Localization of mRNA and small RNAs (sRNAs) is important for understanding their function. Fluorescent
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.
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.
The whole mount HCR RNA-FISH and IHC methods allow easy investigation of 3D spatial gene expression patterns in entire plant tissues.
- NSF-PAR ID:
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Plant Methods
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Localization of mRNA and small RNAs (sRNAs) is important for understanding their function. Fluorescent
in situhybridization (FISH) has been used extensively in animal systems to study the localization and expression of sRNAs. However, current methods for fluorescent in situdetection of sRNA in plant tissues are less developed. Here we report a protocol (sRNA‐FISH) for efficient fluorescent detection of sRNAs in plants. This protocol is suitable for application in diverse plant species and tissue types. The use of locked nucleic acid probes and antibodies conjugated with different fluorophores allows the detection of two sRNAs in the same sample. Using this method, we have successfully detected the co‐localization of miR2275 and a 24‐nucleotide phased small interfering RNA in maize anther tapetal and archesporial cells. We describe how to overcome the common problem of the wide range of autofluorescence in embedded plant tissue using linear spectral unmixing on a laser scanning confocal microscope. For highly autofluorescent samples, we show that multi‐photon fluorescence excitation microscopy can be used to separate the target sRNA‐FISH signal from background autofluorescence. In contrast to colorimetric in situhybridization, sRNA‐FISH signals can be imaged using super‐resolution microscopy to examine the subcellular localization of sRNAs. We detected maize miR2275 by super‐resolution structured illumination microscopy and direct stochastic optical reconstruction microscopy. In this study, we describe how we overcame the challenges of adapting FISH for imaging in plant tissue and provide a step‐by‐step sRNA‐FISH protocol for studying sRNAs at the cellular and even subcellular level.
Cleft palate is one of the most prevalent birth defects. Mice are useful for studying palate development because of their morphological and genetic similarities to humans. In mice, palate development occurs between embryonic days (E)11.5 to 15.5. Single cell transcriptional profiles of palate cell populations have been a valuable resource for the craniofacial research community, but we lack a single cell transcriptional profile for anterior palate at E13.5, at the transition from proliferation to shelf elevation.
A detailed single cell RNA sequencing analysis reveals heterogeneity in expression profiles of the cell populations of the E13.5 anterior palate. Hybridization chain reaction RNA fluorescent in situ hybridization (HCR RNA FISH) reveals epithelial populations segregate into layers. Mesenchymal populations spatially segregate into four domains. One of these mesenchymal populations expresses ligands and receptors distinct from the rest of the mesenchyme, suggesting that these cells have a unique function. RNA velocity analysis shows two terminal cell states that contribute to either the proximal or distal palatal regions emerge from a single progenitor pool.
This single cell resolution expression data and detailed analysis from E13.5 anterior palate provides a powerful resource for mechanistic insight into secondary palate morphogenesis for the craniofacial research community.
Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers.
The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker.
Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB.
The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
Cell type specialization is a hallmark of complex multicellular organisms and is usually established through implementation of cell-type-specific gene expression programs. The multicellular green alga
Volvox carterihas just two cell types, germ and soma, that have previously been shown to have very different transcriptome compositions which match their specialized roles. Here we interrogated another potential mechanism for differentiation in V. carteri, cell type specific alternative transcript isoforms (CTSAI). Methods
We used pre-existing predictions of alternative transcripts and de novo transcript assembly with HISAT2 and Ballgown software to compile a list of loci with two or more transcript isoforms, identified a small subset that were candidates for CTSAI, and manually curated this subset of genes to remove false positives. We experimentally verified three candidates using semi-quantitative RT-PCR to assess relative isoform abundance in each cell type.
Of the 1978 loci with two or more predicted transcript isoforms 67 of these also showed cell type isoform expression biases. After curation 15 strong candidates for CTSAI were identified, three of which were experimentally verified, and their predicted gene product functions were evaluated in light of potential cell type specific roles. A comparison of genes with predicted alternative splicing from
Chlamydomonas reinhardtii, a unicellular relative of V. carteri, identified little overlap between ortholog pairs with alternative splicing in both species. Finally, we interrogated cell type expression patterns of 126 V . carteripredicted RNA binding protein (RBP) encoding genes and found 40 that showed either somatic or germ cell expression bias. These RBPs are potential mediators of CTSAI in V. carteriand suggest possible pre-adaptation for cell type specific RNA processing and a potential path for generating CTSAI in the early ancestors of metazoans and plants. Conclusions
We predicted numerous instances of alternative transcript isoforms in Volvox, only a small subset of which showed cell type specific isoform expression bias. However, the validated examples of CTSAI supported existing hypotheses about cell type specialization in
V. carteri,and also suggested new hypotheses about mechanisms of functional specialization for their gene products. Our data imply that CTSAI operates as a minor but important component of V. cartericellular differentiation and could be used as a model for how alternative isoforms emerge and co-evolve with cell type specialization.
The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3′-untranslated region (3′-UTR) of mRNA produces transcripts with shorter or longer 3′-UTR. Often, 3′-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3′-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3′-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3′-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3′-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations.
APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3′-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3′-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3′-UTR annotation and read coverage on the 3′-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user’s manual are freely available at
APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3′-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3′-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3′-UTR APA events and improve genome annotation.
APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3′-UTR APA events. The pipeline integrates both RNA-seq and 3′-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.