Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

A systematic review of single-cell RNA sequencing applications and innovations

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

Discovering the Potential Role of the C2 DUSP2+ MCs Subgroup in Lung Adenocarcinoma

OBJECTIVE

In both industrialized and developing nations worldwide, lung adenocarcinoma is one of the deadliest malignant tumors and the primary cause of cancer-related deaths. Its cellular heterogeneity is unclear to the fullest extent, although in recent years, its prevalence in younger individuals has increased. Therefore, it is urgent to deepen the understanding of lung adenocarcinoma and explore new therapeutic methods.

METHODS

CytoTRACE, Monocle, SCENIC, and enrichment analysis were used to analyze the single cell RNA data, we characterized the biological characteristics of mast cells (MCs) in lung adenocarcinoma patient samples. CellChat was used to analyze and validate the interaction between MCs and tumor cells in lung adenocarcinoma. Prognostic models were used to evaluate and predict the development trend and outcome of a patient's disease, such as the survival time of cancer patients. The python package SCENIC was used to evaluate the enrichment of transcription factors and the activity of regulators in lung adenocarcinoma cell subgroups. CCK-8 assay could validate the activity of a specific cell subgroup sequenced in single cell sequencing to confirm the role of this cell subgroup in tumor proliferation.

RESULTS

Our analysis identified seven major cell types, further grouping MCs within them and identifying four distinct subgroups, including MCs with high DUSP2 expression, which showed some tumor-related characteristics. In addition, we identified the key signaling receptor EGFR and validated it through in vitro knockdown experiments, demonstrating its role in promoting cancer. In addition, we established an independent prognostic indicator, the DUSP2+ MCs risk score, which showed an association between groups with high risk scores and poor outcomes.

CONCLUSION

These findings shed light on the complex interactions in the lung adenocarcinoma tumor microenvironment and suggest that targeting specific MCs subgroups, particularly through the EGFR signaling pathway, may provide new therapeutic strategies.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

Genomic and Transcriptomic Approaches Advance the Diagnosis and Prognosis of Neurodegenerative Diseases

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), represent a growing societal challenge due to their irreversible progression and significant impact on patients, caregivers, and healthcare systems. Despite advances in clinical and imaging-based diagnostics, these diseases are often detected at advanced stages, limiting the effectiveness of therapeutic interventions. Recent breakthroughs in genomic and transcriptomic technologies, including whole-genome sequencing, single-cell RNA sequencing (scRNA-seq), and CRISPR-based screens, have revolutionized the field, offering new avenues for early diagnosis and personalized prognosis. Genomic approaches have elucidated disease-specific genetic risk factors and molecular pathways, while transcriptomic studies have identified stage-specific biomarkers that correlate with disease progression and severity. Furthermore, genome-wide association studies (GWAS), polygenic risk scores (PRS), and spatial transcriptomics are enabling the stratification of patients based on their risk profiles and prognostic trajectories. Advances in functional genomics have uncovered actionable targets, such as ATXN2 in ALS and TREM2 in AD, paving the way for tailored therapeutic strategies. Despite these achievements, challenges remain in translating genomic discoveries into clinical practice due to disease heterogeneity and the complexity of neurodegenerative pathophysiology. Future integration of genetic technologies holds promise for transforming diagnostic and prognostic paradigms, offering hope for improved patient outcomes and precision medicine approaches.

ImageDoubler: image-based doublet identification in single-cell sequencing

Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets-where two or more cells are sequenced together-disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques.

A chemogenetic technology using insect Ionotropic Receptors to stimulate target cell populations in the mammalian brain

Chemogenetics uses artificially-engineered proteins to modify the activity of cells, notably neurons, in response to small molecules. Although a common set of chemogenetic tools are the G protein-coupled receptor-based DREADDs, there has been great hope for ligand-gated, ion channel-type chemogenetic tools that directly impact neuronal excitability. We have devised such a technology by exploiting insect Ionotropic Receptors (IRs), a highly divergent subfamily of ionotropic glutamate receptors that evolved to detect diverse environmental chemicals. Here, we review a series of studies developing and applying this "IR-mediated neuronal activation" (IRNA) technology with the Drosophila melanogaster IR84a/IR8a complex, which detects phenyl-containing ligands. We also discuss how variants of IRNA could be produced by modifying the composition of the IR complex, using natural or engineered subunits, which would enable artificial activation of different cell populations in the brain in response to distinct chemicals.

Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches

Transcriptional regulation controls cellular functions through interactions between transcription factors (TFs) and their chromosomal targets. However, understanding the fate conversion potential of multiple TFs in an inducible manner remains limited. Here, we introduce iTF-seq as a method for identifying individual TFs that can alter cell fate toward specific lineages at a single-cell level. iTF-seq enables time course monitoring of transcriptome changes, and with biotinylated individual TFs, it provides a multi-omics approach to understanding the mechanisms behind TF-mediated cell fate changes. Our iTF-seq study in mouse embryonic stem cells identified multiple TFs that trigger rapid transcriptome changes indicative of differentiation within a day of induction. Moreover, cells expressing these potent TFs often show a slower cell cycle and increased cell death. Further analysis using bioChIP-seq revealed that GCM1 and OTX2 act as pioneer factors and activators by increasing gene accessibility and activating the expression of lineage specification genes during cell fate conversion. iTF-seq has utility in both mapping cell fate conversion and understanding cell fate conversion mechanisms.

TYROBP/DAP12 knockout in Huntington's disease Q175 mice cell-autonomously decreases microglial expression of disease-associated genes and non-cell-autonomously mitigates astrogliosis and motor deterioration

INTRODUCTION

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by an expansion of the CAG trinucleotide repeat in the Huntingtin gene (HTT). Immune activation is abundant in the striatum of HD patients. Detection of active microglia at presymptomatic stages suggests that microgliosis is a key early driver of neuronal dysfunction and degeneration. Recent studies showed that deletion of Tyrobp, a microglial protein, ameliorates neuronal dysfunction in Alzheimer's disease amyloidopathy and tauopathy mouse models while decreasing components of the complement subnetwork.

OBJECTIVE

While TYROBP/DAP12-mediated microglial activation is detrimental for some diseases such as peripheral nerve injury, it is beneficial for other diseases. We sought to determine whether the TYROBP network is implicated in HD and whether Tyrobp deletion impacts HD striatal function and transcriptomics.

METHODS

To test the hypothesis that Tyrobp deficiency would be beneficial in an HD model, we placed the Q175 HD mouse model on a Tyrobp-null background. We characterized these mice with a combination of behavioral testing, immunohistochemistry, transcriptomic and proteomic profiling. Further, we evaluated the gene signature in isolated Q175 striatal microglia, with and without Tyrobp.

RESULTS

Comprehensive analysis of publicly available human HD transcriptomic data revealed that the TYROBP network is overactivated in the HD putamen. The Q175 mice showed morphologic microglial activation, reduced levels of post-synaptic density-95 protein and motor deficits at 6 and 9 months of age, all of which were ameliorated on the Tyrobp-null background. Gene expression analysis revealed that lack of Tyrobp in the Q175 model does not prevent the decrease in the expression of striatal neuronal genes but reduces pro-inflammatory pathways that are specifically active in HD human brain, including genes identified as detrimental in neurodegenerative diseases, e.g. C1q and members of the Ccr5 signaling pathway. Integration of transcriptomic and proteomic data revealed that astrogliosis and complement system pathway were reduced after Tyrobp deletion, which was further validated by immunofluorescence analysis.

CONCLUSIONS

Our data provide molecular and functional support demonstrating that Tyrobp deletion prevents many of the abnormalities in the HD Q175 mouse model, suggesting that the Tyrobp pathway is a potential therapeutic candidate for Huntington's disease.

Decoding the tumor microenvironment and molecular mechanism: unraveling cervical cancer subpopulations and prognostic signatures through scRNA-Seq and bulk RNA-seq analyses

Background

Cervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated.

Methods

We utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes.

Results

Through our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator-the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines.

Conclusion

This study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches.

cnnImpute: missing value recovery for single cell RNA sequencing data

The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized our ability to explore cellular diversity and unravel the complexities of intricate diseases. However, due to the inherently low signal-to-noise ratio and the presence of an excessive number of missing values, scRNA-seq data analysis encounters unique challenges. Here, we present cnnImpute, a novel convolutional neural network (CNN) based method designed to address the issue of missing data in scRNA-seq. Our approach starts by estimating missing probabilities, followed by constructing a CNN-based model to recover expression values with a high likelihood of being missing. Through comprehensive evaluations, cnnImpute demonstrates its effectiveness in accurately imputing missing values while preserving the integrity of cell clusters in scRNA-seq data analysis. It achieved superior performance in various benchmarking experiments. cnnImpute offers an accurate and scalable method for recovering missing values, providing a useful resource for scRNA-seq data analysis.

Single-cell RNA sequencing to understand host-virus interactions

Single-cell RNA sequencing (scRNA-seq) has allowed for the profiling of host and virus transcripts and host-virus interactions at single-cell resolution. This review summarizes the existing scRNA-seq technologies together with their strengths and weaknesses. The applications of scRNA-seq in various virological studies are discussed in depth, which broaden the understanding of the immune atlas, host-virus interactions, and immune repertoire. scRNA-seq can be widely used for virology in the near future to better understand the pathogenic mechanisms and discover more effective therapeutic strategies.

Single-nucleus transcriptomic analysis reveals the relationship between gene expression in oligodendrocyte lineage and major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common mental illness that affects millions of people worldwide and imposes a heavy burden on individuals, families and society. Previous studies on MDD predominantly focused on neurons and employed bulk homogenates of brain tissues. This paper aims to decipher the relationship between oligodendrocyte lineage (OL) development and MDD at the single-cell resolution level.

METHODS

Here, we present the use of a guided regularized random forest (GRRF) algorithm to explore single-nucleus RNA sequencing profiles (GSE144136) of the OL at four developmental stages, which contains dorsolateral prefrontal cortex of 17 healthy controls (HC) and 17 MDD cases, generated by Nagy C et al. We prioritized and ordered differentially expressed genes (DEGs) based on Nagy et al., which could predominantly discriminate cells in the four developmental stages and two adjacent developmental stages of the OL. We further screened top-ranked genes that distinguished between HC and MDD in four developmental stages. Moreover, we estimated the performance of the GRRF model via the area under the curve value. Additionally, we validated the pivotal candidate gene Malat1 in animal models.

RESULTS

We found that, among the four developmental stages, the onset development of OL (OPC2) possesses the best predictive power for distinguishing HC and MDD, and long noncoding RNA MALAT1 has top-ranked importance value in candidate genes of four developmental stages. In addition, results of fluorescence in situ hybridization assay showed that Malat1 plays a critical role in the occurrence of depression.

CONCLUSIONS

Our work elucidates the mechanism of MDD from the perspective of OL development at the single-cell resolution level and provides novel insight into the occurrence of depression.

Mechanisms of multi-omics and network pharmacology to explain traditional chinese medicine for vascular cognitive impairment: A narrative review

BACKGROUND

The term "vascular cognitive impairment" (VCI) describes various cognitive conditions that include vascular elements. It increases the risk of morbidity and mortality in the elderly population and is the most common cognitive impairment associated with cerebrovascular disease. Understanding the etiology of VCI may aid in identifying approaches to target its possible therapy for the condition. Treatment of VCI has focused on vascular risk factors. There are no authorized conventional therapies available right now. The medications used to treat VCI are solely approved for symptomatic relief and are not intended to prevent or slow the development of VCI.

PURPOSE

The function of Chinese medicine in treating VCI has not yet been thoroughly examined. This review evaluates the preclinical and limited clinical evidence to comprehend the "multi-component, multi-target, multi-pathway" mechanism of Traditional Chinese medicine (TCM). It investigates the various multi-omics approaches in the search for the pathological mechanisms of VCI, as well as the new research strategies, in the hopes of supplying supportive evidence for the clinical treatment of VCI.

METHODS

This review used the Preferred Reporting Items for Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. Using integrated bioinformatics and network pharmacology approaches, a thorough evaluation and analysis of 25 preclinical studies published up to July 1, 2023, were conducted to shed light on the mechanisms of TCM for vascular cognitive impairment. The studies for the systematic review were located using the following databases: PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect.

RESULTS

We discovered that the multi-omics analysis approach would hasten the discovery of the role of TCM in the treatment of VCI. It will explore components, compounds, targets, and pathways, slowing the progression of VCI from the perspective of inhibiting oxidative stress, stifling neuroinflammation, increasing cerebral blood flow, and inhibiting iron deposition by a variety of molecular mechanisms, which have significant implications for the treatment of VCI.

CONCLUSION

TCM is a valuable tool for developing dementia therapies, and further research is needed to determine how TCM components may affect the operation of the neurovascular unit. There are still some limitations, although several research have offered invaluable resources for searching for possible anti-dementia medicines and treatments. To gain new insights into the molecular mechanisms that precisely modulate the key molecules at different levels during pharmacological interventions-a prerequisite for comprehending the mechanism of action and determining the potential therapeutic value of the drugs-further research should employ more standardized experimental methods as well as more sophisticated science and technology. Given the results of this review, we advocate integrating chemical and biological component analysis approaches in future research on VCI to provide a more full and objective assessment of the standard of TCM. With the help of bioinformatics, a multi-omics analysis approach will hasten the discovery of the role of TCM in the treatment of VCI, which has significant implications for the treatment of VCI.

scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data

BACKGROUND

The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train.

RESULTS

To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducted interpretability analysis for each dimension (pathway) of cell representation in the embedding space.

CONCLUSIONS

The results showed that the cell representations obtained by scInterpreter are full of biological significance. Through weight sorting, we found several new genes related to pathways in PBMC dataset. In general, scInterpreter is an effective and interpretable integration tool. It is expected that scInterpreter will bring great convenience to the study of single-cell transcriptomics.

The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery

Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.

Particle sorting method based on swirl induction

Fluid-based methods for particle sorting demonstrate increasing appeal in many areas of biosciences due to their biocompatibility and cost-effectiveness. Herein, we construct a microfluidic sorting system based on a swirl microchip. The impact of microchannel velocity on the swirl stagnation point as well as particle movement is analyzed through simulation and experiment. Moreover, the quantitative mapping relationship between flow velocity and particle position distribution is established. With this foundation established, a particle sorting method based on swirl induction is proposed. Initially, the particle is captured by a swirl. Then, the Sorting Region into which the particle aims to enter is determined according to the sorting condition and particle characteristic. Subsequently, the velocities of the microchannels are adjusted to control the swirl, which will induce the particle to enter its corresponding Induction Region. Thereafter, the velocities are adjusted again to change the fluid field and drive the particle into a predetermined Sorting Region, hence the sorting is accomplished. We have extensively conducted experiments taking particle size or color as a sorting condition. An outstanding sorting success rate of 98.75% is achieved when dealing with particles within the size range of tens to hundreds of micrometers in radius, which certifies the effectiveness of the proposed sorting method. Compared to the existing sorting techniques, the proposed method offers greater flexibility. The adjustment of sorting conditions or particle parameters no longer requires complex chip redesign, because such sorting tasks can be successfully realized through simple microchannel velocities control.

Bibliometric analysis of research on neurodegenerative diseases and single-cell RNA sequencing: Opportunities and challenges

Neurodegeneration, characterized by the progressive deterioration in neuronal structure or function, presents an elusive mechanism. The use of single-cell RNA sequencing (scRNA-seq) technology in the clinic is becoming increasingly prevalent in recent decades. This technology offers unparalleled cell-level insights into neurodegenerative diseases, establishing itself as a potent tool for elucidating these diseases underlying mechanisms. Here, we made a deep investigation for scRNA-seq research in neurodegenerative diseases using bibliometric analysis from 2009 to 2022. We observed a robust upward trajectory in the number of publications on this subject. The United States stood out as the principal contributor to this expanding field. Specifically, the University of California System exhibited notable research prowess in this field. Alzheimer disease and Parkinson disease were the diseases most frequently investigated. Key research hotspots include the creation of a molecular brain atlas and identification of vulnerable neuronal subpopulations and potential therapeutic targets at the transcriptomic level.

The effect of hyperthyroidism on cognitive function, neuroinflammation, and necroptosis in APP/PS1 mice

BACKGROUND

Increasing evidence has linked the thyroid dysfunction to the pathogenesis of dementia. Evidence from clinical studies has demonstrated that hypothyroidism is related to an increased risk of dementia. But the association of hyperthyroidism with dementia is largely unknown.

METHODS

We used the adenovirus containing thyrotropin receptor (TSHR) amino acid residues 1-289 (Ad-TSHR289)-induced Graves' disease (GD) phenotype in Alzheimer's disease (AD) model mice (APP/PS1 mice) to evaluate the effect of hyperthyroidism on the cognitive function and β-amyloid (Aβ) accumulation.

RESULTS

GD mice exhibited a stable long-term hyperthyroidism and cognitive deficits. Single Cell RNA-sequencing analysis indicated that microglia function played a critical role in the pathophysiological processes in GD mice. Neuroinflammation and polarization of microglia (M1/M2 phenotype) and activated receptor-interacting serine/threonine protein kinase 3 (RIPK3)/mixed lineage kinase domain-like pseudo-kinase (MLKL)-mediated necroptosis contributed to the pathological process, including Aβ deposition and neuronal loss. RIPK3 inhibitor could inhibit GD-mediated Aβ accumulation and neuronal loss.

CONCLUSIONS

Our findings reveal that GD hyperthyroidism aggravates cognitive deficits in AD mice and induces Aβ deposition and neuronal loss by inducing neuroinflammation and RIPK3/MLKL-mediated necroptosis.

CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server

MOTIVATION

Single-cell sequencing technology has become a routine in studying many biological problems. A core step of analyzing single-cell data is the assignment of cell clusters to specific cell types. Reference-based methods are proposed for predicting cell types for single-cell clusters. However, the scalability and lack of preprocessed reference datasets prevent them from being practical and easy to use.

RESULTS

Here, we introduce a reference-based cell annotation web server, CellAnn, which is super-fast and easy to use. CellAnn contains a comprehensive reference database with 204 human and 191 mouse single-cell datasets. These reference datasets cover 32 organs. Furthermore, we developed a cluster-to-cluster alignment method to transfer cell labels from the reference to the query datasets, which is superior to the existing methods with higher accuracy and higher scalability. Finally, CellAnn is an online tool that integrates all the procedures in cell annotation, including reference searching, transferring cell labels, visualizing results, and harmonizing cell annotation labels. Through the user-friendly interface, users can identify the best annotation by cross-validating with multiple reference datasets. We believe that CellAnn can greatly facilitate single-cell sequencing data analysis.

AVAILABILITY AND IMPLEMENTATION

The web server is available at www.cellann.io, and the source code is available at https://github.com/Pinlyu3/CellAnn_shinyapp.

Integrating spatial and single-nucleus transcriptomic data elucidates microglial-specific responses in female cynomolgus macaques with depressive-like behaviors

Major depressive disorder represents a serious public health challenge worldwide; however, the underlying cellular and molecular mechanisms are mostly unknown. Here, we profile the dorsolateral prefrontal cortex of female cynomolgus macaques with social stress-associated depressive-like behaviors using single-nucleus RNA-sequencing and spatial transcriptomics. We find gene expression changes associated with depressive-like behaviors mostly in microglia, and we report a pro-inflammatory microglia subpopulation enriched in the depressive-like condition. Single-nucleus RNA-sequencing data result in the identification of six enriched gene modules associated with depressive-like behaviors, and these modules are further resolved by spatial transcriptomics. Gene modules associated with huddle and sit alone behaviors are expressed in neurons and oligodendrocytes of the superficial cortical layer, while gene modules associated with locomotion and amicable behaviors are enriched in microglia and astrocytes in mid-to-deep cortical layers. The depressive-like behavior associated microglia subpopulation is enriched in deep cortical layers. In summary, our findings show cell-type and cortical layer-specific gene expression changes and identify one microglia subpopulation associated with depressive-like behaviors in female non-human primates.

Single-Cell RNA Sequencing and Its Applications in the Study of Psychiatric Disorders

Neuroscience is currently one of the most challenging research fields owing to the enormous complexity of the mammalian nervous system. We are yet to understand precise transcriptional programs that govern cell fate during neurodevelopment, resolve the connectome of the mammalian brain, and determine the etiology of various neurodegenerative and psychiatric disorders. Technological advances in the past decade, notably single-cell RNA sequencing, have enabled huge progress in our understanding of such features. Our current knowledge of the transcriptome is largely derived from bulk RNA sequencing, which reveals only the average gene expression of millions of cells, potentially missing out on minor transcriptome differences between cells detectable only at single-cell resolution. Since 2009, several single-cell RNA sequencing techniques have emerged that enable the accurate classification of neuronal and glial cell subtypes beyond classical molecular markers and electrophysiological features and allow the identification of previously unknown cell types. Furthermore, it enables the interrogation of molecular and disease-relevant mechanisms and offers further possibilities for the discovery of new drug targets and disease biomarkers. This review intends to familiarize the reader with the main single-cell RNA sequencing techniques developed throughout the past decade and discusses their application in the fields of brain cell taxonomy, neurodevelopment, and psychiatric disorders.

Ethanol-induced transcriptional and translational changes in Aldh1l1-Egfp/Rpl10a cortical astrocyte cultures

The role astrocytes play in brain development and function has garnered greater attention as the diversity of roles they are involved in has become apparent. We have previously shown that ethanol-exposed astrocytes alter neuronal neurite outgrowth in an in vitro co-culture system and that ethanol alters the astrocyte-produced extracellular matrix (ECM) in vitro, with similar alterations in vivo. In this study, we utilized the translating ribosome affinity purification (TRAP) procedure in Aldh1l1-EGFP/Rpl10a transgenic mouse primary cortical astrocyte cultures to transcriptionally and translationally profile the astrocyte response to ethanol. We found a large number of differences between the total RNA pool and the translating RNA pool, indicating that the transcriptional state of astrocytes may not always reflect the translational state of astrocytes. In addition, there was a considerable overlap between ethanol-dysregulated genes in the total RNA pool and the translating RNA pool. Comparisons to published datasets indicate the in vitro model used here is most similar to PD1 or PD7 in vivo cortical astrocytes, and the ethanol-regulated genes showed a significant overlap with models of chronic ethanol exposure in astrocytes, a model of third-trimester ethanol exposure in the hippocampus and cerebellum, and an acute model of ethanol exposure in the hippocampus. These findings will further our understanding of the effects of ethanol on astrocyte gene expression and protein translation and how these changes may alter brain development and support the use of in vitro astrocyte cultures as models of neonatal astrocytes.

Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges

With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.

The gut microbiome modulates the transformation of microglial subtypes

Clinical and animal studies have shown that gut microbiome disturbances can affect neural function and behaviors via the microbiota-gut-brain axis, and may be implicated in the pathogenesis of several brain diseases. However, exactly how the gut microbiome modulates nervous system activity remains obscure. Here, using a single-cell nucleus sequencing approach, we sought to characterize the cell type-specific transcriptomic changes in the prefrontal cortex and hippocampus derived from germ-free (GF), specific pathogen free, and colonized-GF mice. We found that the absence of gut microbiota resulted in cell-specific transcriptomic changes. Furthermore, microglia transcriptomes were preferentially influenced, which could be effectively reversed by microbial colonization. Significantly, the gut microbiome modulated the mutual transformation of microglial subpopulations in the two regions. Cross-species analysis showed that the transcriptome changes of these microglial subpopulations were mainly associated with Alzheimer's disease (AD) and major depressive disorder (MDD), which were further supported by animal behavioral tests. Our findings demonstrate that gut microbiota mainly modulate the mutual transformation of microglial subtypes, which may lead to new insights into the pathogenesis of AD and MDD.

Single cell molecular alterations reveal target cells and pathways of conditioned fear memory

OBJECTIVES

Recent evidence indicates that hippocampus is important for conditioned fear memory (CFM). Though few studies consider the roles of various cell types' contribution to such a process, as well as the accompanying transcriptome changes during this process. The purpose of this study was to explore the transcriptional regulatory genes and the targeted cells that are altered by CFM reconsolidation.

METHODS

A fear conditioning experiment was established on adult male C57 mice, after day 3 tone-cued CFM reconsolidation test, hippocampus cells were dissociated. Using single cell RNA sequencing (scRNA-seq) technique, alterations of transcriptional genes expression were detected and cell cluster analysis were performed and compared with those in sham group.

RESULTS

Seven non-neuronal and eight neuronal cell clusters (including four known neurons and four newly identified neuronal subtypes) has been explored. Among them, CA subtype 1 has characteristic gene markers of Ttr and Ptgds, which is speculated to be the outcome of acute stress and promotes the production of CFM. The results of KEGG pathway enrichment indicate the differences in the expression of certain molecular protein functional subunits in long-term potentiation (LTP) pathway between two types of neurons (DG and CA1) and astrocytes, thus providing a new transcriptional perspective for the role of hippocampus in the CFM reconsolidation. More importantly, the correlation between the reconsolidation of CFM and neurodegenerative diseases-linked genes is substantiated by the results from cell-cell interactions and KEGG pathway enrichment. Further analysis shows that the reconsolidation of CFM inhibits the risk-factor genes App and ApoE in Alzheimer's Disease (AD) and activates the protective gene Lrp1.

CONCLUSIONS

This study reports the transcriptional genes expression changes of hippocampal cells driven by CFM, which confirm the involvement of LTP pathway and suggest the possibility of CFM-like behavior in preventing AD. However, the current research is limited to normal C57 mice, and further studies on AD model mice are needed to prove this preliminary conclusion.

Cell Type-Specific Nuclei Markers: The Need for Human Brain Research to Go Nuclear

Identifying and interrogating cell type-specific populations within the heterogeneous milieu of the human brain is paramount to resolving the processes of normal brain homeostasis and the pathogenesis of neurological disorders. While brain cell type-specific markers are well established, most are localized on cellular membranes or within the cytoplasm, with limited literature describing those found in the nucleus. Due to the complex cytoarchitecture of the human brain, immunohistochemical studies require well-defined cell-specific nuclear markers for more precise and efficient quantification of the cellular populations. Furthermore, efficient nuclear markers are required for cell type-specific purification and transcriptomic interrogation of archived human brain tissue through nuclei isolation-based RNA sequencing. To sate the growing demand for robust cell type-specific nuclear markers, we thought it prudent to comprehensively review the current literature to identify and consolidate a novel series of robust cell type-specific nuclear markers that can assist researchers across a range of neuroscientific disciplines. The following review article collates and discusses several key and prospective cell type-specific nuclei markers for each of the major human brain cell types; it then concludes by discussing the potential applications of cell type-specific nuclear workflows and the power of nuclear-based neuroscientific research.

Cyclic Multiplex Fluorescent Immunohistochemistry Protocol to Phenotype Glial Cells in Formalin-Fixed Paraffin-Embedded Human Brain Sections

There is a growing interest in expanding the multiplexing capability of immunohistochemistry to achieve a deeper phenotyping of various cell types in health and disease. Here, we describe a protocol of cyclic multiplex fluorescent immunohistochemistry that enables the labeling of up to 16 antigens on the same formalin-fixed paraffin-embedded section using "off-the-shelf," commercially available, primary antibodies as well as fluorescently conjugated secondary antibodies. Key steps include the denaturing/stripping of the antibodies by microwaving and the quenching of any remaining fluorescent signal between the cycles of otherwise traditional multiplexed fluorescent immunohistochemistry. We have successfully applied this protocol to characterize astrocytic and microglial responses to Aβ plaques and neurofibrillary tangles in Alzheimer's disease brains, but it could be easily adapted to other user's needs regarding cell types, disease, and organ.

Single-cell RNA sequencing reveals the role of cell heterogeneity in the sex difference in primary hyperparathyroidism

OBJECTIVE

To explore the difference in parathyroid tissue-derived cells between male and female PHPT patients.

METHODS

Resected parathyroid tissues were collected from PHPT patients of both sexes. Single cells were isolated and sequenced for RNA expression profiles. The cell sequencing data were annotated by cell type, followed by population analysis, functional analysis, pathway analysis, cell communication analysis, differential gene expression analysis, and pseudotime trajectory analysis. The subcluster analyses were also performed in the parathyroid cells.

RESULTS

No substantial difference in the cell population, function, or communication is found between the two sexes. The interferon-a response, oxidative phosphorylation, and reactive oxygen species pathways are up-regulated in females than in male patients, mainly contributed by fibroblast cells, endothelial cells, parathyroid cells, and myeloid cells, which also have significantly more up-regulated pathways and cellular interactions than the other three cell types. The subcluster analysis of parathyroid cells identified five subpopulations: SPARCL1-OC and ISG15-OC are predominant in females, while more S100A13-PCC and PTHLH-OC are found in males. The cellular functions are also elevated in females compared with males. Cells from female patients show a higher expression level of parathyroid hormone (PTH) but a lower expression level of parathyroid hormone-like hormone (PTHLH). The cell pseudotime trajectory and pathway analyses show that the oxyphil cells may be more mature and functionally active than the chief cells in both sexes.

CONCLUSION

These findings suggest that the sex difference in PHPT may be caused by the differentially expressed genes and activated pathways in different cell types in the parathyroid tissue. The heterogeneity of parathyroid cell subpopulations, especially in oxyphil cells, may be associated with the sex differences in PHPT pathogenesis.

FTH1- and SAT1-Induced Astrocytic Ferroptosis Is Involved in Alzheimer’s Disease: Evidence from Single-Cell Transcriptomic Analysis

Objectives: Despite significant advances in neuroscience, the mechanisms of AD are not fully understood. Single-cell RNA sequencing (scRNA-seq) techniques provide potential solutions to analyze cellular composition of complex brain tissue and explore cellular and molecular biological mechanisms of AD. Methods: We investigated cellular heterogeneity in AD via utilization of bioinformatic analysis of scRNA-seq in AD patients and healthy controls from the Gene Expression Omnibus (GEO) database. The “GOplot” package was applied to explore possible biological processes in oligodendrocytes, astrocytes, and oligodendrocyte progenitor cells (OPCs). Expression patterns and biological functions of differentially expressed genes (DEGs) from scRNA-seq data were validated in RNA sequencing data. DEGs in astrocytes interacted with ferroptosis-related genes in FerrDb. CCK-8 and EdU assays were performed to measure cell proliferation ability. ROS, Fe²⁺ level, mitochondrial membrane potentials, iron concentrations, and total iron binding capacity (TIBC) in serum were evaluated. Y-maze and elevated maze were used to measure anxiety-like behavior. Autonomous and exploration behaviors or learning and memory ability in mice were analyzed using open field test and novel object recognition test. Results: Multiple clusters were identified, including oligodendrocytes, astrocytes, OPCs, neurons, microglia, doublets, and endothelial cells. Astrocytes were significantly decreased in AD, while oligodendrocytes and OPCs increased. Cell-to-cell ligand–receptor interaction analysis revealed that astrocytes, neurons, and OPCs mainly established contacts with other cells via the NRG3–ERBB4 ligand–receptor pair. GO and KEGG analyses found that astrocytes were enriched in the ferroptosis pathway. FTH1 and SAT1 in astrocytes were identified as hub mRNAs associated with ferroptosis. Serum iron concentration of 5xFAD mice was higher than that of WT, and emotional and cognitive function were significantly impaired as compared to WT. Serum iron concentration was negatively correlated with number of astrocytes and percentage of time spent entering the novelty arm in the Y-maze test, while it was positively correlated with percentage of time spent in the central area. Meanwhile, number of astrocytes was negatively correlated with percentage of time spent in the central area, while it was positively correlated with percentage of time spent entering the novelty arm. Conclusions: Through scRNA-seq analysis, we found that ferroptosis was activated in astrocytes and may contribute to the pathophysiological process in the entorhinal cortex. FTH1 and SAT1 were identified to impact astrocyte ferroptosis. Emotional and cognitive impairment in AD was associated with astrocyte ferroptosis. Our findings provide clues to reveal the pathophysiological processes following AD at the cellular level and highlight potential drug targets for the treatment of AD.

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.

Interactome overlap between risk genes of epilepsy and targets of anti-epileptic drugs

Aanti-epileptic drugs have been used for treating epilepsy for decades, meanwhile, more than one hundred genes have been identified to be associated with risk of epilepsy; however, the interaction mechanism between anti-epileptic drugs and risk genes of epilepsy was still not clearly understood. In this study, we systematically explored the interaction of epilepsy risk genes and anti-epileptic drug targets through a network-based approach. Our results revealed that anti-epileptic drug targets were significantly over-represented in risk genes of epilepsy with 17 overlapping genes and P-value = 2.2 ×10 −16. We identified a significantly localized PPI network with 55 epileptic risk genes and 94 anti-epileptic drug target genes, and network overlap analysis showed significant interactome overlap between risk genes and drug targets with P-value = 0.04. Besides, genes from PPI network were significantly enriched in the co-expression network of epilepsy with 22 enriched genes and P-value = 1.3 ×10 −15; meanwhile, cell type enrichment analysis indicated genes in this network were significantly enriched in 4 brain cell types (Interneuron, Medium Spiny Neuron, CA1 pyramidal Neuron, and Somatosensory pyramidal Neuron). These results provide evidence for significant interactions between epilepsy risk genes and anti-epileptic drug targets from the perspective of network biology.

Sample-multiplexing approaches for single-cell sequencing

Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.

Profiling 26,000 Aplysia californica neurons by single cell mass spectrometry reveals neuronal populations with distinct neuropeptide profiles

Neuropeptides are a chemically diverse class of cell-to-cell signaling molecules that are widely expressed throughout the central nervous system, often in a cell-specific manner. While cell-to-cell differences in neuropeptides is expected, it is often unclear how exactly neuropeptide expression varies among neurons. Here we created a microscopy-guided, high-throughput single cell matrix-assisted laser desorption/ionization mass spectrometry approach to investigate the neuropeptide heterogeneity of individual neurons in the central nervous system of the neurobiological model Aplysia californica, the California sea hare. In all, we analyzed more than 26,000 neurons from 18 animals and assigned 866 peptides from 66 prohormones by mass matching against an in silico peptide library generated from known Aplysia prohormones retrieved from the UniProt database. Louvain-Jaccard (LJ) clustering of mass spectra from individual neurons revealed 40 unique neuronal populations, or LJ clusters, each with a distinct neuropeptide profile. Prohormones and their related peptides were generally found in single cells from ganglia consistent with the prohormones' previously known ganglion localizations. Several LJ clusters also revealed the cellular colocalization of behaviorally related prohormones, such as an LJ cluster exhibiting achatin and neuropeptide Y, which are involved in feeding, and another cluster characterized by urotensin II, small cardiac peptide, sensorin A, and FRFa, which have shown activity in the feeding network or are present in the feeding musculature. This mass spectrometry-based approach enables the robust categorization of large cell populations based on single cell neuropeptide content and is readily adaptable to the study of a range of animals and tissue types.

Single-Cell RNA Sequencing for Analyzing the Intestinal Tract in Healthy and Diseased Individuals

The intestinal tract is composed of different cell lineages with distinct functions and gene expression profiles, providing uptake of nutrients and protection against insults to the gut lumen. Changes in or damage to the cellulosity or local environment of the intestinal tract can cause various diseases. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for profiling and analyzing individual cell data, making it possible to resolve rare and intermediate cell states that are hardly observed at the bulk level. In this review, we discuss the application of intestinal tract scRNA-seq in identifying novel cell subtypes and states, targets, and explaining the molecular mechanisms involved in intestinal diseases. Finally, we provide future perspectives on using single-cell techniques to discover molecular and cellular targets and biomarkers as a new approach for developing novel therapeutics for intestinal diseases.

Transcriptional Alterations of Mouse Trigeminal Ganglion Neurons Following Orofacial Inflammation Revealed by Single-Cell Analysis

Orofacial inflammation leads to transcriptional alterations in trigeminal ganglion (TG) neurons. However, diverse alterations and regulatory mechanisms following orofacial inflammatory pain in different types of TG neurons remain unclear. Here, orofacial inflammation was induced by injection of complete Freund’s adjuvant (CFA) in mice. After 7 days, we performed single-cell RNA-sequencing on TG cells of mice from control and treatment groups. We identified primary sensory neurons, Schwann cells, satellite glial cells, oligodendrocyte-like cells, immune cells, fibroblasts, and endothelial cells in TG tissue. After principal component analysis and hierarchical clustering, we identified six TG neuronal subpopulations: peptidergic nociceptors (PEP1 and PEP2), non-peptidergic nociceptors (NP1 and NP2), C-fiber low-threshold mechanoreceptors (cLTMR) and myelinated neurons (Nefh-positive neurons, NF) based on annotated marker gene expression. We also performed differential gene expression analysis among TG neuronal subtypes, identifying several differential genes involved in the inflammatory response, neuronal excitability, neuroprotection, and metabolic processes. Notably, we identified several potential novel targets associated with pain modulation, including Arl6ip1, Gsk3b, Scn7a, and Zbtb20 in PEP1, Rgs7bp in PEP2, and Bhlha9 in cLTMR. The established protein–protein interaction network identified some hub genes, implying their critical involvement in regulating orofacial inflammatory pain. Our study revealed the heterogeneity of TG neurons and their diverse neuronal transcriptomic responses to orofacial inflammation, providing a basis for the development of therapeutic strategies for orofacial inflammatory pain.

Eleutheroside E alleviates cerebral ischemia-reperfusion injury in a 5-hydroxytryptamine receptor 2C (Htr2c)-dependent manner in rats

Stroke is the central disorder underlined by ischemia-reperfusion (I/R) injury. Eleutheroside E (EE) is administered as the shield in some ischemia tissues with anti-inflammatory action. However, whether EE defends I/R-induced damage in the brain remains unknown. Here, we demonstrated that EE significantly alleviated the cerebral I/R injury and reduced the apoptosis of hippocampal neuron cells in rats. During the anti-apoptosis process, EE significantly upregulated the expression of 5-hydroxytryptamine receptor 2C (Htr2c) gene. Silencing Htr2c expression dramatically weakened the protective effect of EE on I/R-induced apoptosis of rat hippocampal neuron. EE-regulated Htr2c also remarkably inhibited the expression of caspase-3, −6 and −7, thereby suggesting a plausible anti-apoptosis mechanism associated with Htr2c/caspase axis. These findings elicit the potentially clinical strategy that targets Htr2c to improve outcome of ischemia brain.

Recent Major Transcriptomics and Epitranscriptomics Contributions toward Personalized and Precision Medicine

With the advent of genome-wide screening methods-beginning with microarray technologies and moving onto next generation sequencing methods-the era of precision and personalized medicine was born. Genomics led the way, and its contributions are well recognized. However, "other-omics" fields have rapidly emerged and are becoming as important toward defining disease causes and exploring therapeutic benefits. In this review, we focus on the impacts of transcriptomics, and its extension-epitranscriptomics-on personalized and precision medicine efforts. There has been an explosion of transcriptomic studies particularly in the last decade, along with a growing number of recent epitranscriptomic studies in several disease areas. Here, we summarize and overview major efforts for cancer, cardiovascular disease, and neurodevelopmental disorders (including autism spectrum disorder and intellectual disability) for transcriptomics/epitranscriptomics in precision and personalized medicine. We show that leading advances are being made in both diagnostics, and in investigative and landscaping disease pathophysiological studies. As transcriptomics/epitranscriptomics screens become more widespread, it is certain that they will yield vital and transformative precision and personalized medicine contributions in ways that will significantly further genomics gains.

Advances in single-cell sequencing and its application to musculoskeletal system research

In recent years, single-cell sequencing (SCS) technologies have continued to advance with improved operating procedures and reduced cost, leading to increasing practical adoption among researchers. These emerging technologies have superior abilities to analyse cell heterogeneity at a single-cell level, which have elevated multi-omics research to a higher level. In some fields of research, application of SCS has enabled many valuable discoveries, and musculoskeletal system offers typical examples. This article reviews some major scientific issues and recent advances in musculoskeletal system. In addition, combined with SCS technologies, the research of cell or tissue heterogeneity in limb development and various musculoskeletal system clinical diseases also provides new possibilities for treatment strategies. Finally, this article discusses the challenges and future development potential of SCS and recommends the direction of future applications of SCS to musculoskeletal medicine.

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.

Single-Cell RNA Sequencing Reveals Multiple Pathways and the Tumor Microenvironment Could Lead to Chemotherapy Resistance in Cervical Cancer

Background

Cervical cancer is one of the most common gynecological cancers worldwide. The tumor microenvironment significantly influences the therapeutic response and clinical outcome. However, the complex tumor microenvironment of cervical cancer and the molecular mechanisms underlying chemotherapy resistance are not well studied. This study aimed to comprehensively analyze cells from pretreated and chemoresistant cervical cancer tissues to generate a molecular census of cell populations.

Methods

Biopsy tissues collected from patients with cervical squamous cell carcinoma, cervical adenocarcinoma, and chronic cervicitis were subjected to single-cell RNA sequencing using the 10× Genomics platform. Unsupervised clustering analysis of cells was performed to identify the main cell types, and important cell clusters were reclustered into subpopulations. Gene expression profiles and functional enrichment analysis were used to explore gene expression and functional differences between cell subpopulations in cervicitis and cervical cancer samples and between chemoresistant and chemosensitive samples.

Results

A total of 24,371 cells were clustered into nine separate cell types, including immune and non-immune cells. Differentially expressed genes between chemoresistant and chemosensitive patients enriched in the phosphoinositide 3-kinase (PI3K)/AKT pathway were involved in tumor development, progression, and apoptosis, which might lead to chemotherapy resistance.

Conclusions

Our study provides a comprehensive overview of the cancer microenvironment landscape and characterizes its gene expression and functional difference in chemotherapy resistance. Consequently, our study deepens the insights into cervical cancer biology through the identification of gene markers for diagnosis, prognosis, and therapy.

Isolating and culturing of single microbial cells by laser ejection sorting technology

Single cell isolation and cultivation play an important role in studying physiology, gene expression and functions of microorganisms. A series of single-cell isolation technologies have been developed, among which single-cell ejection technology is one of the most promising. Single cell ejection technology has applied Laser Induced Forward Transfer Technique (LIFT) to isolate bacteria but the viability (or recovery rate) of cells after sorting has not been clarified in the current research progress. In this work, to keep the cells alive as much as possible, we propose a three-layer LIFT system (top layer: 25-nm aluminum film; second layer: 3 μm agar media; third layer: liquid containing bacterial) for the isolation and cultivation of single Gram-negative (E. coli), Gram-positive (Lactobacillus rhamnosus GG, LGG), and eukaryotic microorganisms (Saccharomyces cerevisiae). The experiment results showed that the average survival rates for ejected pure single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for LGG. In addition, we successfully isolated and cultured the GFP expressing E. coli JM109 from the mixture containing complex communities of soil bacteria by fluorescence signal. The average survival rate of E. coli JM109 was demonstrated to be 25.3%. In this study, the isolated and cultured single colonies were further confirmed by colony PCR and sequencing. Such precise sorting and cultivation technique of live single microbial cells could be coupled with other microscopic approaches to isolate single microorganisms with specific functions, revealing their roles in the natural community. Importance We developed a laser induced forward transfer (LIFT) technology to accurately isolate single live microbial cells. The cultivation recovery rates of the ejected single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for Lactobacillus rhamnosus GG (LGG). Coupled LIFT with fluorescent microscope, we demonstrated that single cells of GFP expressing E. coli JM109 were sorted according to fluorescence signal from a complex community of soil bacteria, and subsequently cultured with 25% cultivation recovery rate. This single cell live sorting technology could isolate single microbes with specific functions, revealing their roles in the natural community.

The elusive varicose astrocytes

The evolutionary pattern of different astrocyte types across animal species remains unresolved. In a recent study, Falcone and colleagues revealed that varicose projection astrocytes, a rare form of astrocyte characterized by long varicosities-containing processes, are exclusively found in hominoid brains while being absent from other primate brains.

Transcriptomics of the depressed and PTSD brain

Stress is the response of an organism to demands for change, yet excessive or chronic stress contributes to nearly all psychiatric disorders. The advent of high-throughput transcriptomic methods such as single cell RNA sequencing poses new opportunities to understand the neurobiology of stress, yet substantial barriers to understanding stress remain. Stress adaptation is an organismal survival mechanism conserved across all organisms, yet there is an infinity of potential stressful experiences. Unraveling shared and separate transcriptional programs for adapting to stressful experience remains a challenge, despite methodological and analytic advances. Here we review the state of the field focusing on the technologies used to study the transcriptome for the stress neurobiologist, and also attempt to identify central questions about the heterogeneity of stress for those applying transcriptomic approaches. We further explore how postmortem transcriptome studies aided by preclinical animal models are converging on common molecular pathways for adaptation to aversive experience. Finally, we discuss approaches to integrate large genomic datasets with human neuroimaging and other datasets.

The role of manganese dysregulation in neurological disease: emerging evidence

Manganese is an essential trace metal. The dysregulation of manganese seen in a broad spectrum of neurological disorders reflects its importance in brain development and key neurophysiological processes. Historically, the observation of acquired manganism in miners and people who misuse drugs provided early evidence of brain toxicity related to manganese exposure. The identification of inherited manganese transportopathies, which cause neurodevelopmental and neurodegenerative syndromes, further corroborates the neurotoxic potential of this element. Moreover, manganese dyshomoeostasis is also implicated in Parkinson's disease and other neurodegenerative conditions, such as Alzheimer's disease and Huntington's disease. Ongoing and future research will facilitate the development of better targeted therapeutical strategies than are currently available for manganese-associated neurological disorders.

Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

Precision treatment exploration of breast cancer based on heterogeneity analysis of lncRNAs at the single-cell level

BACKGROUND

Breast cancer (BC) is a complex disease with high heterogeneity, which often leads to great differences in treatment results. Current common molecular typing method is PAM50, which shows positive results for precision medicine; however, room for improvement still remains because of the different prognoses of subtypes. Therefore, in this article, we used lncRNAs, which are more tissue-specific and developmental stage-specific than other RNAs, as typing markers and combined single-cell expression profiles to retype BC, to provide a new method for BC classification and explore new precise therapeutic strategies based on this method.

METHODS

Based on lncRNA expression profiles of 317 single cells from 11 BC patients, SC3 was used to retype BC, and differential expression analysis and enrichment analysis were performed to identify biological characteristics of new subtypes. The results were validated for survival analysis using data from TCGA. Then, the downstream regulatory genes of lncRNA markers of each subtype were searched by expression correlation analysis, and these genes were used as targets to screen therapeutic drugs, thus proposing new precision treatment strategies according to the different subtype compositions of patients.

RESULTS

Seven lncRNA subtypes and their specific biological characteristics are obtained. Then, 57 targets and 210 drugs of 7 subtypes were acquired. New precision medicine strategies were proposed according to the different compositions of patient subtypes.

CONCLUSIONS

For patients with different subtype compositions, we propose a strategy to select different drugs for different patients, which means using drugs targeting multi subtype or combinations of drugs targeting a single subtype to simultaneously kill different cancer cells by personalized treatment, thus reducing the possibility of drug resistance and even recurrence.

Interconnections between circadian clocks and metabolism

Circadian rhythms evolved through adaptation to daily light/dark changes in the environment; they are believed to be regulated by the core circadian clock interlocking feedback loop. Recent studies indicate that each core component executes general and specific functions in metabolism. Here, we review the current understanding of the role of these core circadian clock genes in the regulation of metabolism using various genetically modified animal models. Additionally, emerging evidence shows that exposure to environmental stimuli, such as artificial light, unbalanced diet, mistimed eating, and exercise, remodels the circadian physiological processes and causes metabolic disorders. This Review summarizes the reciprocal regulation between the circadian clock and metabolism, highlights remaining gaps in knowledge about the regulation of circadian rhythms and metabolism, and examines potential applications to human health and disease.