CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present 'Worm Perturb-Seq (WPS)', a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking 'pairwise modularity' in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells.

Multigenerational cell tracking of DNA replication and heritable DNA damage

Cell heterogeneity is a universal feature of life. Although biological processes affected by cell-to-cell variation are manifold, from developmental plasticity to tumour heterogeneity and differential drug responses, the sources of cell heterogeneity remain largely unclear1,2. Mutational and epigenetic signatures from cancer (epi)genomics are powerful for deducing processes that shaped cancer genome evolution3-5. However, retrospective analyses face difficulties in resolving how cellular heterogeneity emerges and is propagated to subsequent cell generations. Here, we used multigenerational single-cell tracking based on endogenously labelled proteins and custom-designed computational tools to elucidate how oncogenic perturbations induce sister cell asymmetry and phenotypic heterogeneity. Dual CRISPR-based genome editing enabled simultaneous tracking of DNA replication patterns and heritable endogenous DNA lesions. Cell lineage trees of up to four generations were tracked in asynchronously growing cells, and time-resolved lineage analyses were combined with end-point measurements of cell cycle and DNA damage markers through iterative staining. Besides revealing replication and repair dynamics, damage inheritance and emergence of sister cell heterogeneity across multiple cell generations, through combination with single-cell transcriptomics, we delineate how common oncogenic events trigger multiple routes towards polyploidization with distinct outcomes for genome integrity. Our study provides a framework to dissect phenotypic plasticity at the single-cell level and sheds light onto cellular processes that may resemble early events during cancer development.

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Single-cell RNA-seq (scRNAseq) struggles to capture the cellular heterogeneity of transcripts within individual cells due to the prevalence of highly abundant and ubiquitous transcripts, which can obscure the detection of biologically distinct transcripts expressed up to several orders of magnitude lower levels. To address this challenge, here we introduce single-cell CRISPRclean (scCLEAN), a molecular method that globally recomposes scRNAseq libraries, providing a benefit that cannot be recapitulated with deeper sequencing. scCLEAN utilizes the programmability of CRISPR/Cas9 to target and remove less than 1% of the transcriptome while redistributing approximately half of reads, shifting the focus toward less abundant transcripts. We experimentally apply scCLEAN to both heterogeneous immune cells and homogenous vascular smooth muscle cells to demonstrate its ability to uncover biological signatures in different biological contexts. We further emphasize scCLEAN's versatility by applying it to a third-generation sequencing method, single-cell MAS-Seq, to increase transcript-level detection and discovery. Here we show the possible utility of scCLEAN across a wide array of human tissues and cell types, indicating which contexts this technology proves beneficial and those in which its application is not advisable.

Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.

Single-cell technology for cell-based drug delivery and pharmaceutical research

Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

Endothelial-to-mesenchymal transition gene signature derived from single-cell transcriptomics of human atherosclerotic tissue associates with stable plaque histological characteristics

BACKGROUND

Endothelial cells within atherosclerotic plaques can differentiate into a mesenchymal-like phenotype through endothelial-to-mesenchymal transition (EndoMT). Our understanding of the molecular mechanisms underlying EndoMT in human atherosclerosis remains limited. Current gene expression signatures are often derived from in vitro experiments or animal studies and typically reflect genes upregulated in fully differentiated mesenchymal cell states, while genes upregulated during the process are omitted. To address this knowledge gap, we utilized in silico lineage tracing in single-cell transcriptomic (scRNA-seq) data from human plaque tissues to identify the EndoMT gene expression signature.

METHODS AND RESULTS

We constructed three candidate EndoMT lineages across subpopulations of ECs and SMCs in human carotid scRNA-seq data (n = 46). We examined gene expression over the course of these lineages and identified a core signature of 73 genes upregulated in EndoMT. Upregulation of those genes was confirmed in EndoMT trajectories of other human datasets derived from plaque tissue and in Cdh5-CreERT2 Rosa-eYFP apoE-/- lineage-traced mice. Analysis of human carotid plaque bulk RNA-seq data (632 patients) found the association of core gene signature with fibrous and more stable histological phenotypes.

CONCLUSION

This study defines the core gene signature of EndoMT in human atherosclerotic plaques, which can serve as a reference for future studies and gene set enrichment analysis.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction

With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the large-scale scRNA-seq data, we try to design a novel graph convolutional network with an adaptive aggregation mechanism. Based on the assumption that the aggregation order of different cells would be different, a graph convolutional network with an adaptive aggregation-based dimensionality reduction algorithm for scRNA-seq data is developed, named scAGCN. In scAGCN, a preprocessing consisting of quality control and feature selection is implemented. Then, an approximate nearest neighbor graph is rapidly constructed. Finally, a graph convolutional network with an adaptive aggregation mechanism is constructed, in which the neighborhood selection strategy based on node distribution and similarity boxplots is designed, and the aggregation function is optimized by defining a similarity measurement between neighborhood nodes and the central node. The results show that scAGCN outperforms existing dimensionality reduction methods on 15 real scRNA-seq datasets, especially in 10 large-scale scRNA-seq datasets.

Genome-wide association study and multi-ancestry meta-analysis identify common variants associated with carotid artery intima-media thickness

Carotid artery intima-media thickness (cIMT) is a measurement of subclinical atherosclerosis that predicts future cardiovascular events, including stroke and myocardial infarction. Genome-wide association studies (GWAS) have identified only a fraction of the genetic variants associated with cIMT. We performed the largest GWAS for cIMT involving up to 131,000 individuals. For the first time, we meta-analysed a diverse range of ancestries including populations with African, Asian (Chinese), Brazilian, European, and Hispanic ancestries. Our study identified 59 independent loci (53 loci from the multi-ancestry single variant analysis of which 19 are novel, P<5×10-8; 6 novel in gene-based analysis from single variant analysis, P=2.6×10-6, 2 novel in meta-regression) associated with cIMT. Gene-based, tissue-expression and gene-set enrichment analyses revealed novel genes of potential interest and highlighted significant relationships between vascular tissues (aorta, coronary and tibial arteries) and genetic associations. We found that circulatory levels of seven proteins, including ACAN, BCAM, DUT, ERI1, APOE, FN1, and GLRX were potentially causally associated with cIMT levels. We found a strong genome-wide correlation between cIMT and various cardiometabolic, smoking phenotypes, and lipid traits. Using Mendelian randomisation, our analyses provide robust evidence for causal associations between cIMT and several clinically relevant traits, including lipids, blood pressure, and waist circumference. Our study extends our genetic knowledge of atherosclerosis and highlights potential causal relations between risk factors, atherosclerosis and clinical diagnoses.

Mg2+ influx mediated by TRPM7 triggers the initiation of muscle stem cell activation

Muscle satellite cells (MuSCs) respond immediately to environmental cues upon skeletal muscle injuries. Despite decades of research into muscle regeneration, the specific molecular factors that trigger the transition of MuSCs from a quiescent to an active state remain largely unidentified. Here, we identify transient receptor potential melastatin 7 (TRPM7), an Mg2+-permeable ion channel, as a critical regulator of MuSC activation. Trpm7 deletion in MuSCs reduced Mg2+ influx, impairing myofiber regeneration and leading to decreased MuSC numbers and cell cycle arrest during regeneration. These changes were linked to disrupted mTOR signaling, which drives the transition of MuSCs from G0 to GAlert phase. In addition, Trpm7-deficient MuSCs exhibited impaired early responses, including quiescent projection retraction and AP-1 induction. Mg2+ supplementation rescued these defects, restoring normal MuSC activation. Our findings reveal a previously unrecognized mechanism where Mg2+ permeation through TRPM7 is essential for MuSC activation and efficient skeletal muscle regeneration, highlighting TRPM7 as a critical regulator of muscle repair.

Stem cell culture conditions affect in vitro differentiation potential and mouse gastruloid formation

Aggregating low numbers of mouse embryonic stem cells (mESCs) and inducing Wnt signalling generates 'gastruloids', self-organising complex structures that display an anteroposterior organisation of cell types derived from all three germ layers. Current gastruloid protocols display considerable heterogeneity between experiments in terms of morphology, elongation efficiency, and cell type composition. We therefore investigated whether altering the mESC pluripotency state would provide more consistent results. By growing three mESC lines from two different genetic backgrounds in different intervals of ESLIF and 2i medium the pluripotency state of cells was modulated, and mESC culture as well as the resulting gastruloids were analysed. Microscopic analysis showed a pre-culture-specific effect on gastruloid formation, in terms of aspect ratio and reproducibility. RNA-seq analysis of the mESC start population confirmed that short-term pulses of 2i and ESLIF modulate the pluripotency state, and result in different cellular states. Since multiple epigenetic regulators were detected among the top differentially expressed genes, we further analysed genome-wide DNA methylation and H3K27me3 distributions. We observed epigenetic differences between conditions, most dominantly in the promoter regions of developmental regulators. Lastly, when we investigated the cell type composition of gastruloids grown from these different pre-cultures, we observed that mESCs subjected to 2i-ESLIF preceding aggregation generated gastruloids more consistently, including more complex mesodermal contributions as compared to the ESLIF-only control. These results indicate that optimisation of the mESCs pluripotency state allows the modulation of cell differentiation during gastruloid formation.

Intraplaque haemorrhage quantification and molecular characterisation using attention based multiple instance learning

Intraplaque haemorrhage (IPH) represents a critical feature of plaque vulnerability as it is robustly associated with adverse cardiovascular events, including stroke and myocardial infarction. How IPH drives plaque instability is unknown. However, its identification and quantification in atherosclerotic plaques is currently performed manually, with high interobserver variability, limiting its accurate assessment in large cohorts. Leveraging the Athero-Express biobank, an ongoing study comprising a comprehensive dataset of histological, transcriptional, and clinical information from 2,595 carotid endarterectomy patients, we developed an attention-based additive multiple instance learning (MIL) framework to automate the detection and quantification of IPH across whole-slide images of nine distinct histological stains. We demonstrate that routinely available Haematoxylin and Eosin (H&E) staining outperformed all other plaque relevant Immunohistochemistry (IHC) stains tested (AUROC = 0.86), underscoring its utility in quantifying IPH. When combining stains through ensemble models, we see that H&E + CD68 (a macrophage marker) as well as H&E + Verhoeff-Van Gieson elastic fibers staining (EVG) leads to a substantial improvement (AUROC = 0.92). Using our model, we could derive IPH area from the MIL-derived patch-level attention scores, enabling not only classification but precise localisation and quantification of IPH area in each plaque, facilitating downstream analyses of its association and cellular composition with clinical outcomes. By doing so, we demonstrate that IPH presence and area are the most significant predictors of both preoperative symptom presentation and major adverse cardiovascular events (MACE), outperforming manual scoring methods. Automating IPH detection also allowed us to characterise IPH on a molecular level at scale. Pairing IPH measurements with single-cell transcriptomic analyses revealed key molecular pathways involved in IPH, including TNF-α signalling, extracellular matrix remodelling and the presence of foam cells. This study represents the largest effort in the cardiovascular field to integrate digital pathology, machine learning, and molecular data to predict and characterize IPH which leads to better understanding how it drives symptoms and MACE. Our model provides a scalable, interpretable, and reproducible method for plaque phenotyping, enabling the derivation of plaque phenotypes for predictive modelling of MACE outcomes.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Single-cell genome and transcriptome sequencing without upfront whole-genome amplification reveals cell state plasticity of melanoma subclones

Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method that explores genetic alterations and their functional consequences in single cells at scale.

Sp1 mechanotransduction regulates breast cancer cell invasion in response to multiple tumor-mimicking extracellular matrix cues

Breast cancer progression is marked by extracellular matrix (ECM) remodeling, including increased stiffness, faster stress relaxation, and elevated collagen levels. In vitro experiments have revealed a role for each of these factors to individually promote malignant behavior, but their combined effects remain unclear. To address this, we developed alginate-collagen hydrogels with independently tunable stiffness, stress relaxation, and collagen density. We show that these combined tumor-mimicking ECM cues reinforced invasive morphologies and promoted spheroid invasion in breast cancer and mammary epithelial cells. High stiffness and low collagen density in slow-relaxing matrices led to the greatest cell migration speed and displacement. RNA-seq revealed Sp1 target gene enrichment in response to both individual and combined ECM cues, with a greater enrichment observed under multiple cues. Notably, high expression of Sp1 target genes upregulated by fast stress relaxation correlated with poor patient survival. Mechanistically, we found that phosphorylated-Sp1 (T453) was increasingly located in the nucleus in stiff and/or fast relaxing matrices, which was regulated by PI3K and ERK1/2 signaling, as well as actomyosin contractility. This study emphasizes how multiple ECM cues in complex microenvironments reinforce malignant traits and supports an emerging role for Sp1 as a mechanoresponsive transcription factor.

Embracing the heterogeneity of neural stem cells in the subventricular zone

Neural stem cells (NSCs) of the subventricular zone (SVZ) could be a potential source for brain repair. These are heterogeneous cells with distinct activation states. To identify NSCs in the SVZ, different markers are used, including Gfap, Nestin, and Sox2. A comparison of these different methods to assess if the NSC marker used is selective toward specific NSC states is currently lacking. Here, we integrated six previously published single-cell RNA sequencing datasets from the adult mouse SVZ, where different methods were used to identify NSCs. Our data show that the approach used to isolate NSCs favors certain cell states over others. Our analyses underscore the importance of enriching for the NSC population of interest to increase data granularity. We also observed that cells with lower gene expression can be assigned incorrectly to clusters. We provide a framework for choosing the most optimal approach to enrich for NSC states of interest.

Long-range enhancer-controlled genes are hypersensitive to regulatory factor perturbations

Cell-type-specific gene activation is regulated by enhancers, sometimes located at large genomic distances from target gene promoters. Whether distal enhancers require specific factors to orchestrate gene regulation remains unclear. Here, we used enhancer distance-controlled reporter screens to find candidate factors. We depleted them and employed activity-by-contact predictions to genome-wide classify genes based on enhancer distance. Predicted distal enhancers typically control tissue-restricted genes and often are strong enhancers. We find cohesin, but also mediator, most specifically required for long-range activation, with cohesin repressing short-range gene activation and prioritizing distal over proximal HBB genes competing for shared enhancers. Long-range controlled genes are also most sensitive to perturbations of other regulatory proteins and to BET inhibitor JQ1, this being more a consequence of their distinct enhancer features than distance. Our work predicts that lengthening of intervening sequences can help limit the expression of target genes to specialized cells with optimal trans-factor environments.

Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment

Stem cell-based models of human heart tissue and cardiac differentiation employ monolayer and 3D organoid cultures with different properties, cell type composition, and maturity. Here we show how cardiac monolayer, embryoid body, and engineered heart tissue trajectories compare in a single-cell roadmap of atrial and ventricular differentiation conditions. Using a multiomic approach and gene-regulatory network inference, we identified regulators of the epicardial, atrial, and ventricular cardiomyocyte lineages. We identified ZNF711 as a regulatory switch and safeguard for cardiomyocyte commitment. We show that ZNF711 ablation prevents cardiomyocyte differentiation in the absence of retinoic acid, causing progenitors to be diverted more prominently to epicardial and other lineages. Retinoic acid rescues this shift in lineage commitment and promotes atrial cardiomyocyte differentiation by regulation of shared and complementary target genes, showing interplay between ZNF711 and retinoic acid in cardiac lineage commitment.

Systemic coordination of whole-body tissue remodeling during local regeneration in sea anemones

The complexity of regeneration extends beyond local wound responses, eliciting systemic processes across the entire organism. However, the functional relevance and coordination of distant molecular processes remain unclear. In the cnidarian Nematostella vectensis, we show that local regeneration triggers a systemic homeostatic response, leading to coordinated whole-body remodeling. Leveraging spatial transcriptomics, endogenous protein tagging, and live imaging, we comprehensively dissect this systemic response at the organismal scale. We identify proteolysis as a critical process driven by both local and systemic upregulation of metalloproteases. We show that metalloproteinase expression levels and activity scale with the extent of tissue loss. This proportional response drives long-range tissue and extracellular matrix movement. Our findings demonstrate the adaptive nature of the systematic response in regeneration, enabling the organism to maintain shape homeostasis while coping with a wide range of injuries.

Analyze the Diversity and Function of Immune Cells in the Tumor Microenvironment From the Perspective of Single-Cell RNA Sequencing

BACKGROUND

Cancer development is closely associated with complex alterations in the tumor microenvironment (TME). Among these, immune cells within the TME play a huge role in personalized tumor diagnosis and treatment.

OBJECTIVES

This review aims to summarize the diversity of immune cells in the TME, their impact on patient prognosis and treatment response, and the contributions of single-cell RNA sequencing (scRNA-seq) in understanding their functional heterogeneity.

METHODS

We analyzed recent studies utilizing scRNA-seq to investigate immune cell populations in the TME, focusing on their interactions and regulatory mechanisms.

RESULTS

ScRNA-seq reveals the functional heterogeneity of immune cells, enhances our understanding of their role in tumor antibody responses, and facilitates the construction of immune cell interaction networks. These insights provide guidance for the development of cancer immunotherapies and personalized treatment approaches.

CONCLUSION

Applying scRNA-seq to immune cell analysis in the TME offers a novel pathway for personalized cancer treatment. Despite its promise, several challenges remain, highlighting the need for further advancements to fully integrate scRNA-seq into clinical applications.

Unraveling aging from transcriptomics

Research into aging constitutes a pivotal endeavor aimed at elucidating the underlying biological mechanisms governing aging and age-associated diseases, as well as promoting healthy longevity. Recent advances in transcriptomic technologies, such as bulk RNA sequencing (RNA-seq), single-cell transcriptomics, and spatial transcriptomics, have revolutionized our ability to study aging at unprecedented resolution and scale. These technologies present novel opportunities for the discovery of biomarkers, elucidation of molecular pathways, and development of targeted therapeutic strategies for age-related disorders. This review surveys recent breakthroughs in different types of transcripts on aging, such as mRNA, long noncoding (lnc)RNA, tRNA, and miRNA, highlighting key findings and discussing their potential implications for future studies in this field.

kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq

The term 'RNA-seq' refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, single cells or single nuclei. The kallisto, bustools and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data. Execution of this protocol requires basic familiarity with a command line environment. With this protocol, quantification of a moderately sized RNA-seq dataset can be completed within minutes.

scCobra allows contrastive cell embedding learning with domain adaptation for single cell data integration and harmonization

The rapid advancement of single-cell technologies has created an urgent need for effective methods to integrate and harmonize single-cell data. Technical and biological variations across studies complicate data integration, while conventional tools often struggle with reliance on gene expression distribution assumptions and over-correction. Here, we present scCobra, a deep generative neural network designed to overcome these challenges through contrastive learning with domain adaptation. scCobra effectively mitigates batch effects, minimizes over-correction, and ensures biologically meaningful data integration without assuming specific gene expression distributions. It enables online label transfer across datasets with batch effects, allowing continuous integration of new data without retraining. Additionally, scCobra supports batch effect simulation, advanced multi-omic integration, and scalable processing of large datasets. By integrating and harmonizing datasets from similar studies, scCobra expands the available data for investigating specific biological problems, improving cross-study comparability, and revealing insights that may be obscured in isolated datasets.

NLRP3-mediated glutaminolysis controls microglial phagocytosis to promote Alzheimer's disease progression

Activation of the NLRP3 inflammasome has been implicated in the pathogenesis of Alzheimer's disease (AD) via the release of IL-1β and ASC specks. However, whether NLRP3 is involved in pathways beyond this remained unknown. Here, we found that Aβ deposition in vivo directly triggered NLRP3 activation in APP/PS1 mice, which model many features of AD. Loss of NLRP3 increased glutamine- and glutamate-related metabolism and increased expression of microglial Slc1a3, which was associated with enhanced mitochondrial and metabolic activity. The generation of α-ketoglutarate during this process impacted cellular function, including increased clearance of Aβ peptides as well as epigenetic and gene transcription changes. This pathway was conserved between murine and human cells. Critically, we could mimic this effect pharmacologically using NLRP3-specific inhibitors, but only with chronic NLRP3 inhibition. Together, these data demonstrate an additional role for NLRP3, where it can modulate mitochondrial and metabolic function, with important downstream consequences for the progression of AD.

GPLD1+ cancer stem cells contribute to chemotherapy resistance and tumour relapse in intestinal cancer

Cancer stem cells (CSCs) play a central role in cancer progression, therapy resistance, and disease recurrence. With the use of a quadruple-mutant mouse intestinal cancer organoid model and single-cell RNA-sequencing analysis, we have now identified glycosylphosphatidylinositol-specific phospholipase D1 (GPLD1), an enzyme that catalyzes the cleavage of glycosylphosphatidylinositol (GPI) anchors of membrane proteins, as a marker of slowly cycling CSCs. Ablation of Gpld1+ cells in combination with 5-fluorouracil treatment greatly attenuated cell viability in and regrowth of the intestinal cancer organoids. In addition, we identified serine protease 8 (PRSS8) as a key substrate of GPLD1 in human colorectal cancer cells. GPLD1 cleaves the GPI anchor of PRSS8 and thereby mediates release of the protease from the plasma membrane, resulting in the activation of Wnt signalling and promotion of the epithelial-mesenchymal transition (EMT) in the cancer cells. Pharmacological inhibition of GPLD1 suppressed Wnt signalling activity and EMT in association with upregulation of the amount of functional PRSS8 at the plasma membrane. Our findings suggest that targeting of GPLD1 in colorectal cancer might contribute to a new therapeutic strategy that is based on suppression of Wnt signalling and EMT-related cancer progression driven by CSCs.

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

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

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

The transcriptome provides a highly informative molecular phenotype to connect genotype to phenotype and is most frequently measured by RNA-sequencing (RNA-seq). Therefore, an ultimate goal is to perturb every gene and measure changes in the transcriptome. However, this remains challenging, especially in intact organisms due to different experimental and computational challenges. Here, we present 'Worm Perturb-Seq (WPS)', which provides high-resolution RNA-seq profiles for hundreds of replicate perturbations at a time in a living animal. WPS introduces multiple experimental advances that combine strengths of bulk and single cell RNA-seq, and that further provides an analytical framework, EmpirDE, that leverages the unique power of the large WPS datasets. EmpirDE identifies differentially expressed genes (DEGs) by using gene-specific empirical null distributions, rather than control conditions alone, thereby systematically removing technical biases and improving statistical rigor. We applied WPS to 103 Caenhorhabditis elegans nuclear hormone receptors (NHRs) to delineate a Gene Regulatory Network (GRN) and found that this GRN presents a striking 'pairwise modularity' where pairs of NHRs regulate shared target genes. We envision that the experimental and analytical advances of WPS should be useful not only for C. elegans, but will be broadly applicable to other models, including human cells.

Single-cell sequencing applications in acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous group of malignancies with poor prognosis. AML result from the proliferation of immature myeloid cells blocked at a variable stage of differentiation. Beyond inter-patient heterogeneity, AMLs are characterized by genetic and phenotypic intra-patient heterogeneity. Despite major advances in deciphering AML biology with bulk sequencing studies, pivotal questions remain unanswered. Analyses at the single-cell level could thus transform our understanding of these neoplasms. We review recent progresses in single-cell sequencing technologies from cell processing to bioinformatic pipelines. We next discuss how single-cell applications have helped understand the genetic and functional intra-leukemic heterogeneity, emphasizing aspects related to leukemic stem cells, clonal evolution and measurable residual disease (MRD) monitoring. We finally delineate how single-cell technologies could be implemented in routine clinical practice to improve patient management.

The establishment of the anther somatic niche with single-cell sequencing

The anther is the developmental housing of pollen and therefore the male gametes of flowering plants. The meiotic cells from which pollen are derived must differentiate de novo from somatic anther cells and synchronously develop with the rest of the anther. Anthropogenic control over another development has become crucial for global agriculture so as to maintain inbred lines and generate hybrid seeds of many crops. Understanding the genes that underlie the proper differentiation, developmental landmarks, and functions of each anther cell type is thus fundamental to both basic and applied plant sciences. We investigated the development of the somatic niche of the maize (Zea mays) anther using single-cell RNA-seq (scRNA-seq). Extensive background knowledge on the birth then pace and pattern of cell division of the maize anther cell types and published examples of cell-type gene expression from in situ hybridization allowed us to identify the primary cell types within the anther lobe, as well as the connective cells between the four lobes. We established the developmental trajectories of somatic cell types from pre-meiosis to post-meiosis, identified putative marker genes for the somatic cell types that previously lacked any known specific functions, and addressed the possibility that tapetal cells sequentially differentiate. This comprehensive scRNA-seq dataset of the somatic niche of the maize anther will serve as a baseline for future analyses investigating male-sterile genotypes and the impact of environmental conditions on male fertility in flowering plants.

Single-cell multiomics to advance cell therapy

Single-cell RNA-sequencing (scRNAseq) was first introduced in 2009 and has evolved with many technological advancements over the last decade. Not only are there several scRNAseq platforms differing in many aspects, but there are also a large number of computational pipelines available for downstream analyses which are being developed at an exponential rate. Such computational data appear in many scientific publications in virtually every field of study; thus, investigators should be able to understand and interpret data in this rapidly evolving field. Here, we discuss key differences in scRNAseq platforms, crucial steps in scRNAseq experiments, standard downstream analyses and introduce newly developed multimodal approaches. We then discuss how single-cell omics has been applied to advance the field of cell therapy.

Single-cell sequencing of human Langerhans cells identifies altered gene expression profiles in patients with atopic dermatitis

Atopic dermatitis (AD) is characterized by dysregulated T cell immunity and skin microbiome dysbiosis with predominance of Staphylococcus aureus, which is associated with exacerbating AD skin inflammation. Specific glycosylation patterns of S. aureus cell wall structures amplify skin inflammation through interaction with Langerhans cells (LCs). Nevertheless, the role of LCs in AD remains poorly characterized. Here, we performed single cell RNA sequencing of primary epidermal LCs and dermal T cells, isolated from skin biopsies of AD patients and healthy control subjects, alongside specific glycoanalysis of S. aureus strains isolated from the AD lesions. Our findings revealed 4 LC subpopulations ie, 2 steady-state clusters [LC1 and LC1H] and 2 proinflammatory/matured subsets [LC2 and migratory LCs]. The latter 2 subsets were enriched in AD skin. AD LCs showed enhanced expression of C-type lectin receptors, the high-affinity IgE receptor, and activation of prostaglandin and leukotriene biosynthesis pathways, upregulated transcriptional signatures related to T cell activation pathways, and increased expression of CCL17 compared with healthy LCs. Correspondingly, T helper 2 and T regulatory cell populations were increased in AD lesions. Complementary, we performed bulk RNA sequencing of primary LCs stimulated with the S. aureus strains isolated from the AD lesions, which showed upregulation of T helper 2-related pathways. Our study provides proof-of-concept for a role of LCs in connecting the S. aureus-T cell axis in the AD inflammatory cycle.

Machine learning-optimized targeted detection of alternative splicing

RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that greatly enriches for splicing-informative junction-spanning reads. Local splicing variation sequencing (LSV-seq) utilizes multiplexed reverse transcription from highly scalable pools of primers anchored near splicing events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower sequencing depth. Leveraging deep learning splicing code predictions, we used LSV-seq to target events with low coverage in GTEx RNA-seq data and newly discover hundreds of tissue-specific splicing events. Our results demonstrate the ability of LSV-seq to quantify splicing of events of interest at high-throughput and with exceptional sensitivity.

lncRNAlyzr: Enrichment Analysis for lncRNA Sets

lncRNAs make up a large portion of the human genome affecting many biological processes in normal physiology and diseases. However, human lncRNAs are understudied compared to protein-coding genes. While there are many tools for performing gene set enrichment analysis for coding genes, few tools exist for lncRNA enrichment analysis. lncRNAlyzr is a webserver application designed for lncRNAs enrichment analysis. lncRNAlyzr has a database containing 33 lncRNA set libraries created by computing correlations between lncRNAs and annotated coding gene sets. After users submit a set of lncRNAs to lncRNAlyzr, the enrichment analysis results are visualized as ball-and-stick subnetworks where nodes are lncRNAs connected to enrichment terms from across selected lncRNA set libraries. To demonstrate lncRNAlyzr, it was used to analyze the effects of knocking down the lncRNA CYTOR in K562 cells. Overall, lncRNAlyzr is an enrichment analysis tool for lncRNAs aiming to further our understanding of lncRNAs functional modules. lncRNAlyzr is available from: https://lncrnalyzr.maayanlab.cloud.

lncRNA NORAD modulates STAT3/STAT1 balance and innate immune responses in human cells via interaction with STAT3

Long non-coding RNAs (lncRNAs) are pivotal regulators of cellular processes. Here we reveal an interaction between the lncRNA NORAD, noted for its role in DNA stability, and the immune related transcription factor STAT3 in embryonic and differentiated human cells. Results from NORAD knockdown experiments implicate NORAD in facilitating STAT3 nuclear localization and suppressing antiviral gene activation. In NORAD-deficient cells, STAT3 remains cytoplasmic, allowing STAT1 to enhance antiviral activity. Analysis of RNA expression data from in vitro experiments and clinical samples demonstrates reduced NORAD upon viral infection. Additionally, evolutionary conservation analysis suggests that this regulatory function of NORAD is restricted to humans, potentially owing to the introduction of an Alu element in hominoids. Our findings thus suggest that NORAD functions as a modulator of STAT3-mediated immune suppression, adding to the understanding of lncRNAs in immune regulation and evolutionary adaptation in host defense mechanisms.

Loss of PI5P4Kα Slows the Progression of a Pten Mutant Basal Cell Model of Prostate Cancer

Although early prostate cancer depends on the androgen receptor signaling pathway, which is predominant in luminal cells, there is much to be understood about the contribution of epithelial basal cells in cancer progression. Herein, we observe cell type-specific differences in the importance of the metabolic enzyme phosphatidylinositol 5-phosphate 4-kinase alpha (PI5P4Kα; gene name PIP4K2A) in the prostate epithelium. We report the development of a basal cell-specific genetically engineered mouse model targeting Pip4k2a alone or in combination with the tumor suppressor phosphatase and tensin homolog (Pten). PI5P4Kα is enriched in basal cells, and no major histopathologic changes were detectable following gene deletion. Notably, the combined loss of Pip4k2a slowed the development of Pten mutant mouse prostatic intraepithelial neoplasia. Through the inclusion of a lineage tracing reporter, we utilize single-cell RNA sequencing to evaluate changes resulting from in vivo downregulation of Pip4k2a and characterize cell populations influenced in the established Probasin-Cre- and cytokeratin 5-Cre-driven genetically engineered mouse model. Transcriptomic pathway analysis points toward the disruption of lipid metabolism as a mechanism for reduced tumor progression. This was functionally supported by shifts of carnitine lipids in LNCaP prostate cancer cells treated with siPIP4K2A. Overall, these data nominate PI5P4Kα as a target for PTEN mutant prostate cancer. Implications: PI5P4Kα is enriched in prostate basal cells, and its targeted loss slows the progression of a model of advanced prostate cancer.

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.

Cross-species comparison reveals that Hmga1 reduces H3K27me3 levels to promote cardiomyocyte proliferation and cardiac regeneration

In contrast to adult mammalian hearts, the adult zebrafish heart efficiently replaces cardiomyocytes lost after injury. Here we reveal shared and species-specific injury response pathways and a correlation between Hmga1, an architectural non-histone protein, and regenerative capacity, as Hmga1 is required and sufficient to induce cardiomyocyte proliferation and required for heart regeneration. In addition, Hmga1 was shown to reactivate developmentally silenced genes, likely through modulation of H3K27me3 levels, poising them for a pro-regenerative gene program. Furthermore, AAV-mediated Hmga1 expression in injured adult mouse hearts led to controlled cardiomyocyte proliferation in the border zone and enhanced heart function, without cardiomegaly and adverse remodeling. Histone modification mapping in mouse border zone cardiomyocytes revealed a similar modulation of H3K27me3 marks, consistent with findings in zebrafish. Our study demonstrates that Hmga1 mediates chromatin remodeling and drives a regenerative program, positioning it as a promising therapeutic target to enhance cardiac regeneration after injury.

pscAdapt: Pre-Trained Domain Adaptation Network Based on Structural Similarity for Cell Type Annotation in Single Cell RNA-seq Data

Cell type annotation refers to the process of categorizing and labeling cells to identify their specific cell types, which is crucial for understanding cell functions and biological processes. Although many methods have been developed for automated cell type annotation, they often encounter challenges such as batch effects due to variations in data distribution across platforms and species, thereby compromising their performance. To address batch effects, in this study, a pre-trained domain adaptation model based on structural similarity, named pscAdapt, is proposed for cell type annotation. Specifically, a pre-trained strategy is employed to initialize model parameters to learn the data distribution of source domain. This strategy is also combined with an adversarial learning strategy to train the domain adaptation network for achieving domain level alignment and reducing domain discrepancy. Furthermore, to better distinguish different types of cells, a structural similarity loss is designed, aiming to shorten distances between cells of the same type and increase distances between cells of different types in feature space, thus achieving cell level alignment and enhancing the discriminability of cell types. Comprehensive experiments were conducted on simulated datasets, cross-platforms datasets and cross-species datasets to validate the effectiveness of pscAdapt, results of which demonstrate that pscAdapt outperforms several popular cell type annotation methods.

Single-cell omics and heterogeneity of neuroglial cells

Our bodies contain a rich diversity of cell types with unique physiologic properties. Interestingly, cells within our bodies contain the same DNA content, yet they can vary dramatically with respect to their molecular, structural, and functional properties. The need to better understand cellular complexity and diversity in biologic systems has led to a technical revolution in the field through the development of sophisticated single-cell "omic" approaches. This allows the investigation of the genome, epigenome, transcriptome, and proteome of individual cells derived from complex samples or tissues, such as nervous system tissue. These methods are allowing scientists to detect distinct cell populations and cellular states in different species (including rodent and human) and molecular transitions of cell populations across the lifespan. Recent studies have revealed that astrocytes, oligodendrocytes, and microglia exhibit greater molecular and functional heterogeneity than originally thought and innovative single-cell technologies have allowed a more comprehensive and less biased view of this cellular diversity. The chapter begins by providing a primer of single-cell transcriptomic and spatial transcriptomic approaches that have been particularly influential in uncovering single-cell diversity of neuroglial cells in the brain. It then takes a closer look at how these technologies have been pivotal in defining neuroglial cell subtypes and for determining their spatial relationships within the CNS. Then, it concludes with discussion of how the recent technical advances and discoveries have provoked new questions about the origin, organization, and functional purpose of diverse neuroglial cell subtypes.

Multi-omics analysis reveals distinct gene regulatory mechanisms between primary and organoid-derived human hepatocytes

Hepatic organoid cultures are a powerful model to study liver development and diseases in vitro. However, hepatocyte-like cells differentiated from these organoids remain immature compared to primary human hepatocytes (PHHs), which are the benchmark in the field. Here, we applied integrative single-cell transcriptome and chromatin accessibility analysis to reveal gene regulatory mechanisms underlying these differences. We found that, in mature human hepatocytes, activator protein 1 (AP-1) factors co-occupy regulatory regions with hepatocyte-specific transcription factors, including HNF4A, suggesting their potential cooperation in governing hepatic gene expression. Comparative analysis identified distinct transcription factor sets that are specifically active in either PHHs or intrahepatic cholangiocyte organoid (ICO)-derived human hepatocytes. ELF3 was one of the factors uniquely expressed in ICO-derived hepatocytes, and its expression negatively correlated with hepatic marker gene expression. Functional analysis further revealed that ELF3 depletion increased the expression of key hepatic markers in ICO-derived hepatocytes. Our integrative analysis provides insights into the transcriptional regulatory networks of PHHs and hepatic organoids, thereby informing future strategies for developing improved hepatic models.

Hyperglycaemia induces diet-dependent defects of the left-right axis by lowering intracellular pH

Pregestational diabetes is a risk factor for congenital anomalies, including heterotaxy syndrome, a rare birth defect characterized by the abnormal arrangement of organs relative to the left-right (L-R) body axis. To provide insight into the underlying mechanism by which diabetes induces heterotaxy, we here analyzed the L-R axis of mouse embryos of diabetic dams. Various Pitx2 expression patterns indicative of disruption of L-R axis formation were apparent in such embryos. Expression of Nodal at the node, which triggers a Nodal-Pitx2 expression cascade in lateral plate mesoderm, showed marked regression associated with L-R axis malformation. This regression was similar to that apparent in Wnt3a-/- embryos, and canonical Wnt signalling was indeed found to be downregulated in embryos of diabetic dams. RNA sequencing revealed dysregulation of glycolysis in embryos of diabetic dams, and high glucose lowered intracellular pH in the primitive streak, leading to the suppression of Wnt signalling and the regression of Nodal expression. Of note, maternal vitamin A intake increased the incidence of L-R axis defects in embryos of diabetic dams, with dysregulation of retinoic acid metabolism being apparent in these embryos and in Wnt3a-/- embryos. Our results shed light on the mechanisms underlying embryopathies associated with maternal diabetes and suggest the importance of diet for prevention of heterotaxy.

Dynamics of liver cancer cellular taxa revealed through single-cell RNA sequencing: Advances and challenges

Liver cancer is a leading cause of death worldwide, representing a substantial public health challenge. The advent of single-cell RNA sequencing has significantly advanced our understanding of cellular dynamics from the onset of liver cancer to therapeutic intervention. This technology has unveiled profound insights into cancer heterogeneity and the tumor microenvironment (TME), enabling the identification of key molecular drivers and phenotypic landscapes of liver cancer at a single-cell resolution. This review highlights recent advancements in mapping functional cell subsets, phenotypic alterations, and the diversity of the TME. These insights are pivotal for advancing targeted therapies and developing prognostic tools. Moreover, this review covers the ongoing challenges and advances from tumor initiation to progression, offering a detailed perspective on advancing personalized treatment.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges, and future perspectives on transcriptomic profiling

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic approach with the power to identify rare cells, discover new cellular subclusters, and describe novel genes. scRNA-seq can simultaneously reveal dynamic shifts in cellular phenotypes and heterogeneities in cellular subtypes. Since the publication of the first protocol on scRNA-seq in 2009, this evolving technology has continued to improve, through the use of cell-specific barcodes, adoption of droplet-based systems, and development of advanced computational methods. Despite induction of the cellular stress response during the tissue dissociation process, scRNA-seq remains a popular technology, and commercially available scRNA-seq methods have been applied to the brain. Recent advances in spatial transcriptomics now allow the researcher to capture the positional context of transcriptional activity, strengthening our knowledge of cellular organization and cell-cell interactions in spatially intact tissues. A combination of spatial transcriptomic data with proteomic, metabolomic, or chromatin accessibility data is a promising direction for future research. Herein, we provide an overview of the workflow, data analyses methods, and pros and cons of scRNA-seq technology. We also summarize the latest achievements of scRNA-seq in stroke and acute traumatic brain injury, and describe future applications of scRNA-seq and spatial transcriptomics.

The Sensory Shark: High-quality Morphological, Genomic and Transcriptomic Data for the Small-spotted Catshark Scyliorhinus Canicula Reveal the Molecular Bases of Sensory Organ Evolution in Jawed Vertebrates

Cartilaginous fishes (chondrichthyans: chimeras and elasmobranchs -sharks, skates, and rays) hold a key phylogenetic position to explore the origin and diversifications of jawed vertebrates. Here, we report and integrate reference genomic, transcriptomic, and morphological data in the small-spotted catshark Scyliorhinus canicula to shed light on the evolution of sensory organs. We first characterize general aspects of the catshark genome, confirming the high conservation of genome organization across cartilaginous fishes, and investigate population genomic signatures. Taking advantage of a dense sampling of transcriptomic data, we also identify gene signatures for all major organs, including chondrichthyan specializations, and evaluate expression diversifications between paralogs within major gene families involved in sensory functions. Finally, we combine these data with 3D synchrotron imaging and in situ gene expression analyses to explore chondrichthyan-specific traits and more general evolutionary trends of sensory systems. This approach brings to light, among others, novel markers of the ampullae of Lorenzini electrosensory cells, a duplication hotspot for crystallin genes conserved in jawed vertebrates, and a new metazoan clade of the transient-receptor potential (TRP) family. These resources and results, obtained in an experimentally tractable chondrichthyan model, open new avenues to integrate multiomics analyses for the study of elasmobranchs and jawed vertebrates.

The Impact of Resident Adipose Tissue Macrophages on Adipocyte Homeostasis and Dedifferentiation

Obesity is concurrent with immunological dysregulation, resulting in chronic low-grade inflammation and cellular dysfunction. In pancreatic islets, this loss of function has been correlated with mature β-cells dedifferentiating into a precursor-like state through constant exposure to inflammatory stressors. As mature adipocytes likewise have the capability to dedifferentiate in vitro and in vivo, we wanted to analyze this cellular change in relation to adipose tissue (AT) inflammation and adipose tissue macrophage (ATM) activity. Using our organotypic AT explant culture method combined with a double-reporter mouse model for labeling ATMs and mature adipocytes, we were able to visualize and quantify dedifferentiated fat (DFAT) cells in AT explants. Preliminary testing showed increased dedifferentiation after tamoxifen (TAM) stimulation, making TAM-dependent lineage-tracing models unsuitable for quantification of naturally occurring DFAT cells. The regulatory role of ATMs in adipocyte dedifferentiation was shown through macrophage depletion using Plexxicon 5622 or clodronate liposomes, which significantly increased DFAT cell levels. Subsequent bulk RNA sequencing of macrophage-depleted explants revealed enrichment of the tumor necrosis factor α (TNFα) signaling pathway as well as downregulation of associated genes. Direct stimulation with TNFα decreased adipocyte dedifferentiation, while application of a TNFα-neutralizing antibody did not significantly alter DFAT cell levels. Our findings suggest a regulatory role of resident ATMs in maintaining the mature adipocyte phenotype and preventing excessive adipocyte dedifferentiation. The specific regulatory pathways as well as the impact that DFAT cells might have on ATMs, and vice versa, are subject to further investigation.

RAG-seq: NSR-primed and Transposase Tagmentation-mediated Strand-specific Total RNA Sequencing in Single Cells

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity with unprecedented resolution. However, many current methods are limited in capturing full-length transcripts and discerning strand orientation. Here, we present RAG-seq, an innovative strand-specific total RNA sequencing technique that combines not-so-random (NSR) primers with Tn5 transposase-mediated tagmentation. RAG-seq overcomes previous limitations by delivering comprehensive transcript coverage and maintaining strand orientation, which are essential for accurate quantification of overlapping genes and detection of antisense transcripts. Through optimized reverse transcription with oligo-dT primers, rRNA depletion via Depletion of Abundant Sequences by Hybridization (DASH), and linear amplification, RAG-seq enhances sensitivity and reproducibility, especially for low-input samples and single cells. Application to mouse oocytes and early embryos highlights RAG-seq's superior performance in identifying stage-specific antisense transcripts, shedding light on their regulatory roles during early development. This advancement represents a significant leap in transcriptome analysis within complex biological contexts.

An acute microglial metabolic response controls metabolism and improves memory

Chronic high-fat feeding triggers metabolic dysfunction including obesity, insulin resistance, and diabetes. How high-fat intake first triggers these pathophysiological states remains unknown. Here, we identify an acute microglial metabolic response that rapidly translates intake of high-fat diet (HFD) to a surprisingly beneficial effect on metabolism and spatial/learning memory. High-fat intake rapidly increases palmitate levels in cerebrospinal fluid and triggers a wave of microglial metabolic activation characterized by mitochondrial membrane activation and fission as well as metabolic skewing toward aerobic glycolysis. These effects are detectable throughout the brain and can be detected within as little as 12 hr of HFD exposure. In vivo, microglial ablation and conditional DRP1 deletion show that the microglial metabolic response is necessary for the acute effects of HFD. 13C-tracing experiments reveal that in addition to processing via β-oxidation, microglia shunt a substantial fraction of palmitate toward anaplerosis and re-release of bioenergetic carbons into the extracellular milieu in the form of lactate, glutamate, succinate, and intriguingly, the neuroprotective metabolite itaconate. Together, these data identify microglia as a critical nutrient regulatory node in the brain, metabolizing away harmful fatty acids and liberating the same carbons as alternate bioenergetic and protective substrates for surrounding cells. The data identify a surprisingly beneficial effect of short-term HFD on learning and memory.

Single-cell RNA sequencing of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases

Alternative splicing contributes to complex traits, but whether this differs in trait-relevant cell types across diverse genetic ancestries is unclear. Here we describe cell-type-specific, sex-biased and ancestry-biased alternative splicing in ~1 M peripheral blood mononuclear cells from 474 healthy donors from the Asian Immune Diversity Atlas. We identify widespread sex-biased and ancestry-biased differential splicing, most of which is cell-type-specific. We identify 11,577 independent cis-splicing quantitative trait loci (sQTLs), 607 trans-sGenes and 107 dynamic sQTLs. Colocalization between cis-eQTLs and trans-sQTLs revealed a cell-type-specific regulatory relationship between HNRNPLL and PTPRC. We observed an enrichment of cis-sQTL effects in autoimmune and inflammatory disease heritability. Specifically, we functionally validated an Asian-specific sQTL disrupting the 5' splice site of TCHP exon 4 that putatively modulates the risk of Graves' disease in East Asian populations. Our work highlights the impact of ancestral diversity on splicing and provides a roadmap to dissect its role in complex diseases at single-cell resolution.

Trained innate immunity in response to nuclear antigens in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease directed against nuclear antigens, including those derived from apoptotic microparticles (MPs) and neutrophil extracellular traps (NETs). Here we investigated whether nuclear autoantigens can induce trained immunity in SLE patients. Trained immunity is a de facto innate immune memory elicited by an initial stimulus that induces a more vigorous long-term inflammatory response to subsequent stimuli. Isolated monocytes were stimulated with SLE-typical nuclear antigens, neutrophil extracellular traps (NETs), and apoptotic microparticles (MPs) or plasma from SLE patients. After five days of rest, cells were restimulated with Toll-like receptor (TLR) agonists, and cytokine production was measured using ELISA. Functional, transcriptomic and epigenetic changes in monocytes from SLE patients were evaluated by ex vivo stimulations, flow cytometric analysis, RNA sequencing, and chromatin immunoprecipitation (ChIP) sequencing for histone 3 lysine 4 trimethylation. We found that in vitro, both MPs and NETs, as well as plasma from SLE patients, can induce trained immunity. Furthermore, circulating monocytes from SLE patients produce increased levels of pro-inflammatory cytokines after stimulation with TLR ligands, indicating trained immunity. This is accompanied by deregulation in histone 3 lysine 4 trimethylation and increased expression of metabolism and inflammation-related genes. Our findings demonstrate that trained immunity can develop against nuclear antigens and that trained immunity is involved in the immunological dysregulation in SLE patients.