destiny: diffusion maps for large-scale single-cell data in R

Single-cell and spatial transcriptomics analysis of human adrenal aging

OBJECTIVE

The human adrenal cortex comprises three functionally and structurally distinct layers that produce layer-specific steroid hormones. With aging, the human adrenal cortex undergoes functional and structural alteration or "adrenal aging", leading to the unbalanced production of steroid hormones. Given the marked species differences in adrenal biology, the underlying mechanisms of human adrenal aging have not been sufficiently studied. This study was designed to elucidate the mechanisms linking the functional and structural alterations of the human adrenal cortex.

METHODS

We conducted single-cell RNA sequencing and spatial transcriptomics analysis of the aged human adrenal cortex.

RESULTS

The data of this study suggest that the layer-specific alterations of multiple signaling pathways underlie the abnormal layered structure and layer-specific changes in steroidogenic cells. We also highlighted that macrophages mediate age-related adrenocortical cell inflammation and senescence.

CONCLUSIONS

This study is the first detailed analysis of the aged human adrenal cortex at single-cell resolution and helps to elucidate the mechanism of human adrenal aging, thereby leading to a better understanding of the pathophysiology of age-related disorders associated with adrenal aging.

The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft

BACKGROUND

Much of our knowledge of organ rejection after transplantation is derived from rodent models.

METHODS

We used single-nucleus RNA sequencing to investigate the inflammatory myocardial microenvironment in human pediatric cardiac allografts at different stages after transplantation. We distinguished donor- from recipient-derived cells using naturally occurring genetic variants embedded in single-nucleus RNA sequencing data.

RESULTS

Donor-derived tissue resident macrophages, which accompany the allograft into the recipient, are lost over time after transplantation. In contrast, monocyte-derived macrophages from the recipient populate the heart within days after transplantation and form 2 macrophage populations: recipient MP1 and recipient MP2. Recipient MP2s have cell signatures similar to donor-derived resident macrophages; however, they lack signatures of pro-reparative phagocytic activity typical of donor-derived resident macrophages and instead express profibrotic genes. In contrast, recipient MP1s express genes consistent with hallmarks of cellular rejection. Our data suggest that recipient MP1s activate a subset of natural killer cells, turning them into a cytotoxic cell population through feed-forward signaling between recipient MP1s and natural killer cells.

CONCLUSIONS

Our findings reveal an imbalance of donor-derived and recipient-derived macrophages in the pediatric cardiac allograft that contributes to allograft failure.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Disruption of maternal vascular remodeling by a fetal endoretrovirus-derived gene in preeclampsia

BACKGROUND

Preeclampsia, one of the most lethal pregnancy-related diseases, is associated with the disruption of uterine spiral artery remodeling during placentation. However, the early molecular events leading to preeclampsia remain unknown.

RESULTS

By analyzing placentas from preeclampsia, non-preeclampsia, and twin pregnancies with selective intrauterine growth restriction, we show that the pathogenesis of preeclampsia is attributed to immature trophoblast and maldeveloped endothelial cells. Delayed epigenetic reprogramming during early extraembryonic tissue development leads to generation of excessive immature trophoblast cells. We find reduction of de novo DNA methylation in these trophoblast cells results in selective overexpression of maternally imprinted genes, including the endoretrovirus-derived gene PEG10 (paternally expressed gene 10). PEG10 forms virus-like particles, which are transferred from the trophoblast to the closely proximate endothelial cells. In normal pregnancy, only a low amount of PEG10 is transferred to maternal cells; however, in preeclampsia, excessive PEG10 disrupts maternal vascular development by inhibiting TGF-beta signaling.

CONCLUSIONS

Our study reveals the intricate epigenetic mechanisms that regulate trans-generational genetic conflict and ultimately ensure proper maternal-fetal interface formation.

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators

Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.

scRNA-seq data from the larval Drosophila ventral cord provides a resource for studying motor systems function and development

The Drosophila larval ventral nerve cord (VNC) shares many similarities with the spinal cord of vertebrates and has emerged as a major model for understanding the development and function of motor systems. Here, we use high-quality scRNA-seq, validated by anatomical identification, to create a comprehensive census of larval VNC cell types. We show that the neural lineages that comprise the adult VNC are already defined, but quiescent, at the larval stage. Using fluorescence-activated cell sorting (FACS)-enriched populations, we separate all motor neuron bundles and link individual neuron clusters to morphologically characterized known subtypes. We discovered a glutamate receptor subunit required for basal neurotransmission and homeostasis at the larval neuromuscular junction. We describe larval glia and endorse the general view that glia perform consistent activities throughout development. This census represents an extensive resource and a powerful platform for future discoveries of cellular and molecular mechanisms in repair, regeneration, plasticity, homeostasis, and behavioral coordination.

Role of macrophages in regression of myocardial fibrosis following alleviation of left ventricular pressure overload

Sustained hemodynamic pressure overload (PO) produced by murine transverse aortic constriction (TAC) causes myocardial fibrosis; removal of TAC (unTAC) returns left ventricle (LV) hemodynamic load to normal and results in significant, but incomplete regression of myocardial fibrosis. However, the cellular mechanisms that result in these outcomes have not been defined. The objective was to determine temporal changes in myocardial macrophage phenotype in TAC and unTAC and determine whether macrophage depletion alters collagen degradation after unTAC. Myocardial macrophage abundance and phenotype were assessed by immunohistochemistry, flow cytometry, and gene expression by RT-PCR in control (non-TAC), 2 wk, 4 wk TAC, and 2 wk, 4 wk, and 6 wk unTAC. Myocardial cytokine profiles and collagen-degrading enzymes were determined by immunoassay and immunoblots. Initial collagen degradation was detected with collagen-hybridizing peptide (CHP). At unTAC, macrophages were depleted with clodronate liposomes, and endpoints were measured at 2 wk unTAC. Macrophage number had a defined temporal pattern: increased in 2 wk and 4 wk TAC, followed by increases at 2 wk unTAC (over 4 wk TAC) that then decreased at 4 wk and 6 wk unTAC. At 2 wk unTAC, macrophage area was significantly increased and was regionally associated with CHP reactivity. Cytokine profiles in unTAC reflected a proinflammatory milieu versus the TAC-induced profibrotic milieu. Single-cell sequencing analysis of 2 wk TAC versus 2 and 6 wk unTAC revealed distinct macrophage gene expression profiles at each time point demonstrating unique macrophage populations in unTAC versus TAC myocardium. Clodronate liposome depletion at unTAC reduced CHP reactivity and decreased cathepsin K and proMMP2. We conclude that temporal changes in number and phenotype of macrophages play a critical role in both TAC-induced development and unTAC-mediated partial, but incomplete, regression of myocardial fibrosis.NEW & NOTEWORTHY Our novel findings highlight the dynamic changes in myocardial macrophage populations that occur in response to PO and after alleviation of PO. Our data demonstrated, for the first time, a potential benefit of macrophages in contributing to collagen degradation and the partial regression of interstitial fibrosis following normalization of hemodynamic load.

Steroids-producing nodules: a two-layered adrenocortical nodular structure as a precursor lesion of cortisol-producing adenoma

BACKGROUND

The human adrenal cortex consists of three functionally and structurally distinct layers; zona glomerulosa, zona fasciculata (zF), and zona reticularis (zR), and produces adrenal steroid hormones in a layer-specific manner; aldosterone, cortisol, and adrenal androgens, respectively. Cortisol-producing adenomas (CPAs) occur mostly as a result of somatic mutations associated with the protein kinase A pathway. However, how CPAs develop after adrenocortical cells acquire genetic mutations, remains poorly understood.

METHODS

We conducted integrated approaches combining the detailed histopathologic studies with genetic, RNA-sequencing, and spatially resolved transcriptome (SRT) analyses for the adrenal cortices adjacent to human adrenocortical tumours.

FINDINGS

Histopathological analysis revealed an adrenocortical nodular structure that exhibits the two-layered zF- and zR-like structure. The nodular structures harbour GNAS somatic mutations, known as a driver mutation of CPAs, and confer cell proliferative and autonomous steroidogenic capacities, which we termed steroids-producing nodules (SPNs). RNA-sequencing coupled with SRT analysis suggests that the expansion of the zF-like structure contributes to the formation of CPAs, whereas the zR-like structure is characterised by a macrophage-mediated immune response.

INTERPRETATION

We postulate that CPAs arise from a precursor lesion, SPNs, where two distinct cell populations might contribute differently to adrenocortical tumorigenesis. Our data also provide clues to the molecular mechanisms underlying the layered structures of human adrenocortical tissues.

FUNDING

KAKENHI, The Uehara Memorial Foundation, Daiwa Securities Health Foundation, Kaibara Morikazu Medical Science Promotion Foundation, Secom Science and Technology Foundation, ONO Medical Research Foundation, and Japan Foundation for Applied Enzymology.

Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis

Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.

Decoding spatiotemporal transcriptional dynamics and epithelial fibroblast crosstalk during gastroesophageal junction development through single cell analysis

The gastroesophageal squamocolumnar junction (GE-SCJ) is a critical tissue interface between the esophagus and stomach, with significant relevance in the pathophysiology of gastrointestinal diseases. Despite this, the molecular mechanisms underlying GE-SCJ development remain unclear. Using single-cell transcriptomics, organoids, and spatial analysis, we examine the cellular heterogeneity and spatiotemporal dynamics of GE-SCJ development from embryonic to adult mice. We identify distinct transcriptional states and signaling pathways in the epithelial and mesenchymal compartments of the esophagus and stomach during development. Fibroblast-epithelial interactions are mediated by various signaling pathways, including WNT, BMP, TGF-β, FGF, EGF, and PDGF. Our results suggest that fibroblasts predominantly send FGF and TGF-β signals to the epithelia, while epithelial cells mainly send PDGF and EGF signals to fibroblasts. We observe differences in the ligands and receptors involved in cell-cell communication between the esophagus and stomach. Our findings provide insights into the molecular mechanisms underlying GE-SCJ development and fibroblast-epithelial crosstalk involved, paving the way to elucidate mechanisms during adaptive metaplasia development and carcinogenesis.

Integrative Single Cell Atlas Revealed Intratumoral Heterogeneity Generation from an Adaptive Epigenetic Cell State in Human Bladder Urothelial Carcinoma

Intratumor heterogeneity (ITH) of bladder cancer (BLCA) contributes to therapy resistance and immune evasion affecting clinical prognosis. The molecular and cellular mechanisms contributing to BLCA ITH generation remain elusive. It is found that a TM4SF1-positive cancer subpopulation (TPCS) can generate ITH in BLCA, evidenced by integrative single cell atlas analysis. Extensive profiling of the epigenome and transcriptome of all stages of BLCA revealed their evolutionary trajectories. Distinct ancestor cells gave rise to low-grade noninvasive and high-grade invasive BLCA. Epigenome reprograming led to transcriptional heterogeneity in BLCA. During early oncogenesis, epithelial-to-mesenchymal transition generated TPCS. TPCS has stem-cell-like properties and exhibited transcriptional plasticity, priming the development of transcriptionally heterogeneous descendent cell lineages. Moreover, TPCS prevalence in tumor is associated with advanced stage cancer and poor prognosis. The results of this study suggested that bladder cancer interacts with its environment by acquiring a stem cell-like epigenomic landscape, which might generate ITH without additional genetic diversification.

An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data

Analysis of cell-cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell-cell communication based on the known interactions between molecules, such as ligands, receptors and extracellular matrix. However, the prior information is not quite adequate and only involves a fraction of cellular communications, producing many false-positive or false-negative results. To this end, we propose an improved hierarchical variational autoencoder (HiVAE) based model to fully use single-cell RNA-seq data for automatically estimating CCC. Specifically, the HiVAE model is used to learn the potential representation of cells on known ligand-receptor genes and all genes in single-cell RNA-seq data, respectively, which are then utilized for cascade integration. Subsequently, transfer entropy is employed to measure the transmission of information flow between two cells based on the learned representations, which are regarded as directed communication relationships. Experiments are conducted on single-cell RNA-seq data of the human skin disease dataset and the melanoma dataset, respectively. Results show that the HiVAE model is effective in learning cell representations, and transfer entropy could be used to estimate the communication scores between cell types.

Primary nasal viral infection rewires the tissue-scale memory response

The nasal mucosa is frequently the initial site of respiratory viral infection, replication, and transmission. Recent work has started to clarify the independent responses of epithelial, myeloid, and lymphoid cells to viral infection in the nasal mucosa, but their spatiotemporal coordination and relative contributions remain unclear. Furthermore, understanding whether and how primary infection shapes tissue-scale memory responses to secondary challenge is critical for the rational design of nasal-targeting therapeutics and vaccines. Here, we generated a single-cell RNA-sequencing (scRNA-seq) atlas of the murine nasal mucosa sampling three distinct regions before and during primary and secondary influenza infection. Primary infection was largely restricted to respiratory mucosa and induced stepwise changes in cell type, subset, and state composition over time. Type I Interferon (IFN)-responsive neutrophils appeared 2 days post infection (dpi) and preceded transient IFN-responsive/cycling epithelial cell responses 5 dpi, which coincided with broader antiviral monocyte and NK cell accumulation. By 8 dpi, monocyte-derived macrophages (MDMs) expressing Cxcl9 and Cxcl16 arose alongside effector cytotoxic CD8 and Ifng-expressing CD4 T cells. Following viral clearance (14 dpi), rare, previously undescribed Krt13+ nasal immune-interacting floor epithelial (KNIIFE) cells expressing multiple genes with immune communication potential increased concurrently with tissue-resident memory T (TRM)-like cells and early IgG+/IgA+ plasmablasts. Proportionality analysis coupled with cell-cell communication inference, alongside validation by in situ microscopy, underscored the CXCL16-CXCR6 signaling axis between MDMs and effector CD8 T cells 8dpi and KNIIFE cells and TRM cells 14 dpi. Secondary influenza challenge with a homologous or heterologous strain administered 60 dpi induced an accelerated and coordinated myeloid and lymphoid response without epithelial proliferation, illustrating how tissue-scale memory to natural infection engages both myeloid and lymphoid cells to reduce epithelial regenerative burden. Together, this atlas serves as a reference for viral infection in the upper respiratory tract and highlights the efficacy of local coordinated memory responses upon rechallenge.

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.1.

Fallopian tube single cell analysis reveals myeloid cell alterations in high-grade serous ovarian cancer

Most high-grade serous ovarian cancers (HGSCs) likely initiate from fallopian tube (FT) epithelia. While epithelial subtypes have been characterized using single-cell RNA-sequencing (scRNA-Seq), heterogeneity of other compartments and their involvement in tumor progression are poorly defined. Integrated analysis of human FT scRNA-Seq and HGSC-related tissues, including tumors, revealed greater immune and stromal transcriptional diversity than previously reported. We identified abundant monocytes in FTs across two independent cohorts. The ratio of macrophages to monocytes is similar between benign FTs, ovaries, and adjacent normal tissues but significantly greater in tumors. FT-defined monocyte and macrophage signatures, cell-cell communication, and gene set enrichment analyses identified monocyte- and macrophage-specific interactions and functional pathways in paired tumors and adjacent normal tissues. Further reanalysis of HGSC scRNA-Seq identified monocyte and macrophage subsets associated with neoadjuvant chemotherapy. Taken together, our work provides data that an altered FT myeloid cell composition could inform the discovery of early detection markers for HGSC.

Application of Mass Cytometry Platforms to Solid Organ Transplantation

Transplantation serves as the cornerstone of treatment for patients with end-stage organ disease. The prevalence of complications, such as allograft rejection, infection, and malignancies, underscores the need to dissect the complex interactions of the immune system at the single-cell level. In this review, we discuss studies using mass cytometry or cytometry by time-of-flight, a cutting-edge technology enabling the characterization of immune populations and cell-to-cell interactions in granular detail. We review the application of mass cytometry in human and experimental animal studies in the context of transplantation, uncovering invaluable contributions of the tool to understanding rejection and other transplant-related complications. We discuss recent innovations that have the potential to streamline and standardize mass cytometry workflows for application to multisite clinical trials. Additionally, we introduce imaging mass cytometry, a technique that couples the power of mass cytometry with spatial context, thereby mapping cellular interactions within tissue microenvironments. The synergistic integration of mass cytometry and imaging mass cytometry data with other omics data sets and high-dimensional data platforms to further define immune dynamics is discussed. In conclusion, mass cytometry technologies, when integrated with other tools and data, shed light on the intricate landscape of the immune response in transplantation. This approach holds significant potential for enhancing patient outcomes by advancing our understanding and facilitating the development of new diagnostics and therapeutics.

Single Cell High Dimensional Analysis of Human Peripheral Blood Mononuclear Cells Reveals Unique Intermediate Monocyte Subsets Associated with Sex Differences in Coronary Artery Disease

Monocytes are associated with human cardiovascular disease progression. Monocytes are segregated into three major subsets: classical (cMo), intermediate (iMo), and nonclassical (nMo). Recent studies have identified heterogeneity within each of these main monocyte classes, yet the extent to which these subsets contribute to heart disease progression is not known. Peripheral blood mononuclear cells (PBMC) were obtained from 61 human subjects within the Coronary Assessment of Virginia (CAVA) Cohort. Coronary atherosclerosis severity was quantified using the Gensini Score (GS). We employed high-dimensional single-cell transcriptome and protein methods to define how human monocytes differ in subjects with low to severe coronary artery disease. We analyzed 487 immune-related genes and 49 surface proteins at the single-cell level using Antibody-Seq (Ab-Seq). We identified six subsets of myeloid cells (cMo, iMo, nMo, plasmacytoid DC, classical DC, and DC3) at the single-cell level based on surface proteins, and we associated these subsets with coronary artery disease (CAD) incidence based on Gensini score (GS) in each subject. Only frequencies of iMo were associated with high CAD (GS > 32), adj.p = 0.024. Spearman correlation analysis with GS from each subject revealed a positive correlation with iMo frequencies (r = 0.314, p = 0.014) and further showed a robust sex-dependent positive correlation in female subjects (r = 0.663, p = 0.004). cMo frequencies did not correlate with CAD severity. Key gene pathways differed in iMo among low and high CAD subjects and between males and females. Further single-cell analysis of iMo revealed three iMo subsets in human PBMC, distinguished by the expression of HLA-DR, CXCR3, and CD206. We found that the frequency of immunoregulatory iMo_HLA-DR+CXCR3+CD206+ was associated with CAD severity (adj.p = 0.006). The immunoregulatory iMo subset positively correlated with GS in both females (r = 0.660, p = 0.004) and males (r = 0.315, p = 0.037). Cell interaction analyses identified strong interactions of iMo with CD4+ effector/memory T cells and Tregs from the same subjects. This study shows the importance of iMo in CAD progression and suggests that iMo may have important functional roles in modulating CAD risk, particularly among females.

Single-Nucleus RNA-Seq Characterizes the Cell Types Along the Neuronal Lineage in the Adult Human Subependymal Zone and Reveals Reduced Oligodendrocyte Progenitor Abundance with Age

The subependymal zone (SEZ), also known as the subventricular zone (SVZ), constitutes a neurogenic niche that persists during postnatal life. In humans, the neurogenic potential of the SEZ declines after the first year of life. However, studies discovering markers of stem and progenitor cells highlight the neurogenic capacity of progenitors in the adult human SEZ, with increased neurogenic activity occurring under pathological conditions. In the present study, the complete cellular niche of the adult human SEZ was characterized by single-nucleus RNA sequencing, and compared between four youth (age 16-22) and four middle-aged adults (age 44-53). We identified 11 cellular clusters including clusters expressing marker genes for neural stem cells (NSCs), neuroblasts, immature neurons, and oligodendrocyte progenitor cells. The relative abundance of NSC and neuroblast clusters did not differ between the two age groups, indicating that the pool of SEZ NSCs does not decline in this age range. The relative abundance of oligodendrocyte progenitors and microglia decreased in middle-age, indicating that the cellular composition of human SEZ is remodeled between youth and adulthood. The expression of genes related to nervous system development was higher across different cell types, including NSCs, in youth as compared with middle-age. These transcriptional changes suggest ongoing central nervous system plasticity in the SEZ in youth, which declined in middle-age.

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.

Human surface ectoderm and amniotic ectoderm are sequentially specified according to cellular density

Mechanisms specifying amniotic ectoderm and surface ectoderm are unresolved in humans due to their close similarities in expression patterns and signal requirements. This lack of knowledge hinders the development of protocols to accurately model human embryogenesis. Here, we developed a human pluripotent stem cell model to investigate the divergence between amniotic and surface ectoderms. In the established culture system, cells differentiated into functional amnioblast-like cells. Single-cell RNA sequencing analyses of amnioblast differentiation revealed an intermediate cell state with enhanced surface ectoderm gene expression. Furthermore, when the differentiation started at the confluent condition, cells retained the expression profile of surface ectoderm. Collectively, we propose that human amniotic ectoderm and surface ectoderm are specified along a common nonneural ectoderm trajectory based on cell density. Our culture system also generated extraembryonic mesoderm-like cells from the primed pluripotent state. Together, this study provides an integrative understanding of the human nonneural ectoderm development and a model for embryonic and extraembryonic human development around gastrulation.

Single-cell RNA-Seq reveals the earliest lineage specification and X chromosome dosage compensation in bovine preimplantation embryos

Lineage specification and X chromosome dosage compensation are two crucial biological processes that occur during preimplantation embryonic development. Although extensively studied in mice, the timing and regulation of these processes remain elusive in other species, including humans. Previous studies have suggested conserved principles of human and bovine early development. This study aims to provide fundamental insights into these programs and the regulation using a bovine embryo model by employing single-cell transcriptomics and genome editing approaches. The study analyzes the transcriptomes of 286 individual cells and reveals that bovine trophectoderm/inner cell mass transcriptomes diverge at the early blastocyst stage, after cavitation but before blastocyst expansion. The study also identifies transcriptomic markers and provides the timing of lineage specification events in the bovine embryo. Importantly, we find that SOX2 is required for the first cell decision program in bovine embryos. Moreover, the study shows the occurrence of X chromosome dosage compensation from morula to late blastocyst and reveals that this compensation results from downregulation of X-linked genes in female embryonic cells. The transcriptional atlas generated by this study is expected to be widely useful in improving our understanding of mammalian early embryo development.

Characterization of the human fetal gonad and reproductive tract by single-cell transcriptomics

During human fetal development, sex differentiation occurs not only in the gonads but also in the adjacent developing reproductive tract. However, while the cellular composition of male and female human fetal gonads is well described, that of the adjacent developing reproductive tract remains poorly characterized. Here, we performed single-cell transcriptomics on male and female human fetal gonads together with the adjacent developing reproductive tract from first and second trimesters, highlighting the morphological and molecular changes during sex differentiation. We validated different cell populations of the developing reproductive tract and gonads and compared the molecular signatures between the first and second trimesters, as well as between sexes, to identify conserved and sex-specific features. Together, our study provides insights into human fetal sex-specific gonadogenesis and development of the reproductive tract beyond the gonads.

Cortical somatostatin long-range projection neurons and interneurons exhibit divergent developmental trajectories

The mammalian cerebral cortex contains an extraordinary diversity of cell types that emerge by implementing different developmental programs. Delineating when and how cellular diversification occurs is particularly challenging for cortical inhibitory neurons because they represent a small proportion of all cortical cells and have a protracted development. Here, we combine single-cell RNA sequencing and spatial transcriptomics to characterize the emergence of neuronal diversity among somatostatin-expressing (SST+) cells in mice. We found that SST+ inhibitory neurons segregate during embryonic stages into long-range projection (LRP) neurons and two types of interneurons, Martinotti cells and non-Martinotti cells, following distinct developmental trajectories. Two main subtypes of LRP neurons and several subtypes of interneurons are readily distinguishable in the embryo, although interneuron diversity is likely refined during early postnatal life. Our results suggest that the timing for cellular diversification is unique for different subtypes of SST+ neurons and particularly divergent for LRP neurons and interneurons.

Cooperative pro-tumorigenic adaptation to oncogenic RAS through epithelial-to-mesenchymal plasticity

In breast cancers, aberrant activation of the RAS/MAPK pathway is strongly associated with mesenchymal features and stemness traits, suggesting an interplay between this mitogenic signaling pathway and epithelial-to-mesenchymal plasticity (EMP). By using inducible models of human mammary epithelial cells, we demonstrate herein that the oncogenic activation of RAS promotes ZEB1-dependent EMP, which is necessary for malignant transformation. Notably, EMP is triggered by the secretion of pro-inflammatory cytokines from neighboring RAS-activated senescent cells, with a prominent role for IL-6 and IL-1α. Our data contrast with the common view of cellular senescence as a tumor-suppressive mechanism and EMP as a process promoting late stages of tumor progression in response to signals from the tumor microenvironment. We highlighted here a pro-tumorigenic cooperation of RAS-activated mammary epithelial cells, which leverages on oncogene-induced senescence and EMP to trigger cellular reprogramming and malignant transformation.

A fast, scalable and versatile tool for analysis of single-cell omics data

Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.

A new genomic framework to categorize pediatric acute myeloid leukemia

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 887 pAML into 23 mutually distinct molecular categories, including new major entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3 or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a new prognostic framework for pAML based on these updated molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

Single-cell multi-omics profiling of human preimplantation embryos identifies cytoskeletal defects during embryonic arrest

Human in vitro fertilized embryos exhibit low developmental capabilities, and the mechanisms that underlie embryonic arrest remain unclear. Here using a single-cell multi-omics sequencing approach, we simultaneously analysed alterations in the transcriptome, chromatin accessibility and the DNA methylome in human embryonic arrest due to unexplained reasons. Arrested embryos displayed transcriptome disorders, including a distorted microtubule cytoskeleton, increased genomic instability and impaired glycolysis, which were coordinated with multiple epigenetic reprogramming defects. We identified Aurora A kinase (AURKA) repression as a cause of embryonic arrest. Mechanistically, arrested embryos induced through AURKA inhibition resembled the reprogramming abnormalities of natural embryonic arrest in terms of the transcriptome, the DNA methylome, chromatin accessibility and H3K4me3 modifications. Mitosis-independent sequential activation of the zygotic genome in arrested embryos showed that YY1 contributed to human major zygotic genome activation. Collectively, our study decodes the reprogramming abnormalities and mechanisms of human embryonic arrest and the key regulators of zygotic genome activation.

Human iPSC modeling recapitulates in vivo sympathoadrenal development and reveals an aberrant developmental subpopulation in familial neuroblastoma

Studies defining normal and disrupted human neural crest cell development have been challenging given its early timing and intricacy of development. Consequently, insight into the early disruptive events causing a neural crest related disease such as pediatric cancer neuroblastoma is limited. To overcome this problem, we developed an in vitro differentiation model to recapitulate the normal in vivo developmental process of the sympathoadrenal lineage which gives rise to neuroblastoma. We used human in vitro pluripotent stem cells and single-cell RNA sequencing to recapitulate the molecular events during sympathoadrenal development. We provide a detailed map of dynamically regulated transcriptomes during sympathoblast formation and illustrate the power of this model to study early events of the development of human neuroblastoma, identifying a distinct subpopulation of cell marked by SOX2 expression in developing sympathoblast obtained from patient derived iPSC cells harboring a germline activating mutation in the anaplastic lymphoma kinase (ALK) gene.

Deterministic reprogramming of neutrophils within tumors

Neutrophils are increasingly recognized as key players in the tumor immune response and are associated with poor clinical outcomes. Despite recent advances characterizing the diversity of neutrophil states in cancer, common trajectories and mechanisms governing the ontogeny and relationship between these neutrophil states remain undefined. Here, we demonstrate that immature and mature neutrophils that enter tumors undergo irreversible epigenetic, transcriptional, and proteomic modifications to converge into a distinct, terminally differentiated dcTRAIL-R1+ state. Reprogrammed dcTRAIL-R1+ neutrophils predominantly localize to a glycolytic and hypoxic niche at the tumor core and exert pro-angiogenic function that favors tumor growth. We found similar trajectories in neutrophils across multiple tumor types and in humans, suggesting that targeting this program may provide a means of enhancing certain cancer immunotherapies.

The chromatin landscape of healthy and injured cell types in the human kidney

There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.

Self-organized BMP signaling dynamics underlie the development and evolution of digit segmentation patterns in birds and mammals

Repeating patterns of synovial joints are a highly conserved feature of articulated digits, with variations in joint number and location resulting in diverse digit morphologies and limb functions across the tetrapod clade. During the development of the amniote limb, joints form iteratively within the growing digit ray, as a population of distal progenitors alternately specifies joint and phalanx cell fates to segment the digit into distinct elements. While numerous molecular pathways have been implicated in this fate choice, it remains unclear how they give rise to a repeating pattern. Here, using single-cell RNA sequencing and spatial gene expression profiling, we investigate the transcriptional dynamics of interphalangeal joint specification in vivo. Combined with mathematical modeling, we predict that interactions within the BMP signaling pathway-between the ligand GDF5, the inhibitor NOGGIN, and the intracellular effector pSMAD-result in a self-organizing Turing system that forms periodic joint patterns. Our model is able to recapitulate the spatiotemporal gene expression dynamics observed in vivo, as well as phenocopy digit malformations caused by BMP pathway perturbations. By contrasting in silico simulations with in vivo morphometrics of two morphologically distinct digits, we show how changes in signaling parameters and growth dynamics can result in variations in the size and number of phalanges. Together, our results reveal a self-organizing mechanism that underpins amniote digit segmentation and its evolvability and, more broadly, illustrate how Turing systems based on a single molecular pathway may generate complex repetitive patterns in a wide variety of organisms.

Immune determinants of CAR-T cell expansion in solid tumor patients receiving GD2 CAR-T cell therapy

Chimeric antigen receptor T cells (CAR-Ts) have remarkable efficacy in liquid tumors, but limited responses in solid tumors. We conducted a Phase I trial (NCT02107963) of GD2 CAR-Ts (GD2-CAR.OX40.28.z.iC9), demonstrating feasibility and safety of administration in children and young adults with osteosarcoma and neuroblastoma. Since CAR-T efficacy requires adequate CAR-T expansion, patients were grouped into good or poor expanders across dose levels. Patient samples were evaluated by multi-dimensional proteomic, transcriptomic, and epigenetic analyses. T cell assessments identified naive T cells in pre-treatment apheresis associated with good expansion, and exhausted T cells in CAR-T products with poor expansion. Myeloid cell assessment identified CXCR3+ monocytes in pre-treatment apheresis associated with good expansion. Longitudinal analysis of post-treatment samples identified increased CXCR3- classical monocytes in all groups as CAR-T numbers waned. Together, our data uncover mediators of CAR-T biology and correlates of expansion that could be utilized to advance immunotherapies for solid tumor patients.

Combinatorial single-cell profiling of major chromatin types with MAbID

Gene expression programs result from the collective activity of numerous regulatory factors. Studying their cooperative mode of action is imperative to understand gene regulation, but simultaneously measuring these factors within one sample has been challenging. Here we introduce Multiplexing Antibodies by barcode Identification (MAbID), a method for combinatorial genomic profiling of histone modifications and chromatin-binding proteins. MAbID employs antibody-DNA conjugates to integrate barcodes at the genomic location of the epitope, enabling combined incubation of multiple antibodies to reveal the distributions of many epigenetic markers simultaneously. We used MAbID to profile major chromatin types and multiplexed measurements without loss of individual data quality. Moreover, we obtained joint measurements of six epitopes in single cells of mouse bone marrow and during mouse in vitro differentiation, capturing associated changes in multifactorial chromatin states. Thus, MAbID holds the potential to gain unique insights into the interplay between gene regulatory mechanisms, especially for low-input samples and in single cells.

Single-cell transcriptomic profiling reveals a pathogenic role of cytotoxic CD4+ T cells in giant cell arteritis

Giant cell arteritis (GCA) is a systemic vasculitis mediated by an aberrant immunological response against the blood vessel wall. Although the pathogenic mechanisms that drive GCA have not yet been elucidated, there is strong evidence that CD4+ T cells are key drivers of the inflammatory process occurring in this vasculitis. The aim of this study was to further delineate the role of CD4+ T cells in GCA by applying single-cell RNA sequencing and T cell receptor (TCR) repertoire profiling to 114.799 circulating CD4+ T cells from eight GCA patients in two different clinical states, active and in remission, and eight healthy controls. Our results revealed an expansion of cytotoxic CD4+ T lymphocytes (CTLs) in active GCA patients, which expressed higher levels of cytotoxic and chemotactic genes when compared to patients in remission and controls. Accordingly, differentially expressed genes in CTLs of active patients were enriched in pathways related to granzyme-mediated apoptosis, inflammation, and the recruitment of different immune cells, suggesting a role of this cell type in the inflammatory and vascular remodelling processes occurring in GCA. CTLs also exhibited a higher clonal expansion in active patients with respect to those in remission. Drug repurposing analysis prioritized maraviroc, which targeted CTLs, as potentially repositionable for this vasculitis. In addition, effector regulatory T cells (Tregs) were decreased in GCA and showed lower expression of genes involved in their suppressive activity. These findings provide further insights into the pathogenic role of CD4+ T cells in GCA and suggest targeting CTLs as a potential therapeutic option.

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation

In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.

Thinking process templates for constructing data stories with SCDNEY

Background

Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery.

Methods

We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more.

Results

Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression.

Conclusions

Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.

Broad H3K4me3 Domain Is Associated with Spatial Coherence during Mammalian Embryonic Development

It is well known that the chromatin states play a major role in cell-fate decision and cell-identity maintenance; however, the spatial variation of chromatin states in situ remains poorly characterized. Here, by leveraging recently available spatial-CUT&Tag data, we systematically characterized the global spatial organization of the H3K4me3 profiles in a mouse embryo. Our analysis identified a subset of genes with spatially coherent H3K4me3 patterns, which together delineate the tissue boundaries. The spatially coherent genes are strongly enriched with tissue-specific transcriptional regulators. Remarkably, their corresponding genomic loci are marked by broad H3K4me3 domains, which is distinct from the typical H3K4me3 signature. Spatial transition across tissue boundaries is associated with continuous shortening of the broad H3K4me3 domains as well as expansion of H3K27me3 domains. Our analysis reveals a strong connection between the genomic and spatial variation of chromatin states, which may play an important role in embryonic development.

Secretory GFP reconstitution labeling of neighboring cells interrogates cell-cell interactions in metastatic niches

Cancer cells inevitably interact with neighboring host tissue-resident cells during the process of metastatic colonization, establishing a metastatic niche to fuel their survival, growth, and invasion. However, the underlying mechanisms in the metastatic niche are yet to be fully elucidated owing to the lack of methodologies for comprehensively studying the mechanisms of cell-cell interactions in the niche. Here, we improve a split green fluorescent protein (GFP)-based genetically encoded system to develop secretory glycosylphosphatidylinositol-anchored reconstitution-activated proteins to highlight intercellular connections (sGRAPHIC) for efficient fluorescent labeling of tissue-resident cells that neighbor on and putatively interact with cancer cells in deep tissues. The sGRAPHIC system enables the isolation of metastatic niche-associated tissue-resident cells for their characterization using a single-cell RNA sequencing platform. We use this sGRAPHIC-leveraged transcriptomic platform to uncover gene expression patterns in metastatic niche-associated hepatocytes in a murine model of liver metastasis. Among the marker genes of metastatic niche-associated hepatocytes, we identify Lgals3, encoding galectin-3, as a potential pro-metastatic factor that accelerates metastatic growth and invasion.

ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate

MOTIVATION

Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory.

RESULTS

We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation.

AVAILABILITY AND IMPLEMENTATION

ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.

scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis

INTRODUCTION

Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data.

RESULTS

scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics.

CONCLUSIONS

The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.

Mapping cardiac remodeling in chronic kidney disease

Patients with advanced chronic kidney disease (CKD) mostly die from sudden cardiac death and recurrent heart failure. The mechanisms of cardiac remodeling are largely unclear. To dissect molecular and cellular mechanisms of cardiac remodeling in CKD in an unbiased fashion, we performed left ventricular single-nuclear RNA sequencing in two mouse models of CKD. Our data showed a hypertrophic response trajectory of cardiomyocytes with stress signaling and metabolic changes driven by soluble uremia-related factors. We mapped fibroblast to myofibroblast differentiation in this process and identified notable changes in the cardiac vasculature, suggesting inflammation and dysfunction. An integrated analysis of cardiac cellular responses to uremic toxins pointed toward endothelin-1 and methylglyoxal being involved in capillary dysfunction and TNFα driving cardiomyocyte hypertrophy in CKD, which was validated in vitro and in vivo. TNFα inhibition in vivo ameliorated the cardiac phenotype in CKD. Thus, interventional approaches directed against uremic toxins, such as TNFα, hold promise to ameliorate cardiac remodeling in CKD.

Single-cell and spatial architecture of primary liver cancer

Primary liver cancer (PLC) poses a leading threat to human health, and its treatment options are limited. Meanwhile, the investigation of homogeneity and heterogeneity among PLCs remains challenging. Here, using single-cell RNA sequencing, spatial transcriptomic and bulk multi-omics, we elaborated a molecular architecture of 3 PLC types, namely hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC). Taking a high-resolution perspective, our observations revealed that CHC cells exhibit internally discordant phenotypes, whereas ICC and HCC exhibit distinct tumor-specific features. Specifically, ICC was found to be the primary source of cancer-associated fibroblasts, while HCC exhibited disrupted metabolism and greater individual heterogeneity of T cells. We further revealed a diversity of intermediate-state cells residing in the tumor-peritumor junctional zone, including a congregation of CPE+ intermediate-state endothelial cells (ECs), which harbored the molecular characteristics of tumor-associated ECs and normal ECs. This architecture offers insights into molecular characteristics of PLC microenvironment, and hints that the tumor-peritumor junctional zone could serve as a targeted region for precise therapeutical strategies.

Human skeletal muscle organoids model fetal myogenesis and sustain uncommitted PAX7 myogenic progenitors

In vitro culture systems that structurally model human myogenesis and promote PAX7+ myogenic progenitor maturation have not been established. Here we report that human skeletal muscle organoids can be differentiated from induced pluripotent stem cell lines to contain paraxial mesoderm and neuromesodermal progenitors and develop into organized structures reassembling neural plate border and dermomyotome. Culture conditions instigate neural lineage arrest and promote fetal hypaxial myogenesis toward limb axial anatomical identity, with generation of sustainable uncommitted PAX7 myogenic progenitors and fibroadipogenic (PDGFRa+) progenitor populations equivalent to those from the second trimester of human gestation. Single-cell comparison to human fetal and adult myogenic progenitor /satellite cells reveals distinct molecular signatures for non-dividing myogenic progenitors in activated (CD44High/CD98+/MYOD1+) and dormant (PAX7High/FBN1High/SPRY1High) states. Our approach provides a robust 3D in vitro developmental system for investigating muscle tissue morphogenesis and homeostasis.

In Situ Spatial Reconstruction of Distinct Normal and Pathological Cell Populations Within the Human Adrenal Gland

The human adrenal gland consists of concentrically organized, functionally distinct regions responsible for hormone production. Dysregulation of adrenocortical cell differentiation alters the proportion and organization of the functional zones of the adrenal cortex leading to disease. Current models of adrenocortical cell differentiation are based on mouse studies, but there are known organizational and functional differences between human and mouse adrenal glands. This study aimed to investigate the centripetal differentiation model in the human adrenal cortex and characterize aldosterone-producing micronodules (APMs) to better understand adrenal diseases such as primary aldosteronism. We applied spatially resolved in situ transcriptomics to human adrenal tissue sections from 2 individuals and identified distinct cell populations and their positional relationships. The results supported the centripetal differentiation model in humans, with cells progressing from the outer capsule to the zona glomerulosa, zona fasciculata, and zona reticularis. Additionally, we characterized 2 APMs in a 72-year-old woman. Comparison with earlier APM transcriptomes indicated a subset of core genes, but also heterogeneity between APMs. The findings contribute to our understanding of normal and pathological cellular differentiation in the human adrenal cortex.

Non-classical circulating monocytes expressing high levels of microsomal prostaglandin E2 synthase-1 tag an aberrant IFN-response in systemic sclerosis

Systemic sclerosis (SSc) is a complex disease that affects the connective tissue, causing fibrosis. SSc patients show altered immune cell composition and activation in the peripheral blood (PB). PB monocytes (Mos) are recruited into tissues where they differentiate into macrophages, which are directly involved in fibrosis. To understand the role of CD14+ PB Mos in SSc, a single-cell transcriptome analysis (scRNA-seq) was conducted on 8 SSc patients and 8 controls. Using unsupervised clustering methods, CD14+ cells were assigned to 11 clusters, which added granularity to the known monocyte subsets: classical (cMos), intermediate (iMos) and non-classical Mos (ncMos) or type 2 dendritic cells. NcMos were significantly overrepresented in SSc patients and showed an active IFN-signature and increased expression levels of PTGES, in addition to monocyte motility and adhesion markers. We identified a SSc-related cluster of IRF7+ STAT1+ iMos with an aberrant IFN-response. Finally, a depletion of M2 polarised cMos in SSc was observed. Our results highlighted the potential of PB Mos as biomarkers for SSc and provided new possibilities for putative drug targets for modulating the innate immune response in SSc.

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.

Transcriptional linkage analysis with in vivo AAV-Perturb-seq

The ever-growing compendium of genetic variants associated with human pathologies demands new methods to study genotype-phenotype relationships in complex tissues in a high-throughput manner1,2. Here we introduce adeno-associated virus (AAV)-mediated direct in vivo single-cell CRISPR screening, termed AAV-Perturb-seq, a tuneable and broadly applicable method for transcriptional linkage analysis as well as high-throughput and high-resolution phenotyping of genetic perturbations in vivo. We applied AAV-Perturb-seq using gene editing and transcriptional inhibition to systematically dissect the phenotypic landscape underlying 22q11.2 deletion syndrome3,4 genes in the adult mouse brain prefrontal cortex. We identified three 22q11.2-linked genes involved in known and previously undescribed pathways orchestrating neuronal functions in vivo that explain approximately 40% of the transcriptional changes observed in a 22q11.2-deletion mouse model. Our findings suggest that the 22q11.2-deletion syndrome transcriptional phenotype found in mature neurons may in part be due to the broad dysregulation of a class of genes associated with disease susceptibility that are important for dysfunctional RNA processing and synaptic function. Our study establishes a flexible and scalable direct in vivo method to facilitate causal understanding of biological and disease mechanisms with potential applications to identify genetic interventions and therapeutic targets for treating disease.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

AXL-initiated paracrine activation of pSTAT3 enhances mesenchymal and vasculogenic supportive features of tumor-associated macrophages

Tumor-associated macrophages (TAMs) are integral to the development of complex tumor microenvironments (TMEs) and can execute disparate cellular programs in response to extracellular cues. However, upstream signaling processes underpinning this phenotypic plasticity remain to be elucidated. Here, we report that concordant AXL-STAT3 signaling in TAMs is triggered by lung cancer cells or cancer-associated fibroblasts in the cytokine milieu. This paracrine action drives TAM differentiation toward a tumor-promoting "M2-like" phenotype with upregulation of CD163 and putative mesenchymal markers, contributing to TAM heterogeneity and diverse cellular functions. One of the upregulated markers, CD44, mediated by AXL-IL-11-pSTAT3 signaling cascade, enhances macrophage ability to interact with endothelial cells and facilitate formation of primitive vascular networks. We also found that AXL-STAT3 inhibition can impede the recruitment of TAMs in a xenograft mouse model, thereby suppressing tumor growth. These findings suggest the potential application of AXL-STAT3-related markers to quantitatively assess metastatic potential and inform therapeutic strategies in lung cancer.

Microbial activity contributes to spatial heterogeneity of wetland methane fluxes

The emission of methane from wetlands is spatially heterogeneous, as concurrently measured surface fluxes can vary by orders of magnitude within the span of a few meters. Despite extensive study and the climatic significance of these emissions, it remains unclear what drives large, within-site variations. While geophysical factors (e.g., soil temperature) are known to correlate with methane (CH4) flux, measurable variance in these parameters often declines as spatial and temporal scales become finer. As methane emitted from wetlands is the direct, net product of microbial metabolisms which both produce and degrade CH4, it stands to reason that characterizing the spatial variability of microbial communities within a wetland-both horizontally and vertically-may help explain observed variances in flux. To that end, we surveyed microbial communities to a depth of 1 m across an ombrotrophic peat bog in Maine, USA using amplicon sequencing and gene expression techniques. Surface methane fluxes and geophysical factors were concurrently measured. Across the first meter of peat at the site, we observed significant changes in the abundance and composition of methanogenic taxa at every depth sampled, with variance in methanogen abundance explaining 70% of flux heterogeneity at a subset of plots. Among measured environmental factors, only peat depth emerged as correlated with flux, and had significant impact on the abundance and composition of methane-cycling communities. These conclusions suggest that a heightened awareness of how microbial communities are structured and spatially distributed within wetlands could offer improved insights into predicting CH4 flux dynamics. IMPORTANCE Globally, wetlands are one of the largest sources of methane (CH4), a greenhouse gas with a warming impact significantly greater than CO2. Methane produced in wetlands is the byproduct of a group of microorganisms which convert organic carbon into CH4. Despite our knowledge of how this process works, it is still unclear what drives dramatic, localized (<10 m) variance in emission rates from the surface of wetlands. While environmental conditions, like soil temperature or water table depth, correlate with methane flux when variance in these factors is large (e.g., spring vs fall), the explanatory power of these variables decline when spatial and temporal scales become smaller. As methane fluxes are the direct product of microbial activity, we profiled how the microbial community varied, both horizontally and vertically, across a peat bog in Maine, USA, finding that variance in microbial communities was likely contributing to much of the observed variance in flux.