from dissociated tissues or embryos, Seurat can be employed to estimate a
cell’s likely position within spatial domains of the original tissue or
embryo. As software matures and techniques improve in resolution,
spatial transcriptomic technologies like Spatial Transcriptomics,
Slide-Seq, and Seurat can provide more accurate spatial transcriptomic distributions [37, 38].
An outcome sought from this long list of computational
options is a list of genes to be used in follow-up mechanistic studies.
The question of how to reduce the size of that list varies with the
goals in the system. In the case of the EMT, one approach might be
to eliminate RNAs that are constitutively expressed since the EMT
is fundamentally a change. Then, the direction of change and its
timing can be considered using trajectories of RNAs and clustering
programs. To that, data on perturbations, either based on known
transcription factor control or perhaps on known drug effects can
provide differential expression data that helps narrow the candidate
list. Ultimately the goal is to identify candidates that are essential to
the EMT and will help the investigator understand how the process
works. To that end scRNA-seq provides an excellent tool.
Acknowledgements
The authors thank members of the McClay and the Wray labs for
their critical input. We also appreciate the help provided by the
Duke Core facility and the Benfey lab in the Biology Department.
Support for this project was provided by NIH to DRM (RO1 HD
14483 and PO1 HD037105), and by NSF to GAW
(IOS-1457305) and AJM for his NSF predoctoral fellowship
(DGE-1644868). GS is supported by a Career Award at the Scientific Interface from the Burroughs Welcome Fund, and by funds
from the Klarman Cell Observatory.
References
1. Schafer G, Narasimha M, Vogelsang E, Leptin
M (2014) Cadherin switching during the formation and differentiation of the Drosophila
mesoderm - implications for epithelial-to-mesenchymal transitions. J Cell Sci 127
(Pt
7):1511–1522.
https://doi.org/10.
1242/jcs.139485
2. Schindler AJ, Sherwood DR (2013) Morphogenesis of the Caenorhabditis elegans vulva.
Wiley Interdiscip Rev Dev Biol 2(1):75–95.
https://doi.org/10.1002/wdev.87
3. Saunders LR, McClay DR (2014) Sub-circuits
of a gene regulatory network control a developmental epithelial-mesenchymal transition.
Development 141(7):1503–1513. https://
doi.org/10.1242/dev.101436
4. Cao J, Packer JS, Ramani V, Cusanovich DA,
Huynh C, Daza R, Qiu X, Lee C, Furlan SN,
312
Abdull J. Massri et al.