An official website of the United States government
The choice of fixation method significantly impacts tissue morphology and visualization of gene expression and proteins after in situ hybridization chain reaction (HCR) or immunohistochemistry (IHC), respectively. In this study, we compared the effects of paraformaldehyde (PFA) and trichloroacetic acid (TCA) fixation techniques prior to HCR and IHC on chicken embryos. Our findings underscore the importance of optimizing fixation methods for accurate visualization and subsequent interpretation of HCR and IHC results, with implications for probe and antibody validation and tissue-specific protein localization studies. We found that TCA fixation resulted in larger and more circular nuclei and neural tubes compared to PFA fixation. Additionally, TCA fixation altered the subcellular fluorescence signal intensity of various proteins, including transcription factors, cytoskeletal proteins, and cadherins. Notably, TCA fixation revealed protein signals in tissues that may be inaccessible with PFA fixation. In contrast, TCA fixation proved ineffective for mRNA visualization. These results highlight the need for optimization of fixation protocols depending on the target and model system, emphasizing the importance of methodological considerations in biological analyses.
more »
« less
A rapid and sensitive, multiplex, whole mount RNA fluorescence in situ hybridization and immunohistochemistry protocol
Background In the past few years, there has been an explosion in single-cell transcriptomics datasets, yet in vivo confirmation of these datasets is hampered in plants due to lack of robust validation methods. Likewise, modeling of plant development is hampered by paucity of spatial gene expression data. RNA fluorescence in situ hybridization (FISH) enables investigation of gene expression in the context of tissue type. Despite development of FISH methods for plants, easy and reliable whole mount FISH protocols have not yet been reported. Results We adapt a 3-day whole mount RNA-FISH method for plant species based on a combination of prior protocols that employs hybridization chain reaction (HCR), which amplifies the probe signal in an antibody-free manner. Our whole mount HCR RNA-FISH method shows expected spatial signals with low background for gene transcripts with known spatial expression patterns in Arabidopsis inflorescences and monocot roots. It allows simultaneous detection of three transcripts in 3D. We also show that HCR RNA-FISH can be combined with endogenous fluorescent protein detection and with our improved immunohistochemistry (IHC) protocol. ConclusionsThe whole mount HCR RNA-FISH and IHC methods allow easy investigation of 3D spatial gene expression patterns in entire plant tissues.
more »
« less
- Award ID(s):
- 2319036
- PAR ID:
- 10535650
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Plant Methods
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 1746-4811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
High-resolution profiling reveals coupled transcriptional and translational regulation of transgenesAbstract Concentrations of RNAs and proteins provide important determinants of cell fate. Robust gene circuit design requires an understanding of how the combined actions of individual genetic components influence both messenger RNA (mRNA) and protein levels. Here, we simultaneously measure mRNA and protein levels in single cells using hybridization chain reaction Flow-FISH (HCR Flow-FISH) for a set of commonly used synthetic promoters. We find that promoters generate differences in both the mRNA abundance and the effective translation rate of these transcripts. Stronger promoters not only transcribe more RNA but also show higher effective translation rates. While the strength of the promoter is largely preserved upon genome integration with identical elements, the choice of polyadenylation signal and coding sequence can generate large differences in the profiles of the mRNAs and proteins. We used long-read direct RNA sequencing to define the transcription start and splice sites of common synthetic promoters and independently vary the defined promoter and 5′ UTR sequences in HCR Flow-FISH. Together, our high-resolution profiling of transgenic mRNAs and proteins offers insight into the impact of common synthetic genetic components on transcriptional and translational mechanisms. By developing a novel framework for quantifying expression profiles of transgenes, we have established a system for building more robust transgenic systems.more » « less
-
null (Ed.)RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants.more » « less
-
Abstract Spatially-resolved RNA profiling has now been widely used to understand cells’ structural organizations and functional roles in tissues, yet it is challenging to reconstruct the whole spatial transcriptomes due to various inherent technical limitations in tissue section preparation and RNA capture and fixation in the application of the spatial RNA profiling technologies. Here, we introduce a graph-guided neural tensor decomposition (GNTD) model for reconstructing whole spatial transcriptomes in tissues. GNTD employs a hierarchical tensor structure and formulation to explicitly model the high-order spatial gene expression data with a hierarchical nonlinear decomposition in a three-layer neural network, enhanced by spatial relations among the capture spots and gene functional relations for accurate reconstruction from highly sparse spatial profiling data. Extensive experiments on 22 Visium spatial transcriptomics datasets and 3 high-resolution Stereo-seq datasets as well as simulation data demonstrate that GNTD consistently improves the imputation accuracy in cross-validations driven by nonlinear tensor decomposition and incorporation of spatial and functional information, and confirm that the imputed spatial transcriptomes provide a more complete gene expression landscape for downstream analyses of cell/spot clustering for tissue segmentation, and spatial gene expression clustering and visualizations.more » « less
-
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.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1186/s13007-023-01108-9
