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1. Space: the final frontier — achieving single-cell, spatially resolved transcriptomics in plants

   https://doi.org/10.1042/ETLS20200274  

   Gurazada, Sai Guna; Cox, Kevin L.; Czymmek, Kirk J.; Meyers, Blake C. (February 2021, Emerging Topics in Life Sciences)

   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. 

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2. RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data

   https://doi.org/10.1093/nar/gkaa1049  

   Li, Keren; Hope, C Matthew; Wang, Xiaozhong A; Wang, Ji-Ping (November 2020, Nucleic Acids Research)

   null (Ed.)

   Abstract Ribosome profiling, also known as Ribo-seq, has become a popular approach to investigate regulatory mechanisms of translation in a wide variety of biological contexts. Ribo-seq not only provides a measurement of translation efficiency based on the relative abundance of ribosomes bound to transcripts, but also has the capacity to reveal dynamic and local regulation at different stages of translation based on positional information of footprints across individual transcripts. While many computational tools exist for the analysis of Ribo-seq data, no method is currently available for rigorous testing of the pattern differences in ribosome footprints. In this work, we develop a novel approach together with an R package, RiboDiPA, for Differential Pattern Analysis of Ribo-seq data. RiboDiPA allows for quick identification of genes with statistically significant differences in ribosome occupancy patterns for model organisms ranging from yeast to mammals. We show that differential pattern analysis reveals information that is distinct and complimentary to existing methods that focus on translational efficiency analysis. Using both simulated Ribo-seq footprint data and three benchmark data sets, we illustrate that RiboDiPA can uncover meaningful pattern differences across multiple biological conditions on a global scale, and pinpoint characteristic ribosome occupancy patterns at single codon resolution. 

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3. Nonparametric expression analysis using inferential replicate counts

   https://doi.org/10.1093/nar/gkz622  

   Zhu, Anqi; Srivastava, Avi; Ibrahim, Joseph G; Patro, Rob; Love, Michael I (August 2019, Nucleic Acids Research)

   Abstract A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test. 

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4. SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

   https://doi.org/10.1093/bioinformatics/btae412  

   Aihara, Gohta; Clifton, Kalen; Chen, Mayling; Li, Zhuoyan; Atta, Lyla; Miller, Brendan_F; Satija, Rahul; Hickey, John_W; Fan, Jean; Martelli, ed., Pier_Luigi (June 2024, Bioinformatics)

   Abstract MotivationSpatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. ResultsTo enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures. Availability and implementationSEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. 

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5. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

   https://doi.org/10.1063/5.0091135  

   Heydari, A. Ali; Sindi, Suzanne S. (March 2023, Biophysics Reviews)

   Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications. 

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