TY - JOUR
T1 - Space
T2 - The final frontier- A chieving single-cell, spatially resolved transcriptomics in plants
AU - Gurazada, Sai Guna Ranjan
AU - Cox, Kevin L.
AU - Czymmek, Kirk J.
AU - Meyers, Blake C.
N1 - Funding Information:
This work was supported by a Howard Hughes Medical Institute Hanna H. Gray Fellowship to K.L.C., and NSF MCB award 1945854 to B.C.M. and K.J.C. Additional support for the work comes from the Donald Danforth Plant Science Center, including the Advanced Bioimaging Laboratory (RRID:SCR_018951).
Publisher Copyright:
© 2021 Journal of Turkish Sleep Medicine. All rights reserved.
PY - 2021/5
Y1 - 2021/5
N2 - Single-cell RNA-seq is a tool that generates a high resolution of transcriptional data that can be used to understand regulatory networks in biological systems. In plants, several methods have been established for transcriptional analysis in tissue sections, cell types, and/or single cells. These methods typically require cell sorting, transgenic plants, protoplasting, or other damaging or laborious processes. Additionally, the majority of these technologies lose most or all spatial resolution during implementation. Those that offer a high spatial resolution for RNA lack breadth in the number of transcripts characterized. Here, we briefly review the evolution of spatial transcriptomics methods and we highlight recent advances and current challenges in sequencing, imaging, and computational aspects toward achieving 3D spatial transcriptomics of plant tissues with a resolution approaching single cells. We also provide a perspective on the potential opportunities to advance this novel methodology in plants.
AB - Single-cell RNA-seq is a tool that generates a high resolution of transcriptional data that can be used to understand regulatory networks in biological systems. In plants, several methods have been established for transcriptional analysis in tissue sections, cell types, and/or single cells. These methods typically require cell sorting, transgenic plants, protoplasting, or other damaging or laborious processes. Additionally, the majority of these technologies lose most or all spatial resolution during implementation. Those that offer a high spatial resolution for RNA lack breadth in the number of transcripts characterized. Here, we briefly review the evolution of spatial transcriptomics methods and we highlight recent advances and current challenges in sequencing, imaging, and computational aspects toward achieving 3D spatial transcriptomics of plant tissues with a resolution approaching single cells. We also provide a perspective on the potential opportunities to advance this novel methodology in plants.
UR - http://www.scopus.com/inward/record.url?scp=85107090697&partnerID=8YFLogxK
U2 - 10.1042/ETLS20200274
DO - 10.1042/ETLS20200274
M3 - Review article
C2 - 33522561
AN - SCOPUS:85107090697
SN - 2397-8554
VL - 5
SP - 179
EP - 188
JO - Emerging Topics in Life Sciences
JF - Emerging Topics in Life Sciences
IS - 2
ER -