Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing

Single-cell transcriptome sequencing atlas of cassava tuberous root

INTRODUCTION

Single-cell transcriptome sequencing (ScRNA-seq) has emerged as an effective method for examining cell differentiation and development. In non-model plants, it hasn't been employed very much, especially in sink organs that are abundant in secondary metabolites.

RESULTS

In this study, we sequenced the single-cell transcriptomes at two developmental phases of cassava tuberous roots using the technology known as 10x Genomics (S1, S2). In total, 14,566 cells were grouped into 15 different cell types, primarily based on the marker genes of model plants known to exist. In the pseudotime study, the cell differentiation trajectory was defined, and the difference in gene expression between the two stages on the pseudotime axis was compared. The differentiation process of the vascular tissue and cerebral tissue was identified by the trajectory. We discovered the rare cell type known as the casparian strip via the use of up-regulated genes and pseudotime analysis, and we explained how it differentiates from endodermis. The successful creation of a protoplast isolation technique for organs rich in starch was also described in our study.

DISCUSSION

Together, we created the first high-resolution single-cell transcriptome atlas of cassava tuberous roots, which made significant advancements in our understanding of how these roots differentiate and develop.

Protoplast Isolation for Plant Single-Cell RNA-seq

The growth and development of plants depends on diversified gene expression in different cell types. Compared to traditional bulk RNA sequencing, droplet-based single-cell RNA sequencing (scRNA-seq) allows for transcriptome profiling of individual cells within heterogeneous tissues. scRNA-seq provides a high-resolution atlas of cellular characterization and vastly improves our understandings of the interactions between individual cells and the microenvironment. However, the difficulty in protoplast isolation has limited the application of single-cell sequencing technology in plant research. Here we describe a high-efficiency protoplast isolation protocol for scRNA-seq.

Single Cell RNA-Sequencing in Arabidopsis Root Tissues

Droplet-based single-cell RNA-sequencing (scRNA-seq) empowers transcriptomic profiling with an unprecedented resolution, facilitating insights into the cellular heterogeneity of tissues, developmental progressions, stress-response dynamics, and more at single-cell level. In this chapter, we describe the experimental workflow of processing Arabidopsis root tissue into protoplasts and generating single-cell transcriptomes. We also describe the general computational workflow of visualizing and utilizing scRNA-seq data. This protocol can be used as a starting point for establishing a scRNA-seq workflow.

An optimized FACS-free single-nucleus RNA sequencing (snRNA-seq) method for plant science research

Recently, single-cell RNA sequencing (scRNA-seq) provides unprecedented power for accurately understanding gene expression regulatory mechanisms. However, scRNA-seq studies have limitations in plants, due to difficulty in protoplast isolation that requires enzymatic digestion of the cell walls from various plant tissues. Therefore, to overcome this problem, we developed a nuclei isolation approach that does not rely on Fluorescence Activated Cell Sorting (FACS). We validated the robustness of the FACS-free single-nucleus RNA sequencing (snRNA-seq) methodology in mature Arabidopsis plant tissue by comparing it to scRNA-seq results based on protoplasts extracted from the same batch of leaf materials. Sequencing results demonstrated the high quality of snRNA-seq data, as well as its utility in cell type classification and marker gene identification. This approach also showed several advantages, including the ability to use frozen samples, taking less suspension preparation time, and reducing biased cellular coverage and dissociation-induced transcriptional artifacts. Surprisingly, snRNA-seq detected two epidermal pavement cell clusters, while scRNA-seq only had one. Furthermore, we hypothesized that these two epidermal cells represent the top and lower epidermis based on differences in expression patterns of cluster-specific expressed genes. In summary, this study has advanced the application of snRNA-seq in Arabidopsis leaves and confirmed the advantages of snRNA-seq in plant research.

Analysis of Chromatin Accessibility, Histone Modifications, and Transcriptional States in Specific Cell Types Using Flow Cytometry

The past two decades in biomedical research have experienced an explosion of cell type-specific and single-cell studies, especially concerning the concomitant dissection of regulatory and transcriptional landscapes of those under investigation. Additionally, leveraging next-generation sequencing (NGS) platforms efforts have been undertaken to evaluate the effects of chromatin accessibility, histone modifications, or even transcription factor binding sites. We have shown that Fluorescence-Activated Nuclear Sorting (FANS) is an effective means to characterize the transcriptomes of nuclei from different tissues. In light of our own technical and experimental developments, we extend this effort to combine FACS/FANS with Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) for profiling individual cell types according to their chromatin and transcriptional states.

Lipid transporter LSR1 positively regulates leaf senescence in Arabidopsis

Senescence is the final stage in the life history of a leaf, whereby plants relocate nutrients from leaves to other developing organs. Recent efforts have begun to focus on understanding the network-based molecular mechanism that incorporates various environmental signals and leaf age information and involves a complex process with the coordinated actions of multiple pathways. Here, we identified a novel participant, named LSR1 (Leaf Senescence Related 1), that involved in the regulation of leaf senescence. Loss-of-function lsr1-1 mutant showed delayed leaf senescence whereas the overexpression of LSR1 accelerated senescence. LSR1 encodes a lipid transfer protein, and the results show that the protein is located in chloroplast and intercellular space. The LSR1 may be involved in the regulation of leaf senescence by transporting lipids in plants.

Plant Single-Cell/Nucleus RNA-seq Workflow

Single-cell transcriptomics technologies allow researchers to investigate how individual cells, in complex multicellular organisms, differentially use their common genomic DNA. In plant biology, these technologies were recently applied to reveal the transcriptomes of various plant cells isolated from different organs and different species and in response to environmental stresses. These first studies support the potential of single-cell transcriptomics technology to decipher the biological function of plant cells, their developmental programs, cell-type-specific gene networks, programs controlling plant cell response to environmental stresses, etc. In this chapter, we provide information regarding the critical steps and important information to consider when developing an experimental design in plant single-cell biology. We also describe the current status of bioinformatics tools used to analyze single-cell RNA-seq datasets and how additional emerging technologies such as spatial transcriptomics and long-read sequencing technologies will provide additional information on the differential use of the genome by plant cells.

Chromatin accessibility illuminates single-cell regulatory dynamics of rice root tips

BACKGROUND

Root development and function have central roles in plant adaptation to the environment. The modification of root traits has additionally been a major driver of crop performance since the green revolution; however, the molecular underpinnings and the regulatory programmes defining root development and response to environmental stress remain largely unknown. Single-cell reconstruction of gene regulatory programmes provides an important tool to understand the cellular phenotypic variation in complex tissues and their response to endogenous and environmental stimuli. While single-cell transcriptomes of several plant organs have been elucidated, the underlying chromatin landscapes associated with cell type-specific gene expression remain largely unexplored.

RESULTS

To comprehensively delineate chromatin accessibility during root development of an important crop, we applied single-cell ATAC-seq (scATAC-seq) to 46,758 cells from rice root tips under normal and heat stress conditions. Our data revealed cell type-specific accessibility variance across most of the major cell types and allowed us to identify sets of transcription factors which associate with accessible chromatin regions (ACRs). Using root hair differentiation as a model, we demonstrate that chromatin and gene expression dynamics during cell type differentiation correlate in pseudotime analyses. In addition to developmental trajectories, we describe chromatin responses to heat and identify cell type-specific accessibility changes to this key environmental stimulus.

CONCLUSIONS

We report chromatin landscapes during rice root development at single-cell resolution. Our work provides a framework for the integrative analysis of regulatory dynamics in this important crop organ at single-cell resolution.

Cell-specific pathways recruited for symbiotic nodulation in the Medicago truncatula legume

Medicago truncatula is a model legume species that has been studied for decades to understand the symbiotic relationship between legumes and soil bacteria collectively named rhizobia. This symbiosis called nodulation is initiated in roots with the infection of root hair cells by the bacteria, as well as the initiation of nodule primordia from root cortical, endodermal, and pericycle cells, leading to the development of a new root organ, the nodule, where bacteria fix and assimilate the atmospheric dinitrogen for the benefit of the plant. Here, we report the isolation and use of the nuclei from mock and rhizobia-inoculated roots for the single nuclei RNA-seq (sNucRNA-seq) profiling to gain a deeper understanding of early responses to rhizobial infection in Medicago roots. A gene expression map of the Medicago root was generated, comprising 25 clusters, which were annotated as specific cell types using 119 Medicago marker genes and orthologs to Arabidopsis cell-type marker genes. A focus on root hair, cortex, endodermis, and pericycle cell types, showing the strongest differential regulation in response to a short-term (48 h) rhizobium inoculation, revealed not only known genes and functional pathways, validating the sNucRNA-seq approach, but also numerous novel genes and pathways, allowing a comprehensive analysis of early root symbiotic responses at a cell type-specific level.

Review: Challenges and perspectives in applying single nuclei RNA-seq technology in plant biology

Plant single-cell RNA-seq technology quantifies the abundance of plant transcripts at a single-cell resolution. Deciphering the transcriptomes of each plant cell, their regulation during plant cell development, and their response to environmental stresses will support the functional study of genes, the establishment of precise transcriptional programs, the prediction of more accurate gene regulatory networks, and, in the long term, the design of de novo gene pathways to enhance selected crop traits. In this review, we will discuss the opportunities, challenges, and problems, and share tentative solutions associated with the generation and analysis of plant single-cell transcriptomes. We will discuss the benefit and limitations of using plant protoplasts vs. nuclei to conduct single-cell RNA-seq experiments on various plant species and organs, the functional annotation of plant cell types based on their transcriptomic profile, the characterization of the dynamic regulation of the plant genes during cell development or in response to environmental stress, the need to characterize and integrate additional layers of -omics datasets to capture new molecular modalities at the single-cell level and reveal their causalities, the deposition and access to single-cell datasets, and the accessibility of this technology to plant scientists.

Plant synthetic biology innovations for biofuels and bioproducts

Plant-based biosynthesis of fuels, chemicals, and materials promotes environmental sustainability, which includes decreases in greenhouse gas emissions, water pollution, and loss of biodiversity. Advances in plant synthetic biology (synbio) should improve precision and efficacy of genetic engineering for sustainability. Applicable synbio innovations include genome editing, gene circuit design, synthetic promoter development, gene stacking technologies, and the design of environmental sensors. Moreover, recent advancements in developing spatially resolved and single-cell omics contribute to the discovery and characterization of cell-type-specific mechanisms and spatiotemporal gene regulations in distinct plant tissues for the expression of cell- and tissue-specific genes, resulting in improved bioproduction. This review highlights recent plant synbio progress and new single-cell molecular profiling towards sustainable biofuel and biomaterial production.

Single-cell RNA-seq reveals fate determination control of an individual fibre cell initiation in cotton (Gossypium hirsutum)

Cotton fibre is a unicellular seed trichome, and lint fibre initials per seed as a factor determines fibre yield. However, the mechanisms controlling fibre initiation from ovule epidermis are not understood well enough. Here, with single-cell RNA sequencing (scRNA-seq), a total of 14 535 cells were identified from cotton ovule outer integument of Xu142_LF line at four developmental stages (1.5, 1, 0.5 days before anthesis and the day of anthesis). Three major cell types, fibre, non-fibre epidermis and outer pigment layer were identified and then verified by RNA in situ hybridization. A comparative analysis on scRNA-seq data between Xu142 and its fibreless mutant Xu142 fl further confirmed fibre cluster definition. The developmental trajectory of fibre cell was reconstructed, and fibre cell was identified differentiated at 1 day before anthesis. Gene regulatory networks at four stages revealed the spatiotemporal pattern of core transcription factors, and MYB25-like and HOX3 were demonstrated played key roles as commanders in fibre differentiation and tip-biased diffuse growth respectively. A model for early development of a single fibre cell was proposed here, which sheds light on further deciphering mechanism of plant trichome and the improvement of cotton fibre yield.

Altering the balance between AOX1A and NDB2 expression affects a common set of transcripts in Arabidopsis

Stress-responsive components of the mitochondrial alternative electron transport pathway have the capacity to improve tolerance of plants to abiotic stress, particularly the alternative oxidase AOX1A but also external NAD(P)H dehydrogenases such as NDB2, in Arabidopsis. NDB2 and AOX1A can cooperate to entirely circumvent the classical electron transport chain in Arabidopsis mitochondria. Overexpression of AOX1A or NDB2 alone can have slightly negative impacts on plant growth under optimal conditions, while simultaneous overexpression of NDB2 and AOX1A can reverse these phenotypic effects. We have taken a global transcriptomic approach to better understand the molecular shifts that occur due to overexpression of AOX1A alone and with concomitant overexpression of NDB2. Of the transcripts that were significantly up- or down- regulated in the AOX1A overexpression line compared to wild type (410 and 408, respectively), the majority (372 and 337, respectively) reverted to wild type levels in the dual overexpression line. Several mechanisms for the AOX1A overexpression phenotype are proposed based on the functional classification of these 709 genes, which can be used to guide future experiments. Only 28 genes were uniquely up- or down-regulated when NDB2 was overexpressed in the AOX1A overexpression line. On the other hand, many unique genes were deregulated in the NDB2 knockout line. Furthermore, several changes in transcript abundance seen in the NDB2 knockout line were consistent with changes in the AOX1A overexpression line. The results suggest that an imbalance in AOX1A:NDB2 protein levels caused by under- or over-expression of either component, triggers a common set of transcriptional responses that may be important in mitochondrial redox regulation. The most significant changes were transcripts associated with photosynthesis, secondary metabolism and oxidative stress responses.

A mutation in THREONINE SYNTHASE 1 uncouples proliferation and transition domains of the root apical meristem: experimental evidence and in silico proposed mechanism

A continuum from stem to transit-amplifying to a differentiated cell state is a common theme in multicellular organisms. In the plant root apical meristem (RAM), transit-amplifying cells are organized into two domains: cells from the proliferation domain (PD) are displaced to the transition domain (TD), suggesting that both domains are necessarily coupled. Here, we show that in the Arabidopsis thaliana mto2-2 mutant, in which threonine (Thr) synthesis is affected, the RAM lacks the PD. Through a combination of cell length profile analysis, mathematical modeling and molecular markers, we establish that the PD and TD can be uncoupled. Remarkably, although the RAM of mto2-2 is represented solely by the TD, the known factors of RAM maintenance and auxin signaling are expressed in the mutant. Mathematical modeling predicts that the stem cell niche depends on Thr metabolism and that, when disturbed, the normal continuum of cell states becomes aborted.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

MINI-EX: Integrative inference of single-cell gene regulatory networks in plants

Multicellular organisms, such as plants, are characterized by highly specialized and tightly regulated cell populations, establishing specific morphological structures and executing distinct functions. Gene regulatory networks (GRNs) describe condition-specific interactions of transcription factors (TFs) regulating the expression of target genes, underpinning these specific functions. As efficient and validated methods to identify cell-type-specific GRNs from single-cell data in plants are lacking, limiting our understanding of the organization of specific cell types in both model species and crops, we developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy. Next, regulons are assigned to different cell types, leveraging cell-specific expression, and candidate regulators are prioritized using network centrality measures, functional annotations, and expression specificity. This embedded prioritization strategy offers a unique and efficient means to unravel signaling cascades in specific cell types controlling a biological process of interest. We demonstrate the stability of MINI-EX toward input data sets with low number of cells and its robustness toward missing data, and show that it infers state-of-the-art networks with a better performance compared with other related single-cell network tools. MINI-EX successfully identifies key regulators controlling root development in Arabidopsis and rice, leaf development in Arabidopsis, and ear development in maize, enhancing our understanding of cell-type-specific regulation and unraveling the roles of different regulators controlling the development of specific cell types in plants.

Single-nuclei transcriptome analysis of the shoot apex vascular system differentiation in Populus

Differentiation of stem cells in the plant apex gives rise to aerial tissues and organs. Presently, we lack a lineage map of the shoot apex cells in woody perennials - a crucial gap considering their role in determining primary and secondary growth. Here, we used single-nuclei RNA-sequencing to determine cell type-specific transcriptomes of the Populus vegetative shoot apex. We identified highly heterogeneous cell populations clustered into seven broad groups represented by 18 transcriptionally distinct cell clusters. Next, we established the developmental trajectories of the epidermis, leaf mesophyll and vascular tissue. Motivated by the high similarities between Populus and Arabidopsis cell population in the vegetative apex, we applied a pipeline for interspecific single-cell gene expression data integration. We contrasted the developmental trajectories of primary phloem and xylem formation in both species, establishing the first comparison of vascular development between a model annual herbaceous and a woody perennial plant species. Our results offer a valuable resource for investigating the principles underlying cell division and differentiation conserved between herbaceous and perennial species while also allowing us to examine species-specific differences at single-cell resolution.

A Novel Algorithm for Feature Selection Using Penalized Regression with Applications to Single-Cell RNA Sequencing Data

With the emergence of single-cell RNA sequencing (scRNA-seq) technology, scientists are able to examine gene expression at single-cell resolution. Analysis of scRNA-seq data has its own challenges, which stem from its high dimensionality. The method of machine learning comes with the potential of gene (feature) selection from the high-dimensional scRNA-seq data. Even though there exist multiple machine learning methods that appear to be suitable for feature selection, such as penalized regression, there is no rigorous comparison of their performances across data sets, where each poses its own challenges. Therefore, in this paper, we analyzed and compared multiple penalized regression methods for scRNA-seq data. Given the scRNA-seq data sets we analyzed, the results show that sparse group lasso (SGL) outperforms the other six methods (ridge, lasso, elastic net, drop lasso, group lasso, and big lasso) using the metrics area under the receiver operating curve (AUC) and computation time. Building on these findings, we proposed a new algorithm for feature selection using penalized regression methods. The proposed algorithm works by selecting a small subset of genes and applying SGL to select the differentially expressed genes in scRNA-seq data. By using hierarchical clustering to group genes, the proposed method bypasses the need for domain-specific knowledge for gene grouping information. In addition, the proposed algorithm provided consistently better AUC for the data sets used.

Salinity induces discontinuous protoxylem via a DELLA-dependent mechanism promoting salt tolerance in Arabidopsis seedlings

Salinity is detrimental to plants and developmental adjustments limiting salt uptake and transport is therefore important for acclimation to high salt. These parameters may be influenced by xylem morphology, however how plant root xylem development is affected by salt stress remains unclear. Using molecular and genetic techniques and detailed phenotypic analyses, we demonstrate that salt causes distinct effects on Arabidopsis seedling root xylem and reveal underlying molecular mechanisms. Salinity causes intermittent inhibition of protoxylem cell differentiation, generating protoxylem gaps, in Arabidopsis and several other eudicot seedlings. The extent of protoxylem gaps in seedlings positively correlates with salt tolerance. Reduced gibberellin signalling is required for protoxylem gap formation. Mutant analyses reveal that the xylem differentiation regulator VASCULAR RELATED NAC DOMAIN 6 (VND6), along with secondary cell wall producing and cell wall modifying enzymes, including EXPANSIN A1 (EXP1), are involved in protoxylem gap formation, in a DELLA-dependent manner. Salt stress is likely to reduce levels of bioactive gibberellins, stabilising DELLAs, which in turn activates multiple factors modifying protoxylem differentiation. Salt stress impacts seedling survival and formation of protoxylem gaps may be a measure to enhance salt tolerance.

Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT

We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than the popular tool Cell Ranger. COPILOT then offers non-arbitrary background noise removal by comparing distributions of low-quality and high-quality cells. Together, this protocol streamlines the processing workflow and provides an easy entry for new scRNA-seq users. For complete details on the use and execution of this protocol, please refer to Shahan et al. (2022).

Cell-Type-Specific Profiling of the Arabidopsis thaliana Membrane Protein-Encoding Genes

Membrane proteins work in large complexes to perceive and transduce external signals and to trigger a cellular response leading to the adaptation of the cells to their environment. Biochemical assays have been extensively used to reveal the interaction between membrane proteins. However, such analyses do not reveal the unique and complex composition of the membrane proteins of the different plant cell types. Here, we conducted a comprehensive analysis of the expression of Arabidopsis membrane proteins in the different cell types composing the root. Specifically, we analyzed the expression of genes encoding membrane proteins interacting in large complexes. We found that the transcriptional profiles of membrane protein-encoding genes differ between Arabidopsis root cell types. This result suggests that different cell types are characterized by specific sets of plasma membrane proteins, which are likely a reflection of their unique biological functions and interactions. To further explore the complexity of the Arabidopsis root cell membrane proteomes, we conducted a co-expression analysis of genes encoding interacting membrane proteins. This study confirmed previously reported interactions between membrane proteins, suggesting that the co-expression of genes at the single cell-type level can be used to support protein network predictions.

Glucose-driven TOR-FIE-PRC2 signalling controls plant development

Nutrients and energy have emerged as central modulators of developmental programmes in plants and animals1-3. The evolutionarily conserved target of rapamycin (TOR) kinase is a master integrator of nutrient and energy signalling that controls growth. Despite its key regulatory roles in translation, proliferation, metabolism and autophagy2-5, little is known about how TOR shapes developmental transitions and differentiation. Here we show that glucose-activated TOR kinase controls genome-wide histone H3 trimethylation at K27 (H3K27me3) in Arabidopsis thaliana, which regulates cell fate and development6-10. We identify FERTILIZATION-INDEPENDENT ENDOSPERM (FIE), an indispensable component of Polycomb repressive complex 2 (PRC2), which catalyses H3K27me3 (refs. 6-8,10-12), as a TOR target. Direct phosphorylation by TOR promotes the dynamic translocation of FIE from the cytoplasm to the nucleus. Mutation of the phosphorylation site on FIE abrogates the global H3K27me3 landscape, reprogrammes the transcriptome and disrupts organogenesis in plants. Moreover, glucose-TOR-FIE-PRC2 signalling modulates vernalization-induced floral transition. We propose that this signalling axis serves as a nutritional checkpoint leading to epigenetic silencing of key transcription factor genes that specify stem cell destiny in shoot and root meristems and control leaf, flower and silique patterning, branching and vegetative-to-reproduction transition. Our findings reveal a fundamental mechanism of nutrient signalling in direct epigenome reprogramming, with broad relevance for the developmental control of multicellular organisms.

Investigation of Brassica and its relative genomes in the post-genomics era

The Brassicaceae family includes many economically important crop species, as well as cosmopolitan agricultural weed species. In addition, Arabidopsis thaliana, a member of this family, is used as a molecular model plant species. The genus Brassica is mesopolyploid, and the genus comprises comparatively recently originated tetrapolyploid species. With these characteristics, Brassicas have achieved the commonly accepted status of model organisms for genomic studies. This paper reviews the rapid research progress in the Brassicaceae family from diverse omics studies, including genomics, transcriptomics, epigenomics, and three-dimensional (3D) genomics, with a focus on cultivated crops. The morphological plasticity of Brassicaceae crops is largely due to their highly variable genomes. The origin of several important Brassicaceae crops has been established. Genes or loci domesticated or contributing to important traits are summarized. Epigenetic alterations and 3D structures have been found to play roles in subgenome dominance, either in tetraploid Brassica species or their diploid ancestors. Based on this progress, we propose future directions and prospects for the genomic investigation of Brassicaceae crops.

Root Cap to Soil Interface: A Driving Force Toward Plant Adaptation and Development

Land plants have developed robust roots to grow in diverse soil ecosystems. The distal end of the root tip has a specialized organ called the 'root cap'. The root cap assists the roots in penetrating the ground, absorbing water and minerals, avoiding heavy metals and regulating the rhizosphere microbiota. Furthermore, root-cap-derived auxin governs the lateral root patterning and directs root growth under varying soil conditions. The root cap formation is hypothesized as one of the key innovations during root evolution. Morphologically diversified root caps in early land plant lineage and later in angiosperms aid in improving the adaptation of roots and, thereby, plants in diverse soil environments. This review article presents a retrospective view of the root cap's important morphological and physiological characteristics for the root-soil interaction and their response toward various abiotic and biotic stimuli. Recent single-cell RNAseq data shed light on root cap cell-type-enriched genes. We compiled root cap cell-type-enriched genes from Arabidopsis, rice, maize and tomato and analyzed their transcription factor (TF) binding site enrichment. Further, the putative gene regulatory networks derived from root-cap-enriched genes and their TF regulators highlight the species-specific biological functions of root cap genes across the four plant species.

Transcriptional analysis of Ceratopteris richardii young sporophyte reveals conservation of stem cell factors in the root apical meristem

Gene expression in roots has been assessed in different plant species in studies ranging from complete organs to specific cell layers, and more recently at the single cell level. While certain genes or functional categories are expressed in the root of all or most plant species, lineage-specific genes have also been discovered. An increasing amount of transcriptomic data is available for angiosperms, while a limited amount of data is available for ferns, and few studies have focused on fern roots. Here, we present a de novo transcriptome assembly from three different parts of the Ceratopteris richardii young sporophyte. Differential gene expression analysis of the root tip transcriptional program showed an enrichment of functional categories related to histogenesis and cell division, indicating an active apical meristem. Analysis of a diverse set of orthologous genes revealed conserved expression in the root meristem, suggesting a preserved role for different developmental roles in this tissue, including stem cell maintenance. The reconstruction of evolutionary trajectories for ground tissue specification genes suggests a high degree of conservation in vascular plants, but not for genes involved in root cap development, showing that certain genes are absent in Ceratopteris or have intricate evolutionary paths difficult to track. Overall, our results suggest different processes of conservation and divergence of genes involved in root development.

A root phloem pole cell atlas reveals common transcriptional states in protophloem-adjacent cells

Single-cell sequencing has recently allowed the generation of exhaustive root cell atlases. However, some cell types are elusive and remain underrepresented. Here we use a second-generation single-cell approach, where we zoom in on the root transcriptome sorting with specific markers to profile the phloem poles at an unprecedented resolution. Our data highlight the similarities among the developmental trajectories and gene regulatory networks common to protophloem sieve element (PSE)-adjacent lineages in relation to PSE enucleation, a key event in phloem biology. As a signature for early PSE-adjacent lineages, we have identified a set of DNA-binding with one finger (DOF) transcription factors, the PINEAPPLEs (PAPL), that act downstream of PHLOEM EARLY DOF (PEAR) genes and are important to guarantee a proper root nutrition in the transition to autotrophy. Our data provide a holistic view of the phloem poles that act as a functional unit in root development.

The receptor kinase SRF3 coordinates iron-level and flagellin dependent defense and growth responses in plants

Iron is critical for host–pathogen interactions. While pathogens seek to scavenge iron to spread, the host aims at decreasing iron availability to reduce pathogen virulence. Thus, iron sensing and homeostasis are of particular importance to prevent host infection and part of nutritional immunity. While the link between iron homeostasis and immunity pathways is well established in plants, how iron levels are sensed and integrated with immune response pathways remains unknown. Here we report a receptor kinase SRF3, with a role in coordinating root growth, iron homeostasis and immunity pathways via regulation of callose synthases. These processes are modulated by iron levels and rely on SRF3 extracellular and kinase domains which tune its accumulation and partitioning at the cell surface. Mimicking bacterial elicitation with the flagellin peptide flg22 phenocopies SRF3 regulation upon low iron levels and subsequent SRF3-dependent responses. We propose that SRF3 is part of nutritional immunity responses involved in sensing external iron levels.

Iron homeostasis is known to influence plant immune signaling. Here the authors characterize SRF3, a receptor kinase that acts as a negative regulator of callose synthesis, that is required for root responses to iron deficiency and pathogen signals.

Arabidopsis plants engineered for high root sugar secretion enhance the diversity of soil microorganisms

Plants secrete sugars from their roots into the soil, presumably to support beneficial plant-microbe interactions. Accordingly, manipulation of sugar secretion might be a viable strategy to enhance plant health and productivity. To evaluate the effect of increased root sugar secretion on plant performance and the soil microbiome, we overexpressed glucose and sucrose-specific membrane transporters in root epidermal cells of the model plant Arabidopsis thaliana. These plants showed strongly increased rates of sugar secretion in a hydroponic culture system. When grown on soil, the transporter-overexpressor plants displayed a higher photosynthesis rate, but reduced shoot growth compared to the wild-type control. Amplicon sequencing and qPCR analysis of rhizosphere soil samples indicated a limited effect on the total abundance of bacteria and fungi, but a strong effect on community structure in soil samples associated with the overexpressors. Notable changes included the increased abundance of bacteria belonging to the genus Rhodanobacter and the fungi belonging to the genus Cutaneotrichosporon, while Candida species abundance was reduced. The potential influences of the altered soil microbiome on plant health and productivity are discussed. The results indicate that the engineering of sugar secretion can be a viable pathway to enhancing the carbon sequestration rate and optimizing the soil microbiome. This article is protected by copyright. All rights reserved.

An overview of bioinformatics, genomics, and transcriptomics resources for bryophytes

Bryophytes are useful models for the study of plant evolution, development, plant-fungal symbiosis, stress responses, and gametogenesis. Additionally, their dominant haploid gametophytic phase makes them great models for functional genomics research, allowing straightforward genome editing and gene knockout via CRISPR or homologous recombination. Until 2016, however, the only bryophyte genome sequence published was that of Physcomitrium patens. Throughout recent years, several other bryophyte genomes and transcriptome datasets became available, enabling better comparative genomics in evolutionary studies. The increase in the number of bryophyte genome and transcriptome resources available has yielded a plethora of annotations, databases, and bioinformatics tools to access the new data, which covers the large diversity of this clade and whose biology comprises features such as association with arbuscular mycorrhiza fungi, sex chromosomes, low gene redundancy, or loss of RNA editing genes for organellar transcripts. Here we provide a guide to resources available for bryophytes with regards to genome and transcriptome databases and bioinformatics tools.

Transcriptional landscapes of de novo root regeneration from detached Arabidopsis leaves revealed by time-lapse and single-cell RNA sequencing analyses

Detached Arabidopsis thaliana leaves can regenerate adventitious roots, providing a platform for studying de novo root regeneration (DNRR). However, the comprehensive transcriptional framework of DNRR remains elusive. Here, we provide a high-resolution landscape of transcriptome reprogramming from wound response to root organogenesis in DNRR and show key factors involved in DNRR. Time-lapse RNA sequencing (RNA-seq) of the entire leaf within 12 h of leaf detachment revealed rapid activation of jasmonate, ethylene, and reactive oxygen species (ROS) pathways in response to wounding. Genetic analyses confirmed that ethylene and ROS may serve as wound signals to promote DNRR. Next, time-lapse RNA-seq within 5 d of leaf detachment revealed the activation of genes involved in organogenesis, wound-induced regeneration, and resource allocation in the wounded region of detached leaves during adventitious rooting. Genetic studies showed that BLADE-ON-PETIOLE1/2, which control aboveground organs, PLETHORA3/5/7, which control root organogenesis, and ETHYLENE RESPONSE FACTOR115, which controls wound-induced regeneration, are involved in DNRR. Furthermore, single-cell RNA-seq data revealed gene expression patterns in the wounded region of detached leaves during adventitious rooting. Overall, our study not only provides transcriptome tools but also reveals key factors involved in DNRR from detached Arabidopsis leaves.

A combinatorial indexing strategy for low-cost epigenomic profiling of plant single cells

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs in animal models. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we developed a combinatorial index and dual PCR barcode strategy to profile the Arabidopsis thaliana root single-cell epigenome without any specialized equipment. We generated chromatin accessibility profiles for 13 576 root nuclei with an average of 12 784 unique Tn5 integrations per cell. Integration of the single-cell assay for transposase-accessible chromatin sequencing and RNA sequencing data sets enabled the identification of 24 cell clusters with unique transcription, chromatin, and cis-regulatory signatures. Comparison with single-cell data generated using the commercial microfluidic platform from 10X Genomics revealed that this low-cost combinatorial index method is capable of unbiased identification of cell-type-specific chromatin accessibility. We anticipate that, by removing cost, instrumentation, and other technical obstacles, this method will be a valuable tool for routine investigation of single-cell epigenomes and provide new insights into plant growth and development and plant interactions with the environment.

scCloudMine: A cloud-based app for visualization, comparison, and exploration of single-cell transcriptomic data

scCloudMine is a cloud-based application for visualization, comparison, and exploration of single-cell transcriptome data. It does not require an on-site, high-power computing server, installation, or associated expertise and expense. Users upload their own or publicly available scRNA-seq datasets after pre-processing for visualization using a web browser. The data can be viewed in two color modes-Cluster, representing cell identity, and Values, showing levels of expression-and data can be queried using keywords or gene identification number(s). Using the app to compare studies, we determined that some genes frequently used as cell-type markers are in fact study specific. The apparent cell-specific expression of PHO1;H3 differed between GFP-tagging and scRNA-seq studies. Some phosphate transporter genes were induced by protoplasting, but they retained cell specificity, suggesting that cell-specific responses to stress (i.e., protoplasting) can occur. Examination of the cell specificity of hormone response genes revealed that 132 hormone-responsive genes display restricted expression and that the jasmonate response gene TIFY8 is expressed in endodermal cells, in contrast to previous reports. It also appears that JAZ repressors have cell-type-specific functions. These features identified using scCloudMine highlight the need for resources to enable biological researchers to compare their datasets of interest under a variety of parameters. scCloudMine enables researchers to form new hypotheses and perform comparative studies and allows for the easy re-use of data from this emerging technology by a wide variety of users who may not have access or funding for high-performance on-site computing and support.

Advances and applications of single-cell omics technologies in plant research

Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.

A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

Cellular heterogeneity in growth and differentiation results in organ patterning. Single-cell transcriptomics allows characterization of gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during this methodology. To recover the original location of cells in the developing organ is essential to link gene activity with cellular identity and function in plants. Here, we propose a method to reconstruct genome-wide gene expression patterns of individual cells in a 3D flower meristem by combining single-nuclei RNA-seq with microcopy-based 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the method is used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com.

Single-cell transcriptomics allows gene expression heterogeneity to be assessed at cellular resolution but the original location of each cell is unknown. Here the authors combine single nuclei RNA-seq with 3D spatial reconstruction of floral meristems to link gene activities with morphology.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms

What makes a mouse a mouse, and not a hamster? Differences in gene regulation between the two organisms play a critical role. Comparative analysis of gene coexpression networks provides a general framework for investigating the evolution of gene regulation across species. Here, we compare coexpression networks from 37 species and quantify the conservation of gene activity 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. We find that ancient genes are expressed in multiple cell types and have well conserved coexpression patterns, however they are expressed at different levels across cell types. Thus, differential regulation of ancient gene programs contributes to transcriptional cell identity. We propose that this differential regulation may play a role in cell diversification in both the animal and plant kingdoms.

scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells

The ubiquitous dropout problem in single-cell RNA sequencing technology causes a large amount of data noise in the gene expression profile. For this reason, we propose an evolutionary sparse imputation (ESI) algorithm for single-cell transcriptomes, which constructs a sparse representation model based on gene regulation relationships between cells. To solve this model, we design an optimization framework based on nondominated sorting genetics. This framework takes into account the topological relationship between cells and the variety of gene expression to iteratively search the global optimal solution, thereby learning the Pareto optimal cell-cell affinity matrix. Finally, we use the learned sparse relationship model between cells to improve data quality and reduce data noise. In simulated datasets, scESI performed significantly better than benchmark methods with various metrics. By applying scESI to real scRNA-seq datasets, we discovered scESI can not only further classify the cell types and separate cells in visualization successfully but also improve the performance in reconstructing trajectories differentiation and identifying differentially expressed genes. In addition, scESI successfully recovered the expression trends of marker genes in stem cell differentiation and can discover new cell types and putative pathways regulating biological processes.

The molecular basis of zinc homeostasis in cereals

Plants require zinc (Zn) as an essential cofactor for diverse molecular, cellular and physiological functions. Zn is crucial for crop yield, but is one of the most limiting micronutrients in soils. Grasses like rice, wheat, maize and barley are crucial sources of food and nutrients for humans. Zn deficiency in these species therefore not only reduces annual yield but also directly results in Zn malnutrition of more than two billion people in the world. There has been good progress in understanding Zn homeostasis and Zn deficiency mechanisms in plants. However, our current knowledge of monocots, including grasses, remains insufficient. In this review, we provide a summary of our knowledge of molecular Zn homeostasis mechanisms in monocots, with a focus on important cereal crops. We additionally highlight divergences in Zn homeostasis of monocots and the dicot model Arabidopsis thaliana, as well as important gaps in our knowledge that need to be addressed in future research on Zn homeostasis in cereal monocots.

Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods

An essential step in the analysis of single-cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single-cell predictive marker (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single-cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non-Arabidopsis species, which expands the number of marker genes for this cell type by 35-154%. Our results represent a new approach to identifying cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.

Single-Cell RNA Sequencing for Plant Research: Insights and Possible Benefits

In recent years, advances in single-cell RNA sequencing (scRNA-seq) technologies have continued to change our views on biological systems by increasing the spatiotemporal resolution of our analysis to single-cell resolution. Application of scRNA-seq to plants enables the comprehensive characterization of both common and rare cell types and cell states, uncovering new cell types and revealing how cell types relate to each other spatially and developmentally. This review provides an overview of scRNA-seq methodologies, highlights the application of scRNA-seq in plant science, justifies why scRNA-seq is a master player of sequencing, and explains the role of single-cell transcriptomics technologies in environmental stress adaptation, alongside the challenges and prospects of single-cell transcriptomics. Collectively, we put forward a central role of single-cell sequencing in plant research.

Applications of cell- and tissue-specific 'omics to improve plant productivity

The individual tissues and cell types of plants each have characteristic properties that contribute to the function of the plant as a whole. These are reflected by unique patterns of gene expression, protein and metabolite content, which enable cell-type-specific patterns of growth, development and physiology. Gene regulatory networks act within the cell types to govern the production and activity of these components. For the broader organism to grow and reproduce successfully, cell-type-specific activity must also function within the context of surrounding cell types, which is achieved by coordination of signalling pathways. We can investigate how gene regulatory networks are constructed and function using integrative 'omics technologies. Historically such experiments in plant biological research have been performed at the bulk tissue level, to organ resolution at best. In this review, we describe recent advances in cell- and tissue-specific 'omics technologies that allow investigation at much improved resolution. We discuss the advantages of these approaches for fundamental and translational plant biology, illustrated through the examples of specialised metabolism in medicinal plants and seed germination. We also discuss the challenges that must be overcome for such approaches to be adopted widely by the community.

A rice single cell transcriptomic atlas defines the developmental trajectories of rice floret and inflorescence meristems

Rice inflorescence development determines yield and relies on the activity of axillary meristems (AMs); however, high-resolution analysis of its early development is lacking. Here, we have used high-throughput single-cell RNA sequencing to profile 37 571 rice inflorescence cells and constructed a genome-scale gene expression resource covering the inflorescence-to-floret transition during early reproductive development. The differentiation trajectories of florets and AMs were reconstructed, and discrete cell types and groups of regulators in the highly heterogeneous young inflorescence were identified and then validated by in situ hybridization and with fluorescent marker lines. Our data demonstrate that a WOX transcription factor, DWARF TILLER1, regulates flower meristem activity, and provide evidence for the role of auxin in rice inflorescence branching by exploring the expression and biological role of the auxin importer OsAUX1. Our comprehensive transcriptomic atlas of early rice inflorescence development, supported by genetic evidence, provides single-cell-level insights into AM differentiation and floret development.

Single-cell RNA-sequencing of Nicotiana attenuata corolla cells reveals the biosynthetic pathway of a floral scent

High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population. Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.

An Efficient and Universal Protoplast Isolation Protocol Suitable for Transient Gene Expression Analysis and Single-Cell RNA Sequencing

The recent advent of single-cell RNA sequencing (scRNA-seq) has enabled access to the developmental landscape of a complex organ by monitoring the differentiation trajectory of every specialized cell type at the single-cell level. A main challenge in this endeavor is dissociating plant cells from the rigid cell walls and some species are recalcitrant to such cellular isolation. Here, we describe the establishment of a simple and efficient protocol for protoplast preparation in Chirita pumila, which includes two consecutive digestion processes with different enzymatic buffers. Using this protocol, we generated viable cell suspensions suitable for an array of expression analyses, including scRNA-seq. The universal application of this protocol was further tested by successfully isolating high-quality protoplasts from multiple organs (petals, fruits, tuberous roots, and gynophores) from representative species on the key branches of the angiosperm lineage. This work provides a robust method in plant science, overcoming barriers to isolating protoplasts in diverse plant species and opens a new avenue to study cell type specification, tissue function, and organ diversification in plants.

Single-cell genomics revolutionizes plant development studies across scales

Understanding the development of tissues, organs and entire organisms through the lens of single-cell genomics has revolutionized developmental biology. Although single-cell transcriptomics has been pioneered in animal systems, from an experimental perspective, plant development holds some distinct advantages: cells do not migrate in relation to one another, and new organ formation (of leaves, roots, flowers, etc.) continues post-embryonically from persistent stem cell populations known as meristems. For a time, plant studies lagged behind animal or cell culture-based, single-cell approaches, largely owing to the difficulty in dissociating plant cells from their rigid cell walls. Recent intensive development of single-cell and single-nucleus isolation techniques across plant species has opened up a wide range of experimental approaches. This has produced a rapidly expanding diversity of information across tissue types and species, concomitant with the creative development of methods. In this brief Spotlight, we highlight some of the technical developments and how they have led to profiling single-cell genomics in various plant organs. We also emphasize the contribution of single-cell genomics in revealing developmental trajectories among different cell types within plant organs. Furthermore, we present efforts toward comparative analysis of tissues and organs at a single-cell level. Single-cell genomics is beginning to generate comprehensive information relating to how plant organs emerge from stem cell populations.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

An update on principles of m6A targeting

N⁶-methyladenosine (m⁶A) is of fundamental importance in gene regulation. The function of m⁶A is achieved through proteins that recognize m⁶A, known as EVOLUTIONARILY CONSERVED C-TERMINAL REGIONS (ECTs) in arabidopsis (Arabidopsis thaliana). mRNA targets of ECTs and their interaction with m⁶A-containing motifs remain to be revealed. In this forum article, I highlight recent advances in m⁶A targeting.

Single-cell RNA sequencing technologies and applications: A brief overview

Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.