Statistical significance of variables driving systematic variation in high-dimensional data

Single-nucleus multiomics unravels the genetic mechanisms underlying musk secretion in Chinese forest musk deer (Moschus berezovskii)

Musk secreted by the musk glands in male forest musk deer (FMD; Moschus berezovskii) is highly valued for its pharmaceutical and perfumery applications. However, the regulatory mechanisms underlying musk secretion are not well understood. This study aimed to investigate the genes and transcription factors involved in musk secretion across different periods and ages. We analyzed the musk glands of adult male FMD during the non-secretory and secretory periods, as well as juvenile and adult male FMD during the secretory period, using single-cell multiome ATAC+gene expression technique. Our analysis identified 13 cell types, including acinar cells of Types 1 and 2. Chromatin accessibility analysis and gene expression data confirmed that the genes Map3k2, Hsd17b12, and Jun are critical for musk secretion. Additionally, EHF, NR4A2, and FOXO1 proteins play crucial regulatory roles. Weighted gene co-expression network analysis (WGCNA) highlighted the importance of GnRH signaling pathway in musk secretion. Gene set enrichment analysis (GSEA) showed that the steroid hormone biosynthesis pathway is notably enriched in acinar cells. Furthermore, intercellular communication appears to influence both the initiation and maintenance of musk secretion. These findings provide valuable insights into the molecular pathways of musk secretion in FMD, offering potential avenues for increasing musk production and developing treatment for inflammation and tumors.

Transcriptional landscape of sweetpotato root tip development at the single-cell level

Single-cell transcriptome sequencing (scRNA-seq) is a powerful tool for describing the transcriptome dynamics of plant development but has not yet been utilized to analyze the tissue ontology of sweetpotato. This study established a stable method for isolating single protoplast cells for scRNA-seq to reveal the cell heterogeneity of sweetpotato root tip meristems at the single-cell level. The study analyzed 12,172 single cells and 27,355 genes in the root tips of the sweetpotato variety Guangshu 87, which were distributed into 15 cell clusters. Pseudo-time analysis showed that there were transitional cells in the apical development trajectory of mature cell types from stem cell niches. Furthermore, we identified novel development regulators of sweetpotato tubers via trajectory analysis. The transcription factor IbGATA4 was highly expressed in the adventitious roots during the development of sweetpotato root tips, where it may regulate the development of sweetpotato root tips. In addition, significant differences were observed in the transcriptional profiles of cell types between sweetpotato, Arabidopsis thaliana, and maize. This study mapped the single-cell transcriptome of sweetpotato root tips, laying a foundation for studying the types, functions, differentiation, and development of sweetpotato root tip cells.

The frequency of CD38+ alveolar macrophages correlates with early control of M. tuberculosis in the murine lung

Tuberculosis, caused by Mycobacterium tuberculosis, remains an enduring global health challenge due to the limited efficacy of existing treatments. Although much research has focused on immune failure, the role of host macrophage biology in controlling the disease remains underappreciated. Here we show, through multi-modal single-cell RNA sequencing in a murine model, that different alveolar macrophage subsets play distinct roles in either advancing or controlling the disease. Initially, alveolar macrophages that are negative for the CD38 marker are the main infected population. As the infection progresses, CD38+ monocyte-derived and tissue-resident alveolar macrophages emerge as significant controllers of bacterial growth. These macrophages display a unique chromatin organization pre-infection, indicative of epigenetic priming for pro-inflammatory responses. Moreover, intranasal BCG immunization increases the numbers of CD38+ macrophages, enhancing their capability to restrict Mycobacterium tuberculosis growth. Our findings highlight the dynamic roles of alveolar macrophages in tuberculosis and open pathways for improved vaccines and therapies.

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

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

A single-nucleus transcriptomic atlas characterizes cell types and their molecular features in the ovary of adult Nile tilapia

In fish species, there is limited analysis of signature transcriptome profiles at the single-cell level in gonadal cells. Here, the molecular signatures of distinct ovarian cell categories in adult Nile tilapia (Oreochromis niloticus) were analysed using single-nucleus RNA sequencing (snRNA-seq). We identified four cell types (oogonia, oocytes, granulosa cell, and thecal cell) based on their specifically expressed genes and biological functions. Similarly, we found some key pathways involved in ovarian development that may affect germline-somatic interactions. A cell-to-cell communication network between the distinct cell types was constructed. We found that the bidirectional communication is mandatory for the development of germ cells and somatic cells in fish ovaries, and the granulosa cells and thecal cells play a central regulating role in the cell network in fish ovary. Additionally, we identified some novel candidate marker genes for various types of ovarian cells and also validated them using in situ hybridization. Our work reveals an ovarian atlas at the cellular and molecular levels and contributes to providing insights into oogenesis and gonad development in fish.

Single-nucleus transcriptomics and chromatin accessibility analysis of musk gland development in Chinese forest musk deer (Moschus berezovskii)

Musk secreted by male forest musk deer (Moschus berezovskii) musk glands is an invaluable component of medicine and perfume. Musk secretion depends on musk gland maturation; however, the mechanism of its development remains elusive. Herein, using single cell multiome ATAC + gene expression coupled with several bioinformatic analyses, a dynamic transcriptional cell atlas of musk gland development was revealed, and key genes and transcription factors affecting its development were determined. Twelve cell types, including two different types of acinar cells (Clusters 0 and 10) were identified. Single-nucleus RNA and single-nucleus ATAC sequencing analyses revealed that seven core target genes associated with musk secretion (Hsd17b2, Acacb, Lss, Vapa, Aldh16a1, Aldh7a1, and Sqle) were regulated by 12 core transcription factors (FOXO1, CUX2, RORA, RUNX1, KLF6, MGA, NFIC, FOXO3, ETV5, NR3C1, HSF4, and MITF) during the development of Cluster 0 acinar cells. Kyoto Encyclopedia of Genes and Genomes enrichment showed significant changes in the pathways associated with musk secretion during acinar cell development. Gene set variation analysis also revealed that certain pathways associated with musk secretion were enriched in 6-year-old acinar cells. A gene co-expression network was constructed during acinar cell development to provide a precise understanding of the connections between transcription factors, genes, and pathways. Finally, intercellular communication analysis showed that intercellular communication is involved in musk gland development. This study provides crucial insights into the changes and key factors underlying musk gland development, which serve as valuable resources for studying musk secretion mechanisms and promoting the protection of this endangered species.

AUTS2 disruption causes neuronal differentiation defects in human cerebral organoids through hyperactivation of the WNT/β-catenin pathway

Individuals with the Autism Susceptibility Candidate 2 (AUTS2) gene disruptions exhibit symptoms such as intellectual disability, microcephaly, growth retardation, and distinct skeletal and facial differences. The role of AUTS2 in neurodevelopment has been investigated using animal and embryonic stem cell models. However, the precise molecular mechanisms of how AUTS2 influences neurodevelopment, particularly in humans, are not thoroughly understood. Our study employed a 3D human cerebral organoid culture system, in combination with genetic, genomic, cellular, and molecular approaches, to investigate how AUTS2 impacts neurodevelopment through cellular signaling pathways. We used CRISPR/Cas9 technology to create AUTS2-deficient human embryonic stem cells and then generated cerebral organoids with these cells. Our transcriptomic analyses revealed that the absence of AUTS2 in cerebral organoids reduces the populations of cells committed to the neuronal lineage, resulting in an overabundance of cells with a transcription profile resembling that of choroid plexus (ChP) cells. Intriguingly, we found that AUTS2 negatively regulates the WNT/β-catenin signaling pathway, evidenced by its overactivation in AUTS2-deficient cerebral organoids and in luciferase reporter cells lacking AUTS2. Importantly, treating the AUTS2-deficient cerebral organoids with a WNT inhibitor reversed the overexpression of ChP genes and increased the downregulated neuronal gene expression. This study offers new insights into the role of AUTS2 in neurodevelopment and suggests potential targeted therapies for neurodevelopmental disorders.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

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.

CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung

Tuberculosis, caused by M.tuberculosis (Mtb), remains an enduring global health challenge, especially given the limited efficacy of current therapeutic interventions. Much of existing research has focused on immune failure as a driver of tuberculosis. However, the crucial role of host macrophage biology in controlling the disease remains underappreciated. While we have gained deeper insights into how alveolar macrophages (AMs) interact with Mtb, the precise AM subsets that mediate protection and potentially prevent tuberculosis progression have yet to be identified. In this study, we employed multi-modal scRNA-seq analyses to evaluate the functional roles of diverse macrophage subpopulations across different infection timepoints, allowing us to delineate the dynamic landscape of controller and permissive AM populations during the course of infection. Our analyses at specific time-intervals post-Mtb challenge revealed macrophage populations transitioning between distinct anti- and pro-inflammatory states. Notably, early in Mtb infection, CD38- AMs showed a muted response. As infection progressed, we observed a phenotypic shift in AMs, with CD38+ monocyte-derived AMs (moAMs) and a subset of tissue-resident AMs (TR-AMs) emerging as significant controllers of bacterial growth. Furthermore, scATAC-seq analysis of naïve lungs demonstrated that CD38+ TR-AMs possessed a distinct chromatin signature prior to infection, indicative of epigenetic priming and predisposition to a pro-inflammatory response. BCG intranasal immunization increased the numbers of CD38+ macrophages, substantially enhancing their capability to restrict Mtb growth. Collectively, our findings emphasize the pivotal, dynamic roles of different macrophage subsets in TB infection and reveal rational pathways for the development of improved vaccines and immunotherapeutic strategies.

A Thy-1-negative immunofibroblast population emerges as a key determinant of fibrotic outcomes to biomaterials

Fibrosis-associated fibroblasts have been identified across various fibrotic disorders, but not in the context of biomaterials, fibrotic encapsulation, and the foreign body response. In other fibrotic disorders, a fibroblast subpopulation defined by Thy-1 loss is strongly correlated with fibrosis yet we do not know what promotes Thy-1 loss. We have previously shown that Thy-1 is an integrin regulator enabling normal fibroblast mechanosensing, and here, leveraging nonfibrotic microporous annealed particle (MAP) hydrogels versus classical fibrotic bulk hydrogels, we demonstrate that Thy1-/- mice mount a fibrotic response to MAP gels that includes inflammatory signaling. We found that a distinct and cryptic α-smooth muscle actin-positive Thy-1- fibroblast population emerges in response to interleuklin-1β (IL-1β) and tumor necrosis factor-α (TNFα). Furthermore, IL-1β/TNFα-induced Thy-1- fibroblasts consist of two distinct subpopulations that are strongly proinflammatory. These findings illustrate the emergence of a unique proinflammatory, profibrotic fibroblast subpopulation that is central to fibrotic encapsulation of biomaterials.

Phenotypic and spatial heterogeneity of brain myeloid cells after stroke is associated with cell ontogeny, tissue damage, and brain connectivity

Acute stroke triggers extensive changes to myeloid immune cell populations in the brain that may be targets for limiting brain damage and enhancing repair. Immunomodulatory approaches will be most effective with precise manipulation of discrete myeloid cell phenotypes in time and space. Here, we investigate how stroke alters mononuclear myeloid cell composition and phenotypes at single-cell resolution and key spatial patterns. Our results show that multiple reactive microglial states and monocyte-derived populations contribute to an extensive myeloid cell repertoire in post-stroke brains. We identify important overlaps and distinctions among different cell types/states that involve ontogeny- and spatial-related properties. Notably, brain connectivity with infarcted tissue underpins the pattern of local and remote altered cell accumulation and reactivity. Our discoveries suggest a global but anatomically governed brain myeloid cell response to stroke that comprises diverse phenotypes arising through intrinsic cell ontogeny factors interacting with exposure to spatially organized brain damage and neuro-axonal cues.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach

Pediatric T-cell Acute Lymphoblastic Leukemia (T-ALL) relapses are still associated with a dismal outcome, justifying the search for new therapeutic targets and relapse biomarkers. Using single-cell RNA sequencing (scRNAseq) data from three paired samples of pediatric T-ALL at diagnosis and relapse, we first conducted a high-dimensional weighted gene co-expression network analysis (hdWGCNA). This analysis highlighted several gene co-expression networks (GCNs) and identified relapse-associated hub genes, which are considered potential driver genes. Shared relapse-expressed genes were found to be related to antigen presentation (HLA, B2M), cytoskeleton remodeling (TUBB, TUBA1B), translation (ribosomal proteins, EIF1, EEF1B2), immune responses (MIF, EMP3), stress responses (UBC, HSP90AB1/AA1), metabolism (FTH1, NME1/2, ARCL4C), and transcriptional remodeling (NF-κB family genes, FOS-JUN, KLF2, or KLF6). We then utilized sparse partial least squares discriminant analysis to select from a pool of 481 unique leukemic hub genes, which are the genes most discriminant between diagnosis and relapse states (comprising 44, 35, and 31 genes, respectively, for each patient). Applying a Cox regression method to these patient-specific genes, along with transcriptomic and clinical data from the TARGET-ALL AALL0434 cohort, we generated three model gene signatures that efficiently identified relapsed patients within the cohort. Overall, our approach identified new potential relapse-associated genes and proposed three model gene signatures associated with lower survival rates for high-score patients.

Transient expression of the neuropeptide galanin modulates peripheral‑to‑central connectivity in the somatosensory thalamus during whisker development in mice

The significance of transient neuropeptide expression during postnatal brain development is unknown. Here, we show that galanin expression in the ventrobasal thalamus of infant mice coincides with whisker map development and modulates subcortical circuit wiring. Time-resolved neuroanatomy and single-nucleus RNA-seq identified complementary galanin (Gal) and galanin receptor 1 (Galr1) expression in the ventrobasal thalamus and the principal sensory nucleus of the trigeminal nerve (Pr5), respectively. Somatodendritic galanin release from the ventrobasal thalamus was time-locked to the first postnatal week, when Gal1R+ Pr5 afferents form glutamatergic (Slc17a6+) synapses for the topographical whisker map to emerge. RNAi-mediated silencing of galanin expression disrupted glutamatergic synaptogenesis, which manifested as impaired whisker-dependent exploratory behaviors in infant mice, with behavioral abnormalities enduring into adulthood. Pharmacological probing of receptor selectivity in vivo corroborated that target recognition and synaptogenesis in the thalamus, at least in part, are reliant on agonist-induced Gal1R activation in inbound excitatory axons. Overall, we suggest a neuropeptide-dependent developmental mechanism to contribute to the topographical specification of a fundamental sensory neurocircuit in mice.

Molecularly stratified hypothalamic astrocytes are cellular foci for obesity

Astrocytes safeguard the homeostasis of the central nervous system1,2. Despite their prominent morphological plasticity under conditions that challenge the brain's adaptive capacity3-5, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile6,7. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of 'neurocentric' algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification6-8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes1. Simplistically, therefore, astrocytes are still grouped as 'resting' vs. 'reactive', the latter referring to pathological states marked by various inducible genes3,9,10. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.

Single-cell RNA-seq reveals a link of ovule abortion and sugar transport in Camellia oleifera

Camellia oleifera is the most important woody oil crop in China. Seed number per fruit is an important yield trait in C. oleifera. Ovule abortion is generally observed in C. oleifera and significantly decreases the seed number per fruit. However, the mechanisms of ovule abortion remain poorly understood at present. Single-cell RNA sequencing (scRNA-seq) was performed using mature ovaries of two C. oleifera varieties with different ovule abortion rates (OARs). In total, 20,526 high-quality cells were obtained, and 18 putative cell clusters were identified. Six cell types including female gametophyte, protoxylem, protophloem, procambium, epidermis, and parenchyma cells were identified from three main tissue types of ovule, placenta, and pericarp inner layer. A comparative analysis on scRNA-seq data between high- and low-OAR varieties demonstrated that the overall expression of CoSWEET and CoCWINV in procambium cells, and CoSTP in the integument was significantly upregulated in the low-OAR variety. Both the infertile ovule before pollination and the abortion ovule producing after compatible pollination might be attributed to selective abortion caused by low sugar levels in the apoplast around procambium cells and a low capability of hexose uptake in the integument. Here, the first single-cell transcriptional landscape is reported in woody crop ovaries. Our investigation demonstrates that ovule abortion may be related to sugar transport in placenta and ovules and sheds light on further deciphering the mechanism of regulating sugar transport and the improvement of seed yield in C. oleifera.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

Tick innate immune responses to hematophagy and Ehrlichia infection at single-cell resolution

Introduction

Ticks rely on robust cellular and humoral responses to control microbial infection. However, several aspects of the tick's innate immune system remain uncharacterized, most notably that of the immune cells (called hemocytes), which are known to play a significant role in cellular and humoral responses. Despite the importance of hemocytes in regulating microbial infection, our understanding of their basic biology and molecular mechanisms remains limited. Therefore, we believe that a more detailed understanding of the role of hemocytes in the interactions between ticks and tick-borne microbes is crucial to illuminating their function in vector competence and to help identify novel targets for developing new strategies to block tick-borne pathogen transmission.

Methods

This study examined hemocytes from the lone star tick (Amblyomma americanum) at the transcriptomic level using the 10X genomics single-cell RNA sequencing platform to analyze hemocyte populations from unfed, partially blood-fed, and Ehrlichia chaffeensis-infected ticks. The functional role of differentially expressed hemocyte markers in hemocyte proliferation and Ehrlichia dissemination was determined using an RNA interference approach.

Results and discussion

Our data exhibit the identification of fourteen distinct hemocyte populations. Our results uncover seven distinct lineages present in uninfected and Ehrlichia-infected hemocyte clusters. The functional characterization of hemocytin, cystatin, fibronectin, and lipocalin demonstrate their role in hemocyte population changes, proliferation, and Ehrlichia dissemination.

Conclusion

Our results uncover the tick immune responses to Ehrlichia infection and hematophagy at a single-cell resolution. This work opens a new field of tick innate immunobiology to understand the role of hemocytes, particularly in response to prolonged blood-feeding (hematophagy), and tick-microbial interactions.

Evidence for a selective link between cooperation and individual recognition

The ability to recognize others is a frequent assumption of models of the evolution of cooperation. At the same time, cooperative behavior has been proposed as a selective agent favoring the evolution of individual recognition abilities. Although theory predicts that recognition and cooperation may co-evolve, data linking recognition abilities and cooperative behavior with evidence of selection are elusive. Here, we provide evidence of a selective link between individual recognition and cooperation in the paper wasp Polistes fuscatus through a combination of clinal, common garden, and population genomics analyses. We identified latitudinal clines in both rates of cooperative nesting and color pattern diversity, consistent with a selective link between recognition and cooperation. In behavioral experiments, we replicated previous results demonstrating individual recognition in cooperative and phenotypically diverse P. fuscatus from New York. In contrast, wasps from a less cooperative and phenotypically uniform Louisiana population showed no evidence of individual recognition. In a common garden experiment, groups of wasps from northern populations formed more stable and individually biased associations, indicating that recognition facilitates group stability. The strength of recent positive selection on cognition-associated loci likely to mediate individual recognition is substantially greater in northern compared with southern P. fuscatus populations. Collectively, these data suggest that individual recognition and cooperative nesting behavior have co-evolved in P. fuscatus because recognition helps stabilize social groups. This work provides evidence of a specific cognitive phenotype under selection because of social interactions, supporting the idea that social behavior can be a key driver of cognitive evolution.

Inference after latent variable estimation for single-cell RNA sequencing data

In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell's state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this article, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study and apply count splitting to a data set of pluripotent stem cells differentiating to cardiomyocytes.

Galectin-1 as a marker for microglia activation in the aging brain

Microglia cells, the immune cells residing in the brain, express immune regulatory molecules that have a central role in the manifestation of age-related brain characteristics. Our hypothesis suggests that galectin-1, an anti-inflammatory member of the beta-galactoside-binding lectin family, regulates microglia and neuroinflammation in the aging brain. Through our in-silico analysis, we discovered a subcluster of microglia in the aged mouse brain that exhibited increased expression of galectin-1 mRNA. In our Western blotting experiments, we observed a decrease in galectin-1 protein content in our rat primary cortical cultures over time. Additionally, we found that the presence of lipopolysaccharide, an immune activator, significantly increased the expression of galectin-1 protein in microglial cells. Utilizing flow cytometry, we determined that a portion of the galectin-1 protein was localized on the surface of the microglial cells. As cultivation time increased, we observed a decrease in the expression of activation-coupled molecules in microglial cells, indicating cellular exhaustion. In our mixed rat primary cortical cell cultures, we noted a transition of amoeboid microglial cells labeled with OX42(CD11b/c) to a ramified, branched phenotype during extended cultivation, accompanied by a complete disappearance of galectin-1 expression. By analyzing the transcriptome of a distinct microglial subpopulation in an animal model of aging, we established a correlation between chronological aging and galectin-1 expression. Furthermore, our in vitro study demonstrated that galectin-1 expression is associated with the functional activation state of microglial cells exhibiting specific amoeboid morphological characteristics. Based on our findings, we identify galectin-1 as a marker for microglia activation in the context of aging.

Breast Cancer Macrophage Heterogeneity and Self-renewal are Determined by Spatial Localization

Tumor-infiltrating macrophages support critical steps in tumor progression, and their accumulation in the tumor microenvironment (TME) is associated with adverse outcomes and therapeutic resistance across human cancers. In the TME, macrophages adopt diverse phenotypic alterations, giving rise to heterogeneous immune activation states and induction of cell cycle. While the transcriptional profiles of these activation states are well-annotated across human cancers, the underlying signals that regulate macrophage heterogeneity and accumulation remain incompletely understood. Here, we leveraged a novel ex vivo organotypic TME (oTME) model of breast cancer, in vivo murine models, and human samples to map the determinants of functional heterogeneity of TME macrophages. We identified a subset of F4/80highSca-1+ self-renewing macrophages maintained by type-I interferon (IFN) signaling and requiring physical contact with cancer-associated fibroblasts. We discovered that the contact-dependent self-renewal of TME macrophages is mediated via Notch4, and its inhibition abrogated tumor growth of breast and ovarian carcinomas in vivo, as well as lung dissemination in a PDX model of triple-negative breast cancer (TNBC). Through spatial multi-omic profiling of protein markers and transcriptomes, we found that the localization of macrophages further dictates functionally distinct but reversible phenotypes, regardless of their ontogeny. Whereas immune-stimulatory macrophages (CD11C+CD86+) populated the tumor epithelial nests, the stroma-associated macrophages (SAMs) were proliferative, immunosuppressive (Sca-1+CD206+PD-L1+), resistant to CSF-1R depletion, and associated with worse patient outcomes. Notably, following cessation of CSF-1R depletion, macrophages rebounded primarily to the SAM phenotype, which was associated with accelerated growth of mammary tumors. Our work reveals the spatial determinants of macrophage heterogeneity in breast cancer and highlights the disruption of macrophage self-renewal as a potential new therapeutic strategy.

Cellular heterogeneity of the developing worker honey bee (Apis mellifera) pupa: a single cell transcriptomics analysis

It is estimated that animals pollinate 87.5% of flowering plants worldwide and that managed honey bees (Apis mellifera) account for 30-50% of this ecosystem service to agriculture. In addition to their important role as pollinators, honey bees are well-established insect models for studying learning and memory, behavior, caste differentiation, epigenetic mechanisms, olfactory biology, sex determination, and eusociality. Despite their importance to agriculture, knowledge of honey bee biology lags behind many other livestock species. In this study, we have used scRNA-Seq to map cell types to different developmental stages of the worker honey bee (prepupa at day 11 and pupa at day 15) and sought to determine their gene expression signatures. To identify cell-type populations, we examined the cell-to-cell network based on the similarity of the single-cells transcriptomic profiles. Grouping similar cells together we identified 63 different cell clusters of which 17 clusters were identifiable at both stages. To determine genes associated with specific cell populations or with a particular biological process involved in honey bee development, we used gene coexpression analysis. We combined this analysis with literature mining, the honey bee protein atlas, and gene ontology analysis to determine cell cluster identity. Of the cell clusters identified, 17 were related to the nervous system and sensory organs, 7 to the fat body, 19 to the cuticle, 5 to muscle, 4 to compound eye, 2 to midgut, 2 to hemocytes, and 1 to malpighian tubule/pericardial nephrocyte. To our knowledge, this is the first whole single-cell atlas of honey bees at any stage of development and demonstrates the potential for further work to investigate their biology at the cellular level.

Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the "SingleR." Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand-receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug-gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

Time space and single-cell resolved tissue lineage trajectories and laterality of body plan at gastrulation

Understanding of the molecular drivers of lineage diversification and tissue patterning during primary germ layer development requires in-depth knowledge of the dynamic molecular trajectories of cell lineages across a series of developmental stages of gastrulation. Through computational modeling, we constructed at single-cell resolution, a spatio-temporal transcriptome of cell populations in the germ-layers of gastrula-stage mouse embryos. This molecular atlas enables the inference of molecular network activity underpinning the specification and differentiation of the germ-layer tissue lineages. Heterogeneity analysis of cellular composition at defined positions in the epiblast revealed progressive diversification of cell types. The single-cell transcriptome revealed an enhanced BMP signaling activity in the right-side mesoderm of late-gastrulation embryo. Perturbation of asymmetric BMP signaling activity at late gastrulation led to randomization of left-right molecular asymmetry in the lateral mesoderm of early-somite-stage embryo. These findings indicate the asymmetric BMP activity during gastrulation may be critical for the symmetry breaking process.

Sulindac selectively induces autophagic apoptosis of GABAergic neurons and alters motor behaviour in zebrafish

Nonsteroidal anti-inflammatory drugs compose one of the most widely used classes of medications, but the risks for early development remain controversial, especially in the nervous system. Here, we utilized zebrafish larvae to assess the potentially toxic effects of nonsteroidal anti-inflammatory drugs and found that sulindac can selectively induce apoptosis of GABAergic neurons in the brains of zebrafish larvae brains. Zebrafish larvae exhibit hyperactive behaviour after sulindac exposure. We also found that akt1 is selectively expressed in GABAergic neurons and that SC97 (an Akt1 activator) and exogenous akt1 mRNA can reverse the apoptosis caused by sulindac. Further studies showed that sulindac binds to retinoid X receptor alpha (RXRα) and induces autophagy in GABAergic neurons, leading to activation of the mitochondrial apoptotic pathway. Finally, we verified that sulindac can lead to hyperactivity and selectively induce GABAergic neuron apoptosis in mice. These findings suggest that excessive use of sulindac may lead to early neurodevelopmental toxicity and increase the risk of hyperactivity, which could be associated with damage to GABAergic neurons.

Single-Cell Transcriptome and Pigment Biochemistry Analysis Reveals the Potential for the High Nutritional and Medicinal Value of Purple Sea Cucumbers

The sea cucumber Apostichopus japonicus has important nutritional and medicinal value. Unfortunately, we know little of the source of active chemicals in this animal, but the plentiful pigments of these animals are thought to function in intriguing ways for translation into clinical and food chemistry usage. Here, we found key cell groups with the gene activity predicted for the color morphology of sea cucumber body using single-cell RNA-seq. We refer to these cell populations as melanocytes and quinocytes, which are responsible for the synthesis of melanin and quinone pigments, respectively. We integrated analysis of pigment biochemistry with the transcript profiles to illuminate the molecular mechanisms regulating distinct pigment formation in echinoderms. In concert with the correlated pigment analysis from each color morph, this study expands our understanding of medically important pigment production, as well as the genetic mechanisms for color morphs, and provides deep datasets for exploring advancements in the fields of bioactives and nutraceuticals.

Human resident liver myeloid cells protect against metabolic stress in obesity

Although multiple populations of macrophages have been described in the human liver, their function and turnover in patients with obesity at high risk of developing non-alcoholic fatty liver disease (NAFLD) and cirrhosis are currently unknown. Herein, we identify a specific human population of resident liver myeloid cells that protects against the metabolic impairment associated with obesity. By studying the turnover of liver myeloid cells in individuals undergoing liver transplantation, we find that liver myeloid cell turnover differs between humans and mice. Using single-cell techniques and flow cytometry, we determine that the proportion of the protective resident liver myeloid cells, denoted liver myeloid cells 2 (LM2), decreases during obesity. Functional validation approaches using human 2D and 3D cultures reveal that the presence of LM2 ameliorates the oxidative stress associated with obese conditions. Our study indicates that resident myeloid cells could be a therapeutic target to decrease the oxidative stress associated with NAFLD.

Single-Cell RNA sequencing of leaf sheath cells reveals the mechanism of rice resistance to brown planthopper (Nilaparvata lugens)

The brown planthopper (BPH) (Nilaparvata lugens) sucks rice sap causing leaves to turn yellow and wither, often leading to reduced or zero yields. Rice co-evolved to resist damage by BPH. However, the molecular mechanisms, including the cells and tissues, involved in the resistance are still rarely reported. Single-cell sequencing technology allows us to analyze different cell types involved in BPH resistance. Here, using single-cell sequencing technology, we compared the response offered by the leaf sheaths of the susceptible (TN1) and resistant (YHY15) rice varieties to BPH (48 hours after infestation). We found that the 14,699 and 16,237 cells (identified via transcriptomics) in TN1 and YHY15 could be annotated using cell-specific marker genes into nine cell-type clusters. The two rice varieties showed significant differences in cell types (such as mestome sheath cells, guard cells, mesophyll cells, xylem cells, bulliform cells, and phloem cells) in the rice resistance mechanism to BPH. Further analysis revealed that although mesophyll, xylem, and phloem cells are involved in the BPH resistance response, the molecular mechanism used by each cell type is different. Mesophyll cell may regulate the expression of genes related to vanillin, capsaicin, and ROS production, phloem cell may regulate the cell wall extension related genes, and xylem cell may be involved in BPH resistance response by controlling the expression of chitin and pectin related genes. Thus, rice resistance to BPH is a complicated process involving multiple insect resistance factors. The results presented here will significantly promote the investigation of the molecular mechanisms underlying the resistance of rice to insects and accelerate the breeding of insect-resistant rice varieties.

Capturing sex-specific and hypofertility-linked effects of assisted reproductive technologies on the cord blood DNA methylome

BACKGROUND

Children conceived through assisted reproduction are at an increased risk for growth and genomic imprinting disorders, often linked to DNA methylation defects. It has been suggested that assisted reproductive technology (ART) and underlying parental infertility can induce epigenetic instability, specifically interfering with DNA methylation reprogramming events during germ cell and preimplantation development. To date, human studies exploring the association between ART and DNA methylation defects have reported inconsistent or inconclusive results, likely due to population heterogeneity and the use of technologies with limited coverage of the epigenome. In our study, we explored the epigenetic risk of ART by comprehensively profiling the DNA methylome of 73 human cord blood samples of singleton pregnancies (n = 36 control group, n = 37 ART/hypofertile group) from a human prospective longitudinal birth cohort, the 3D (Design, Develop, Discover) Study, using a high-resolution sequencing-based custom capture panel that examines over 2.4 million autosomal CpGs in the genome.

RESULTS

We identified evidence of sex-specific effects of ART/hypofertility on cord blood DNA methylation patterns. Our genome-wide analyses identified ~ 46% more CpGs affected by ART/hypofertility in female than in male infant cord blood. We performed a detailed analysis of three imprinted genes which have been associated with altered DNA methylation following ART (KCNQ1OT1, H19/IGF2 and GNAS) and found that female infant cord blood was associated with DNA hypomethylation. When compared to less invasive procedures such as intrauterine insemination, more invasive ARTs (in vitro fertilization, intracytoplasmic sperm injection, embryo culture) resulted in more marked and distinct effects on the cord blood DNA methylome. In the in vitro group, we found a close to fourfold higher proportion of significantly enriched Gene Ontology terms involved in development than in the in vivo group.

CONCLUSIONS

Our study highlights the ability of a sensitive, targeted, sequencing-based approach to uncover DNA methylation perturbations in cord blood associated with hypofertility and ART and influenced by offspring sex and ART technique invasiveness.

Selective inference for k-means clustering

We consider the problem of testing for a difference in means between clusters of observations identified via k -means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. In recent work, Gao et al. (2022) considered a related problem in the context of hierarchical clustering. Unfortunately, their solution is highly-tailored to the context of hierarchical clustering, and thus cannot be applied in the setting of k -means clustering. In this paper, we propose a p-value that conditions on all of the intermediate clustering assignments in the k -means algorithm. We show that the p-value controls the selective Type I error for a test of the difference in means between a pair of clusters obtained using k -means clustering in finite samples, and can be efficiently computed. We apply our proposal on hand-written digits data and on single-cell RNA-sequencing data.

Isolating peripheral blood mononuclear cells from HIV-infected patients for single-cell RNA sequencing and integration analysis

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) allows the dissection of transcriptional changes in immune cells with HIV infection. Here, we isolate PBMCs from HIV-infected individuals. After counting the cell number and verifying cell viability, we perform scRNA-seq for PBMCs on the 10× Genomics protocol and the Illumina NovaSeq 6000 sequencing platform. Furthermore, we analyze the function and cellular trajectories of B cell subsets and B cell receptor (BCR) repertoire after filtering raw sequences data and normalizing gene expression. For complete details on the use and execution of this protocol, please refer to Jia et al. (2022).¹.

The early neutrophil-committed progenitors aberrantly differentiate into immunoregulatory monocytes during emergency myelopoiesis

Inflammatory stimuli cause a state of emergency myelopoiesis leading to neutrophil-like monocyte expansion. However, their function, the committed precursors, or growth factors remain elusive. In this study we find that Ym1⁺Ly6C^hi monocytes, an immunoregulatory entity of neutrophil-like monocytes, arise from progenitors of neutrophil 1 (proNeu1). Granulocyte-colony stimulating factor (G-CSF) favors the production of neutrophil-like monocytes through previously unknown CD81⁺CX3CR1^lo monocyte precursors. GFI1 promotes the differentiation of proNeu2 from proNeu1 at the cost of producing neutrophil-like monocytes. The human counterpart of neutrophil-like monocytes that also expands in response to G-CSF is found in CD14⁺CD16^- monocyte fraction. The human neutrophil-like monocytes are discriminated from CD14⁺CD16^- classical monocytes by CXCR1 expression and the capacity to suppress T cell proliferation. Collectively, our findings suggest that the aberrant expansion of neutrophil-like monocytes under inflammatory conditions is a process conserved between mouse and human, which may be beneficial for the resolution of inflammation.

FDX1 regulates cellular protein lipoylation through direct binding to LIAS

Ferredoxins are a family of iron-sulfur (Fe-S) cluster proteins that serve as essential electron donors in numerous cellular processes that are conserved through evolution. The promiscuous nature of ferredoxins as electron donors enables them to participate in many metabolic processes including steroid, heme, vitamin D and Fe-S cluster biosynthesis in different organisms. However, the unique natural function(s) of each of the two human ferredoxins (FDX1 and FDX2) are still poorly characterized. We recently reported that FDX1 is both a crucial regulator of copper ionophore induced cell death and serves as an upstream regulator of cellular protein lipoylation, a mitochondrial lipid-based post translational modification naturally occurring on four mitochondrial enzymes that are crucial for TCA cycle function. Here we show that FDX1 regulates protein lipoylation by directly binding to the lipoyl synthase (LIAS) enzyme and not through indirect regulation of cellular Fe-S cluster biosynthesis. Metabolite profiling revealed that the predominant cellular metabolic outcome of FDX1 loss-of-function is manifested through the regulation of the four lipoylation-dependent enzymes ultimately resulting in loss of cellular respiration and sensitivity to mild glucose starvation. Transcriptional profiling of cells growing in either normal or low glucose conditions established that FDX1 loss-of-function results in the induction of both compensatory metabolism related genes and the integrated stress response, consistent with our findings that FDX1 loss-of-functions is conditionally lethal. Together, our findings establish that FDX1 directly engages with LIAS, promoting cellular protein lipoylation, a process essential in maintaining cell viability under low glucose conditions.

Single-nucleus RNA and ATAC sequencing uncovers the molecular and cellular characteristics in the musk gland of Chinese forest musk deer (Moschus berezovskii)

The Chinese forest musk deer (FMD; Moschus berezovskii) is an endangered artiodactyl mammal. Musk secreted by the musk gland of male has extremely high economic and medicinal value. However, the molecular and cellular characteristics of the musk gland have not been studied. Here, we investigated the diversity and transcriptional composition of musk gland cell types and the effect of cell type-specific chromatin accessibility on gene expression using single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq) association analysis. Based on uniform manifold approximation and projection (UMAP) analysis, we identified 13 cell types from the musk gland, which included two different acinar cells (cluster 0 and cluster 10). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that many pathways related to musk secretion were enriched in acinar cells. Our analysis also revealed acinar cell core transcription factors and core target genes, and further constructed acinar cell-specific regulatory networks. In cluster 0, 11 core target genes (Nedd4l, Adcy9, Akr1c1, Vapb, Me1, Acsl1, Acss3, Srd5a1, Scnn1a, Acadm, and Nceh1) possibly related to musk secretion were regulated by 24 core transcription factors (SP3, NFIC, NR6A1, EHF, RUNX1, TFAP2A, RREB1, GRHL2, NFIB, ELF1, MAX, KLF5, REL, HES1, POU2F3, TFDP1, NR2C1, ATF7, MEIS1, NR4A2, NFIA, PBX1, ZNF652, and NFKB1). In cluster 10, four core target genes (Akr1c1, Pcca, Atp1b1, and Sgk1) possibly related to musk secretion were regulated by 10 core transcription factors (BARX2, EHF, PBX1, RUNX1, NFIB, FOXP1, KLF3, KLF6, ETV6, and NR3C2). Moreover, the credibility of snRNA-seq and snATAC-seq data was verified by fluorescence in situ hybridization and immunohistochemistry. Finally, cell communication analysis demonstrated that the two types of acinar cells mainly have communications in musk secretion-related processes. In conclusion, we provided important insights and invaluable resources for the molecular and cellular characteristics of the musk gland, which will lay a foundation for the study of musk secretion mechanism in the future.

Profiling Transcriptional Heterogeneity with Seq-Well S3: A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq

Seq-Well is a high-throughput, picowell-based single-cell RNA-seq technology that can be used to simultaneously profile the transcriptomes of thousands of cells (Gierahn et al. Nat Methods 14(4):395-398, 2017). Relative to its reverse-emulsion-droplet-based counterparts, Seq-Well addresses key cost, portability, and scalability limitations. Recently, we introduced an improved molecular biology for Seq-Well to enhance the information content that can be captured from individual cells using the platform. This update, which we call Seq-Well S3 (S3: Second-Strand Synthesis), incorporates a second-strand-synthesis step after reverse transcription to boost the detection of cellular transcripts normally missed when running the original Seq-Well protocol (Hughes et al. Immunity 53(4):878-894.e7, 2020). This chapter provides details and tips on how to perform Seq-Well S3, along with general pointers on how to subsequently analyze the resultant single-cell RNA-seq data.

Multi-omic analyses in immune cell development with lessons learned from T cell development

Traditionally, flow cytometry has been the preferred method to characterize immune cells at the single-cell level. Flow cytometry is used in immunology mostly to measure the expression of identifying markers on the cell surface, but-with good antibodies-can also be used to assess the expression of intracellular proteins. The advent of single-cell RNA-sequencing has paved the road to study immune development at an unprecedented resolution. Single-cell RNA-sequencing studies have not only allowed us to efficiently chart the make-up of heterogeneous tissues, including their most rare cell populations, it also increasingly contributes to our understanding how different omics modalities interplay at a single cell resolution. Particularly for investigating the immune system, this means that these single-cell techniques can be integrated to combine and correlate RNA and protein data at the single-cell level. While RNA data usually reveals a large heterogeneity of a given population identified solely by a combination of surface protein markers, the integration of different omics modalities at a single cell resolution is expected to greatly contribute to our understanding of the immune system.

Multilineage commitment of Sca-1+ cells in reshaping vein grafts

Vein graft failure remains a significant clinical problem. Similar to other vascular diseases, stenosis of vein grafts is caused by several cell lines; however, the sources of these cells remain unclear. The objective of this study was to investigate the cellular sources that reshape vein grafts. By analyzing transcriptomics data and constructing inducible lineage-tracing mouse models, we investigated the cellular components of vein grafts and their fates. The sc-RNAseq data suggested that Sca-1+ cells were vital players in vein grafts and might serve as progenitors for multilineage commitment. By generating a vein graft model in which the venae cavae from C57BL/6J wild-type mice were transplanted adjacent to the carotid arteries of Sca-1(Ly6a)-CreERT2; Rosa26-tdTomato mice, we demonstrated that the recipient Sca-1+ cells dominated reendothelialization and the formation of adventitial microvessels, especially at the perianastomotic regions. In turn, using chimeric mouse models, we confirmed that the Sca-1+ cells that participated in reendothelialization and the formation of adventitial microvessels all had a non-bone-marrow origin, whereas bone-marrow-derived Sca-1+ cells differentiated into inflammatory cells in vein grafts. Furthermore, using a parabiosis mouse model, we confirmed that non-bone-marrow-derived circulatory Sca-1+ cells were vital for the formation of adventitial microvessels, whereas Sca-1+ cells derived from local carotid arteries were the source of endothelium restoration. Using another mouse model in which venae cavae from Sca-1 (Ly6a)-CreERT2; Rosa26-tdTomato mice were transplanted adjacent to the carotid arteries of C57BL/6J wild-type mice, we confirmed that the donor Sca-1+ cells were mainly responsible for smooth muscle cells commitment in the neointima, particularly at the middle bodies of vein grafts. In addition, we provided evidence that knockdown/knockout of Pdgfrα in Sca-1+ cells decreased the cell potential to generate SMCs in vitro and decreased number of intimal SMCs in vein grafts. Our findings provided cell atlases of vein grafts, which demonstrated that recipient carotid arteries, donor veins, non-bone-marrow circulation, and the bone marrow provided diverse Sca-1+ cells/progenitors that participated in the reshaping of vein grafts.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Single-cell transcriptomics reveals distinct cell response between acute and chronic pulmonary infection of Pseudomonas aeruginosa

Knowledge of the changes in the immune microenvironment during pulmonary bacterial acute and chronic infections is limited. The dissection of immune system may provide a basis for effective therapeutic strategies against bacterial infection. Here, we describe a single immune cell atlas of mouse lungs after acute and chronic Pseudomonas aeruginosa infection using single-cell transcriptomics, multiplex immunohistochemistry, and flow cytometry. Our single-cell transcriptomic analysis revealed large-scale comprehensive changes in immune cell composition and high variation in cell-cell interactions after acute and chronic P. aeruginosa infection. Bacterial infection reprograms the genetic architecture of immune cell populations. We identified specific immune cell types, including Cxcl2+ B cells and interstitial macrophages, in response to acute and chronic infection, such as their proportions, distribution, and functional status. Importantly, the patterns of immune cell response are drastically different between acute and chronic infection models. The distinct molecular signatures highlight the importance of the highly dynamic cell-cell interaction process in different pathological conditions, which has not been completely revealed previously. These findings provide a comprehensive and unbiased immune cellular landscape for respiratory pathogenesis in mice, enabling further understanding of immunologic mechanisms in infection and inflammatory diseases.

The integrated transcriptome bioinformatics analysis identifies key genes and cellular components for proliferative diabetic retinopathy

Proliferative Diabetic Retinopathy (PDR) is a chronic complication of Diabetes and the main cause of blindness among the world's working population at present. While there have been many studies on the pathogenesis of PDR, its intrinsic molecular mechanisms have not yet been fully elucidated. In recent years, several studies have employed bulk RNA-sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) to profile differentially expressed genes (DEGs) and cellular components associated with PDR. This study adds to this expanding body of work by identifying PDR's target genes and cellular components by conducting an integrated transcriptome bioinformatics analysis. This study integrately examined two public bulk RNA-seq datasets(including 11 PDR patients and 7 controls) and one single-cell RNA-seq datasets(including 5 PDR patients) of Fibro (Vascular) Membranes (FVMs) from PDR patients and control. A total of 176 genes were identified as DEGs between PDR patients and control among both bulk RNA-seq datasets. Based on these DEGs, 14 proteins were identified in the protein overlap within the significant ligand-receptor interactions of retinal FVMs and Protein-Protein Interaction (PPI) network, three of which were associated with PDR (CD44, ICAM1, POSTN), and POSTN might act as key ligand. This finding may provide novel gene signatures and therapeutic targets for PDR.

From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis

Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1-100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.

Single-cell RNA-seq uncovered hemocyte functional subtypes and their differentiational characteristics and connectivity with morphological subpopulations in Litopenaeus vannamei

Hemocytes play central roles in shrimp immune system, whereas whose subclasses have not yet been completely defined. At present, the morphological classification of hemocytes is inadequate to classify the complete hemocyte repertoire and elucidate the functions and differentiation and maturation processes. Based on single-cell RNA sequencing (scRNA-seq) of hemocytes in healthy Litopenaeus vannamei, combined with RNA-FISH and flow cytometric sorting, we identified three hemocyte clusters including TGase+ cells, CTL+ cells and Crustin+ cells, and further determined their functional properties, potential differentiation trajectory and correspondence with morphological subpopulations. The TGase+ cells were mainly responsible for the coagulation, exhibiting distinguishable characteristics of hyalinocyte, and appeared to be developmentally arrested at an early stage of hemocyte differentiation. The CTL+ cells and Crustin+ cells arrested at terminal stages of differentiation mainly participated in recognizing foreign pathogens and initiating immune defense responses, owning distinctive features of granule-containing hemocytes. Furthermore, we have revealed the functional sub-clusters of three hemocyte clusters and their potential differentiation pathways according to the expression of genes involved in cell cycle, cell differentiation and immune response, and the successive differentiation and maturation of hyalinocytes to granule-containing hemocytes have also mapped. The results revealed the diversity of shrimp hemocytes and provide new theoretical rationale for hemocyte classification, which also facilitate systematic research on crustacean immunity.

Primary specification of blastocyst trophectoderm by scRNA-seq: New insights into embryo implantation

Mechanisms of implantation such as determination of the attachment pole, fetal-maternal communication, and underlying causes of implantation failure are largely unexplored. Here, we performed single-cell RNA sequencing on peri-implantation embryos from both humans and mice to explore trophectoderm (TE) development and embryo-endometrium cross-talk. We found that the transcriptomes of polar and mural TE diverged after embryos hatched from the zona pellucida in both species, with polar TE being more mature than mural TE. The implantation poles show similarities in cell cycle activities, as well as in expression of genes critical for implantation and placentation. Embryos that either fail to attach in vitro or fail to implant in vivo show abnormalities in pathways related to energy production, protein metabolism, and 18S ribosomal RNA m⁶A methylation. These findings uncover the gene expression characteristics of humans and mice TE differentiation during the peri-implantation period and provide new insights into embryo implantation.

High-throughput muscle fiber typing from RNA sequencing data

Background

Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested.

Methods

By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22).

Results

The correlation between the sequencing-based method and the other two were r_ATPas = 0.44 [0.13–0.67], [95% CI], and r_myosin = 0.83 [0.61–0.93], with p = 5.70 × 10^–3 and 2.00 × 10^–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads.

Conclusions

This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13395-022-00299-4.

Linking the genotypes and phenotypes of cancer cells in heterogenous populations via real-time optical tagging and image analysis

Linking single-cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, could help unravel molecular mechanisms driving the growth of tumour-cell subpopulations. Here we show that a custom-built optical microscope with an ultrawide field of view, fast automated image analysis and a dye activatable by visible light enables the screening and selective photolabelling of cells of interest in large heterogeneous cell populations on the basis of specific functional cellular dynamics, such as fast migration, morphological variation, small-molecule uptake or cell division. Combining such functional single-cell selection with single-cell RNA sequencing allowed us to (1) functionally annotate the transcriptomic profiles of fast-migrating and spindle-shaped MCF10A cells, of fast-migrating MDA-MB-231 cells and of patient-derived head-and-neck squamous carcinoma cells, and (2) identify critical genes and pathways driving aggressive migration and mesenchymal-like morphology in these cells. Functional single-cell selection upstream of single-cell sequencing does not depend on molecular biomarkers, allows for the enrichment of sparse subpopulations of cells, and can facilitate the identification and understanding of the molecular mechanisms underlying functional phenotypes.

Handling high-dimensional data with missing values by modern machine learning techniques

High-dimensional data have been regarded as one of the most important types of big data in practice. It happens frequently in practice including genetic study, financial study, and geographical study. Missing data in high dimensional data analysis should be handled properly to reduce nonresponse bias. We discuss some modern machine learning techniques including penalized regression approaches, tree-based approaches, and deep learning (DL) for handling missing data with high dimensionality. Specifically, our proposed methods can be used for estimating general parameters of interest including population means and percentiles with imputation-based estimators, propensity score estimators, and doubly robust estimators. We compare those methods through some limited simulation studies and a real application. Both simulation studies and real application show the benefits of DL and XGboost approaches compared with other methods in terms of balancing bias and variance.