Tuning dCas9's ability to block transcription enables robust, noiseless knockdown of bacterial genes

Over the past few years, tools that make use of the Cas9 nuclease have led to many breakthroughs, including in the control of gene expression. The catalytically dead variant of Cas9 known as dCas9 can be guided by small RNAs to block transcription of target genes, in a strategy also known as CRISPRi. Here, we reveal that the level of complementarity between the guide RNA and the target controls the rate at which RNA polymerase "kicks out" dCas9 from the target and completes transcription. We use this mechanism to precisely and robustly reduce gene expression by defined relative amounts. Alternatively, tuning repression by changing dCas9 concentration is noisy and promoter-strength dependent. We demonstrate broad applicability of this method to the study of genetic regulation and cellular physiology. First, we characterize feedback strength of a model auto-repressor. Second, we study the impact of amount variations of cell-wall synthesizing enzymes on cell morphology. Finally, we multiplex the system to obtain any combination of fractional repression of two genes.

Microfluidic technologies for cell deformability cytometry

Microfluidic detection methods for cell deformability cytometry have been regarded as powerful tools for single-cell analysis of cellular mechanical phenotypes, thus having been widely applied in the fields of cell preparation, separation, clinical diagnostics and so on. Featured with traits like easy operations, low cost and high throughput, such methods have shown great potentials on investigating physiological state and pathological changes during cellular deformation. Herein, a review on the advancements of microfluidic-based cell deformation cytometry is presented. We discuss several representative microfluidic-based cell deformability cytometry methods with their frontiers in practical applications. Finally, we analyze the current status and propose the remaining challenges with future perspectives and development directions.

A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease

Introduction

Recent advances in generating massive single-cell/nucleus transcriptomic data have shown great potential for facilitating the identification of cell type-specific Alzheimer's disease (AD) pathobiology and drug-target discovery for therapeutic development.

Methods

We developed The Alzheimer's Cell Atlas (TACA) by compiling an AD brain cell atlas consisting of over 1.1 million cells/nuclei across 26 data sets, covering major brain regions (hippocampus, cerebellum, prefrontal cortex, and so on) and cell types (astrocyte, microglia, neuron, oligodendrocytes, and so on). We conducted nearly 1400 differential expression comparisons to identify cell type-specific molecular alterations (e.g., case vs healthy control, sex-specific, apolipoprotein E (APOE) ε4/ε4, and TREM2 mutations). Each comparison was followed by protein-protein interaction module detection, functional enrichment analysis, and omics-informed target and drug (over 700,000 perturbation profiles) screening. Over 400 cell-cell interaction analyses using 6000 ligand-receptor interactions were conducted to identify the cell-cell communication networks in AD.

Results

All results are integrated into TACA (https://taca.lerner.ccf.org/), a new web portal with cell type-specific, abundant transcriptomic information, and 12 interactive visualization tools for AD.

Discussion

We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for drug discovery.

Highlights

We compiled an Alzheimer's disease (AD) brain cell atlas consisting of more than 1.1 million cells/nuclei transcriptomes from 26 data sets, covering major brain regions (cortex, hippocampus, cerebellum) and cell types (e.g., neuron, oligodendrocyte, astrocyte, and microglia).We conducted over 1400 differential expression (DE) comparisons to identify cell type-specific gene expression alterations. Major comparison types are (1) AD versus healthy control; (2) sex-specific DE, (3) genotype-driven DE (i.e., apolipoprotein E [APOE] ε4/ε4 vs APOE ε3/ε3; TREM2R47H vs common variants) analysis; and (4) others. Each comparison was further followed by (1) human protein-protein interactome network module analysis, (2) pathway enrichment analysis, and (3) gene-set enrichment analysis.For drug screening, we conducted gene set enrichment analysis for all the comparisons with over 700,000 drug perturbation profiles connecting more than 10,000 human genes and 13,000 drugs/compounds.A total of over 400 analyses of cell-cell interactions against 6000 experimentally validated ligand-receptor interactions were conducted to reveal the disease-relevant cell-cell communications in AD.

Droplet-Based Glycan and RNA Sequencing for Profiling the Distinct Cellular Glyco-States in Single Cells

Plate-based single-cell glycan and RNA sequencing (scGR-seq) is previously developed to realize the integrated analysis of glycome and transcriptome in single cells. However, the sample size is limited to only a few hundred cells. Here, a droplet-based scGR-seq is developed to address this issue by adopting a 10x Chromium platform to simultaneously profile ten thousand cells' glycome and transcriptome in single cells. To establish droplet-based scGR-seq, a comparative analysis of two distinct cell lines is performed: pancreatic ductal adenocarcinoma cells and normal pancreatic duct cells. Droplet-based scGR-seq revealed distinct glycan profiles between the two cell lines that showed a strong correlation with the results obtained by flow cytometry. Next, droplet-based scGR-seq is applied to a more complex sample: peripheral blood mononuclear cells (PBMC) containing various immune cells. The method can systematically map the glycan signature for each immune cell in PBMC as well as glycan alterations by cell lineage. Prediction of the association between the glycan expression and the gene expression using regression analysis ultimately leads to the identification of a glycan epitope that impacts cellular functions. In conclusion, the droplet-based scGR-seq realizes the high-throughput profiling of the distinct cellular glyco-states in single cells.

Automated microarray platform for single-cell sorting and collection of lymphocytes following HIV reactivation

A promising strategy to cure HIV-infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5%, 11.2%, and 12.1% percentage of cells, respectively. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single-cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found.

Leveraging single-cell sequencing analysis and bulk-RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis

Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.

The tumor microenvironment of benign and malignant salivary gland tumors

BACKGROUND

Treatment of salivary gland tumors (SGTs) remains challenging. Little is known about the immune landscape of SGTs. We aimed to characterize the tumor microenvironment in benign and malignant SGTs.

METHODS

Eleven benign and nine malignant tumors were collected from patients undergoing curative intent surgery. Specimens were analyzed using mass cytometry by time-of-flight. Immune cell populations were manually gated, and T cells were clustered using the FlowSOM algorithm. Population frequencies were compared between high-grade and low-grade malignancies, corrected for multiple hypothesis testing.

RESULTS

There were trends towards increased CD4+ and CD8+ T cells among malignant tumors. High-grade malignancies exhibited trends towards higher frequencies of CD8+ PD-1+ CD39+ CD103+ exhausted T cells, CD4+ FoxP3+ TCF-1+ CD127- Tregs, and CD69+ CD25- CD4+ T cells compared to low-grade malignancies.

CONCLUSION

SGTs exhibit significant immunologic diversity. High-grade malignancies tended to have greater infiltration of exhausted CD8+ T cells and Tregs, which may guide future studies for immunotherapy strategies.

Integrating single-cell and multi-omic approaches reveals Euphorbiae Humifusae Herba-dependent mitochondrial dysfunction in non-small-cell lung cancer

Euphorbiae Humifusae Herba (EHH) is a pivotal therapeutic agent with diverse pharmacological effects. However, a substantial gap exists in understanding its pharmacological properties and anti-tumour mechanisms. This study aimed to address this gap by exploring EHH's pharmacological properties, identifying NSCLC therapy-associated protein targets, and elucidating how EHH induces mitochondrial disruption in NSCLC cells, offering insights into novel NSCLC treatment strategies. String database was utilized to explore protein-protein interactions. Subsequently, single-cell analysis and multi-omics further unveiled the impact of EHH-targeted genes on the immune microenvironment of NSCLC, as well as their influence on immunotherapeutic responses. Finally, both in vivo and in vitro experiments elucidated the anti-tumour mechanisms of EHH, specifically through the assessment of mitochondrial ROS levels and alterations in mitochondrial membrane potential. EHH exerts its influence through engagement with a cluster of 10 genes, including the apoptotic gene CASP3. This regulatory impact on the immune milieu within NSCLC holds promise as an indicator for predicting responses to immunotherapy. Besides, EHH demonstrated the capability to induce mitochondrial ROS generation and perturbations in mitochondrial membrane potential in NSCLC cells, ultimately leading to mitochondrial dysfunction and consequent apoptosis of tumour cells. EHH induces mitochondrial disruption in NSCLC cells, leading to cell apoptosis to inhibit the progress of NSCLC.

All the single cells: Single-cell transcriptomics/epigenomics experimental design and analysis considerations for glial biologists

Single-cell transcriptomics, epigenomics, and other 'omics applied at single-cell resolution can significantly advance hypotheses and understanding of glial biology. Omics technologies are revealing a large and growing number of new glial cell subtypes, defined by their gene expression profile. These subtypes have significant implications for understanding glial cell function, cell-cell communications, and glia-specific changes between homeostasis and conditions such as neurological disease. For many, the training in how to analyze, interpret, and understand these large datasets has been through reading and understanding literature from other fields like biostatistics. Here, we provide a primer for glial biologists on experimental design and analysis of single-cell RNA-seq datasets. Our goal is to further the understanding of why decisions are made about datasets and to enhance biologists' ability to interpret and critique their work and the work of others. We review the steps involved in single-cell analysis with a focus on decision points and particular notes for glia. The goal of this primer is to ensure that single-cell 'omics experiments continue to advance glial biology in a rigorous and replicable way.

Integration of single-cell proteomic datasets through distinctive proteins in cell clusters

The use of mass spectrometry and antibody-based sequencing technologies at the single-cell level has led to an increase in single-cell proteomic datasets. Integrating these datasets is crucial to eliminate the batch effect that often arises due to their limited sequencing molecules. Although methods for horizontally integrating high-dimensional single-cell transcriptomic datasets can also be applied to single-cell proteomic datasets, a specialized approach explicitly tailored for low-dimensional proteomic datasets may enhance the integration process. Here, we introduce SCPRO-HI, an algorithm for the horizontal integration of antibody-based single-cell proteomic datasets. It utilizes a hierarchical cell anchoring technique to match cells based on the similarity of distinctive proteins for constituting cell clusters. A novel variational auto-encoder model is employed for correcting batch effects on the protein abundances, eliminating the need for mapping them into a new domain. Moreover, we propose a technique for extending the algorithm to high-dimensional datasets. The performance of the SCPRO-HI algorithm is evaluated using simulated and real-world single-cell proteomic datasets. The findings demonstrate our algorithm outperforms state-of-the-art methods, achieving a 75% higher silhouette score while preserving HVPs 13% better. Furthermore, the algorithm shows competitive performance in transcriptomic datasets, suggesting potential for integrating high-dimensional mass-spectrometry-based proteomic datasets.

Transcriptomics of Diphyllatea (CRuMs) from South Pacific crater lakes confirm new cryptic clades

The Diphyllatea (CRuMs) are heterotrophic protists currently divided into three distinct clades (Diphy I-III). Diphy I are biflagellates in the genus Diphylleia, whereas Diphy II and III represent cryptic clades comprising Collodictyon-type quadriflagellates that were recently distinguished based on rRNA gene phylogenies. Here, we isolated Diphyllatea from freshwater crater lakes on two South Pacific islands and generated high-quality transcriptomes from species representing each clade, including the first transcriptomic data from Diphy III. Phylogenomic analyses support the separation of Diphy II and III, while transcriptome completeness highlights the utility of these data for future studies. Lastly, we discuss the biogeography and ecology of Diphyllatea on these remote islands.

Single-Cell Simultaneous Metabolome and Transcriptome Profiling Revealing Metabolite-Gene Correlation Network

Metabolic studies at the single cell level can directly define the cellular phenotype closest to physiological or disease states. However, the current single cell metabolome (SCM) study using mass spectroscopy has difficulty giving a complete view of the metabolic activity in the cell, and the prediction of the metabolism-phenotype relationship is limited by the potential inconsistency between transcriptomic and metabolic levels. Here, the single-cell simultaneous metabolome and transcriptome profiling method (scMeT-seq) is developed at one single cell, based on sub-picoliter sampling from the cell for the initial metabolome profiling followed by single cell transcriptome sequencing. This design not only provides sufficient cytoplasm for SCM but also nicely keeps the cellular viability for the accurate transcriptomic analysis in the same cell. Integrative analysis of scMeT-seq reveals both dynamical and cell state-specific associations between metabolome and transcriptome in the macrophages with defined metabolic perturbations. Moreover, metabolite signatures are mapped to the single-cell trajectory and gene correlation network of macrophage transition, which allows the unsupervised functional interpretation of metabolome. Thus, the established scMeT-seq should lead to a new perspective in metabolic research by transforming metabolomics from a metabolite snapshot to a functional approach.

A Single-Cell Metabolic Profiling Characterizes Human Aging via SlipChip-SERS

Metabolic dysregulation is a key driver of cellular senescence, contributing to the progression of systemic aging. The heterogeneity of senescent cells and their metabolic shifts are complex and unexplored. A microfluidic SlipChip integrated with surface-enhanced Raman spectroscopy (SERS), termed SlipChip-SERS, is developed for single-cell metabolism analysis. This SlipChip-SERS enables compartmentalization of single cells, parallel delivery of saponin and nanoparticles to release intracellular metabolites and to realize SERS detection with simple slipping operations. Analysis of different cancer cell lines using SlipChip-SERS demonstrated its capability for sensitive and multiplexed metabolic profiling of individual cells. When applied to human primary fibroblasts of different ages, it identified 12 differential metabolites, with spermine validated as a potent inducer of cellular senescence. Prolonged exposure to spermine can induce a classic senescence phenotype, such as increased senescence-associated β-glactosidase activity, elevated expression of senescence-related genes and reduced LMNB1 levels. Additionally, the senescence-inducing capacity of spermine in HUVECs and WRL-68 cells is confirmed, and exogenous spermine treatment increased the accumulation and release of H2O2. Overall, a novel SlipChip-SERS system is developed for single-cell metabolic analysis, revealing spermine as a potential inducer of senescence across multiple cell types, which may offer new strategies for addressing ageing and ageing-related diseases.

Integrating Metabolic RNA Labeling-Based Time-Resolved Single-Cell RNA Sequencing with Spatial Transcriptomics for Spatiotemporal Transcriptomic Analysis

Metabolic RNA labeling-based time-resolved single-cell RNA sequencing (scRNA-seq) has provided unprecedented tools to dissect the temporal dynamics and the complex gene regulatory networks of gene expression. However, this technology fails to reveal the spatial organization of cells in tissues, which also regulates the gene expression by intercellular communication. Herein, it is demonstrated that integrating time-resolved scRNA-seq with spatial transcriptomics is a new paradigm for spatiotemporal analysis. Metabolic RNA labeling-based time-resolved Well-TEMP-seq is first applied to profile the transcriptional dynamics of glioblastoma (GBM) cells and discover two potential pathways of EZH2-mediated mesenchymal transition in GBM. With spatial transcriptomics, it is further revealed that the crosstalk between CCL2+ malignant cells and IL10+ tumor-associated macrophages in the tumor microenvironment through an EZH2-FOSL2-CCL2 axis contributes to the mesenchymal transition in GBM. These discoveries show the power of integrative spatiotemporal scRNA-seq to elucidate the complex gene regulatory mechanism and advance the understanding of cellular processes in disease.

Arabidopsis enters the single-cell proteomics era

This article is a Commentary on Montes et al. (2024), 244: 1750–1759.

Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a common, chronic, and progressive lung disease that severely impacts human health and survival. However, the intricate molecular underpinnings of IPF remains elusive. This study aims to delve into the nuanced molecular interplay of cellular interactions in IPF, thereby laying the groundwork for innovative therapeutic approaches in the clinical field of IPF. Sophisticated bioinformatics methods were employed to identify crucial biomarkers essential for the progression of IPF. The GSE122960 single-cell dataset was obtained from the Gene Expression Omnibus (GEO) compendium, and intercellular communication potentialities were scrutinized via CellChat. The random survival forest paradigm was established using the GSE70866 dataset. Quintessential genes were selected through Kaplan-Meier (KM) curves, while immune infiltration examinations, functional enrichment critiques and nomogram paradigms were inaugurated. Analysis of intercellular communication revealed an intimate potential connections between macrophages and various cell types, pinpointing five cardinal genes influencing the trajectory and prognosis of IPF. The nomogram paradigm, sculpted from these seminal genes, exhibits superior predictive prowess. Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF. Interestingly, we discerned these genes' engagement with the EPITHELIAL_MESENCHYMAL_TRANSITION signalling pathway, which may enhance our understanding of the molecular complexity of IPF.

A primer on single-cell RNA-seq analysis using dendritic cells as a case study

Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of external factors on these phenomena. Limited research has been dedicated to the categorization of developing fetal gut cells. Our study aimed to enhance our understanding of fetal gut development by employing advanced machine-learning techniques on single-cell sequencing data. This dataset consisted of 62,849 samples, each characterized by 33,694 distinct gene features. Four feature ranking algorithms were utilized to sort features according to their significance, resulting in four feature lists. Then, these lists were fed into an incremental feature selection method to extract essential genes, classification rules, and build efficient classifiers. Several important genes were recognized by multiple feature ranking algorithms, such as FGG, MDK, RBP1, RBP2, IGFBP7, and SPON2. These features were key in differentiating specific developing intestinal cells, including epithelial, immune, mesenchymal, and vasculature cells of the colon, duo jejunum, and ileum cells. The classification rules showed special gene expression patterns on some intestinal cell types and the efficient classifiers can be useful tools for identifying intestinal cells.

Unveiling blood pressure-associated genes in aortic cells through integrative analysis of GWAS and RNA modification-associated variants

Background

Genome-wide association studies (GWAS) have identified more than a thousand loci for blood pressure (BP). Functional genes in these loci are cell-type specific. The aim of this study was to elucidate potentially functional genes associated with BP in the aorta through the utilization of RNA modification-associated single-nucleotide polymorphisms (RNAm-SNPs).

Methods

Utilizing large-scale genetic data of 757,601 individuals from the UK Biobank and International Consortium of Blood Pressure consortium, we identified associations between RNAm-SNPs and BP. The association between RNAm-SNPs, gene expression, and BP were examined.

Results

A total of 355 RNAm-SNPs related to m6A, m1A, m5C, m7G, and A-to-I modification were associated with BP. The related genes were enriched in the pancreatic secretion pathway and renin secretion pathway. The BP GWAS signals were significantly enriched with m6A-SNPs, highlighting the potential functional relevance of m6A in physiological processes influencing BP. Notably, m6A-SNPs in CYP11B1, PDE3B, HDAC7, ACE, SLC4A7, PDE1A, FRK, MTHFR, NPPA, CACNA1D, and HDAC9 were identified. Differential methylation and differential expression of the BP genes in FTO-overexpression and METTL14-knockdown vascular smooth muscle cells were detected. RNAm-SNPs were associated with ascending and descending aorta diameter and the genes showed differential methylation between aortic dissection (AD) cases and controls. In scRNA-seq study, we identified ARID5A, HLA-DPB1, HLA-DRA, IRF1, LINC01091, MCL1, MLF1, MLXIPL, NAA16, NADK, RERG, SRM, and USP53 as differential expression genes for AD in aortic cells.

Conclusion

The present study identified RNAm-SNPs in BP loci and elucidated the associations between the RNAm-SNPs, gene expression, and BP. The identified BP-associated genes in aortic cells were associated with AD.

Single-cell RNA analysis of chromodomain-encoding genes in mesenchymal stromal cells of the mouse dental pulp

The chromodomain helicase DNA-binding (CHD) and chromobox (CBX) families of proteins play crucial roles in cell fate decisions, differentiation, and cell proliferation in a broad variety of tissues and cell types. CHD proteins are ATP-dependent epigenetic enzymes actively engaged in transcriptional regulation, DNA replication, and DNA damage repair, whereas CBX proteins are transcriptional repressors mainly involved in the formation of heterochromatin. The pleiotropic effects of CHD and CBX proteins are largely dependent on their versatility to interact with other key components of the epigenetic and transcriptional machinery. Although the function and regulatory modes of CHD and CBX factors are well established in many cell types, little is known about their roles during osteogenic differentiation. A single-cell RNA-sequencing analysis of the mouse incisor dental pulp revealed distinct spatiotemporal expression patterns of CHD- and CBX-encoding genes within different clusters of mesenchymal stromal cells (MSCs) representing various stages of osteogenic differentiation. Additionally, genes encoding interaction partners of CHD and CBX proteins, such as subunits of the trithorax-COMPASS and polycomb chromatin remodeling complexes, exhibited differential co-expression behaviors within MSC subpopulations. Thus, CHD- and CBX-encoding genes show partially overlapping but distinct expression patterns in MSCs, suggesting their differential roles in osteogenic cell fate decisions.

Single-cell and spatial alterations of neural cells and circuits in clinical and translational medicine

The spatiotemporal heterogeneity of neurons, circuits and regulators is being uncovered at a single-cell level, from single-cell gene expression to functional regulations. The classifications, architectonics and functional communications amongst neural cells and circuits within the brain can be clearly delineated using single-cell multiomics and transomics. This Editorial highlights the spatiotemporal heterogeneity of neurons and circuits as well as regulators, initiates the translation of neuronal diversity and spatial organisation at single-cell levels into clinical considerations, and enables the discovery and development of new therapies for neurological diseases. It is predicted that single-cell and spatial multiomics will be integrated with metabolomic profiles and corresponding gene epigenetic modifications. The interactions amongst DNAs, RNAs and proteins in a cell provide details of intracellular functional regulations and new opportunities for the translation of temporospatial diversity of neural cell subtypes/states into clinical practice. The application of single-cell multiomics with four-dimensional genome to the human pathological brain will lead us to a new milestone of the diagnosis and treatment.

Loss of Inpp5d has disease-relevant and sex-specific effects on glial transcriptomes

INTRODUCTION

Inpp5d is genetically associated with Alzheimer's disease risk. Loss of Inpp5d alters amyloid pathology in models of amyloidosis. Inpp5d is expressed predominantly in microglia but its function in brain is poorly understood.

METHODS

We performed single-cell RNA sequencing to study the effect of Inpp5d loss on wild-type mouse brain transcriptomes.

RESULTS

Loss of Inpp5d has sex-specific effects on the brain transcriptome. Affected genes are enriched for multiple neurodegeneration terms. Network analyses reveal a gene co-expression module centered around Inpp5d in female mice. Inpp5d loss alters Pleotrophin (PTN), Prosaposin (PSAP), and Vascular Endothelial Growth Factor A (VEGFA) signaling probability between cell types.

DISCUSSION

Our data suggest that the normal function of Inpp5d is entangled with mechanisms involved in neurodegeneration. We report the effect of Inpp5d loss without pathology and show that this has dramatic effects on gene expression. Our study provides a critical reference for researchers of neurodegeneration, allowing separation of disease-specific changes mediated by Inpp5d in disease from baseline effects of Inpp5d loss.

HIGHLIGHTS

Loss of Inpp5d has different effects in male and female mice. Genes dysregulated by Inpp5d loss relate to neurodegeneration. Total loss of Inpp5d in female mice collapses a conserved gene co-expression module. Loss of microglial Inpp5d affects the transcriptome of other cell types.

Dynamics of The Γδtcr Repertoires During The Dedifferentiation Process and Pilot Implications for Immunotherapy of Thyroid Cancer

γδ T cells are evolutionarily conserved T lymphocytes that manifest unique antitumor efficacy independent of tumor mutation burden (TMB) and conventional human leukocyte antigen (HLA) recognition. However, the dynamic changes in their T cell receptor (TCR) repertoire during cancer progression and treatment courses remain unclear. Here, a comprehensive characterization of γδTCR repertoires are performed in thyroid cancers with divergent differentiation states through cross-sectional studies. The findings revealed a significant correlation between the differentiation states and TCR repertoire diversity. Notably, highly expanded clones are prominently enriched in γδ T cell compartment of dedifferentiated patients. Moreover, by longitudinal investigations of the γδ T cell response to various antitumor therapies, it is found that the emergence and expansion of the Vδ2neg subset may be potentially associated with favorable clinical outcomes after post-radiotherapeutic immunotherapy. These findings are further validated at single-cell resolution in both advanced thyroid cancer patients and a murine model, underlining the importance of further investigations into the role of γδTCR in cancer immunity and therapeutic strategies.

Deep Learning and Single-Cell Sequencing Analyses Unveiling Key Molecular Features in the Progression of Carotid Atherosclerotic Plaque

Rupture of advanced carotid atherosclerotic plaques increases the risk of ischaemic stroke, which has significant global morbidity and mortality rates. However, the specific characteristics of immune cells with dysregulated function and proven biomarkers for the diagnosis of atherosclerotic plaque progression remain poorly characterised. Our study elucidated the role of immune cells and explored diagnostic biomarkers in advanced plaque progression using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis. We identified a subcluster of monocytes with significantly increased infiltration in the advanced plaques. Based on the monocyte signature and machine-learning approaches, we accurately distinguished advanced plaques from early plaques, with an area under the curve (AUC) of 0.899 in independent external testing. Using microenvironment cell populations (MCP) counter and non-negative matrix factorisation, we determined the association between monocyte signatures and immune cell infiltration as well as the heterogeneity of the patient. Finally, we constructed a convolutional neural network deep learning model based on gene-immune correlation, which achieved an AUC of 0.933, a sensitivity of 92.3%, and a specificity of 87.5% in independent external testing for diagnosing advanced plaques. Our findings on unique subpopulations of monocytes that contribute to carotid plaque progression are crucial for the development of diagnostic tools for clinical diseases.