Single-nucleus RNA and ATAC sequencing analyses provide molecular insights into early pod development of peanut fruit

Peanut (Arachis hypogaea L.) is an important leguminous oil and economic crop that produces flowers above ground and fruits underground. Subterranean fruit pod development, which significantly impacts peanut production, involves complex molecular mechanisms that likely require the coordinated regulation of multiple genes in different tissues. To investigate the molecular mechanisms underlying peanut fruit pod development, we characterized the anatomical features of early fruit pod development and integrated the snRNA-seq and snATAC-seq data at single-cell levels. Moreover, we identified distinct cell types, such as meristem, embryo, vascular tissue, cuticular layer, and stele cells within the shell wall. These specific cell types were employed to scrutinize potential molecular changes unique to each cell type during pivotal stages of peanut fruit pod development. The snRNA-seq analyses of DEGs (differential expressed genes) unveiled cell-type-specific insights that were not previously discernible through bulk-RNA transcriptome analysis. For instance, MADS-box genes contributing to the formation of parenchyma cells were identified, and gravity-related genes in the vascular cells were detected, indicating an essential role for vascular cells in peg gravitropism. Overall, our single-nucleus analysis provides comprehensive and novel information on specific cell types, gene expression chromatin accessibility during the early stages of fruit pod development. This information will enhance our comprehension of the mechanisms underlying peanut fruit pod development and contribute to efforts aimed at improving peanut production.

Characterizing dysregulations via cell-cell communications in Alzheimer's brains using single-cell transcriptomes

BACKGROUND

Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Most existing studies have solely focused on molecular perturbations within each cell, but cells live in microenvironments rather than in isolated entities. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing in the human prefrontal cortex to investigate cell-to-cell communication in healthy brains and their perturbations in AD. We uniformly processed the snRNA-seq with strict QCs and labeled canonical cell types consistent with the definitions from the BRAIN Initiative Cell Census Network. From ligand and receptor gene expression, we built a high-confidence cell-to-cell communication network to investigate signaling differences between AD and healthy brains.

RESULTS

Specifically, we first performed broad communication pattern analyses to highlight that biologically related cell types in normal brains rely on largely overlapping signaling networks and that the AD brain exhibits the irregular inter-mixing of cell types and signaling pathways. Secondly, we performed a more focused cell-type-centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other non-neuronal cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFβ, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and from endothelial to neuronal cells for the WNT pathway. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons.

CONCLUSIONS

In summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type-specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, giving the mechanistic intra- and inter-cellular picture of AD.

Single-nucleus transcriptional and chromatin accessible profiles reveal critical cell types and molecular architecture underlying chicken sex determination

INTRODUCTION

Understanding the sex determination mechanisms in birds has great significance for the biological sciences and production in the poultry industry. Sex determination in chickens is a complex process that involves fate decisions of supporting cells such as granulosa or Sertoli cells. However, a systematic understanding of the genetic regulation and cell commitment process underlying sex determination in chickens is still lacking.

OBJECTIVES

We aimed to dissect the molecular characteristics associated with sex determination in the gonads of chicken embryos.

METHODS

Single-nucleus RNA-seq (snRNA-seq) and ATAC-seq (snATAC-seq) analysis were conducted on the gonads of female and male chickens at embryonic day 3.5 (E3.5), E4.5, and E5.5.

RESULTS

Here, we provided a time-course transcriptional and chromatin accessible profiling of gonads during chicken sex determination at single-cell resolution. We uncovered differences in cell composition and developmental trajectories between female and male gonads and found that the divergence of transcription and accessibility in gonadal cells first emerged at E5.5. Furthermore, we revealed key cell-type-specific transcription factors (TFs) and regulatory networks that drive lineage commitment. Sex determination signaling pathways, dominated by BMP signaling, are preferentially activated in males during gonadal development. Further pseudotime analysis of the supporting cells indicated that granulosa cells were regulated mainly by the TEAD gene family and that Sertoli cells were driven by the DMRT1 regulons. Cross-species analysis suggested high conservation of both cell types and cell-lineage-specific TFs across the six vertebrates.

CONCLUSIONS

Overall, our study will contribute to accelerating the development of sex manipulation technology in the poultry industry and the application of chickens as a unique model for studying cell fate decisions.

NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing

BACKGROUND

Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation.

RESULTS

We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed.

CONCLUSIONS

Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

A Single-Nucleus Transcriptome-Wide Association Study Implicates Novel Genes in Depression Pathogenesis

BACKGROUND

Depression, a common psychiatric illness and global public health problem, remains poorly understood across different life stages, which hampers the development of novel treatments.

METHODS

To identify new candidate genes for therapeutic development, we performed differential gene expression analysis of single-nucleus RNA sequencing data from the dorsolateral prefrontal cortex of older adults (n = 424) in relation to antemortem depressive symptoms. Additionally, we integrated genome-wide association study results for depression (n = 500,199) along with genetic tools for inferring the expression of 14,048 unique genes in 7 cell types and 52 cell subtypes to perform a transcriptome-wide association study of depression followed by Mendelian randomization.

RESULTS

Our single-nucleus transcriptome-wide association study analysis identified 68 candidate genes for depression and showed the greatest number being in excitatory and inhibitory neurons. Of the 68 genes, 53 were novel compared to previous studies. Notably, gene expression in different neuronal subtypes had varying effects on depression risk. Traits with high genetic correlations with depression, such as neuroticism, shared more transcriptome-wide association study genes than traits that were not highly correlated with depression. Complementing these analyses, differential gene expression analysis across 52 neocortical cell subtypes showed that genes such as KCNN2, SCAI, WASF3, and SOCS6 were associated with late-life depressive symptoms in specific cell subtypes.

CONCLUSIONS

These 2 sets of analyses illustrate the utility of large single-nucleus RNA sequencing data both to uncover genes whose expression is altered in specific cell subtypes in the context of depressive symptoms and to enhance the interpretation of well-powered genome-wide association studies so that we can prioritize specific susceptibility genes for further analysis and therapeutic development.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

BACKGROUND

Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights.

RESULTS

Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files.

CONCLUSIONS

We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

FIPRESCI: droplet microfluidics based combinatorial indexing for massive-scale 5'-end single-cell RNA sequencing

Single-cell RNA sequencing methods focusing on the 5'-end of transcripts can reveal promoter and enhancer activity and efficiently profile immune receptor repertoire. However, ultra-high-throughput 5'-end single-cell RNA sequencing methods have not been described. We introduce FIPRESCI, 5'-end single-cell combinatorial indexing RNA-Seq, enabling massive sample multiplexing and increasing the throughput of the droplet microfluidics system by over tenfold. We demonstrate FIPRESCI enables the generation of approximately 100,000 single-cell transcriptomes from E10.5 whole mouse embryos in a single-channel experiment, and simultaneous identification of subpopulation differences and T cell receptor signatures of peripheral blood T cells from 12 cancer patients.

Isolation of Nuclei from Human Snap-frozen Liver Tissue for Single-nucleus RNA Sequencing

Single-nucleus RNA sequencing (snRNA-seq) provides a powerful tool for studying cell type composition in heterogenous tissues. The liver is a vital organ composed of a diverse set of cell types; thus, single-cell technologies could greatly facilitate the deconvolution of liver tissue composition and various downstream omics analyses at the cell-type level. Applying single-cell technologies to fresh liver biopsies can, however, be very challenging, and snRNA-seq of snap-frozen liver biopsies requires some optimization given the high nucleic acid content of the solid liver tissue. Therefore, an optimized protocol for snRNA-seq specifically targeted for the use of frozen liver samples is needed to improve our understanding of human liver gene expression at the cell-type resolution. We present a protocol for performing nuclei isolation from snap-frozen liver tissues, as well as guidance on the application of snRNA-seq. We also provide guidance on optimizing the protocol to different tissue and sample types.

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.

Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleate syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters and if nuclei within the cell diversify into heterogeneous states. Here, we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleus cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.

Isolation of Nuclei from Mouse Dorsal Root Ganglia for Single-nucleus Genomics

Primary somatosensory neurons, whose cell bodies reside in the dorsal root ganglion (DRG) and trigeminal ganglion, are specialized to transmit sensory information from the periphery to the central nervous system. Our molecular understanding of peripheral sensory neurons has been limited by both their heterogeneity and low abundance compared with non-neuronal cell types in sensory ganglia. We describe a protocol to isolate nuclei from mouse DRGs using iodixanol density gradient centrifugation, which enriches for neuronal nuclei while still sampling non-neuronal cells such as satellite glia and Schwann cells. This protocol is compatible with a range of downstream applications such as single-nucleus transcriptional and epigenomic assays.

Gene expression variation in Arabidopsis embryos at single-nucleus resolution

Soon after fertilization of egg and sperm, plant genomes become transcriptionally activated and drive a series of coordinated cell divisions to form the basic body plan during embryogenesis. Early embryonic cells rapidly diversify from each other, and investigation of the corresponding gene expression dynamics can help elucidate underlying cellular differentiation programs. However, current plant embryonic transcriptome datasets either lack cell-specific information or have RNA contamination from surrounding non-embryonic tissues. We have coupled fluorescence-activated nuclei sorting together with single-nucleus mRNA-sequencing to construct a gene expression atlas of Arabidopsis thaliana early embryos at single-cell resolution. In addition to characterizing cell-specific transcriptomes, we found evidence that distinct epigenetic and transcriptional regulatory mechanisms operate across emerging embryonic cell types. These datasets and analyses, as well as the approach we devised, are expected to facilitate the discovery of molecular mechanisms underlying pattern formation in plant embryos. This article has an associated 'The people behind the papers' interview.

FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

The broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq

Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues such as adult mammalian brains is challenging. Here, we integrate sucrose-gradient-assisted purification of nuclei with droplet microfluidics to develop a highly scalable single-nucleus RNA-seq approach (sNucDrop-seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ∼18,000 nuclei isolated from cortical tissues of adult mice, we demonstrate that sNucDrop-seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity but also enables in-depth analysis of transient transcriptional states driven by neuronal activity, at single-cell resolution, in vivo.