Power analysis of single-cell RNA-sequencing experiments

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.

Single-Cell RNA Sequencing Reveals Transcriptional Landscape of Neutrophils and Highlights the Role of TREM-1 in EAE

BACKGROUND AND OBJECTIVES

Neutrophils, underestimated in multiple sclerosis (MS), are gaining increased attention for their significant functions in patients with MS and the experimental autoimmune encephalomyelitis (EAE) animal model. However, the precise role of neutrophils in cervical lymph nodes (CLNs), the primary CNS-draining lymph nodes where the autoimmune response is initiated during the progression of EAE, remains poorly understood.

METHODS

Applying single-cell RNA sequencing (scRNA-seq), we constructed a comprehensive immune cell atlas of CLNs during development of EAE. Through this atlas, we concentrated on and uncovered the transcriptional landscape, phenotypic and functional heterogeneity of neutrophils, and their crosstalk with immune cells within CLNs in the neuroinflammatory processes in EAE.

RESULTS

Notably, we observed a substantial increase in the neutrophil population in EAE mice, with a particular emphasis on the significant rise within the CLNs. Neutrophils in CLNs were categorized into 3 subtypes, and we explored the specific roles and developmental trajectories of each distinct neutrophil subtype. Neutrophils were found to engage in extensive interactions with other immune cells, playing crucial roles in T-cell activation. Moreover, our findings highlighted the strong migratory ability of neutrophils to CLNs, partly regulated by triggering the receptor expressed on myeloid cells 1 (TREM-1). Inhibiting TREM1 with LR12 prevents neutrophil migration both in vivo and in vitro. In addition, in patients with MS, we confirmed an increase in peripheral neutrophils with an upregulation of TREM-1.

DISCUSSION

Our research provides a comprehensive and precise single-cell atlas of CLNs in EAE, highlighting the role of neutrophils in regulating the periphery immune response. In addition, TREM-1 emerged as an essential regulator of neutrophil migration to CLNs, holding promise as a potential therapeutic target in MS.

SARS-CoV2 infection in whole lung primarily targets macrophages that display subset-specific responses

Deciphering the initial steps of SARS-CoV-2 infection, that influence COVID-19 outcomes, is challenging because animal models do not always reproduce human biological processes and in vitro systems do not recapitulate the histoarchitecture and cellular composition of respiratory tissues. To address this, we developed an innovative ex vivo model of whole human lung infection with SARS-CoV-2, leveraging a lung transplantation technique. Through single-cell RNA-seq, we identified that alveolar and monocyte-derived macrophages (AMs and MoMacs) were initial targets of the virus. Exposure of isolated lung AMs, MoMacs, classical monocytes and non-classical monocytes (ncMos) to SARS-CoV-2 variants revealed that while all subsets responded, MoMacs produced higher levels of inflammatory cytokines than AMs, and ncMos contributed the least. A Wuhan lineage appeared to be more potent than a D614G virus, in a dose-dependent manner. Amidst the ambiguity in the literature regarding the initial SARS-CoV-2 cell target, our study reveals that AMs and MoMacs are dominant primary entry points for the virus, and suggests that their responses may conduct subsequent injury, depending on their abundance, the viral strain and dose. Interfering on virus interaction with lung macrophages should be considered in prophylactic strategies.

Verzi M. KAT2A and KAT2B prevent double-stranded RNA accumulation and interferon signaling to maintain intestinal stem cell renewal

Histone acetyltransferases KAT2A and KAT2B are paralogs highly expressed in the intestinal epithelium, but their functions are not well understood. In this study, double knockout of murine Kat2 genes in the intestinal epithelium was lethal, resulting in robust activation of interferon signaling and interferon-associated phenotypes including the loss of intestinal stem cells. Use of pharmacological agents and sterile organoid cultures indicated a cell-intrinsic double-stranded RNA trigger for interferon signaling. Acetyl-proteomics and sequencing of immunoprecipitated double-stranded RNA were used to interrogate the mechanism behind this response, which identified mitochondria-encoded double-stranded RNA as the source of intrinsic interferon signaling. Kat2a and Kat2b therefore play an essential role in regulating mitochondrial functions and maintaining intestinal health.

Feature selection followed by a novel residuals-based normalization that includes variance stabilization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are reduction of systematic biases primarily introduced through technical sources and transformation of counts to make them more amenable for the application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Optimizing the design of spatial genomic studies

Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and labor-intensive. Traditionally, tissue slicing for spatial sequencing involves parallel axis-aligned sections, often yielding redundant or correlated information. We propose structured batch experimental design, a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studies-one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor-our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and cost-efficient design strategies, allowing spatial genomics studies to be deployed by smaller, resource-constrained labs.

Dynamic Transcriptional Programs During Single NK Cell Killing: Connecting Form to Function in Cellular Immunotherapy

Introduction

Natural killer (NK) cell-based therapies are a promising new method for treating indolent cancer, however engineering new therapies is complex and progress towards therapy for solid tumors is slow. New methods for determining the underlying intracellular signaling driving the killing phenotype would significantly improve this progress.

Methods

We combined single-cell RNA sequencing with live cell imaging of a model system of NK cell killing to correlate transcriptomic data with functional output. A model of NK cell activity, the NK-92 cell line killing of HeLa cervical cancer cells, was used for these studies. NK cell killing activity was observed by microscopy during co-culture with target HeLa cells and killing activity subsequently manually mapped based on NK cell location and Annexin V expression. NK cells from this culture system were profiled by single-cell RNA sequencing using the 10× Genomics platform, and transcription factor activity inferred using the Viper and DoRothEA R packages. Luminescent microscopy of reporter constructs in the NK cells was then used to correlate activity of inferred transcriptional activity with killing activity.

Results

NK cells had heterogeneous killing activity during 10 h of culture with target HeLa cells. Analysis of the single cell sequencing data identified Nuclear Factor Kappa B (NF-κB), Signal Transducer and Activator of Transcription 1 (STAT1) and MYC activity as potential drivers of NK cell functional phenotype in our model system. Live cell imaging of the transcription factor activity found NF-κB activity was significantly correlated with past killing activity. No correlation was observed between STAT1 or MYC activity and NK cell killing.

Conclusions

Combining luminescent microscopy of transcription factor activity with single-cell RNA sequencing is an effective means of assigning functional phenotypes to inferred transcriptomics data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00812-3.

Applications of single-cell RNA sequencing in spermatogenesis and molecular evolution

Spermatogenic cell heterogeneity is determined by the complex process of spermatogenesis differentiation. However, effectively revealing the regulatory mechanisms underlying mammalian spermatogenic cell development and differentiation via traditional methods is difficult. Advances in technology have led to the emergence of many single-cell transcriptome sequencing protocols, which have partially addressed these challenges. In this review, we detail the principles of 10x Genomics technology and summarize the methods for downstream analysis of single-cell transcriptome sequencing data. Furthermore, we explore the role of single-cell transcriptome sequencing in revealing the heterogeneity of testicular ecological niche cells, delineating the establishment and disruption of testicular immune homeostasis during human spermatogenesis, investigating abnormal spermatogenesis in humans, and, ultimately, elucidating the molecular evolution of mammalian spermatogenesis.

Single-cell RNA sequencing reveals cell-cell communication and potential biomarker in sepsis and septic shock patients

BACKGROUND

Sepsis is a disease characterized by infection-induced multiorgan dysfunction, which can progress to septic shock if not promptly treated. Early identification of sepsis is crucial for its treatment. However, there are currently limited specific biomarkers for sepsis or septic shock. This study aims to identify potential biomarkers for sepsis and septic shock.

METHODS

We analyzed single-cell transcriptomic data of peripheral blood mononuclear cells (PBMCs) from healthy individuals, sepsis and septic shock patients, identified differences in gene expression and cell-cell communication between different cell types during disease progression. Moreover, our analyses were further validated with flow cytometry and bulk RNA-seq data.

RESULTS

Our study elucidates the alterations in cellular proportions and cell-cell communication among healthy controls, sepsis, and septic shock patients. We identified a specific augmentation in the Resistin signaling within sepsis monocytes, mediated via RETN-CAP1 ligand-receptor pairs. Additionally, we observed enhanced IL16 signaling within monocytes from septic shock patients, mediated through IL16-CD4 ligand-receptor pairs. Subsequently, we confirmed our findings by validating the increase in CAP-1+ monocytes in sepsis and IL16+ monocytes in septic shock in mouse models. And a significant upregulation of CAP-1 and IL16 was also observed in the bulk RNA-seq data from patients with sepsis and septic shock. Furthermore, we identified four distinct clusters of CD14+ monocytes, highlighting the heterogeneity of monocytes in the progress of sepsis.

CONCLUSIONS

In summary, our work demonstrates changes in cell-cell communication of healthy controls, sepsis and septic shock, confirming that the molecules CAP-1 and IL16 on monocytes may serve as potential diagnostic markers for sepsis and septic shock, respectively. These findings provide new insights for early diagnosis and stratified treatment of the disease.

Feature selection followed by a novel residuals-based normalization simplifies and improves single-cell gene expression analysis

Normalization is a crucial step in the analysis of single-cell RNA-sequencing (scRNA-seq) counts data. Its principal objectives are to reduce the systematic biases primarily introduced through technical sources and to transform the data to make it more amenable for application of established statistical frameworks. In the standard workflows, normalization is followed by feature selection to identify highly variable genes (HVGs) that capture most of the biologically meaningful variation across the cells. Here, we make the case for a revised workflow by proposing a simple feature selection method and showing that we can perform feature selection before normalization by relying on observed counts. We highlight that the feature selection step can be used to not only select HVGs but to also identify stable genes. We further propose a novel variance stabilization transformation inclusive residuals-based normalization method that in fact relies on the stable genes to inform the reduction of systematic biases. We demonstrate significant improvements in downstream clustering analyses through the application of our proposed methods on biological truth-known as well as simulated counts datasets. We have implemented this novel workflow for analyzing high-throughput scRNA-seq data in an R package called Piccolo.

Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets

BACKGROUND

Normalization is a critical step in the analysis of single-cell RNA-sequencing (scRNA-seq) datasets. Its main goal is to make gene counts comparable within and between cells. To do so, normalization methods must account for technical and biological variability. Numerous normalization methods have been developed addressing different sources of dispersion and making specific assumptions about the count data.

MAIN BODY

The selection of a normalization method has a direct impact on downstream analysis, for example differential gene expression and cluster identification. Thus, the objective of this review is to guide the reader in making an informed decision on the most appropriate normalization method to use. To this aim, we first give an overview of the different single cell sequencing platforms and methods commonly used including isolation and library preparation protocols. Next, we discuss the inherent sources of variability of scRNA-seq datasets. We describe the categories of normalization methods and include examples of each. We also delineate imputation and batch-effect correction methods. Furthermore, we describe data-driven metrics commonly used to evaluate the performance of normalization methods. We also discuss common scRNA-seq methods and toolkits used for integrated data analysis.

CONCLUSIONS

According to the correction performed, normalization methods can be broadly classified as within and between-sample algorithms. Moreover, with respect to the mathematical model used, normalization methods can further be classified into: global scaling methods, generalized linear models, mixed methods, and machine learning-based methods. Each of these methods depict pros and cons and make different statistical assumptions. However, there is no better performing normalization method. Instead, metrics such as silhouette width, K-nearest neighbor batch-effect test, or Highly Variable Genes are recommended to assess the performance of normalization methods.

Understanding glioblastoma at the single-cell level: Recent advances and future challenges

Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Beyond single cells: microfluidics empowering multiomics analysis

Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.

scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data

Single-cell RNA sequencing (scRNA-seq) facilitates the study of cell type heterogeneity and the construction of cell atlas. However, due to its limitations, many genes may be detected to have zero expressions, i.e. dropout events, leading to bias in downstream analyses and hindering the identification and characterization of cell types and cell functions. Although many imputation methods have been developed, their performances are generally lower than expected across different kinds and dimensions of data and application scenarios. Therefore, developing an accurate and robust single-cell gene expression data imputation method is still essential. Considering to maintain the original cell-cell and gene-gene correlations and leverage bulk RNA sequencing (bulk RNA-seq) data information, we propose scINRB, a single-cell gene expression imputation method with network regularization and bulk RNA-seq data. scINRB adopts network-regularized non-negative matrix factorization to ensure that the imputed data maintains the cell-cell and gene-gene similarities and also approaches the gene average expression calculated from bulk RNA-seq data. To evaluate the performance, we test scINRB on simulated and experimental datasets and compare it with other commonly used imputation methods. The results show that scINRB recovers gene expression accurately even in the case of high dropout rates and dimensions, preserves cell-cell and gene-gene similarities and improves various downstream analyses including visualization, clustering and trajectory inference.

Single-cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging, and disease

The brain is spatially organized and contains unique cell types, each performing diverse functions and exhibiting differential susceptibility to neurodegeneration. This is exemplified in Parkinson's disease with the preferential loss of dopaminergic neurons of the substantia nigra pars compacta. Using a Parkinson's transgenic model, we conducted a single-cell spatial transcriptomic and dopaminergic neuron translatomic analysis of young and old mouse brains. Through the high resolving capacity of single-cell spatial transcriptomics, we provide a deep characterization of the expression features of dopaminergic neurons and 27 other cell types within their spatial context, identifying markers of healthy and aging cells, spanning Parkinson's relevant pathways. We integrate gene enrichment and genome-wide association study data to prioritize putative causative genes for disease investigation, identifying CASR as a regulator of dopaminergic calcium handling. These datasets represent the largest public resource for the investigation of spatial gene expression in brain cells in health, aging, and disease.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

SARS-CoV-2 infection causes dopaminergic neuron senescence

COVID-19 patients commonly present with signs of central nervous system and/or peripheral nervous system dysfunction. Here, we show that midbrain dopamine (DA) neurons derived from human pluripotent stem cells (hPSCs) are selectively susceptible and permissive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. SARS-CoV-2 infection of DA neurons triggers an inflammatory and cellular senescence response. High-throughput screening in hPSC-derived DA neurons identified several FDA-approved drugs that can rescue the cellular senescence phenotype by preventing SARS-CoV-2 infection. We also identified the inflammatory and cellular senescence signature and low levels of SARS-CoV-2 transcripts in human substantia nigra tissue of COVID-19 patients. Furthermore, we observed reduced numbers of neuromelanin+ and tyrosine-hydroxylase (TH)+ DA neurons and fibers in a cohort of severe COVID-19 patients. Our findings demonstrate that hPSC-derived DA neurons are susceptible to SARS-CoV-2, identify candidate neuroprotective drugs for COVID-19 patients, and suggest the need for careful, long-term monitoring of neurological problems in COVID-19 patients.

The Shoot Apical Meristem: An Evolutionary Molding of Higher Plants

The shoot apical meristem (SAM) gives rise to the aerial structure of plants by producing lateral organs and other meristems. The SAM is responsible for plant developmental patterns, thus determining plant morphology and, consequently, many agronomic traits such as the number and size of fruits and flowers and kernel yield. Our current understanding of SAM morphology and regulation is based on studies conducted mainly on some angiosperms, including economically important crops such as maize (Zea mays) and rice (Oryza sativa), and the model species Arabidopsis (Arabidopsis thaliana). However, studies in other plant species from the gymnosperms are scant, making difficult comparative analyses that help us understand SAM regulation in diverse plant species. This limitation prevents deciphering the mechanisms by which evolution gave rise to the multiple plant structures within the plant kingdom and determines the conserved mechanisms involved in SAM maintenance and operation. This review aims to integrate and analyze the current knowledge of SAM evolution by combining the morphological and molecular information recently reported from the plant kingdom.

Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer

BACKGROUND/OBJECTIVES

Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4.

SUBJECTS/METHODS

In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays.

RESULTS

The primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4.

CONCLUSIONS

Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.

Single-cell transcriptomics uncover hub genes and cell-cell crosstalk in patients with hypertensive nephropathy

Hypertensive nephropathy (HTN) is one of the leading causes of end-stage renal disease, yet the molecular mechanisms are still unknown. To explore novel mechanisms and gene targets for HTN, the gene expression profiles of renal biopsy samples obtained from 2 healthy living donor controls and 5 HTN patients were determined by single-cell RNA sequencing. Key hub genes expression were validated by the Nephroseq v5 platform. The HTN endothelium upregulated cellular adhesion genes (ICAM2 and CEACAM1), inflammatory genes (ETS2 and IFI6) and apoptosis related genes (CNN3). Proximal tubules in HTN highly expressed hub genes including BBOX1, TPM1, TMSB10, SDC4, and NUP58, which might be potential novel targets for proximal tubular injury. The upregulated genes in tubules of HTN were mainly participating in inflammatory signatures including IFN-γ signature, NF-κB signaling, IL-12 signaling and Wnt signaling pathway. Receptor-ligand interaction analysis indicated potential cell-cell crosstalk between endothelial cells or mesangial cells with other renal resident cells in HTN. Together, our data identify a distinct cell-specific gene expression profile, pathogenic inflammatory signaling and potential cell-cell communications between endothelial cells or mesangial cells with other renal resident cells in HTN. These findings may provide a promising novel landscape for mechanisms and treatment of human HTN.

Cellular Heterogeneity of Activated Primary Human Macrophages and Associated Drug-Gene Networks: From Biology to Precision Therapeutics

BACKGROUND

Interferon-γ (IFNγ) signaling plays a complex role in atherogenesis. IFNγ stimulation of macrophages permits in vitro exploration of proinflammatory mechanisms and the development of novel immune therapies. We hypothesized that the study of macrophage subpopulations could lead to anti-inflammatory interventions.

METHODS

Primary human macrophages activated by IFNγ (M(IFNγ)) underwent analyses by single-cell RNA sequencing, time-course cell-cluster proteomics, metabolite consumption, immunoassays, and functional tests (phagocytic, efferocytotic, and chemotactic). RNA-sequencing data were analyzed in LINCS (Library of Integrated Network-Based Cellular Signatures) to identify compounds targeting M(IFNγ) subpopulations. The effect of compound BI-2536 was tested in human macrophages in vitro and in a murine model of atherosclerosis.

RESULTS

Single-cell RNA sequencing identified 2 major clusters in M(IFNγ): inflammatory (M(IFNγ)i) and phagocytic (M(IFNγ)p). M(IFNγ)i had elevated expression of inflammatory chemokines and higher amino acid consumption compared with M(IFNγ)p. M(IFNγ)p were more phagocytotic and chemotactic with higher Krebs cycle activity and less glycolysis than M(IFNγ)i. Human carotid atherosclerotic plaques contained 2 such macrophage clusters. Bioinformatic LINCS analysis using our RNA-sequencing data identified BI-2536 as a potential compound to decrease the M(IFNγ)i subpopulation. BI-2536 in vitro decreased inflammatory chemokine expression and secretion in M(IFNγ) by shrinking the M(IFNγ)i subpopulation while expanding the M(IFNγ)p subpopulation. BI-2536 in vivo shifted the phenotype of macrophages, modulated inflammation, and decreased atherosclerosis and calcification.

CONCLUSIONS

We characterized 2 clusters of macrophages in atherosclerosis and combined our cellular data with a cell-signature drug library to identify a novel compound that targets a subset of macrophages in atherosclerosis. Our approach is a precision medicine strategy to identify new drugs that target atherosclerosis and other inflammatory diseases.

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Elucidating the cellular and genetic composition of ocular tissues is essential for uncovering the pathophysiology of ocular diseases. Since the introduction of single-cell RNA sequencing (scRNA-seq) in 2009, vision researchers have performed extensive single-cell analyses to better understand transcriptome complexity and heterogeneity of ocular structures. This technology has revolutionized our ability to identify rare cell populations and to make cross-species comparisons of gene expression in both steady state and disease conditions. Importantly, single-cell transcriptomic analyses have enabled the identification of cell-type specific gene markers and signalling pathways between ocular cell populations. While most scRNA-seq studies have been conducted on retinal tissues, large-scale transcriptomic atlases pertaining to the ocular anterior segment have also been constructed in the past three years. This timely review provides vision researchers with an overview of scRNA-seq experimental design, technical limitations, and clinical applications in a variety of anterior segment-related ocular pathologies. We review open-access anterior segment-related scRNA-seq datasets and illustrate how scRNA-seq can be an indispensable tool for the development of targeted therapeutics.

Sex-specific declines in cholinergic-targeting tRNA fragments in the nucleus accumbens in Alzheimer's disease

INTRODUCTION

Females with Alzheimer's disease (AD) suffer accelerated dementia and loss of cholinergic neurons compared to males, but the underlying mechanisms are unknown. Seeking causal contributors to both these phenomena, we pursued changes in transfer RNS (tRNA) fragments (tRFs) targeting cholinergic transcripts (CholinotRFs).

METHODS

We analyzed small RNA-sequencing (RNA-Seq) data from the nucleus accumbens (NAc) brain region which is enriched in cholinergic neurons, compared to hypothalamic or cortical tissues from AD brains; and explored small RNA expression in neuronal cell lines undergoing cholinergic differentiation.

RESULTS

NAc CholinotRFs of mitochondrial genome origin showed reduced levels that correlated with elevations in their predicted cholinergic-associated mRNA targets. Single-cell RNA seq from AD temporal cortices showed altered sex-specific levels of cholinergic transcripts in diverse cell types; inversely, human-originated neuroblastoma cells under cholinergic differentiation presented sex-specific CholinotRF elevations.

DISCUSSION

Our findings support CholinotRFs contributions to cholinergic regulation, predicting their involvement in AD sex-specific cholinergic loss and dementia.

Attention-based deep clustering method for scRNA-seq cell type identification

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.

HybriSeq: Probe-based Device-free Single-cell RNA Profiling

We have developed the HybriSeq method for single-cell RNA profiling, which utilizes in situ hybridization of multiple probes for targeted transcripts, followed by split-pool barcoding and sequencing analysis of the probes. We have shown that HybriSeq can achieve high sensitivity for RNA detection with multiple probes and profile RNA accessibility. The utility of HybriSeq is demonstrated in characterizing cell-to-cell heterogeneities of a panel of 95 cell-cycle-related genes and the probe-probe heterogeneity within a single transcript.

Single-cell sequencing of tumor-associated macrophages in a Drosophila model

Introduction

Tumor-associated macrophages may act to either limit or promote tumor growth, yet the molecular basis for either path is poorly characterized.

Methods

We use a larval Drosophila model that expresses a dominant-active version of the Ras-oncogene (RasV12) to study dysplastic growth during early tumor progression. We performed single-cell RNA-sequencing of macrophage-like hemocytes to characterize these cells in tumor- compared to wild-type larvae. Hemocytes included manually extracted tumor-associated- and circulating cells.

Results and discussion

We identified five distinct hemocyte clusters. In addition to RasV12 larvae, we included a tumor model where the activation of effector caspases was inhibited, mimicking an apoptosis-resistant setting. Circulating hemocytes from both tumor models differ qualitatively from control wild-type cells-they display an enrichment for genes involved in cell division, which was confirmed using proliferation assays. Split analysis of the tumor models further reveals that proliferation is strongest in the caspase-deficient setting. Similarly, depending on the tumor model, hemocytes that attach to tumors activate different sets of immune effectors-antimicrobial peptides dominate the response against the tumor alone, while caspase inhibition induces a shift toward members of proteolytic cascades. Finally, we provide evidence for transcript transfer between hemocytes and possibly other tissues. Taken together, our data support the usefulness of Drosophila to study the response against tumors at the organismic level.

Single-cell transcriptome analysis indicates fatty acid metabolism-mediated metastasis and immunosuppression in male breast cancer

Male breast cancer (MBC) is a rare but aggressive malignancy with cellular and immunological characteristics that remain unclear. Here, we perform transcriptomic analysis for 111,038 single cells from tumor tissues of six MBC and thirteen female breast cancer (FBC) patients. We find that that MBC has significantly lower infiltration of T cells relative to FBC. Metastasis-related programs are more active in cancer cells from MBC. The activated fatty acid metabolism involved with FASN is related to cancer cell metastasis and low immune infiltration of MBC. T cells in MBC show activation of p38 MAPK and lipid oxidation pathways, indicating a dysfunctional state. In contrast, T cells in FBC exhibit higher expression of cytotoxic markers and immune activation pathways mediated by immune-modulatory cytokines. Moreover, we identify the inhibitory interactions between cancer cells and T cells in MBC. Our study provides important information for understanding the tumor immunology and metabolism of MBC.

KAT2 paralogs prevent dsRNA accumulation and interferon signaling to maintain intestinal stem cells

Histone acetyltransferases KAT2A and KAT2B are paralogs highly expressed in the intestinal epithelium, but their functions are not well understood. In this study, double knockout of murine Kat2 genes in the intestinal epithelium was lethal, resulting in robust activation of interferon signaling and interferon-associated phenotypes including the loss of intestinal stem cells. Use of pharmacological agents and sterile organoid cultures indicated a cell-intrinsic double-stranded RNA trigger for interferon signaling. Acetyl-proteomics and dsRIP-seq were employed to interrogate the mechanism behind this response, which identified mitochondria-encoded double-stranded RNA as the source of intrinsic interferon signaling. Kat2a and Kat2b therefore play an essential role in regulating mitochondrial functions as well as maintaining intestinal health.

Development of a versatile LCM-Seq method for spatial transcriptomics of fluorescently tagged cholinergic neuron populations

Single-cell transcriptomics are powerful tools to define neuronal cell types based on co-expressed gene clusters. Limited RNA input in these technologies necessarily compromises transcriptome coverage and accuracy of differential expression analysis. We propose that bulk RNA-Seq of neuronal pools defined by spatial position offers an alternative strategy to overcome these technical limitations. We report a laser-capture microdissection (LCM)-Seq method which allows deep transcriptome profiling of fluorescently tagged neuron populations isolated with LCM from histological sections of transgenic mice. Mild formaldehyde fixation of ZsGreen marker protein, LCM sampling of ∼300 pooled neurons, followed by RNA isolation, library preparation and RNA-Seq with methods optimized for nanogram amounts of moderately degraded RNA enabled us to detect ∼15,000 different transcripts in fluorescently labeled cholinergic neuron populations. The LCM-Seq approach showed excellent accuracy in quantitative studies, allowing us to detect 2891 transcripts expressed differentially between the spatially defined and clinically relevant cholinergic neuron populations of the dorsal caudate-putamen and medial septum. In summary, the LCM-Seq method we report in this study is a versatile, sensitive, and accurate bulk sequencing approach to study the transcriptome profile and differential gene expression of fluorescently tagged neuronal populations isolated from transgenic mice with high spatial precision.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

An information-theoretic approach to single cell sequencing analysis

BACKGROUND

Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information.

RESULTS

Here, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types.

CONCLUSIONS

Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.

LAST-seq: single-cell RNA sequencing by direct amplification of single-stranded RNA without prior reverse transcription and second-strand synthesis

Existing single-cell RNA sequencing (scRNA-seq) methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert single-stranded RNA into double-stranded DNA prior to amplification, with the limited RT/SSS efficiency compromising RNA detectability. Here, we develop a new scRNA-seq method, Linearly Amplified Single-stranded-RNA-derived Transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA molecules without prior RT/SSS. LAST-seq offers a high single-molecule capture efficiency and a low level of technical noise for single-cell transcriptome analyses. Using LAST-seq, we characterize transcriptional bursting kinetics in human cells, revealing a role of topologically associating domains in transcription regulation.

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing

Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.

A current and future perspective on T cell receptor repertoire profiling

T cell receptors (TCR) play a vital role in the immune system's ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools.

Approaches for single-cell RNA sequencing across tissues and cell types

Single-cell sequencing of RNA (scRNA-seq) has advanced our understanding of cellular heterogeneity and signaling in developmental biology and disease. A large number of complementary assays have been developed to profile transcriptomes of individual cells, also in combination with other readouts, such as chromatin accessibility or antibody-based analysis of protein surface markers. As scRNA-seq technologies are advancing fast, it is challenging to establish robust workflows and up-to-date protocols that are best suited to address the large range of research questions. Here, we review scRNA-seq techniques from mRNA end-counting to total RNA in relation to their specific features and outline the necessary sample preparation steps and quality control measures. Based on our experience in dealing with the continuously growing portfolio from the perspective of a central single-cell facility, we aim to provide guidance on how workflows can be best automatized and share our experience in coping with the continuous expansion of scRNA-seq techniques.

Expression quantitative trait locus studies in the era of single-cell omics

Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.

Single-cell RNA sequencing reveals the transcriptomic landscape of kidneys in patients with ischemic acute kidney injury

BACKGROUND

Ischemic acute kidney injury (AKI) is a common syndrome associated with considerable mortality and healthcare costs. Up to now, the underlying pathogenesis of ischemic AKI remains incompletely understood, and specific strategies for early diagnosis and treatment of ischemic AKI are still lacking. Here, this study aimed to define the transcriptomic landscape of AKI patients through single-cell RNA sequencing (scRNA-seq) analysis in kidneys.

METHODS

In this study, scRNA-seq technology was applied to kidneys from two ischemic AKI patients, and three human public scRNA-seq datasets were collected as controls. Differentially expressed genes (DEGs) and cell clusters of kidneys were determined. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, as well as the ligand-receptor interaction between cells, were performed. We also validated several DEGs expression in kidneys from human ischemic AKI and ischemia/reperfusion (I/R) injury induced AKI mice through immunohistochemistry staining.

RESULTS

15 distinct cell clusters were determined in kidney from subjects of ischemic AKI and control. The injured proximal tubules (PT) displayed a proapoptotic and proinflammatory phenotype. PT cells of ischemic AKI had up-regulation of novel pro-apoptotic genes including USP47 , RASSF4 , EBAG9 , IER3 , SASH1 , SEPTIN7 , and NUB1 , which have not been reported in ischemic AKI previously. Several hub genes were validated in kidneys from human AKI and renal I/R injury mice, respectively. Furthermore, PT highly expressed DEGs enriched in endoplasmic reticulum stress, autophagy, and retinoic acid-inducible gene I (RIG-I) signaling. DEGs overexpressed in other tubular cells were primarily enriched in nucleotide-binding and oligomerization domain (NOD)-like receptor signaling, estrogen signaling, interleukin (IL)-12 signaling, and IL-17 signaling. Overexpressed genes in kidney-resident immune cells including macrophages, natural killer T (NKT) cells, monocytes, and dendritic cells were associated with leukocyte activation, chemotaxis, cell adhesion, and complement activation. In addition, the ligand-receptor interactions analysis revealed prominent communications between macrophages and monocytes with other cells in the process of ischemic AKI.

CONCLUSION

Together, this study reveals distinct cell-specific transcriptomic atlas of kidney in ischemic AKI patients, altered signaling pathways, and potential cell-cell crosstalk in the development of AKI. These data reveal new insights into the pathogenesis and potential therapeutic strategies in ischemic AKI.

Comparison of transformations for single-cell RNA-seq data

The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal-component analysis, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.

Recent advances in deciphering hippocampus complexity using single-cell transcriptomics

Single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) technologies have emerged as revolutionary and powerful tools, which have helped in achieving significant progress in biomedical research over the last decade. scRNA-seq and snRNA-seq resolve heterogeneous cell populations from different tissues and help reveal the function and dynamics at the single-cell level. The hippocampus is an essential component for cognitive functions, including learning, memory, and emotion regulation. However, the molecular mechanisms underlying the activity of hippocampus have not been fully elucidated. The development of scRNA-seq and snRNA-seq technologies provides strong support for attaining an in-depth understanding of hippocampal cell types and gene expression regulation from the single-cell transcriptome profiling perspective. This review summarizes the applications of scRNA-seq and snRNA-seq in the hippocampus to further expand our knowledge of the molecular mechanisms related to hippocampal development, health, and diseases.

Oncogenic signaling pathways in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most common (∼90% cases) pancreatic neoplasm and one of the most lethal cancer among all malignances. PDAC harbor aberrant oncogenic signaling that may result from the multiple genetic and epigenetic alterations such as the mutation in driver genes (KRAS, CDKN2A, p53), genomic amplification of regulatory genes (MYC, IGF2BP2, ROIK3), deregulation of chromatin-modifying proteins (HDAC, WDR5) among others. A key event is the formation of Pancreatic Intraepithelial Neoplasia (PanIN) that often results from the activating mutation in KRAS. Mutated KRAS can direct a variety of signaling pathways and modulate downstream targets including MYC, which play an important role in cancer progression. In this review, we discuss recent literature shedding light on the origins of PDAC from the perspective of major oncogenic signaling pathways. We highlight how MYC directly and indirectly, with cooperation with KRAS, affect epigenetic reprogramming and metastasis. Additionally, we summarize the recent findings from single cell genomic approaches that highlight heterogeneity in PDAC and tumor microenvironment, and provide molecular avenues for PDAC treatment in the future.

TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration

BACKGROUND

The reactivation of genetic programs from early development is a common mechanism for injury-induced organ regeneration. T-box 3 (TBX3) is a member of the T-box family of transcription factors previously shown to regulate pluripotency and subsequent lineage commitment in a number of tissues, including limb and lung. TBX3 is also involved in lung and heart organogenesis. Here, we provide a comprehensive and thorough characterization of TBX3 and its role during pancreatic organogenesis and regeneration.

RESULTS

We interrogated the level and cell specificity of TBX3 in the developing and adult pancreas at mRNA and protein levels at multiple developmental stages in mouse and human pancreas. We employed conditional mutagenesis to determine its role in murine pancreatic development and in regeneration after the induction of acute pancreatitis. We found that Tbx3 is dynamically expressed in the pancreatic mesenchyme and epithelium. While Tbx3 is expressed in the developing pancreas, its absence is likely compensated by other factors after ablation from either the mesenchymal or epithelial compartments. In an adult model of acute pancreatitis, we found that a lack of Tbx3 resulted in increased proliferation and fibrosis as well as an enhanced inflammatory gene programs, indicating that Tbx3 has a role in tissue homeostasis and regeneration.

CONCLUSIONS

TBX3 demonstrates dynamic expression patterns in the pancreas. Although TBX3 is dispensable for proper pancreatic development, its absence leads to altered organ regeneration after induction of acute pancreatitis.

Dynamical modeling reveals RNA decay mediates the effect of matrix stiffness on aged muscle stem cell fate

Loss of muscle stem cell (MuSC) self-renewal with aging reflects a combination of influences from the intracellular (e.g., post-transcriptional modifications) and extracellular (e.g., matrix stiffness) environment. Whereas conventional single cell analyses have revealed valuable insights into factors contributing to impaired self-renewal with age, most are limited by static measurements that fail to capture nonlinear dynamics. Using bioengineered matrices mimicking the stiffness of young and old muscle, we showed that while young MuSCs were unaffected by aged matrices, old MuSCs were phenotypically rejuvenated by young matrices. Dynamical modeling of RNA velocity vector fields in silico revealed that soft matrices promoted a self-renewing state in old MuSCs by attenuating RNA decay. Vector field perturbations demonstrated that the effects of matrix stiffness on MuSC self-renewal could be circumvented by fine-tuning the expression of the RNA decay machinery. These results demonstrate that post-transcriptional dynamics dictate the negative effect of aged matrices on MuSC self-renewal.

The life and times of endogenous opioid peptides: Updated understanding of synthesis, spatiotemporal dynamics, and the clinical impact in alcohol use disorder

The opioid G-protein coupled receptors (GPCRs) strongly modulate many of the central nervous system structures that contribute to neurological and psychiatric disorders including pain, major depressive disorder, and substance use disorders. To better treat these and related diseases, it is essential to understand the signaling of their endogenous ligands. In this review, we focus on what is known and unknown about the regulation of the over two dozen endogenous peptides with high affinity for one or more of the opioid receptors. We briefly describe which peptides are produced, with a particular focus on the recently proposed possible synthesis pathways for the endomorphins. Next, we describe examples of endogenous opioid peptide expression organization in several neural circuits and how they appear to be released from specific neural compartments that vary across brain regions. We discuss current knowledge regarding the strength of neural activity required to drive endogenous opioid peptide release, clues about how far peptides diffuse from release sites, and their extracellular lifetime after release. Finally, as a translational example, we discuss the mechanisms of action of naltrexone (NTX), which is used clinically to treat alcohol use disorder. NTX is a synthetic morphine analog that non-specifically antagonizes the action of most endogenous opioid peptides developed in the 1960s and FDA approved in the 1980s. We review recent studies clarifying the precise endogenous activity that NTX prevents. Together, the works described here highlight the challenges and opportunities the complex opioid system presents as a therapeutic target.

Exploring the Mechanism of Microfracture in the Treatment of Porcine Full-Thickness Cartilage Defect

BACKGROUND

Microfracture has the most extensive clinical application because of its advantages of a single operation, unified process, and low operation cost. Because research on the repair mechanism of microfractures in the treatment of cartilage defects is not in-depth, this study aimed to elucidate the mechanism.

PURPOSE

To identify the characteristic cell subsets at different repair stages after microfracture, systematically analyze the repair process of the defect area after microfracture, and investigate the mechanism of fibrocartilage repair.

STUDY DESIGN

Descriptive laboratory study.

METHODS

Full-thickness articular cartilage defects and microfractures was established in the right knee of Bama miniature pigs. Single-cell transcriptional assays were used to identify the characteristics of cells isolated from healthy articular cartilage and regenerated tissues.

RESULTS

Microfractures induced mature fibrous repair in the full-thickness cartilage defect six months after surgery, while early stages of repair occurred within six weeks. Based on single-cell sequencing results, eight subsets and specific marker genes were identified. Two processes may occur after microfracture: normal hyaline cartilage regeneration and abnormal fibrocartilage repair. Regulatory chondrocytes, proliferative chondrocytes and cartilage progenitor cells (CPCs) may play important roles in the normal regeneration process. During abnormal repair, CPCs and skeletal stem cells may have different functions, and macrophages and endothelial cells may play important regulatory roles in the formation of fibrochondrocytes.

CONCLUSIONS

Using single-cell transcriptome sequencing, this study investigated the tissue regeneration process and identified key cell subsets after microfracture.

CLINICAL RELEVANCE

These results provide future targets for optimizing the repair effect of microfracture.

In silico tissue generation and power analysis for spatial omics

As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.

Transcriptomic changes in single yeast cells under various stress conditions

BACKGROUND

The stress response of Saccharomyces cerevisiae has been extensively studied in the past decade. However, with the advent of recent technology in single-cell transcriptome profiling, there is a new opportunity to expand and further understanding of the yeast stress response with greater resolution on a system level. To understand transcriptomic changes in baker's yeast S. cerevisiae cells under stress conditions, we sequenced 117 yeast cells under three stress treatments (hypotonic condition, glucose starvation and amino acid starvation) using a full-length single-cell RNA-Seq method.

RESULTS

We found that though single cells from the same treatment showed varying degrees of uniformity, technical noise and batch effects can confound results significantly. However, upon careful selection of samples to reduce technical artifacts and account for batch-effects, we were able to capture distinct transcriptomic signatures for different stress conditions as well as putative regulatory relationships between transcription factors and target genes.

CONCLUSION

Our results show that a full-length single-cell based transcriptomic analysis of the yeast may help paint a clearer picture of how the model organism responds to stress than do bulk cell population-based methods.