Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

Practical Considerations for Single-Cell Genomics

The single-cell revolution in the field of genomics is in full bloom, with clever new molecular biology tricks appearing regularly that allow researchers to explore new modalities or scale up their projects to millions of cells and beyond. Techniques abound to measure RNA expression, DNA alterations, protein abundance, chromatin accessibility, and more, all with single-cell resolution and often in combination. Despite such a rapidly changing technology landscape, there are several fundamental principles that are applicable to the majority of experimental workflows to help users avoid pitfalls and exploit the advantages of the chosen platform. In this overview article, we describe a variety of popular single-cell genomics technologies and address some common questions pertaining to study design, sample preparation, quality control, and sequencing strategy. As the majority of relevant publications currently revolve around single-cell RNA-seq, we will prioritize this genomics modality in our discussion. © 2022 Wiley Periodicals LLC.

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data

Background

Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.

Results

We propose a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Our new scDLC does not require a prior knowledge on the data distribution, but instead, it takes into account the dependency of the most outstanding feature genes in the LSTMs model. LSTMs is a special recurrent neural network, which can learn long-term dependencies of a sequence.

Conclusions

Simulation studies show that our new scDLC performs consistently better than the existing methods in a wide range of settings with large sample sizes. Four real scRNA-seq datasets are also analyzed, and they coincide with the simulation results that our new scDLC always performs the best. The code named “scDLC” is publicly available at https://github.com/scDLC-code/code.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08715-1).

Single-nucleus RNA-sequencing reveals singular gene signatures of human ductal cells during adaptation to insulin resistance

Adaptation to increased insulin demand is mediated by β cell proliferation and neogenesis, among other mechanisms. Although it is known that pancreatic β cells can arise from ductal progenitors, these observations have been limited mostly to the neonatal period. We have recently reported that the duct is a source of insulin-secreting cells in adult insulin-resistant states. To further explore the signaling pathways underlying the dynamic β cell reserve during insulin resistance, we undertook human islet and duct transplantations under the kidney capsule of immunodeficient NOD/SCID-γ (NSG) mouse models that were pregnant, were insulin-resistant, or had insulin resistance superimposed upon pregnancy (insulin resistance + pregnancy), followed by single-nucleus RNA-Seq (snRNA-Seq) on snap-frozen graft samples. We observed an upregulation of proliferation markers (e.g., NEAT1) and expression of islet endocrine cell markers (e.g., GCG and PPY), as well as mature β cell markers (e.g., INS), in transplanted human duct grafts in response to high insulin demand. We also noted downregulation of ductal cell identity genes (e.g., KRT19 and ONECUT2) coupled with upregulation of β cell development and insulin signaling pathways. These results indicate that subsets of ductal cells are able to gain β cell identity and reflect a form of compensation during the adaptation to insulin resistance in both physiological and pathological states.

SAREV: A review on statistical analytics of single-cell RNA sequencing data

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

Interplay Between GH-regulated, Sex-biased Liver Transcriptome and Hepatic Zonation Revealed by Single-Nucleus RNA Sequencing

The zonation of liver metabolic processes is well-characterized; however, little is known about the cell type-specificity and zonation of sexually dimorphic gene expression or its growth hormone (GH)-dependent transcriptional regulators. We address these issues using single-nucleus RNA-sequencing of 32 000 nuclei representing 9 major liver cell types. Nuclei were extracted from livers from adult male and female mice; from males infused with GH continuously, mimicking the female plasma GH pattern; and from mice exposed to TCPOBOP, a xenobiotic agonist ligand of the nuclear receptor CAR that perturbs sex-biased gene expression. Analysis of these rich transcriptomic datasets revealed the following: 1) expression of sex-biased genes and their GH-dependent transcriptional regulators is primarily restricted to hepatocytes and is not a feature of liver nonparenchymal cells; 2) many sex-biased transcripts show sex-dependent zonation within the liver lobule; 3) gene expression is substantially feminized both in periportal and pericentral hepatocytes when male mice are infused with GH continuously; 4) sequencing nuclei increases the sensitivity for detecting thousands of nuclear-enriched long-noncoding RNAs (lncRNAs) and enables determination of their liver cell type-specificity, sex-bias and hepatocyte zonation profiles; 5) the periportal to pericentral hepatocyte cell ratio is significantly higher in male than female liver; and 6) TCPOBOP exposure disrupts both sex-specific gene expression and hepatocyte zonation within the liver lobule. These findings highlight the complex interconnections between hepatic sexual dimorphism and zonation at the single-cell level and reveal how endogenous hormones and foreign chemical exposure can alter these interactions across the liver lobule with large effects both on protein-coding genes and lncRNAs.

Single-cell analysis of the aging female mouse hypothalamus

Alterations in metabolism, sleep patterns, body composition, and hormone status are all key features of aging. While the hypothalamus is a well-conserved brain region that controls these homeostatic and survival-related behaviors, little is known about the intrinsic features of hypothalamic aging. Here, we perform single nuclei RNA-sequencing of 40,064 hypothalamic nuclei from young and aged female mice. We identify cell type-specific signatures of aging in neuronal subtypes as well as astrocytes and microglia. We uncover changes in cell types critical for metabolic regulation and body composition, and in an area of the hypothalamus linked to cognition. Our analysis also reveals an unexpected female-specific feature of hypothalamic aging: the master regulator of X-inactivation, Xist, is elevated with age, particularly in hypothalamic neurons. Moreover, using machine learning, we show that levels of X-chromosome genes, and Xist itself, can accurately predict cellular age. This study identifies critical cell-specific changes of the aging hypothalamus in mammals, and uncovers a potential marker of neuronal aging in females.

Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function

Psychiatric and neurological disorders are influenced by an undetermined number of genes and molecular pathways that may differ among afflicted individuals. Functionally testing and characterizing biological systems is essential to discovering the interrelationship among candidate genes and understanding the neurobiology of behavior. Recent advancements in genetic, genomic, and behavioral approaches are revolutionizing modern neuroscience. Although these tools are often used separately for independent experiments, combining these areas of research will provide a viable avenue for multidimensional studies on the brain. Herein we will briefly review some of the available tools that have been developed for characterizing novel cellular and animal models of human disease. A major challenge will be openly sharing resources and datasets to effectively integrate seemingly disparate types of information and how these systems impact human disorders. However, as these emerging technologies continue to be developed and adopted by the scientific community, they will bring about unprecedented opportunities in our understanding of molecular neuroscience and behavior.

Multi-omics profiling of single nuclei from frozen archived postmortem human pituitary tissue

Concomitant profiling of transcriptome and chromatin accessibility in isolated nuclei can reveal gene regulatory control mechanisms in health and disease. We report a single nucleus multi-omics analysis protocol optimized for frozen archived postmortem human pituitaries that is also effective for frozen ovine and murine pituitaries and human skeletal muscle biopsies. Its main advantages are that (1) it is not limited to fresh tissue, (2) it avoids tissue dissociation-induced transcriptional changes, and (3) it includes a novel, automated quality control pipeline. For complete details on the use and execution of this protocol, please refer to Ruf-Zamojski et al. (2021) and Zhang et al. (2022).

A map of bat virus receptors derived from single-cell multiomics

Bats are considered reservoirs of many lethal zoonotic viruses and have been implicated in several outbreaks of emerging infectious diseases, such as SARS-CoV, MERS-CoV, and SARS-CoV-2. It is necessary to systematically derive the expression patterns of bat virus receptors and their regulatory features for future research into bat-borne viruses and the prediction and prevention of pandemics. Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) of major organ samples collected from Chinese horseshoe bats (Rhinolophus affinis) and systematically checked the expression pattern of bat-related virus receptors and chromatin accessibility across organs and cell types, providing a valuable dataset for studying the nature of infection among bat-borne viruses.

Internal oligo(dT) priming introduces systematic bias in bulk and single-cell RNA sequencing count data

Significant advances in RNA sequencing have been recently made possible by using oligo(dT) primers for simultaneous mRNA enrichment and reverse transcription priming. The associated increase in efficiency has enabled more economical bulk RNA sequencing methods and the advent of high-throughput single-cell RNA sequencing, already one of the most widely adopted methods in transcriptomics. However, the effects of off-target oligo(dT) priming on gene expression quantification have not been appreciated. In the present study, we describe the extent, the possible causes, and the consequences of internal oligo(dT) priming across multiple public datasets obtained from various bulk and single-cell RNA sequencing platforms. To explore and address this issue, we developed a computational algorithm for RNA counting methods, which identifies the sequencing read alignments that likely resulted from internal oligo(dT) priming and removes them from the data. Directly comparing filtered datasets to those obtained by an alternative method reveals significant improvements in gene expression measurement. Finally, we infer a list of human genes whose expression quantification is most likely to be affected by internal oligo(dT) priming and predict that when measured using these methods, the expression of most genes may be inflated by at least 10% whereby some genes are affected more than others.

Inherent constraints on a polyfunctional tissue lead to a reproduction-immunity tradeoff

BACKGROUND

Single tissues can have multiple functions, which can result in constraints, impaired function, and tradeoffs. The insect fat body performs remarkably diverse functions including metabolic control, reproductive provisioning, and systemic immune responses. How polyfunctional tissues simultaneously execute multiple distinct physiological functions is generally unknown. Immunity and reproduction are observed to trade off in many organisms but the mechanistic basis for this tradeoff is also typically not known. Here we investigate constraints and trade-offs in the polyfunctional insect fat body.

RESULTS

Using single-nucleus sequencing, we determined that the Drosophila melanogaster fat body executes diverse basal functions with heterogenous cellular subpopulations. The size and identity of these subpopulations are remarkably stable between virgin and mated flies, as well as before and after infection. However, as an emergency function, the immune response engages the entire tissue and all cellular subpopulations produce induce expression of defense genes. We found that reproductively active females who were given bacterial infection exhibited signatures of ER stress and impaired capacity to synthesize new protein in response to infection, including decreased capacity to produce antimicrobial peptides. Transient provision of a reversible translation inhibitor to mated females prior to infection rescued general protein synthesis, specific production of antimicrobial peptides, and survival of infection.

CONCLUSIONS

The commonly observed tradeoff between reproduction and immunity appears to be driven, in D. melanogaster, by a failure of the fat body to be able to handle simultaneous protein translation demands of reproductive provisioning and immune defense. We suggest that inherent cellular limitations in tissues that perform multiple functions may provide a general explanation for the wide prevalence of physiological and evolutionary tradeoffs.

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.

Dnmt3a knockout in excitatory neurons impairs postnatal synapse maturation and increases the repressive histone modification H3K27me3

Two epigenetic pathways of transcriptional repression, DNA methylation and polycomb repressive complex 2 (PRC2), are known to regulate neuronal development and function. However, their respective contributions to brain maturation are unknown. We found that conditional loss of the de novo DNA methyltransferase Dnmt3a in mouse excitatory neurons altered expression of synapse-related genes, stunted synapse maturation, and impaired working memory and social interest. At the genomic level, loss of Dnmt3a abolished postnatal accumulation of CG and non-CG DNA methylation, leaving adult neurons with an unmethylated, fetal-like epigenomic pattern at ~222,000 genomic regions. The PRC2-associated histone modification, H3K27me3, increased at many of these sites. Our data support a dynamic interaction between two fundamental modes of epigenetic repression during postnatal maturation of excitatory neurons, which together confer robustness on neuronal regulation.

Brain atrophy in prodromal synucleinopathy is shaped by structural connectivity and gene expression

Isolated REM sleep behaviour disorder (iRBD) is a synucleinopathy characterized by abnormal behaviours and vocalizations during REM sleep. Most iRBD patients develop dementia with Lewy bodies, Parkinson's disease, or multiple system atrophy over time. Patients with iRBD exhibit brain atrophy patterns that are reminiscent of those observed in overt synucleinopathies. However, the mechanisms linking brain atrophy to the underlying alpha-synuclein pathophysiology are poorly understood. Our objective was to investigate how the prion-like and regional vulnerability hypotheses of alpha-synuclein might explain brain atrophy in iRBD. Using a multicentric cohort of 182 polysomnography-confirmed iRBD patients who underwent T1-weighted MRI, we performed vertex-based cortical surface and deformation-based morphometry analyses to quantify brain atrophy in patients (67.8 years, 84% men) and 261 healthy controls (66.2 years, 75%) and investigated the morphological correlates of motor and cognitive functioning in iRBD. Next, we applied the agent-based Susceptible-Infected-Removed model (i.e., a computational model that simulates in silico the spread of pathologic alpha-synuclein based on structural connectivity and gene expression) and tested if it recreated atrophy in iRBD by statistically comparing simulated regional brain atrophy to the atrophy observed in patients. The impact of SNCA and GBA gene expression and brain connectivity was then evaluated by comparing the model fit to the one obtained in null models where either gene expression or connectivity was randomized. The results showed that iRBD patients present with cortical thinning and tissue deformation, which correlated with motor and cognitive functioning. Next, we found that the computational model recreated cortical thinning (r = 0.51, p = 0.0007) and tissue deformation (r = 0.52, p = 0.0005) in patients, and that the connectome's architecture along with SNCA and GBA gene expression contributed to shaping atrophy in iRBD. We further demonstrated that the full agent-based model performed better than network measures or gene expression alone in recreating the atrophy pattern in iRBD. In summary, atrophy in iRBD is extensive, correlates with motor and cognitive function, and can be recreated using the dynamics of agent-based modelling, structural connectivity, and gene expression. These findings support the concepts that both prion-like spread and regional susceptibility account for the atrophy observed in prodromal synucleinopathies. Therefore, the agent-based Susceptible-Infected-Removed model may be a useful tool for testing hypotheses underlying neurodegenerative diseases and new therapies aimed at slowing or stopping the spread of alpha-synuclein pathology.

Single-Cell Transcriptomics of Liver Cancer: Hype or Insights?

Hepatocellular carcinoma (HCC) is characterized by its high degrees of both inter- and intratumoral heterogeneity. Its complex tumor microenvironment is also crucial in promoting tumor progression. Recent advances in single-cell RNA sequencing provide an important highway to characterize the underlying pathogenesis and heterogeneity of HCC in an unprecedented degree of resolution. This review discusses the up-to-date discoveries from the latest studies of HCC with respect to the strength of single-cell RNA sequencing. We discuss its use in the dissection of the landscape of the intricate HCC ecosystem and highlight the major features at cellular levels, including the malignant cells, different immune cell types, and the various cell-cell interactions, which are crucial for developing effective immunotherapies. Finally, its translational applications will be discussed. Altogether, these explorations may give us some hints at the tumor growth and progression and drug resistance and recurrence, particularly in this era of personalized medicine.

Single-Cell and Single-Nucleus RNAseq Analysis of Adult Neurogenesis

The complexity of adult neurogenesis is becoming increasingly apparent as we learn more about cellular heterogeneity and diversity of the neurogenic lineages and stem cell niches within the adult brain. This complexity has been unraveled in part due to single-cell and single-nucleus RNA sequencing (sc-RNAseq and sn-RNAseq) studies that have focused on adult neurogenesis. This review summarizes 33 published studies in the field of adult neurogenesis that have used sc- or sn-RNAseq methods to answer questions about the three main regions that host adult neural stem cells (NSCs): the subventricular zone (SVZ), the dentate gyrus (DG) of the hippocampus, and the hypothalamus. The review explores the similarities and differences in methodology between these studies and provides an overview of how these studies have advanced the field and expanded possibilities for the future.

Heterogeneous Skeletal Muscle Cell and Nucleus Populations Identified by Single-Cell and Single-Nucleus Resolution Transcriptome Assays

Single-cell RNA-seq (scRNA-seq) has revolutionized modern genomics, but the large size of myotubes and myofibers has restricted use of scRNA-seq in skeletal muscle. For the study of muscle, single-nucleus RNA-seq (snRNA-seq) has emerged not only as an alternative to scRNA-seq, but as a novel method providing valuable insights into multinucleated cells such as myofibers. Nuclei within myofibers specialize at junctions with other cell types such as motor neurons. Nuclear heterogeneity plays important roles in certain diseases such as muscular dystrophies. We survey current methods of high-throughput single cell and subcellular resolution transcriptomics, including single-cell and single-nucleus RNA-seq and spatial transcriptomics, applied to satellite cells, myoblasts, myotubes and myofibers. We summarize the major myonuclei subtypes identified in homeostatic and regenerating tissue including those specific to fiber type or at junctions with other cell types. Disease-specific nucleus populations were found in two muscular dystrophies, FSHD and Duchenne muscular dystrophy, demonstrating the importance of performing transcriptome studies at the single nucleus level in muscle.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Cre driver mouse lines for thalamocortical circuit mapping

Subpopulations of neurons and associated neural circuits can be targeted in mice with genetic tools in a highly selective manner for visualization and manipulation. However, there are not well-defined Cre "driver" lines that target the expression of Cre recombinase to thalamocortical (TC) neurons. Here, we characterize three Cre driver lines for the nuclei of the dorsal thalamus: Oligodendrocyte transcription factor 3 (Olig3)-Cre, histidine decarboxylase (HDC)-Cre, and corticotropin-releasing hormone (CRH)-Cre. We examined the postnatal distribution of Cre expression for each of these lines with the Cre-dependent reporter CAG-tdTomato (Ai9). Cre-dependent expression of tdTomato reveals that Olig3-Cre expresses broadly within the thalamus, including TC neurons and interneurons, while HDC-Cre and CRH-Cre each have unique patterns of expression restricted to TC neurons within and across the sensory relay nuclei of the dorsal thalamus. Cre expression is present by the time of natural birth in all three lines, underscoring their utility for developmental studies. To demonstrate the utility of these Cre drivers for studying sensory TC circuitry, we targeted the expression of channelrhodopsin-2 to thalamus from the CAG-COP4*H134R/EYFP (Ai32) allele with either HDC-Cre or CRH-Cre. Optogenetic activation of TC afferents in primary visual cortex was sufficient to measure frequency-dependent depression. Thus, these Cre drivers provide selective Cre-dependent gene expression in thalamus suitable for both anatomical and functional studies.

Ceramides and Acute Kidney Injury

Altered lipid metabolism is a characteristic feature and potential driving factor of acute kidney injury (AKI). Of the lipids that accumulate in injured renal tissues, ceramides are potent regulators of metabolism and cell fate. Up-regulation of ceramide synthesis is a common feature shared across several AKI etiologies in vitro and in vivo. Furthermore, ceramide accumulation is an early event in the natural history of AKI that precedes cell death and organ dysfunction. Emerging evidence suggests that inhibition of ceramide accumulation may improve renal outcomes in several models of AKI. This review examines the landscape of ceramide metabolism and regulation in the healthy and injured kidney. Furthermore, we discuss the body of literature regarding ceramides as therapeutic targets for AKI and consider potential mechanisms by which ceramides drive kidney pathogenesis.

Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson's disease

The loss of dopamine (DA) neurons within the substantia nigra pars compacta (SNpc) is a defining pathological hallmark of Parkinson's disease (PD). Nevertheless, the molecular features associated with DA neuron vulnerability have not yet been fully identified. Here, we developed a protocol to enrich and transcriptionally profile DA neurons from patients with PD and matched controls, sampling a total of 387,483 nuclei, including 22,048 DA neuron profiles. We identified ten populations and spatially localized each within the SNpc using Slide-seq. A single subtype, marked by the expression of the gene AGTR1 and spatially confined to the ventral tier of SNpc, was highly susceptible to loss in PD and showed the strongest upregulation of targets of TP53 and NR2F2, nominating molecular processes associated with degeneration. This same vulnerable population was specifically enriched for the heritable risk associated with PD, highlighting the importance of cell-intrinsic processes in determining the differential vulnerability of DA neurons to PD-associated degeneration.

Single-nucleus RNA sequencing identified cells with ependymal cell-like features enriched in neonatal mice after spinal cord injury

The adult mammalian central nervous system has limited regenerative ability, and spinal cord injury (SCI) often causes lifelong motor disability. While regeneration is limited in adults, injured spinal cord tissue can be regenerated and neural function can be almost completely restored in neonates. However, difference of cellular composition in lesion has not been well characterized. To gain insight into the age-dependent cellular reaction after SCI, we performed single-nucleus RNA sequencing, analyzing 4,076 nuclei from sham and injured spinal cords from adult and neonatal mice. Clustering analysis identified 18 cell populations. We identified previously undescribed cells with ependymal cell-like gene expression profile, the number of which was increased in neonates after SCI. Histological analysis revealed that these cells line the central canal under physiological conditions in both adults and neonates. We confirmed that they were enriched in the lesion only in neonates. We further showed that these cells were positive for the cellular markers of ependymal cells, astrocytes and radial glial cells. This study provides a deeper understanding of neonate-specific cellular responses after SCI, which may determine regenerative capacity.

Cell transcriptomic atlas of the non-human primate Macaca fascicularis

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.

Robust decomposition of cell type mixtures in spatial transcriptomics

A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .

It Is Not Just Fat: Dissecting the Heterogeneity of Adipose Tissue Function

PURPOSE OF REVIEW

The purpose of the current review is to summarize findings from the most recent and impactful studies which investigated human and mouse adipose tissue transcriptomes at a single-cell level. We provide perspective about the potential importance of data derived from these single-cell technologies in improving our understanding of the adipose organ and metabolic disease and likely future directions of this approach.

RECENT FINDINGS

The majority of single-cell or single-nuclei studies of the adipose organ so far have focused on investigating the stromal-vascular fraction (SVF) of mouse subcutaneous and intraabdominal white and interscapular brown fat depots. Few studies have also evaluated the impact of additional factors as drivers of adipose phenotypes, such as high-fat diet-induced obesity, adolescence, aging, and cold exposure. Recent studies have also investigated human cell lines and human fat biopsies across a range of body mass index (BMI) and in response to insulin resistance or T2D. These studies have identified numerous previously unexplored subpopulations of adipocyte progenitors, immune cells, and mature adipocytes in both mice and men. Single-cell and single-nuclei technologies have brought an explosion of data that have advanced our understanding of the adipose organ in health and disease. However, we are still at the dawn of achieving a complete and comprehensive map of the mouse and human adipose organ. Multi-modal single-cell approaches to identify both anatomic localization of specific cellular populations and epigenetic mechanisms responsible for observed transcriptomic patterns are underway and will likely provide an even deeper understanding of the adipose organ in response to health and disease.

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.

A protocol to extract cell-type-specific signatures from differentially expressed genes in bulk-tissue RNA-seq

Bulk-tissue RNA-seq is widely used to dissect variation in gene expression levels across tissues and under different experimental conditions. Here, we introduce a protocol that leverages existing single-cell expression data to deconvolve patterns of cell-type-specific gene expression in differentially expressed gene lists from highly heterogeneous tissue. We apply this protocol to interrogate cell-type-specific gene expression and variation in cell type composition between the distinct sublayers of the hippocampal CA1 region of the brain in a rodent model of epilepsy.

For complete details on the use and execution of this protocol, please refer to Cid et al. (2021).

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A protocol to explore gene signatures from bulk RNA-seq at the cell-type-specific level

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Deconvolution of complex gene signatures from highly heterogeneous tissues

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Publicly available single-cell gene expression dataset is retrieved and curated

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Gene signatures across brain regions and disease states are surveyed in scRNA-seq data

Neuronal-glial communication perturbations in murine SOD1G93A spinal cord

Amyotrophic lateral sclerosis (ALS) is an incurable disease characterized by proteinaceous aggregate accumulation and neuroinflammation culminating in rapidly progressive lower and upper motor neuron death. To interrogate cell-intrinsic and inter-cell type perturbations in ALS, single-nucleus RNA sequencing was performed on the lumbar spinal cord in the murine ALS model SOD1^G93A transgenic and littermate control mice at peri-symptomatic onset stage of disease, age 90 days. This work uncovered perturbed tripartite synapse functions, complement activation and metabolic stress in the affected spinal cord; processes evidenced by cell death and proteolytic stress-associated gene sets. Concomitantly, these pro-damage events in the spinal cord co-existed with dysregulated reparative mechanisms. This work provides a resource of cell-specific niches in the ALS spinal cord and asserts that interwoven dysfunctional neuronal-glial communications mediating neurodegeneration are underway prior to overt disease manifestation and are recapitulated, in part, in the human post-mortem ALS spinal cord.

In this paper, single-nucleus RNA sequencing was performed to provide a resource of cell-specific niches in the murine ALS model spinal cord at peri-symptomatic onset stage of disease. The data suggest that dysfunctional neuronal-glial communication occurs prior to disease onset, which is partially recapitulated in human post-mortem ALS spinal cord tissue.

Hyperexcitable arousal circuits drive sleep instability during aging

Sleep quality declines with age; however, the underlying mechanisms remain elusive. We found that hyperexcitable hypocretin/orexin (Hcrt/OX) neurons drive sleep fragmentation during aging. In aged mice, Hcrt neurons exhibited more frequent neuronal activity epochs driving wake bouts, and optogenetic activation of Hcrt neurons elicited more prolonged wakefulness. Aged Hcrt neurons showed hyperexcitability with lower KCNQ2 expression and impaired M-current, mediated by KCNQ2/3 channels. Single-nucleus RNA-sequencing revealed adaptive changes to Hcrt neuron loss in the aging brain. Disruption of Kcnq2/3 genes in Hcrt neurons of young mice destabilized sleep, mimicking aging-associated sleep fragmentation, whereas the KCNQ-selective activator flupirtine hyperpolarized Hcrt neurons and rejuvenated sleep architecture in aged mice. Our findings demonstrate a mechanism underlying sleep instability during aging and a strategy to improve sleep continuity.

Perspectives on Bulk-Tissue RNA Sequencing and Single-Cell RNA Sequencing for Cardiac Transcriptomics

The heart has been the center of numerous transcriptomic studies in the past decade. Even though our knowledge of the key organ in our cardiovascular system has significantly increased over the last years, it is still not fully understood yet. In recent years, extensive efforts were made to understand the genetic and transcriptomic contribution to cardiac function and failure in more detail. The advent of Next Generation Sequencing (NGS) technologies has brought many discoveries but it is unable to comprehend the finely orchestrated interactions between and within the various cell types of the heart. With the emergence of single-cell sequencing more than 10 years ago, researchers gained a valuable new tool to enable the exploration of new subpopulations of cells, cell-cell interactions, and integration of multi-omic approaches at a single-cell resolution. Despite this innovation, it is essential to make an informed choice regarding the appropriate technique for transcriptomic studies, especially when working with myocardial tissue. Here, we provide a primer for researchers interested in transcriptomics using NGS technologies.

Single-cell genomics in plants: current state, future directions, and hurdles to overcome

Single-cell genomics has the potential to revolutionize the study of plant development and tissue-specific responses to environmental stimuli by revealing heretofore unknown players and gene regulatory processes. Here, I focus on the current state of single-cell genomics in plants, emerging technologies and applications, in addition to outlining possible future directions for experiments. I describe approaches to enable cheaper and larger experiments and technologies to measure multiple types of molecules to better model and understand cell types and their different states and trajectories throughout development. Lastly, I discuss the inherent limitations of single-cell studies and the technological hurdles that need to be overcome to widely apply single-cell genomics in crops to generate the greatest possible knowledge gain.

The genome and preliminary single-nuclei transcriptome of Lemna minuta reveals mechanisms of invasiveness

The ability to trace every cell in some model organisms has led to the fundamental understanding of development and cellular function. However, in plants the complexity of cell number, organ size, and developmental time makes this a challenge even in the diminutive model plant Arabidopsis (Arabidopsis thaliana). Duckweed, basal nongrass aquatic monocots, provide an opportunity to follow every cell of an entire plant due to their small size, reduced body plan, and fast clonal growth habit. Here we present a chromosome-resolved genome for the highly invasive Lesser Duckweed (Lemna minuta) and generate a preliminary cell atlas leveraging low cell coverage single-nuclei sequencing. We resolved the 360 megabase genome into 21 chromosomes, revealing a core nonredundant gene set with only the ancient tau whole-genome duplication shared with all monocots, and paralog expansion as a result of tandem duplications related to phytoremediation. Leveraging SMARTseq2 single-nuclei sequencing, which provided higher gene coverage yet lower cell count, we profiled 269 nuclei covering 36.9% (8,457) of the L. minuta transcriptome. Since molecular validation was not possible in this nonmodel plant, we leveraged gene orthology with model organism single-cell expression datasets, gene ontology, and cell trajectory analysis to define putative cell types. We found that the tissue that we computationally defined as mesophyll expressed high levels of elemental transport genes consistent with this tissue playing a role in L. minuta wastewater detoxification. The L. minuta genome and preliminary cell map provide a paradigm to decipher developmental genes and pathways for an entire plant.

Characterization of transcript enrichment and detection bias in single-nucleus RNA-seq for mapping of distinct human adipocyte lineages

Single-cell RNA sequencing (scRNA-seq) enables molecular characterization of complex biological tissues at high resolution. The requirement of single-cell extraction, however, makes it challenging for profiling tissues such as adipose tissue, for which collection of intact single adipocytes is complicated by their fragile nature. For such tissues, single-nucleus extraction is often much more efficient and therefore single-nucleus RNA sequencing (snRNA-seq) presents an alternative to scRNA-seq. However, nuclear transcripts represent only a fraction of the transcriptome in a single cell, with snRNA-seq marked with inherent transcript enrichment and detection biases. Therefore, snRNA-seq may be inadequate for mapping important transcriptional signatures in adipose tissue. In this study, we compare the transcriptomic landscape of single nuclei isolated from preadipocytes and mature adipocytes across human white and brown adipocyte lineages, with whole-cell transcriptome. We show that snRNA-seq is capable of identifying the broad cell types present in scRNA-seq at all states of adipogenesis. However, we also explore how and why the nuclear transcriptome is biased and limited, as well as how it can be advantageous. We robustly characterize the enrichment of nuclear-localized transcripts and adipogenic regulatory lncRNAs in snRNA-seq, while also providing a detailed understanding for the preferential detection of long genes upon using this technique. To remove such technical detection biases, we propose a normalization strategy for a more accurate comparison of nuclear and cellular data. Finally, we show successful integration of scRNA-seq and snRNA-seq data sets with existing bioinformatic tools. Overall, our results illustrate the applicability of snRNA-seq for the characterization of cellular diversity in the adipose tissue.

Diversity of transcriptomic microglial phenotypes in aging and Alzheimer's disease

The morphological plasticity of microglia has fascinated neuroscientists for 100 years. Attempts to classify functional phenotypes are hampered by similarities between endogenous brain microglia and peripheral myeloid cells that can enter the brain under pathological conditions. Recent advances in single-cell -omic methodologies have led to an explosion of data regarding gene expression in microglia. Herein, we review the diversity of microglial phenotypes in healthy brains, aging, and Alzheimer's disease (AD); identify knowledge gaps in the body of evidence; and suggest areas in which new knowledge would be useful. Data from human samples and mouse models are compared and contrasted. Understanding the molecular complexity of the microglial response repertoire will suggest new avenues for therapeutic treatments in AD.

A method for rapid flow-cytometric isolation of endothelial nuclei and RNA from archived frozen brain tissue

Endothelial cells are important contributors to brain development, physiology, and disease. Although RNA sequencing has contributed to the understanding of brain endothelial cell diversity, bulk analysis and single-cell approaches have relied on fresh tissue digestion protocols for the isolation of single endothelial cells and flow cytometry-based sorting on surface markers or transgene expression. These approaches are limited in the analysis of the endothelium in human brain tissues, where fresh samples are difficult to obtain. Here, we developed an approach to examine endothelial RNA expression by using an endothelial-specific marker to isolate nuclei from abundant archived frozen brain tissues. We show that this approach rapidly and reliably extracts endothelial nuclei from frozen mouse brain samples, and importantly, from archived frozen human brain tissues. Furthermore, isolated RNA transcript levels are closely correlated with expression in whole cells from tissue digestion protocols and are enriched in endothelial markers and depleted of markers of other brain cell types. As high-quality RNA transcripts could be obtained from as few as 100 nuclei in archived frozen human brain tissues, we predict that this approach should be useful for both bulk analysis of endothelial RNA transcripts in human brain tissues as well as single-cell analysis of endothelial sub-populations.

Advancing root developmental research through single-cell technologies

Single-cell RNA-sequencing has greatly increased the spatiotemporal resolution of root transcriptomics data, but we are still only scratching the surface of its full potential. Despite the challenges that remain in the field, the orderly aligned structure of the Arabidopsis root meristem makes it specifically suitable for lineage tracing and trajectory analysis. These methods will become even more potent by increasing resolution and specificity using tissue-specific single-cell RNA-sequencing and spatial transcriptomics. Feeding multiple single-cell omics data sets into single-cell gene regulatory networks will accelerate the discovery of regulators of root development in multiple species. By providing transcriptome atlases for virtually any species, single-cell technologies could tempt many root developmental biologists to move beyond the comfort of the well-known Arabidopsis root meristem.

Selective isolation of mouse glial nuclei optimized for reliable downstream omics analyses

BACKGROUND

Isolation of cell types of interest from the brain for molecular applications presents several challenges, including cellular damage during tissue dissociation or enrichment procedures, and low cell number in the tissue in some cases. Techniques have been developed to enrich distinct cell populations using immunopanning or fluorescence activated cell/nuclei sorting. However, these techniques often involve fixation, immunolabeling and DNA staining steps, which could potentially influence downstream omics applications.

NEW METHOD

Taking advantage of readily available genetically modified mice with fluorescent-tagged nuclei, we describe a technique for the purification of cell-type specific brain nuclei, optimized to decrease sample preparation time and to limit potential artefacts for downstream omics applications. We demonstrate the applicability of this approach for the purification of glial cell nuclei and show that the resulting cell-type specific nuclei obtained can be used effectively for omics applications, including ATAC-seq and RNA-seq.

RESULTS

We demonstrate excellent enrichment of fluorescently-tagged glial nuclei, yielding high quality RNA and chromatin. We identify several critical steps during nuclei isolation that help limit nuclei rupture and clumping, including quick homogenization, dilution before filtration and loosening of the pellet before resuspension, thus improving yield. Sorting of fluorescent nuclei can be achieved without fixation, antibody labelling, or DAPI staining, reducing potential artifactual results in RNA-seq and ATAC-seq analyses. We show that reproducible glial cell type-specific profiles can be obtained in transcriptomic and chromatin accessibility assays using this rapid protocol.

COMPARISON WITH EXISTING METHODS

Our method allows for rapid enrichment of glial nuclei populations from the mouse brain with minimal processing steps, while still providing high quality RNA and chromatin required for reliable omics analyses.

CONCLUSIONS

We provide a reproducible method to obtain nucleic material from glial cells in the mouse brain with a quick and limited sample preparation.

Cell Heterogeneity Uncovered by Single-Cell RNA Sequencing Offers Potential Therapeutic Targets for Ischemic Stroke

Ischemic stroke is a detrimental neurological disease characterized by an irreversible infarct core surrounded by an ischemic penumbra, a salvageable region of brain tissue. Unique roles of distinct brain cell subpopulations within the neurovascular unit and peripheral immune cells during ischemic stroke remain elusive due to the heterogeneity of cells in the brain. Single-cell RNA sequencing (scRNA-seq) allows for an unbiased determination of cellular heterogeneity at high-resolution and identification of cell markers, thereby unveiling the principal brain clusters within the cell-type-specific gene expression patterns as well as cell-specific subclusters and their functions in different pathways underlying ischemic stroke. In this review, we have summarized the changes in differentiation trajectories of distinct cell types and highlighted the specific pathways and genes in brain cells that are impacted by stroke. This review is expected to inspire new research and provide directions for investigating the potential pathological mechanisms and novel treatment strategies for ischemic stroke at the level of a single cell.

Recent Progress in Cardiovascular Research Involving Single-Cell Omics Approaches

Cardiovascular diseases are among the leading causes of morbidity and mortality worldwide. Although the spectrum of the heart from development to disease has long been studied, it remains largely enigmatic. The emergence of single-cell omics technologies has provided a powerful toolbox for defining cell heterogeneity, unraveling previously unknown pathways, and revealing intercellular communications, thereby boosting biomedical research and obtaining numerous novel findings over the last 7 years. Not only cell atlases of normal and developing hearts that provided substantial research resources, but also some important findings regarding cell-type-specific disease gene program, could never have been established without single-cell omics technologies. Herein, we briefly describe the latest technological advances in single-cell omics and summarize the major findings achieved by such approaches, with a focus on development and homeostasis of the heart, myocardial infarction, and heart failure.

Immune cells in cardiac homeostasis and disease: emerging insights from novel technologies

The increasing use of single-cell immune profiling and advanced microscopic imaging technologies has deepened our understanding of the cardiac immune system, confirming that the heart contains a broad repertoire of innate and adaptive immune cells. Leucocytes found in the healthy heart participate in essential functions to preserve cardiac homeostasis, not only by defending against pathogens but also by maintaining normal organ function. In pathophysiological conditions, cardiac inflammation is implicated in healing responses after ischaemic or non-ischaemic cardiac injury. The aim of this review is to provide a concise overview of novel methodological advancements to the non-expert readership and summarize novel findings on immune cell heterogeneity and functions in cardiac disease with a focus on myocardial infarction as a prototypic example. In addition, we will briefly discuss how biological sex modulate the cardiac immune response. Finally, we will highlight emerging concepts for novel therapeutic applications, such as targeting immunometabolism and nanomedicine.

Hippocampal neuropathology in suicide: Gaps in our knowledge and opportunities for a breakthrough

Suicide is a major global hazard. There is a need for increasing suicide awareness and effective and evidence-based interventions, targeting both suicidal ideation and conduct. However, anti-suicide pharmacological effects are unsatisfactory. The human hippocampus is vulnerable to neuropsychiatric damages and subsequently releases psychobiological signals. Human hippocampal studies of suicide completers have shown mechanistic changes in neurobiology, which, however, could not reflect the neuropathological 'fingerprints' of fatal suicide ideations and suicide attempts. In this review, we provide several leading theories of suicide, including the serotoninergic system, Wnt pathway and brain-derived neurotrophic factor/tropomyosin receptor kinase B signalling, and discuss the evidence for their roles in suicide and treatment. Moreover, the cognitive dysfunctions associated with suicide risk are discussed, as well as the novel evidence on cognitive therapies that decrease suicidal ideation. We highlight the need to apply multi-omics techniques (including single-nucleus RNA sequencing and mass spectrometry histochemistry) on hippocampal samples from donors who died by suicide or legal euthanasia, to clarify the aetiology of suicide and propose novel therapeutic strategies.

Transcriptomic Crosstalk between Gliomas and Telencephalic Neural Stem and Progenitor Cells for Defining Heterogeneity and Targeted Signaling Pathways

Recent studies have begun to reveal surprising levels of cell diversity in the human brain, both in adults and during development. Distinctive cellular phenotypes point to complex molecular profiles, cellular hierarchies and signaling pathways in neural stem cells, progenitor cells, neuronal and glial cells. Several recent reports have suggested that neural stem and progenitor cell types found in the developing and adult brain share several properties and phenotypes with cells from brain primary tumors, such as gliomas. This transcriptomic crosstalk may help us to better understand the cell hierarchies and signaling pathways in both gliomas and the normal brain, and, by clarifying the phenotypes of cells at the origin of the tumor, to therapeutically address their most relevant signaling pathways.

Single Cell Scale Neuronal and Glial Gene Expression and Putative Cell Phenotypes and Networks in the Nucleus Tractus Solitarius in an Alcohol Withdrawal Time Series

Alcohol withdrawal syndrome (AWS) is characterized by neuronal hyperexcitability, autonomic dysregulation, and severe negative emotion. The nucleus tractus solitarius (NTS) likely plays a prominent role in the neurological processes underlying these symptoms as it is the main viscerosensory nucleus in the brain. The NTS receives visceral interoceptive inputs, influences autonomic outputs, and has strong connections to the limbic system and hypothalamic-pituitary-adrenal axis to maintain homeostasis. Our prior analysis of single neuronal gene expression data from the NTS shows that neurons exist in heterogeneous transcriptional states that form distinct functional subphenotypes. Our working model conjectures that the allostasis secondary to alcohol dependence causes peripheral and central biological network decompensation in acute abstinence resulting in neurovisceral feedback to the NTS that substantially contributes to the observed AWS. We collected single noradrenergic and glucagon-like peptide-1 (GLP-1) neurons and microglia from rat NTS and measured a subset of their transcriptome as pooled samples in an alcohol withdrawal time series. Inflammatory subphenotypes predominate at certain time points, and GLP-1 subphenotypes demonstrated hyperexcitability post-withdrawal. We hypothesize such inflammatory and anxiogenic signaling contributes to alcohol dependence via negative reinforcement. Targets to mitigate such dysregulation and treat dependence can be identified from this dataset.

Evaluating microglial phenotypes using single-cell technologies

Recent single-cell technologies have enabled researchers to simultaneously assess the transcriptomes and other modalities of thousands of cells within their spatial context. Here, we have summarized available single-cell methods for dissociated tissues and tissue slides with respect to the specifics of microglial biology. We have focused on next-generation-based technologies. We review the potential of these single-cell sequencing methods and newer multiomics approaches to extend the understanding of microglia function beyond the status quo.

Single nucleus RNA-sequencing: how it's done, applications and limitations

Single nuclei RNA-sequencing (sNuc-Seq) is a methodology which uses isolated nuclei instead of whole cells to profile gene expression. By using droplet microfluidic technologies, users are able to profile thousands of single transcriptomes at high throughput from their chosen tissue. This article aims to introduce sNuc-Seq as a method and its utility in multiple tissue types. Furthermore, we discuss the risks associated with the use of nuclei, which must be considered before committing to a methodology.

Single-cell Transcriptional Changes in Neurodegenerative Diseases

In recent decades, our understanding of the molecular changes involved in neurodegenerative diseases has been transformed. Single-cell RNA sequencing and single-nucleus RNA sequencing technologies have been applied to provide cellular and molecular details of the brain at the single-cell level. This has expanded our knowledge of the central nervous system and provided insights into the molecular vulnerability of brain cell types and underlying mechanisms in neurodegenerative diseases. In this review, we highlight the recent advances and findings related to neurodegenerative diseases using these cutting-edge technologies.