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As the number of single‐cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA‐seq data, while accounting for uncertainty caused by biological and measurement noise. We also introduce single‐cell ANnotation using Variational Inference (scANVI), a semi‐supervised variant of scVI designed to leverage existing cell state annotations. We demonstrate that scVI and scANVI compare favorably to state‐of‐the‐art methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings. In contrast to existing methods, scVI and scANVI integrate multiple datasets with a single generative model that can be directly used for downstream tasks, such as differential expression. Both methods are easily accessible through scvi‐tools. SYNOPSIS This study demonstrates the ability of scVI to integrate single‐cell RNA‐seq datasets in a variety of settings and presents scANVI, a new development based on scVI for automated annotation of cell types and states. In scVI, datasets from different labs and technologies are integrated in a joint latent space. In scANVI, cell type annotations are transferred between datasets and across different scenarios. Uncertainties of differential gene expression in multiple samples are quantified. The performance of scVI and scANVI in data integration and cell state annotation is superior to other related methods. Graphical Abstract This study demonstrates the ability of scVI to integrate single‐cell RNA‐seq datasets in a variety of settings and presents scANVI, a new development based on scVI for automated annotation of cell types and states.

• 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.

Glioblastomas are highly heterogeneous brain tumors. Despite the availability of standard treatment for glioblastoma multiforme (GBM), i.e., Stupp protocol, which involves surgical resection followed by radiotherapy and chemotherapy, glioblastoma remains refractory to treatment and recurrence is inevitable. Moreover, the biology of recurrent glioblastoma remains unclear. Increasing evidence has shown that intratumoral heterogeneity and the tumor microenvironment contribute to therapeutic resistance. However, the interaction between intracellular heterogeneity and drug resistance in recurrent GBMs remains controversial. The aim of this study was to map the transcriptome landscape of cancer cells and the tumor heterogeneity and tumor microenvironment in recurrent and drug‐resistant GBMs at a single‐cell resolution and further explore the mechanism of drug resistance of GBMs. We analyzed six tumor tissue samples from three patients with primary GBM and three patients with recurrent GBM in which recurrence and drug resistance developed after treatment with the standard Stupp protocol using single‐cell RNA sequencing. Using unbiased clustering, nine major cell clusters were identified. Upregulation of the expression of stemness‐related and cell‐cycle‐related genes was observed in recurrent GBM cells. Compared with the initial GBM tissues, recurrent GBM tissues showed a decreased proportion of microglia, consistent with previous reports. Finally, vascular endothelial growth factor A expression and the blood–brain barrier permeability were high, and the O6‐methylguanine DNA methyltransferase‐related signaling pathway was activated in recurrent GBM. Our results delineate the single‐cell map of recurrent glioblastoma, tumor heterogeneity, tumor microenvironment, and drug‐resistance mechanisms, providing new insights into treatment strategies for recurrent glioblastomas. We observed upregulation of the expression of stemness‐related and cell‐cycle‐related genes in recurrent GBM cells. Further, we observed that recurrent GBM tissues showed a decreased proportion of microglia, consistent with previous reports, and that vascular endothelial growth factor A expression and the blood–brain barrier permeability were high, and the O6‐methylguanine DNA methyltransferase‐related signaling pathway was activated in recurrent GBM. Our results provide new insights into treatment strategies for recurrent glioblastomas.

Summary The tea plant is an economically important woody beverage crop. The unique taste of tea is evoked by certain metabolites, especially catechin esters, whereas their precise formation mechanism in different cell types remains unclear. Here, a fast protoplast isolation method was established and the transcriptional profiles of 16 977 single cells from 1st and 3rd leaves were investigated. We first identified 79 marker genes based on six isolated tissues and constructed a transcriptome atlas, mapped developmental trajectories and further delineated the distribution of different cell types during leaf differentiation and genes associated with cell fate transformation. Interestingly, eight differently expressed genes were found to co‐exist at four branch points. Genes involved in the biosynthesis of certain metabolites showed cell‐ and development‐specific characteristics. An unexpected catechin ester glycosyltransferase was characterized for the first time in plants by a gene co‐expression network in mesophyll cells. Thus, the first single‐cell transcriptional landscape in woody crop leave was reported and a novel metabolism pathway of catechin esters in plants was discovered.

• 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.

Summary Stalk rot caused by Fusarium verticillioides (Fv) is one of the most destructive diseases in maize production. The defence response of root system to Fv invasion is important for plant growth and development. Dissection of root cell type‐specific response to Fv infection and its underlying transcription regulatory networks will aid in understanding the defence mechanism of maize roots to Fv invasion. Here, we reported the transcriptomes of 29 217 single cells derived from root tips of two maize inbred lines inoculated with Fv and mock condition, and identified seven major cell types with 21 transcriptionally distinct cell clusters. Through the weighted gene co‐expression network analysis, we identified 12 Fv‐responsive regulatory modules from 4049 differentially expressed genes (DEGs) that were activated or repressed by Fv infection in these seven cell types. Using a machining‐learning approach, we constructed six cell type‐specific immune regulatory networks by integrating Fv‐induced DEGs from the cell type‐specific transcriptomes, 16 known maize disease‐resistant genes, five experimentally validated genes (ZmWOX5b, ZmPIN1a, ZmPAL6, ZmCCoAOMT2, and ZmCOMT), and 42 QTL or QTN predicted genes that are associated with Fv resistance. Taken together, this study provides not only a global view of maize cell fate determination during root development but also insights into the immune regulatory networks in major cell types of maize root tips at single‐cell resolution, thus laying the foundation for dissecting molecular mechanisms underlying disease resistance in maize.

The serious coronavirus disease‐2019 (COVID‐19) was first reported in December 2019 in Wuhan, China. COVID‐19 is an infectious disease caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). Angiotensin converting enzyme 2(ACE2) is the cellular receptor for SARS‐CoV‐2. Considering the critical roles of testicular cells for the transmission of genetic information between generations, we analyzed single‐cell RNA‐sequencing (scRNA‐seq) data of adult human testis. The mRNA expression of ACE2 was expressed in both germ cells and somatic cells. Moreover, the positive rate of ACE2 in testes of infertile men was higher than normal, which indicates that SARS‐CoV‐2 may cause reproductive disorders through pathway activated by ACE2 and the men with reproductive disorder may easily to be infected by SARS‐CoV‐2. The expression level of ACE2 was related to the age, and the mid‐aged with higher positive rate than young men testicular cells. Taken together, this research provides a biological background of the potential route for infection of SARS‐CoV‐2 and may enable rapid deciphering male‐related reproductive disorders induced by COVID‐19.

Single-cell RNA-seq (scRNA-seq) has been highlighted as a powerful tool for the description of human cell transcriptome, but the technology has not been broadly applied in plant cells. Herein, we describe the successful development of a robust protoplast cell isolation system in the peanut leaf. A total of 6,815 single cells were divided into eight cell clusters based on reported marker genes by applying scRNA-seq. Further, a pseudo-time analysis was used to describe the developmental trajectory and interaction network of transcription factors (TFs) of distinct cell types during leaf growth. The trajectory enabled re-investigation of the primordium-driven development processes of the mesophyll and epidermis. These results suggest that palisade cells likely differentiate into spongy cells, while the epidermal cells originated earlier than the primordium. Subsequently, the developed method integrated multiple technologies to efficiently validate the scRNA-seq result in a homogenous cell population. The expression levels of several TFs were strongly correlated with epidermal ontogeny in accordance with obtained scRNA-seq values. Additionally, peanut AHL23 (AT-HOOK MOTIF NUCLEAR LOCALIZED PROTEIN 23), which is localized in nucleus, promoted leaf growth when ectopically expressed in Arabidopsis by modulating the phytohormone pathway. Together, our study displays that application of scRNA-seq can provide new hypotheses regarding cell differentiation in the leaf blade of Arachis hypogaea. We believe that this approach will enable significant advances in the functional study of leaf blade cells in the allotetraploid peanut and other plant species.

The transcriptome, the complete set of RNA molecules within a cell, plays a critical role in regulating physiological processes. The advent of RNA sequencing (RNA-seq) facilitated by Next Generation Sequencing (NGS) technologies, has revolutionized transcriptome research, providing unique insights into gene expression dynamics. This powerful strategy can be applied at both bulk tissue and single-cell levels. Bulk RNA-seq provides a gene expression profile within a tissue sample. Conversely, single-cell RNA sequencing (scRNA-seq) offers resolution at the cellular level, allowing the uncovering of cellular heterogeneity, identification of rare cell types, and distinction between distinct cell populations. As computational tools, machine learning techniques, and NGS sequencing platforms continue to evolve, the field of transcriptome research is poised for significant advancements. Therefore, to fully harness this potential, a comprehensive understanding of bulk RNA-seq and scRNA-seq technologies, including their advantages, limitations, and computational considerations, is crucial. This review provides a systematic comparison of the computational processes involved in both RNA-seq and scRNA-seq, highlighting their fundamental principles, applications, strengths, and limitations, while outlining future directions in transcriptome research.