Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

Hormones mediate long-range cell communication and play vital roles in physiology, metabolism, and health. Traditionally, endocrinologists have focused on one hormone or organ system at a time. Yet, hormone signaling by its very nature connects cells of different organs and involves crosstalk of different hormones. Here, we leverage the organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), to systematically map source and target cells for 84 classes of hormones. This work uncovers previously-uncharacterized sites of hormone regulation, and shows that the hormonal signaling network is densely connected, decentralized, and rich in feedback loops. Evolutionary comparisons of hormonal genes and their expression patterns show that mouse lemur better models human hormonal signaling than mouse, at both the genomic and transcriptomic levels, and reveal primate-specific rewiring of hormone-producing/target cells. This work complements the scale and resolution of classical endocrine studies and sheds light on primate hormone regulation.

A Single-Cell Transcriptome of Bovine Milk Somatic Cells

The production of milk by dairy cows far exceeds the nutritional needs of the calf and is vital for the economical use of dairy cattle. High milk yield is a unique production trait that can be effectively enhanced through traditional selection methods. The process of lactation in cows serves as an excellent model for studying the biological aspects of lactation with the aim of exploring the mechanistic base of this complex trait at the cellular level. In this study, we analyzed the milk transcriptome at the single-cell level by conducting scRNA-seq analysis on milk samples from two Holstein Friesian cows at mid-lactation (75 and 93 days) using the 10× Chromium platform. Cells were pelleted and fat was removed from milk by centrifugation. The cell suspension from each cow was loaded on separate channels, resulting in the recovery of 9313 and 14,544 cells. Library samples were loaded onto two lanes of the NovaSeq 6000 (Illumina) instrument. After filtering at the cell and gene levels, a total of 7988 and 13,973 cells remained, respectively. We were able to reconstruct different cell types (milk-producing cells, progenitor cells, macrophages, monocytes, dendritic cells, T cells, B cells, mast cells, and neutrophils) in bovine milk. Our findings provide a valuable resource for identifying regulatory elements associated with various functions of the mammary gland such as lactation, tissue renewal, native immunity, protein and fat synthesis, and hormonal response.

Deficiency of ASGR1 promotes liver injury by increasing GP73-mediated hepatic endoplasmic reticulum stress

Liver injury is a core pathological process in the majority of liver diseases, yet the genetic factors predisposing individuals to its initiation and progression remain poorly understood. Here we show that asialoglycoprotein receptor 1 (ASGR1), a lectin specifically expressed in the liver, is downregulated in patients with liver fibrosis or cirrhosis and male mice with liver injury. ASGR1 deficiency exacerbates while its overexpression mitigates acetaminophen-induced acute and CCl4-induced chronic liver injuries in male mice. Mechanistically, ASGR1 binds to an endoplasmic reticulum stress mediator GP73 and facilitates its lysosomal degradation. ASGR1 depletion increases circulating GP73 levels and promotes the interaction between GP73 and BIP to activate endoplasmic reticulum stress, leading to liver injury. Neutralization of GP73 not only attenuates ASGR1 deficiency-induced liver injuries but also improves survival in mice received a lethal dose of acetaminophen. Collectively, these findings identify ASGR1 as a potential genetic determinant of susceptibility to liver injury and propose it as a therapeutic target for the treatment of liver injury.

An iron rheostat controls hematopoietic stem cell fate

Mechanisms governing the maintenance of blood-producing hematopoietic stem and multipotent progenitor cells (HSPCs) are incompletely understood, particularly those regulating fate, ensuring long-term maintenance, and preventing aging-associated stem cell dysfunction. We uncovered a role for transitory free cytoplasmic iron as a rheostat for adult stem cell fate control. We found that HSPCs harbor comparatively small amounts of free iron and show the activation of a conserved molecular response to limited iron-particularly during mitosis. To study the functional and molecular consequences of iron restriction, we developed models allowing for transient iron bioavailability limitation and combined single-molecule RNA quantification, metabolomics, and single-cell transcriptomic analyses with functional studies. Our data reveal that the activation of the limited iron response triggers coordinated metabolic and epigenetic events, establishing stemness-conferring gene regulation. Notably, we find that aging-associated cytoplasmic iron loading reversibly attenuates iron-dependent cell fate control, explicating intervention strategies for dysfunctional aged stem cells.

A Panoramic View of Cell Population Dynamics in Mammalian Aging

To elucidate the aging-associated cellular population dynamics throughout the body, here we present PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissue samples, encompassing a range of organs across different life stages, sexes, and genotypes. This comprehensive dataset allowed us to identify more than 3,000 unique cellular states and catalog over 200 distinct aging-associated cell populations experiencing significant depletion or expansion. Our panoramic analysis uncovered temporally structured, organ- and lineage-specific shifts of cellular dynamics during lifespan progression. Moreover, we investigated aging-associated alterations in immune cell populations, revealing both widespread shifts and organ-specific changes. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte-dependent. The breadth and depth of our 'cell-omics' methodology not only enhance our comprehension of cellular aging but also lay the groundwork for exploring the complex regulatory networks among varied cell types in the context of aging and aging-associated diseases.

ScLinear predicts protein abundance at single-cell resolution

Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches.

Predicting cell population-specific gene expression from genomic sequence

Most regulatory elements, especially enhancer sequences, are cell population-specific. One could even argue that a distinct set of regulatory elements is what defines a cell population. However, discovering which non-coding regions of the DNA are essential in which context, and as a result, which genes are expressed, is a difficult task. Some computational models tackle this problem by predicting gene expression directly from the genomic sequence. These models are currently limited to predicting bulk measurements and mainly make tissue-specific predictions. Here, we present a model that leverages single-cell RNA-sequencing data to predict gene expression. We show that cell population-specific models outperform tissue-specific models, especially when the expression profile of a cell population and the corresponding tissue are dissimilar. Further, we show that our model can prioritize GWAS variants and learn motifs of transcription factor binding sites. We envision that our model can be useful for delineating cell population-specific regulatory elements.

Vascular development, remodeling and maturation

The development of the vascular system is crucial in supporting the growth and health of all other organs in the body, and vascular system dysfunction is the major cause of human morbidity and mortality. This chapter discusses three successive processes that govern vascular system development, starting with the differentiation of the primitive vascular system in early embryonic development, followed by its remodeling into a functional circulatory system composed of arteries and veins, and its final maturation and acquisition of an organ specific semi-permeable barrier that controls nutrient uptake into tissues and hence controls organ physiology. Along these steps, endothelial cells forming the inner lining of all blood vessels acquire extensive heterogeneity in terms of gene expression patterns and function, that we are only beginning to understand. These advances contribute to overall knowledge of vascular biology and are predicted to unlock the unprecedented therapeutic potential of the endothelium as an avenue for treatment of diseases associated with dysfunctional vasculature.

BI-3231, an enzymatic inhibitor of HSD17B13, reduces lipotoxic effects induced by palmitic acid in murine and human hepatocytes

17-β-hydroxysteroid dehydrogenase 13 (HSD17B13), a lipid droplet-associated enzyme, is primarily expressed in the liver and plays an important role in lipid metabolism. Targeted inhibition of enzymatic function is a potential therapeutic strategy for treating steatotic liver disease (SLD). The present study is aimed at investigating the effects of the first selective HSD17B13 inhibitor, BI-3231, in a model of hepatocellular lipotoxicity using human cell lines and primary mouse hepatocytes in vitro. Lipotoxicity was induced with palmitic acid in HepG2 cells and freshly isolated mouse hepatocytes and the cells were coincubated with BI-3231 to assess the protective effects. Under lipotoxic stress, triglyceride (TG) accumulation was significantly decreased in the BI-3231-treated cells compared with that of the control untreated human and mouse hepatocytes. In addition, treatment with BI-3231 led to considerable improvement in hepatocyte proliferation, cell differentiation, and lipid homeostasis. Mechanistically, BI-3231 increased the mitochondrial respiratory function without affecting β-oxidation. BI-3231 inhibited the lipotoxic effects of palmitic acid in hepatocytes, highlighting the potential of targeting HSD17B13 as a specific therapeutic approach in steatotic liver disease.NEW & NOTEWORTHY 17-β-Hydroxysteroid dehydrogenase 13 (HSD17B13) is a lipid droplet protein primarily expressed in the liver hepatocytes. HSD17B13 is associated with the clinical outcome of chronic liver diseases and is therefore a target for the development of drugs. Here, we demonstrate the promising therapeutic effect of BI-3231 as a potent inhibitor of HSD17B13 based on its ability to inhibit triglyceride accumulation in lipid droplets (LDs), restore lipid metabolism and homeostasis, and increase mitochondrial activity in vitro.

Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)1-3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition1-7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only8-10, and the three prior genome-wide association studies of GDM11-13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.

scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding

Analyzing single-cell RNA sequencing (scRNA-seq) data remains a challenge due to its high dimensionality, sparsity and technical noise. Recognizing the benefits of dimensionality reduction in simplifying complexity and enhancing the signal-to-noise ratio, we introduce scBiG, a novel graph node embedding method designed for representation learning in scRNA-seq data. scBiG establishes a bipartite graph connecting cells and expressed genes, and then constructs a multilayer graph convolutional network to learn cell and gene embeddings. Through a series of extensive experiments, we demonstrate that scBiG surpasses commonly used dimensionality reduction techniques in various analytical tasks. Downstream tasks encompass unsupervised cell clustering, cell trajectory inference, gene expression reconstruction and gene co-expression analysis. Additionally, scBiG exhibits notable computational efficiency and scalability. In summary, scBiG offers a useful graph neural network framework for representation learning in scRNA-seq data, empowering a diverse array of downstream analyses.

Developmental heterogeneity of vascular cells: Insights into cellular plasticity in atherosclerosis?

Smooth muscle cells, endothelial cells and macrophages display remarkable heterogeneity within the healthy vasculature and under pathological conditions. During development, these cells arise from numerous embryological origins, which confound with different microenvironments to generate postnatal vascular cell diversity. In the atherosclerotic plaque milieu, all these cell types exhibit astonishing plasticity, generating a variety of plaque burdening or plaque stabilizing phenotypes. And yet how developmental origin influences intraplaque cell plasticity remains largely unexplored despite evidence suggesting this may be the case. Uncovering the diversity and plasticity of vascular cells is being revolutionized by unbiased single cell whole transcriptome analysis techniques that will likely continue to pave the way for therapeutic research. Cellular plasticity is only just emerging as a target for future therapeutics, and uncovering how intraplaque plasticity differs across vascular beds may provide key insights into why different plaques behave differently and may confer different risks of subsequent cardiovascular events.

Mac-1 deficiency ameliorates pressure overloaded heart failure through inhibiting macrophage polarization and activation

Persistent pressure overload commonly leads to pathological cardiac hypertrophy and remodeling, ultimately leading to heart failure (HF). Cardiac remodeling is associated with the involvement of immune cells and the inflammatory response in pathogenesis. The macrophage-1 antigen (Mac-1) is specifically expressed on leukocytes and regulates their migration and polarization. Nonetheless, the involvement of Mac-1 in cardiac remodeling and HF caused by pressure overload has not been determined. The Mac-1-knockout (KO) and wild-type (WT) mice were subjected to transverse aortic constriction (TAC) for 6 weeks. Echocardiography and pressure-volume loop assessments were used to evaluate cardiac function, and cardiac remodeling and macrophage infiltration and polarization were estimated by histopathology and molecular techniques. The findings of our study demonstrated that Mac-1 expression was markedly increased in hearts subjected to TAC treatment. Moreover, compared with WT mice, Mac-1-KO mice exhibited dramatically ameliorated TAC-induced cardiac dysfunction, hypertrophy, fibrosis, oxidative stress and apoptosis. The potential positive impacts may be linked to the inhibition of macrophage infiltration and M1 polarization via reductions in NF-kB and STAT1 expression and upregulation of STAT6. In conclusion, this research reveals a new function of Mac-1 deficiency in reducing pathological cardiac remodeling and HF caused by pressure overload. Additionally, inhibiting Mac-1 could be a potential treatment option for patients with HF in a clinical setting.

NCoR1 limits angiogenic capacity by altering Notch signaling

Corepressors negatively regulate gene expression by chromatin compaction. Targeted regulation of gene expression could provide a means to control endothelial cell phenotype. We hypothesize that by targeting corepressor proteins, endothelial angiogenic function can be improved. To study this, the expression and function of nuclear corepressors in human umbilical vein endothelial cells (HUVEC) and in murine organ culture was studied. RNA-seq revealed that nuclear receptor corepressor 1 (NCoR1), silencing mediator of retinoid and thyroid hormone receptors (SMRT) and repressor element-1 silencing transcription factor (REST) are the highest expressed corepressors in HUVECs. Knockout and knockdown strategies demonstrated that the depletion of NCoR1 increased the angiogenic capacity of endothelial cells, whereas depletion of SMRT or REST did not. Interestingly, the effect was VEGF signaling independent. NCoR1 depletion significantly upregulated angiogenesis-associated genes, especially tip cell genes, including ESM1, DLL4 and NOTCH4, as observed by RNA- and ATAC-seq. Confrontation assays comparing cells with and without NCoR1-deficiency revealed that loss of NCoR1 promotes a tip-cell position during spheroid sprouting. Moreover, a proximity ligation assay identified NCoR1 as a direct binding partner of the Notch-signaling-related transcription factor RBPJk. Luciferase assays showed that siRNA-mediated knockdown of NCOR1 promotes RBPJk activity. Furthermore, NCoR1 depletion prompts upregulation of several elements in the Notch signaling cascade. Downregulation of NOTCH4, but not NOTCH1, prevented the positive effect of NCOR1 knockdown on spheroid outgrowth. Collectively, these data indicate that decreasing NCOR1 expression is an attractive approach to promote angiogenic function.

Transcriptomic-based roadmap to the healthy and ozone-exposed lung

The lung is constantly exposed to a myriad of exogenous stressors. Ground-level ozone represents a ubiquitous and extremely reactive anthropogenic toxicant, impacting the health of millions across the globe. While abundant, epidemiological, in vivo, and in vitro data focuses the ozone toxicity in individual cell types (e.g. epithelial type II, alveolar macrophages) or signaling pathways involved in the injury (e.g., akt, glutathione). When appropriately used, bulk and single cell RNA sequencing techniques have the potential to provide complete, and in certain cases unbiased, information of the molecular events taking place in the steady state and injured lung, and even capture the phenotypic diversity of neighboring cells. To this end, this review compiles information pertaining to the latest understanding of lung cell identity and activation in the steady state and ozone exposed lung. In addition, it discusses the value and benefits of multi-omics approaches and other tools developed to predict cell-cell communication and dissect spatial heterogeneity.

Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data

Single-cell RNA sequencing technology is one of the most cost-effective ways to uncover transcriptomic heterogeneity. With the rapid rise of this technology, enormous amounts of scRNA-seq data have been produced. Due to the high dimensionality, noise, sparsity and missing features of the available scRNA-seq data, accurately clustering the scRNA-seq data for downstream analysis is a significant challenge. Many computational methods have been designed to address this issue; nevertheless, the efficacy of the available methods is still inadequate. In addition, most similarity-based methods require a number of clusters as input, which is difficult to achieve in real applications. In this study, we developed a novel computational method for clustering scRNA-seq data by considering both global and local information, named GCFG. This method characterizes the global properties of data by applying concept factorization, and the regularized Gaussian graphical model is utilized to evaluate the local embedding relationship of data. To learn the cell-cell similarity matrix, we integrated the two components, and an iterative optimization algorithm was developed. The categorization of single cells is obtained by applying Louvain, a modularity-based community discovery algorithm, to the similarity matrix. The behavior of the GCFG approach is assessed on 14 real scRNA-seq datasets in terms of ACC and ARI, and comparison results with 17 other competitive methods suggest that GCFG is effective and robust.

Single-Cell RNA-Seq Reveals Coronary Heterogeneity and Identifies CD133+TRPV4high Endothelial Subpopulation in Regulating Flow-Induced Vascular Tone in Mice

BACKGROUND

Single-cell RNA-Seq analysis can determine the heterogeneity of cells between different tissues at a single-cell level. Coronary artery endothelial cells (ECs) are important to coronary blood flow. However, little is known about the heterogeneity of coronary artery ECs, and cellular identity responses to flow. Identifying endothelial subpopulations will contribute to the precise localization of vascular endothelial subpopulations, thus enabling the precision of vascular injury treatment.

METHODS

Here, we performed a single-cell RNA sequencing of 31 962 cells and functional assays of 3 branches of the coronary arteries (right coronary artery/circumflex left coronary artery/anterior descending left coronary artery) in wild-type mice.

RESULTS

We found a compendium of 7 distinct cell types in mouse coronary arteries, mainly ECs, granulocytes, cardiac myocytes, smooth muscle cells, lymphocytes, myeloid cells, and fibroblast cells, and showed spatial heterogeneity between arterial branches. Furthermore, we revealed a subpopulation of coronary artery ECs, CD133+TRPV4high ECs. TRPV4 (transient receptor potential vanilloid 4) in CD133+TRPV4high ECs is important for regulating vasodilation and coronary blood flow.

CONCLUSIONS

Our study elucidates the nature and range of coronary arterial cell diversity and highlights the importance of coronary CD133+TRPV4high ECs in regulating coronary vascular tone.

Interleukin-6: An Under-Appreciated Inducer of Thermogenic Adipocyte Differentiation

Adipose tissue inflammation is a key factor leading to obesity-associated immune disorders, such as insulin resistance, beta cell loss in the pancreatic islets, meta-inflammation, and autoimmunity. Inhibiting adipose tissue inflammation is considered a straightforward approach to abrogate these diseases. However, recent findings show that certain pro-inflammatory cytokines are essential for the proper differentiation and functioning of adipocytes. Lipolysis is stimulated, and the thermogenic competence of adipocytes is unlocked by interleukin-6 (IL-6), a cytokine that was initially recognized as a key trigger of adipose tissue inflammation. Coherently, signal transducer and activator of transcription 3 (STAT3), which is a signal transducer for IL-6, is necessary for thermogenic adipocyte development. Given the impact of thermogenic adipocytes in increasing energy expenditure and reducing body adiposity, functions of IL-6 in the adipose tissue have gained attention recently. In this review, we show that IL-6 signaling may protect from excess fat accumulation by stimulating thermogenesis in adipocytes.

Making mouse transcriptomics deconvolution accessible with immunedeconv

Summary

Transcriptome deconvolution has emerged as a reliable technique to estimate cell-type abundances from bulk RNA sequencing data. Unlike their human equivalents, methods to quantify the cellular composition of complex tissues from murine transcriptomics are sparse and sometimes not easy to use. We extended the immunedeconv R package to facilitate the deconvolution of mouse transcriptomics, enabling the quantification of murine immune-cell types using 13 different methods. Through immunedeconv, we further offer the possibility of tweaking cell signatures used by deconvolution methods, providing custom annotations tailored for specific cell types and tissues. These developments strongly facilitate the study of the immune-cell composition of mouse models and further open new avenues in the investigation of the cellular composition of other tissues and organisms.

Availability and implementation

The R package and the documentation are available at https://github.com/omnideconv/immunedeconv.

Exploring the Molecular and Developmental Dynamics of Endothelial Cell Differentiation

The development and differentiation of endothelial cells (ECs) are fundamental processes with significant implications for both health and disease. ECs, which are found in all organs and blood vessels, play a crucial role in facilitating nutrient and waste exchange and maintaining proper vessel function. Understanding the intricate signaling pathways involved in EC development holds great promise for enhancing vascularization, tissue engineering, and vascular regeneration. Hematopoietic stem cells originating from hemogenic ECs, give rise to diverse immune cell populations, and the interaction between ECs and immune cells is vital for maintaining vascular integrity and regulating immune responses. Dysregulation of vascular development pathways can lead to various diseases, including cancer, where tumor-specific ECs promote tumor growth through angiogenesis. Recent advancements in single-cell genomics and in vivo genetic labeling have shed light on EC development, plasticity, and heterogeneity, uncovering tissue-specific gene expression and crucial signaling pathways. This review explores the potential of ECs in various applications, presenting novel opportunities for advancing vascular medicine and treatment strategies.

Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

LPS and type I and II interferons have opposing effects on epigenetic regulation of LAIR1 expression in mouse and human macrophages

Inhibitory immune receptors are important for maintaining immune homeostasis. We identified epigenetic alterations in 2 members of this group, LAIR1 and LAIR2, in lymphoma patients with inflammatory tissue damage and susceptibility to infection. We predicted that the expression of LAIR genes is controlled by immune mediators acting on transcriptional regulatory elements. Using flow cytometry, quantitative reverse-transcription polymerase chain reaction, and RNA sequencing, we measured LAIR1 and LAIR2 in human and murine immune cell subsets at baseline and posttreatment with immune mediators, including type I and II interferons, tumor necrosis factor α, and lipopolysaccharide (LPS). We identified candidate regulatory elements using epigenome profiling and measured their regulatory activity using luciferase reporters. LAIR1 expression substantially increases during monocyte differentiation to macrophages in both species. In contrast, murine and human macrophages exhibited opposite changes in LAIR1 in response to immune stimuli: human LAIR1 increased with LPS while mouse LAIR1 increased with interferon γ. LAIR genes had distinct patterns of enhancer activity with variable responses to immune stimuli. To identify relevant transcription factors (TFs), we developed integrative bioinformatic techniques applied to TF chromatin immunoprecipitation sequencing, RNA sequencing, and luciferase activity, revealing distinct sets of TFs for each LAIR gene. Most strikingly, LAIR1 TFs include nuclear factor kappa B factors RELA and RELB, while Lair1 and LAIR2 instead include STAT3 and/or STAT5. Regulation by nuclear factor kappa B factors may therefore explain the LPS-induced increase in LAIR1 expression, in contrast to Lair1 decrease. Our findings reveal new insights into transcriptional mechanisms that control distinct expression patterns of LAIR genes in response to inflammatory stimuli in human and murine myeloid and lymphoid cells.

Embigin deficiency leads to delayed embryonic lung development and high neonatal mortality in mice

Embigin (Gp70), a receptor for fibronectin and an ancillary protein for monocarboxylate transporters, is known to regulate stem cell niches in sebaceous gland and bone marrow. Here, we show that embigin expression is at high level during early mouse embryogenesis and that embigin is essential for lung development. Markedly increased neonatal mortality of Emb-/- mice can be explained by the compromised lung maturation: in Emb-/- mice (E17.5) the number and the size of the small airways and distal airspace are significantly smaller, there are fewer ATI and ATII cells, and the alkaline phosphatase activity in amniotic fluid is lower. Emb-/- lungs show less peripheral branching already at E12.5, and embigin is highly expressed in lung primordium. Thus, embigin function is essential at early pseudoglandular stage or even earlier. Furthermore, our RNA-seq analysis and Ki67 staining results support the idea that the development of Emb-/- lungs is rather delayed than defected.

Multi-organ single-cell RNA sequencing in mice reveals early hyperglycemia responses that converge on fibroblast dysregulation

Diabetes causes a range of complications that can affect multiple organs. Hyperglycemia is an important driver of diabetes-associated complications, mediated by biological processes such as dysfunction of endothelial cells, fibrosis, and alterations in leukocyte number and function. Here, we dissected the transcriptional response of key cell types to hyperglycemia across multiple tissues using single-cell RNA sequencing (scRNA-seq) and identified conserved, as well as organ-specific, changes associated with diabetes complications. By studying an early time point of diabetes, we focus on biological processes involved in the initiation of the disease, before the later organ-specific manifestations had supervened. We used a mouse model of type 1 diabetes and performed scRNA-seq on cells isolated from the heart, kidney, liver, and spleen of streptozotocin-treated and control male mice after 8 weeks and assessed differences in cell abundance, gene expression, pathway activation, and cell signaling across organs and within organs. In response to hyperglycemia, endothelial cells, macrophages, and monocytes displayed organ-specific transcriptional responses, whereas fibroblasts showed similar responses across organs, exhibiting altered metabolic gene expression and increased myeloid-like fibroblasts. Furthermore, we found evidence of endothelial dysfunction in the kidney, and of endothelial-to-mesenchymal transition in streptozotocin-treated mouse organs. In summary, our study represents the first single-cell and multi-organ analysis of early dysfunction in type 1 diabetes-associated hyperglycemia, and our large-scale dataset (comprising 67 611 cells) will serve as a starting point, reference atlas, and resource for further investigating the events leading to early diabetic disease.

Female behavior drives the formation of distinct social structures in C57BL/6J versus wild-derived outbred mice in field enclosures

BACKGROUND

Social behavior and social organization have major influences on individual health and fitness. Yet, biomedical research focuses on studying a few genotypes under impoverished social conditions. Understanding how lab conditions have modified social organizations of model organisms, such as lab mice, relative to natural populations is a missing link between socioecology and biomedical science.

RESULTS

Using a common garden design, we describe the formation of social structure in the well-studied laboratory mouse strain, C57BL/6J, in replicated mixed-sex populations over 10-day trials compared to control trials with wild-derived outbred house mice in outdoor field enclosures. We focus on three key features of mouse social systems: (i) territory establishment in males, (ii) female social relationships, and (iii) the social networks formed by the populations. Male territorial behaviors were similar but muted in C57 compared to wild-derived mice. Female C57 sharply differed from wild-derived females, showing little social bias toward cage mates and exploring substantially more of the enclosures compared to all other groups. Female behavior consistently generated denser social networks in C57 than in wild-derived mice.

CONCLUSIONS

C57 and wild-derived mice individually vary in their social and spatial behaviors which scale to shape overall social organization. The repeatable societies formed under field conditions highlights opportunities to experimentally study the interplay between society and individual biology using model organisms.

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.

Chemogenetic activation of microglial Gi signaling decreases microglial surveillance and impairs neuronal synchronization

Microglia actively survey the brain and dynamically interact with neurons to maintain brain homeostasis. Microglial Gi-protein coupled receptors (Gi-GPCRs) play a critical role in microglia-neuron communications. However, the impact of temporally activating microglial Gi signaling on microglial dynamics and neuronal activity in the homeostatic brain remains largely unknown. In this study, we employed Gi-based Designer Receptors Exclusively Activated by Designer Drugs (Gi-DREADD) to selectively and temporally modulate microglial Gi signaling pathway. By integrating this chemogenetic approach with in vivo two-photon imaging, we observed that exogenous activation of microglial Gi signaling transiently inhibited microglial process dynamics, reduced neuronal activity, and impaired neuronal synchronization. These altered neuronal functions were associated with a decrease in interactions between microglia and neuron somata. Altogether, this study demonstrates that acute, exogenous activation of microglial Gi signaling can regulate neuronal circuit function, offering a potential pharmacological target for neuromodulation through microglia.

The phosphodiesterase 2A controls lymphatic junctional maturation via cGMP-dependent notch signaling

The molecular mechanisms by which lymphatic vessels induce cell contact inhibition are not understood. Here, we identify the cGMP-dependent phosphodiesterase 2A (PDE2A) as a selective regulator of lymphatic but not of blood endothelial contact inhibition. Conditional deletion of Pde2a in mouse embryos reveals severe lymphatic dysplasia, whereas blood vessel architecture remains unaltered. In the absence of PDE2A, human lymphatic endothelial cells fail to induce mature junctions and cell cycle arrest, whereas cGMP levels, but not cAMP levels, are increased. Loss of PDE2A-mediated cGMP hydrolysis leads to the activation of p38 signaling and downregulation of NOTCH signaling. However, DLL4-induced NOTCH activation restores junctional maturation and contact inhibition in PDE2A-deficient human lymphatic endothelial cells. In postnatal mouse mesenteries, PDE2A is specifically enriched in collecting lymphatic valves, and loss of Pde2a results in the formation of abnormal valves. Our data demonstrate that PDE2A selectively finetunes a crosstalk of cGMP, p38, and NOTCH signaling during lymphatic vessel maturation.

Logical design of synthetic cis-regulatory DNA for genetic tracing of cell identities and state changes

Descriptive data are rapidly expanding in biomedical research. Instead, functional validation methods with sufficient complexity remain underdeveloped. Transcriptional reporters allow experimental characterization and manipulation of developmental and disease cell states, but their design lacks flexibility. Here, we report logical design of synthetic cis-regulatory DNA (LSD), a computational framework leveraging phenotypic biomarkers and trans-regulatory networks as input to design reporters marking the activity of selected cellular states and pathways. LSD uses bulk or single-cell biomarkers and a reference genome or custom cis-regulatory DNA datasets with user-defined boundary regions. By benchmarking validated reporters, we integrate LSD with a computational ranking of phenotypic specificity of putative cis-regulatory DNA. Experimentally, LSD-designed reporters targeting a wide range of cell states are functional without minimal promoters. Applied to broadly expressed genes from human and mouse tissues, LSD generates functional housekeeper-like sLCRs compatible with size constraints of AAV vectors for gene therapy applications. A mesenchymal glioblastoma reporter designed by LSD outperforms previously validated ones and canonical cell surface markers. In genome-scale CRISPRa screens, LSD facilitates the discovery of known and novel bona fide cell-state drivers. Thus, LSD captures core principles of cis-regulation and is broadly applicable to studying complex cell states and mechanisms of transcriptional regulation.

Quantifying the clusterness and trajectoriness of single-cell RNA-seq data

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.

The Sympathetic-Immune Milieu in Metabolic Health and Diseases: Insights from Pancreas, Liver, Intestine, and Adipose Tissues

Sympathetic innervation plays a crucial role in maintaining energy balance and contributes to metabolic pathophysiology. Recent evidence has begun to uncover the innervation landscape of sympathetic projections and sheds light on their important functions in metabolic activities. Additionally, the immune system has long been studied for its essential roles in metabolic health and diseases. In this review, the aim is to provide an overview of the current research progress on the sympathetic regulation of key metabolic organs, including the pancreas, liver, intestine, and adipose tissues. In particular, efforts are made to highlight the critical roles of the peripheral nervous system and its potential interplay with immune components. Overall, it is hoped to underscore the importance of studying metabolic organs from a comprehensive and interconnected perspective, which will provide valuable insights into the complex mechanisms underlying metabolic regulation and may lead to novel therapeutic strategies for metabolic diseases.

Identification and Validation of Hub Genes Related to Neutrophil Extracellular Traps-Mediated Cell Damage During Myocardial Infarction

Purpose

Studies have shown that neutrophil-mediated formation of neutrophil extracellular traps (NETs) leads to increased inflammatory response and cellular tissue damage during myocardial infarction (MI). We aimed to identify and validate possible hub genes in the process of NETs-mediated cell damage.

Methods

We performed an immune cell infiltration analysis of the MI transcriptome dataset based on CIBERSORT and ssGSEA algorithms. Gene expression profiles of NETs formation (GSE178883) were used to analyze the physiological processes of peripheral blood neutrophils after phorbol myristate acetate (PMA) stimulation. Bioinformatics and machine learning algorithms were utilized to find candidate hub genes based on NETs-related genes and transcriptome datasets (GSE66360 and GSE179828). We generated the receiver operating curve (ROC) to evaluate the diagnostic value of hub genes. Next, the correlation between hub genes and immune cells was analyzed using CIBERSORT, ssGSEA and xCell algorithms. Finally, we used quantitative real-time PCR (qRT-PCR) and immunohistochemistry to verify gene expression.

Results

Immune cell infiltration analysis revealed that inflammatory cells such as neutrophils were highly expressed in the peripheral blood of patients with MI. Functional analysis of differentially expressed genes (DEGs) in GSE178883 indicated that the potential pathogenesis lies in immune terms. Using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, we finally identified the seven hub genes (FCAR, IL1B, MMP9, NFIL3, CXCL2, ICAM1, and ZFP36). The qRT-PCR results showed that IL-1B, MMP9, and NFIL3 mRNA expression was up-regulated in the MI group compared to the control. Immunohistochemical results showed high MMP9, IL-1B, and NFIL3 expression in the infarcted area compared to the non-infarcted area and sham-operated groups.

Conclusion

We identified seven hub genes associated with NETs-mediated cellular damage during MI. Our results may provide insights into the mechanisms of neutrophil-mediated cell injury during MI.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Analysis of brain and blood single-cell transcriptomics in acute and subacute phases after experimental stroke

Cerebral ischemia triggers a powerful inflammatory reaction involving peripheral leukocytes and brain resident cells that contribute to both tissue injury and repair. However, their dynamics and diversity remain poorly understood. To address these limitations, we performed a single-cell transcriptomic study of brain and blood cells 2 or 14 days after ischemic stroke in mice. We observed a strong divergence of post-ischemic microglia, monocyte-derived macrophages and neutrophils over time, while endothelial cells and brain-associated macrophages showed altered transcriptomic signatures at 2 days poststroke. Trajectory inference predicted the in situ trans-differentiation of macrophages from blood monocytes into day 2 and day 14 phenotypes, while neutrophils were projected to be continuously de novo recruited from the blood. Brain single-cell transcriptomes from both female and male aged mice were similar to that of young male mice, but aged and young brains differed in their immune cell composition. Although blood leukocyte analysis also revealed altered transcriptomes after stroke, brain-infiltrating leukocytes displayed higher transcriptomic divergence than their circulating counterparts, indicating that phenotypic diversification occurs within the brain in the early and recovery phases of ischemic stroke. A portal ( https://anratherlab.shinyapps.io/strokevis/ ) is provided to allow user-friendly access to our data.

Adiponectin stimulates Sca1+CD34--adipocyte precursor cells associated with hyperplastic expansion and beiging of brown and white adipose tissue

BACKGROUND

The adipocyte hormone adiponectin improves insulin sensitivity and there is an inverse correlation between adiponectin levels and type-2 diabetes risk. Previous research shows that adiponectin remodels the adipose tissue into a more efficient metabolic sink. For instance, mice that overexpress adiponectin show increased capacity for hyperplastic adipose tissue expansion as evident from smaller and metabolically more active white adipocytes. In contrast, the brown adipose tissue (BAT) of these mice looks "whiter" possibly indicating reduced metabolic activity. Here, we aimed to further establish the effect of adiponectin on adipose tissue expansion and adipocyte mitochondrial function as well as to unravel mechanistic aspects in this area.

METHODS

Brown and white adipose tissues from adiponectin overexpressing (APN tg) mice and littermate wildtype controls, housed at room and cold temperature, were studied by histological, gene/protein expression and flow cytometry analyses. Metabolic and mitochondrial functions were studied by radiotracers and Seahorse-based technology. In addition, mitochondrial function was assessed in cultured adiponectin deficient adipocytes from APN knockout and heterozygote mice.

RESULTS

APN tg BAT displayed increased proliferation prenatally leading to enlarged BAT. Postnatally, APN tg BAT turned whiter than control BAT, confirming previous reports. Furthermore, elevated adiponectin augmented the sympathetic innervation/activation within adipose tissue. APN tg BAT displayed reduced metabolic activity and reduced mitochondrial oxygen consumption rate (OCR). In contrast, APN tg inguinal white adipose tissue (IWAT) displayed enhanced metabolic activity. These metabolic differences between genotypes were apparent also in cultured adipocytes differentiated from BAT and IWAT stroma vascular fraction, and the OCR was reduced in both brown and white APN heterozygote adipocytes. In both APN tg BAT and IWAT, the mesenchymal stem cell-related genes were upregulated along with an increased abundance of Lineage-Sca1+CD34- "beige-like" adipocyte precursor cells. In vitro, the adiponectin receptor agonist Adiporon increased the expression of the proliferation marker Pcna and decreased the expression of Cd34 in Sca1+ mesenchymal stem cells.

CONCLUSIONS

We propose that the seemingly opposite effect of adiponectin on BAT and IWAT is mediated by a common mechanism; while reduced adiponectin levels are linked to lower adipocyte OCR, elevated adiponectin levels stimulate expansion of adipocyte precursor cells that produce adipocytes with intrinsically higher metabolic rate than classical white but lower metabolic rate than classical brown adipocytes. Moreover, adiponectin can modify the adipocytes' metabolic activity directly and by enhancing the sympathetic innervation within a fat depot.

Age-related structural and functional changes of the intracardiac nervous system

BACKGROUND

Although aging is known to be associated with an increased incidence of both atrial and ventricular arrhythmias, there is limited knowledge about how Schwann cells (SC) and the intracardiac nervous system (iCNS) remodel with age. Here we investigate the differences in cardiac SC, parasympathetic nerve fibers, and muscarinic acetylcholine receptor M2 (M2R) expression in young and old mice. Additionally, we examine age-related changes in cardiac responses to sympathomimetic and parasympathomimetic drugs.

METHODS AND RESULTS

Lower SC density, lower SC proliferation and fewer parasympathetic nerve fibers were observed in cardiac and, as a control sciatic nerves from old (20-24 months) compared to young mice (2-3 months). In old mice, chondroitin sulfate proteoglycan 4 (CSPG4) was increased in sciatic but not cardiac nerves. Expression of M2R was lower in ventricular myocardium and ventricular conduction system from old mice compared to young mice, while no significant difference was seen in M2R expression in sino-atrial or atrio-ventricular node pacemaker tissue. Heart rate was slower and PQ intervals were longer in Langendorff-perfused hearts from old mice. Ventricular tachycardia and fibrillation were more frequently observed in response to carbachol administration in hearts from old mice versus those from young mice.

CONCLUSIONS

On the background of reduced presence of SC and parasympathetic nerve fibers, and of lower M2R expression in ventricular cardiomyocytes and conduction system of aged hearts, the propensity of ventricular arrhythmogenesis upon parasympathomimetic drug application is increased. Whether this is caused by an increase in heterogeneity of iCNS structure and function remains to be elucidated.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

SRT-Server: powering the analysis of spatial transcriptomic data

BACKGROUND

Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds.

RESULTS

Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise.

CONCLUSIONS

Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Hyperoxia prevents the dynamic neonatal increases in lung mesenchymal cell diversity

Rapid expansion of the pulmonary microvasculature through angiogenesis drives alveolarization, the final stage of lung development that occurs postnatally and dramatically increases lung gas-exchange surface area. Disruption of pulmonary angiogenesis induces long-term structural and physiologic lung abnormalities, including bronchopulmonary dysplasia, a disease characterized by compromised alveolarization. Although endothelial cells are primary determinants of pulmonary angiogenesis, mesenchymal cells (MC) play a critical and dual role in angiogenesis and alveolarization. Therefore, we performed single cell transcriptomics and in-situ imaging of the developing lung to profile mesenchymal cells during alveolarization and in the context of lung injury. Specific mesenchymal cell subtypes were present at birth with increasing diversity during alveolarization even while expressing a distinct transcriptomic profile from more mature correlates. Hyperoxia arrested the transcriptomic progression of the MC, revealed differential cell subtype vulnerability with pericytes and myofibroblasts most affected, altered cell to cell communication, and led to the emergence of Acta1 expressing cells. These insights hold the promise of targeted treatment for neonatal lung disease, which remains a major cause of infant morbidity and mortality across the world.

Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data

MOTIVATION

Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable.

RESULTS

We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL.

AVAILABILITY

An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch.

CONTACT

dengmh@pku.edu.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Journal Name online.

scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data

The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE.

Exploring Immune Cell Diversity in the Lacrimal Glands of Healthy Mice: A Single-Cell RNA-Sequencing Atlas

The lacrimal gland is responsible for maintaining the health of the ocular surface through the production of tears. However, our understanding of the immune system within the lacrimal gland is currently limited. Therefore, in this study, we utilized single-cell RNA sequencing and bioinformatic analysis to identify and analyze immune cells and molecules present in the lacrimal glands of normal mice. A total of 34,891 cells were obtained from the lacrimal glands of mice and classified into 18 distinct cell clusters using Seurat clustering. Within these cell populations, 26 different immune cell subpopulations were identified, including T cells, innate lymphocytes, macrophages, mast cells, dendritic cells, and B cells. Network analysis revealed complex cell-cell interactions between these immune cells, with particularly significant interactions observed among T cells, macrophages, plasma cells, and dendritic cells. Interestingly, T cells were found to be the main source of ligands for the Thy1 signaling pathway, while M2 macrophages were identified as the primary target of this pathway. Moreover, some of these immune cells were validated using immunohistological techniques. Collectively, these findings highlight the abundance and interactions of immune cells and provide valuable insights into the complexity of the lacrimal gland immune system and its relevance to associated diseases.

Computer-assisted interpretation, in-depth exploration and single cell type annotation of RNA sequence data using k-means clustering algorithm

At now, the majority of approaches rely on manual techniques for annotating cell types subsequent to clustering the data obtained from single-cell RNA sequencing (scRNA-seq). These approaches require a significant amount of physical exertion and depend substantially on the user's skill, perhaps resulting in uneven outcomes and inconsistency in treatment. In this paper, we provide a computer-assisted interpretation of every single cell of a tissue sample, along with an in-depth exploration of an individual cell's molecular, phenotypic and functional attributes. The paper will also perform k-means clustering followed by silhouette validation based on similar phenotype and functional attributes, and also, cell type annotation is performed, where we match a cell's gene profile against some known database by applying certain statistical conditions. Finally, all the genes are mapped spatially on the tissue sample. This paper is an aid to medicine to know which cells are expressed/not expressed in a tissue sample and their spatial location on the tissue sample.

Galectin-3 aggravates microglial activation and tau transmission in tauopathy

Alzheimer's disease is characterized by the accumulation of amyloid-β plaques, aggregation of hyperphosphorylated tau (pTau), and microglia activation. Galectin-3 (Gal3) is a β-galactoside-binding protein that has been implicated in amyloid pathology. Its role in tauopathy remains enigmatic. Here, we showed that Gal3 was upregulated in the microglia of humans and mice with tauopathy. pTau triggered the release of Gal3 from human induced pluripotent stem cell-derived microglia in both its free and extracellular vesicular-associated (EV-associated) forms. Both forms of Gal3 increased the accumulation of pathogenic tau in recipient cells. Binding of Gal3 to pTau greatly enhanced tau fibrillation. Besides Gal3, pTau was sorted into EVs for transmission. Moreover, pTau markedly enhanced the number of EVs released by iMGL in a Gal3-dependent manner, suggesting a role of Gal3 in biogenesis of EVs. Single-cell RNA-Seq analysis of the hippocampus of a mouse model of tauopathy (THY-Tau22) revealed a group of pathogenic tau-evoked, Gal3-associated microglia with altered cellular machineries implicated in neurodegeneration, including enhanced immune and inflammatory responses. Genetic removal of Gal3 in THY-Tau22 mice suppressed microglia activation, reduced the level of pTau and synaptic loss in neurons, and rescued memory impairment. Collectively, Gal3 is a potential therapeutic target for tauopathy.

The adhesion GPCR GPR116/ADGRF5 has a dual function in pancreatic islets regulating somatostatin release and islet development

Glucose homeostasis is maintained by hormones secreted from different cell types of the pancreatic islets and controlled by manifold input including signals mediated through G protein-coupled receptors (GPCRs). RNA-seq analyses revealed expression of numerous GPCRs in mouse and human pancreatic islets, among them Gpr116/Adgrf5. GPR116 is an adhesion GPCR mainly found in lung and required for surfactant secretion. Here, we demonstrate that GPR116 is involved in the somatostatin release from pancreatic delta cells using a whole-body as well as a cell-specific knock-out mouse model. Interestingly, the whole-body GPR116 deficiency causes further changes such as decreased beta-cell mass, lower number of small islets, and reduced pancreatic insulin content. Glucose homeostasis in global GPR116-deficient mice is maintained by counter-acting mechanisms modulating insulin degradation. Our data highlight an important function of GPR116 in controlling glucose homeostasis.

Transcriptional profile of ribosome-associated quality control components and their associated phenotypes in mammalian cells

During protein synthesis, organisms detect translation defects that induce ribosome stalling and result in protein aggregation. The Ribosome-associated Quality Control (RQC) complex, comprising TCF25, LTN1, and NEMF, is responsible for identifying incomplete protein products from unproductive translation events, targeting them for degradation. Although RQC disruption causes adverse effects on vertebrate neurons, data regarding mRNA/protein expression and regulation across tissues are lacking. Employing high-throughput methods, we analyzed public datasets to explore RQC gene expression and phenotypes. Our findings revealed widespread expression of RQC components in human tissues; however, silencing of RQC yielded only mild negative effects on cell growth. Notably, TCF25 exhibited elevated mRNA levels that were not reflected in the protein content. We experimentally demonstrated that this disparity arose from post-translational protein degradation by the proteasome. Additionally, we observed that cellular aging marginally influenced RQC expression, leading to reduced mRNA levels in specific tissues. Our results suggest the necessity of RQC expression in all mammalian tissues. Nevertheless, when RQC falters, alternative mechanisms seem to compensate, ensuring cell survival under nonstress conditions.

Myeloid Zfhx3 deficiency protects against hypercapnia-induced suppression of host defense against influenza A virus

Hypercapnia, elevation of the partial pressure of CO2 in blood and tissues, is a risk factor for mortality in patients with severe acute and chronic lung diseases. We previously showed that hypercapnia inhibits multiple macrophage and neutrophil antimicrobial functions and that elevated CO2 increases the mortality of bacterial and viral pneumonia in mice. Here, we show that normoxic hypercapnia downregulates innate immune and antiviral gene programs in alveolar macrophages (AMØs). We also show that zinc finger homeobox 3 (Zfhx3) - a mammalian ortholog of zfh2, which mediates hypercapnic immune suppression in Drosophila - is expressed in mouse and human macrophages. Deletion of Zfhx3 in the myeloid lineage blocked the suppressive effect of hypercapnia on immune gene expression in AMØs and decreased viral replication, inflammatory lung injury, and mortality in hypercapnic mice infected with influenza A virus. To our knowledge, our results establish Zfhx3 as the first known mammalian mediator of CO2 effects on immune gene expression and lay the basis for future studies to identify therapeutic targets to interrupt hypercapnic immunosuppression in patients with advanced lung disease.

Evidence of Histone H2A.Z Deacetylation and Cardiomyocyte Dedifferentiation in Infarcted/Tip60-depleted Hearts

Myocardial infarction (MI) in the human heart causes death of billions of cardiomyocytes (CMs), resulting in cardiac dysfunction that is incompatible with life or lifestyle. In order to re-muscularize injured myocardium, replacement CMs must be generated via renewed proliferation of surviving CMs. Approaches designed to induce proliferation of CMs after injury have been insufficient. Toward this end, we are targeting the Tip60 acetyltransferase, based on the rationale that its pleiotropic functions conspire to block the CM cell-cycle at several checkpoints. We previously reported that genetic depletion of Tip60 in a mouse model after MI reduces scarring, retains cardiac function, and activates the CM cell-cycle, although it is unclear whether this culminates in the generation of daughter CMs. For pre-existing CMs in the adult heart to resume proliferation, it is becoming widely accepted that they must first dedifferentiate, a process highlighted by loss of maturity, epithelial to mesenchymal transitioning (EMT), and reversion from fatty acid oxidation to glycolytic metabolism, accompanied by softening of the myocardial extracellular matrix. Findings in hematopoietic stem cells, and more recently in neural progenitor cells, have shown that Tip60 induces and maintains the differentiated state via site-specific acetylation of the histone variant H2A.Z. Here, we report that genetic depletion of Tip60 from naïve or infarcted hearts results in the near-complete absence of acetylated H2A.Z in CM nuclei, and that this is accordingly accompanied by altered gene expressions indicative of EMT induction, ECM softening, decreased fatty acid oxidation, and depressed expression of genes that regulate the TCA cycle. These findings, combined with our previous work, support the notion that because Tip60 has multiple targets that combinatorially maintain the differentiated state and inhibit proliferation, its transient therapeutic targeting to ameliorate the effects of cardiac injury should be considered.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.