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Creators/Authors contains: "Gurazada, Sai Guna"

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  1. Abstract The Solanaceae or “nightshade” family is an economically important group with remarkable diversity. To gain a better understanding of how the unique biology of the Solanaceae relates to the family’s small RNA (sRNA) genomic landscape, we downloaded over 255 publicly available sRNA data sets that comprise over 2.6 billion reads of sequence data. We applied a suite of computational tools to predict and annotate two major sRNA classes: (1) microRNAs (miRNAs), typically 20- to 22-nucleotide (nt) RNAs generated from a hairpin precursor and functioning in gene silencing and (2) short interfering RNAs (siRNAs), including 24-nt heterochromatic siRNAs typically functioning to repress repetitive regions of the genome via RNA-directed DNA methylation, as well as secondary phased siRNAs and trans-acting siRNAs generated via miRNA-directed cleavage of a polymerase II-derived RNA precursor. Our analyses described thousands of sRNA loci, including poorly understood clusters of 22-nt siRNAs that accumulate during viral infection. The birth, death, expansion, and contraction of these sRNA loci are dynamic evolutionary processes that characterize the Solanaceae family. These analyses indicate that individuals within the same genus share similar sRNA landscapes, whereas comparisons between distinct genera within the Solanaceae reveal relatively few commonalities.
    Free, publicly-accessible full text available February 28, 2023
  2. Abstract Plant cells communicate information for the regulation of development and responses to external stresses. A key form of this communication is transcriptional regulation, accomplished via complex gene networks operating both locally and systemically. To fully understand how genes are regulated across plant tissues and organs, high resolution, multi-dimensional spatial transcriptional data must be acquired and placed within a cellular and organismal context. Spatial transcriptomics (ST) typically provides a two-dimensional spatial analysis of gene expression of tissue sections that can be stacked to render three-dimensional data. For example, X-ray and light-sheet microscopy provide sub-micron scale volumetric imaging of cellular morphology of tissues, organs, or potentially entire organisms. Linking these technologies could substantially advance transcriptomics in plant biology and other fields. Here, we review advances in ST and 3D microscopy approaches and describe how these technologies could be combined to provide high resolution, spatially organized plant tissue transcript mapping.
  3. Jez, Joseph M. ; Topp, Christopher N. (Ed.)
    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.