Differential abundance testing on single-cell data using k-nearest neighbor graphs

Regulation of endocrine cell alternative splicing revealed by single-cell RNA sequencing in type 2 diabetes pathogenesis

The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.

Brain Chimeroids reveal individual susceptibility to neurotoxic triggers

Interindividual genetic variation affects the susceptibility to and progression of many diseases1,2. However, efforts to study how individual human brains differ in normal development and disease phenotypes are limited by the paucity of faithful cellular human models, and the difficulty of scaling current systems to represent multiple people. Here we present human brain Chimeroids, a highly reproducible, multidonor human brain cortical organoid model generated by the co-development of cells from a panel of individual donors in a single organoid. By reaggregating cells from multiple single-donor organoids at the neural stem cell or neural progenitor cell stage, we generate Chimeroids in which each donor produces all cell lineages of the cerebral cortex, even when using pluripotent stem cell lines with notable growth biases. We used Chimeroids to investigate interindividual variation in the susceptibility to neurotoxic triggers that exhibit high clinical phenotypic variability: ethanol and the antiepileptic drug valproic acid. Individual donors varied in both the penetrance of the effect on target cell types, and the molecular phenotype within each affected cell type. Our results suggest that human genetic background may be an important mediator of neurotoxin susceptibility and introduce Chimeroids as a scalable system for high-throughput investigation of interindividual variation in processes of brain development and disease.

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

An epigenetically distinct HSC subset supports thymic reconstitution

Hematopoietic stem cells (HSCs) with multilineage potential are critical for effective T cell reconstitution and restoration of the adaptive immune system after allogeneic Hematopoietic Cell Transplantation (allo-HCT). The Kit lo subset of HSCs is enriched for multipotential precursors, 1, 2 but their T-cell lineage potential has not been well-characterized. We therefore studied the thymic reconstituting and T-cell potential of Kit lo HSCs. Using a preclinical allo-HCT model, we demonstrate that Kit lo HSCs support better thymic recovery, and T-cell reconstitution resulting in improved T cell responses to infection post-HCT. Furthermore, Kit lo HSCs with augmented BM lymphopoiesis mitigate age-associated thymic alterations, thus enhancing T-cell recovery in middle-aged hosts. We find the frequency of the Kit lo subset declines with age, providing one explanation for the reduced frequency of T-competent HSCs and reduced T-lymphopoietic potential in BM precursors of aged mice. 3, 4, 5 Chromatin profiling revealed that Kit lo HSCs exhibit higher activity of lymphoid-specifying transcription factors (TFs), including Zbtb1 . Deletion of Zbtb1 in Kit lo HSCs diminished their T-cell potential, while reinstating Zbtb1 in megakaryocytic-biased Kit hi HSCs rescued T-cell potential, in vitro and in vivo . Finally, we discover an analogous Kit lo HSC subset with enhanced lymphoid potential in human bone marrow. Our results demonstrate that Kit lo HSCs with enhanced lymphoid potential have a distinct underlying epigenetic program.

Non-stem cell lineages as an alternative origin of intestinal tumorigenesis in the context of inflammation

According to conventional views, colon cancer originates from stem cells. However, inflammation, a key risk factor for colon cancer, has been shown to suppress intestinal stemness. Here, we used Paneth cells as a model to assess the capacity of differentiated lineages to trigger tumorigenesis in the context of inflammation in mice. Upon inflammation, Paneth cell-specific Apc mutations led to intestinal tumors reminiscent not only of those arising in patients with inflammatory bowel disease, but also of a larger fraction of human sporadic colon cancers. The latter is possibly because of the inflammatory consequences of western-style dietary habits, a major colon cancer risk factor. Machine learning methods designed to predict the cell-of-origin of cancer from patient-derived tumor samples confirmed that, in a substantial fraction of sporadic cases, the origins of colon cancer reside in secretory lineages and not in stem cells.

Microglia protect against age-associated brain pathologies

Microglia are brain-resident macrophages that contribute to central nervous system (CNS) development, maturation, and preservation. Here, we examine the consequences of permanent microglial deficiencies on brain aging using the Csf1rΔFIRE/ΔFIRE mouse model. In juvenile Csf1rΔFIRE/ΔFIRE mice, we show that microglia are dispensable for the transcriptomic maturation of other brain cell types. By contrast, with advancing age, pathologies accumulate in Csf1rΔFIRE/ΔFIRE brains, macroglia become increasingly dysregulated, and white matter integrity declines, mimicking many pathological features of human CSF1R-related leukoencephalopathy. The thalamus is particularly vulnerable to neuropathological changes in the absence of microglia, with atrophy, neuron loss, vascular alterations, macroglial dysregulation, and severe tissue calcification. We show that populating Csf1rΔFIRE/ΔFIRE brains with wild-type microglia protects against many of these pathological changes. Together with the accompanying study by Chadarevian and colleagues1, our results indicate that the lifelong absence of microglia results in an age-related neurodegenerative condition that can be counteracted via transplantation of healthy microglia.

Single cell transcriptomic analysis of the canine duodenum in chronic inflammatory enteropathy and health

Chronic inflammatory enteropathy (CIE) is a common condition in dogs causing recurrent or persistent gastrointestinal clinical signs. Pathogenesis is thought to involve intestinal mucosal inflammatory infiltrates, but histopathological evaluation of intestinal biopsies from dogs with CIE fails to guide treatment, inform prognosis, or correlate with clinical remission. We employed single-cell RNA sequencing to catalog and compare the diversity of cells present in duodenal mucosal endoscopic biopsies from 3 healthy dogs and 4 dogs with CIE. Through characterization of 35,668 cells, we identified 31 transcriptomically distinct cell populations, including T cells, epithelial cells, and myeloid cells. Both healthy and CIE samples contributed to each cell population. T cells were broadly subdivided into GZMAhigh (putatively annotated as tissue resident) and IL7Rhigh (putatively annotated as non-resident) T cell categories, with evidence of a skewed proportion favoring an increase in the relative proportion of IL7Rhigh T cells in CIE dogs. Among the myeloid cells, neutrophils from CIE samples exhibited inflammatory (SOD2 and IL1A) gene expression signatures. Numerous differentially expressed genes were identified in epithelial cells, with gene set enrichment analysis suggesting enterocytes from CIE dogs may be undergoing stress responses and have altered metabolic properties. Overall, this work reveals the previously unappreciated cellular heterogeneity in canine duodenal mucosa and provides new insights into molecular mechanisms which may contribute to intestinal dysfunction in CIE. The cell type gene signatures developed through this study may also be used to better understand the subtleties of canine intestinal physiology in health and disease.

Single-cell analysis reveals hypoxia-induced immunosuppressive microenvironment in intrahepatic cholangiocarcinoma

The role of hypoxia in the tumor microenvironment of intrahepatic cholangiocarcinoma (iCCA) remains unclear. Here, we generated a comprehensive atlas of the entire tumor microenvironment and delineated the multifaceted cell-cell interactions to decipher hypoxia-induced pro-tumor immune suppression. We discovered hypoxia is significantly associated with iCCA progression via the activation of HIF1A expression. Moreover, hypoxia-dependent PPARγ-mediated fatty acid oxidation in APOE+ TAMs promoted M2 macrophage polarization by activating the HIF1A-PPARG-CD36 axis. These polarized APOE+ TAMs recruited Treg cell infiltration via the CCL3-CCR5 pair to form an immunosuppressive microenvironment. APOE+ TAMs tended to co-localize spatially with Treg cells in the malignant tissue based on spatial transcriptome data and immunofluorescence analysis results. We identified tumor-reactive CXCL13+ CD8-PreTex with specific high expression of ENTPD1 and ITGAE, which acted as precursors of CD8-Tex and had higher cytotoxicity, lower exhaustion, and more vigorous proliferation. Consequently, CXCL13+ CD8-PreTex functioned as a positive regulator of antitumor immunity by expressing the pro-inflammatory cytokines IFNG and TNF, associated with a better survival outcome. Our study reveals the mechanisms involved in hypoxia-induced immunosuppression and suggests that targeting precursor-exhausted CXCL13+CD8+ T cells might provide a pratical immunotherapeutic approach.

Cell type mapping of inflammatory muscle diseases highlights selective myofiber vulnerability in inclusion body myositis

Inclusion body myositis (IBM) is the most prevalent inflammatory muscle disease in older adults with no effective therapy available. In contrast to other inflammatory myopathies such as subacute, immune-mediated necrotizing myopathy (IMNM), IBM follows a chronic disease course with both inflammatory and degenerative features of pathology. Moreover, causal factors and molecular drivers of IBM progression are largely unknown. Therefore, we paired single-nucleus RNA sequencing with spatial transcriptomics from patient muscle biopsies to map cell-type-specific drivers underlying IBM pathogenesis compared with IMNM muscles and noninflammatory skeletal muscle samples. In IBM muscles, we observed a selective loss of type 2 myonuclei paralleled by increased levels of cytotoxic T and conventional type 1 dendritic cells. IBM myofibers were characterized by either upregulation of cell stress markers featuring GADD45A and NORAD or protein degradation markers including RNF7 associated with p62 aggregates. GADD45A upregulation was preferentially seen in type 2A myofibers associated with severe tissue inflammation. We also noted IBM-specific upregulation of ACHE encoding acetylcholinesterase, which can be regulated by NORAD activity and result in functional denervation of myofibers. Our results provide promising insights into possible mechanisms of myofiber degeneration in IBM and suggest a selective type 2 fiber vulnerability linked to genomic stress and denervation pathways.

SIMBA: single-cell embedding along with features

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

Enhanced CD95 and interleukin 18 signalling accompany T cell receptor Vβ21.3+ activation in multi-inflammatory syndrome in children

Multisystem inflammatory syndrome in children is a post-infectious presentation SARS-CoV-2 associated with expansion of the T cell receptor Vβ21.3+ T-cell subgroup. Here we apply muti-single cell omics to compare the inflammatory process in children with acute respiratory COVID-19 and those presenting with non SARS-CoV-2 infections in children. Here we show that in Multi-Inflammatory Syndrome in Children (MIS-C), the natural killer cell and monocyte population demonstrate heightened CD95 (Fas) and Interleuking 18 receptor expression. Additionally, TCR Vβ21.3+ CD4+ T-cells exhibit skewed differentiation towards T helper 1, 17 and regulatory T cells, with increased expression of the co-stimulation receptors ICOS, CD28 and interleukin 18 receptor. We observe no functional evidence for NLRP3 inflammasome pathway overactivation, though MIS-C monocytes show elevated active caspase 8. This, coupled with raised IL18 mRNA expression in CD16- NK cells on single cell RNA sequencing analysis, suggests interleukin 18 and CD95 signalling may trigger activation of TCR Vβ21.3+ T-cells in MIS-C, driven by increased IL-18 production from activated monocytes and CD16- Natural Killer cells.

Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitis

The success of checkpoint inhibitors (CPIs) for cancer has been tempered by immune-related adverse effects including colitis. CPI-induced colitis is hallmarked by expansion of resident mucosal IFNγ cytotoxic CD8+ T cells, but how these arise is unclear. Here, we track CPI-bound T cells in intestinal tissue using multimodal single-cell and subcellular spatial transcriptomics (ST). Target occupancy was increased in inflamed tissue, with drug-bound T cells located in distinct microdomains distinguished by specific intercellular signaling and transcriptional gradients. CPI-bound cells were largely CD4+ T cells, including enrichment in CPI-bound peripheral helper, follicular helper, and regulatory T cells. IFNγ CD8+ T cells emerged from both tissue-resident memory (TRM) and peripheral populations, displayed more restricted target occupancy profiles, and co-localized with damaged epithelial microdomains lacking effective regulatory cues. Our multimodal analysis identifies causal pathways and constitutes a resource to inform novel preventive strategies.

The future of rapid and automated single-cell data analysis using reference mapping

As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.

Kernel-based testing for single-cell differential analysis

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

Chromatin accessibility during human first-trimester neurodevelopment

The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.

Human skeletal muscle aging atlas

Skeletal muscle aging is a key contributor to age-related frailty and sarcopenia with substantial implications for global health. Here we profiled 90,902 single cells and 92,259 single nuclei from 17 donors to map the aging process in the adult human intercostal muscle, identifying cellular changes in each muscle compartment. We found that distinct subsets of muscle stem cells exhibit decreased ribosome biogenesis genes and increased CCL2 expression, causing different aging phenotypes. Our atlas also highlights an expansion of nuclei associated with the neuromuscular junction, which may reflect re-innervation, and outlines how the loss of fast-twitch myofibers is mitigated through regeneration and upregulation of fast-type markers in slow-twitch myofibers with age. Furthermore, we document the function of aging muscle microenvironment in immune cell attraction. Overall, we present a comprehensive human skeletal muscle aging resource ( https://www.muscleageingcellatlas.org/ ) together with an in-house mouse muscle atlas to study common features of muscle aging across species.

Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols

Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.

The landscape of immune dysregulation in pediatric sepsis at a single-cell resolution

Recognizing immune dysregulation as a hallmark of sepsis pathophysiology, leukocytes have attracted major attention of investigation. While adult and pediatric sepsis are clinically distinct, their immunological delineation remains limited. Single cell technologies facilitated the characterization of immune signatures. We tackled to delineate immunological profiles of pediatric sepsis at a single-cell level by analyzing blood samples from six septic children, at both acute and recovery phases, and four healthy children. 16 single-cell transcriptomic datasets were analyzed and compared to adult sepsis dataset. We showed a unique shift in neutrophil subpopulations and functions between acute and recovery phases, along with the regulatory role of resistin. Neutrophil signatures were comparable between adult and pediatric sepsis. Innate-like CD4 T cells were predominantly and uniquely observed in acute phase of pediatric sepsis. Our study serves as a rich source of information about the phenotypic diversity and trajectory of circulating immune cells during pediatric sepsis.

Age-specific nasal epithelial responses to SARS-CoV-2 infection

Children infected with SARS-CoV-2 rarely progress to respiratory failure. However, the risk of mortality in infected people over 85 years of age remains high. Here we investigate differences in the cellular landscape and function of paediatric (<12 years), adult (30-50 years) and older adult (>70 years) ex vivo cultured nasal epithelial cells in response to infection with SARS-CoV-2. We show that cell tropism of SARS-CoV-2, and expression of ACE2 and TMPRSS2 in nasal epithelial cell subtypes, differ between age groups. While ciliated cells are viral replication centres across all age groups, a distinct goblet inflammatory subtype emerges in infected paediatric cultures and shows high expression of interferon-stimulated genes and incomplete viral replication. In contrast, older adult cultures infected with SARS-CoV-2 show a proportional increase in basaloid-like cells, which facilitate viral spread and are associated with altered epithelial repair pathways. We confirm age-specific induction of these cell types by integrating data from in vivo COVID-19 studies and validate that our in vitro model recapitulates early epithelial responses to SARS-CoV-2 infection.

Fine needle aspiration of human lymph nodes reveals cell populations and soluble interactors pivotal to immunological priming

Lymph node (LN) fine needle aspiration (LN FNA) represents a powerful technique for minimally invasive sampling of human LNs in vivo and has been used effectively to directly study aspects of the human germinal center response. However, systematic deep phenotyping of the cellular populations and cell-free proteins recovered by LN FNA has not been performed. Thus, we studied human cervical LN FNAs as a proof-of-concept and used single-cell RNA-sequencing and proteomic analysis to benchmark this compartment, define the purity of LN FNA material, and facilitate future studies in this immunologically pivotal environment. Our data provide evidence that LN FNAs contain bone-fide LN-resident innate immune populations, with minimal contamination of blood material. Examination of these populations reveals unique biology not predictable from equivalent blood-derived populations. LN FNA supernatants represent a specific source of lymph- and lymph node-derived proteins, and can, aided by transcriptomics, identify likely receptor-ligand interactions. This represents the first description of the types and abundance of immune cell populations and cell-free proteins that can be efficiently studied by LN FNA. These findings are of broad utility for understanding LN physiology in health and disease, including infectious or autoimmune perturbations, and in the case of cervical nodes, neuroscience.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Single cell analysis reveals the roles and regulatory mechanisms of type-I interferons in Parkinson's disease

The pathogenesis of Parkinson's disease (PD) is strongly associated with neuroinflammation, and type I interferons (IFN-I) play a crucial role in regulating immune and inflammatory responses. However, the specific features of IFN in different cell types and the underlying mechanisms of PD have yet to be fully described. In this study, we analyzed the GSE157783 dataset, which includes 39,024 single-cell RNA sequencing results for five PD patients and six healthy controls from the Gene Expression Omnibus database. After cell type annotation, we intersected differentially expressed genes in each cell subcluster with genes collected in The Interferome database to generate an IFN-I-stimulated gene set (ISGs). Based on this gene set, we used the R package AUCell to score each cell, representing the IFN-I activity. Additionally, we performed monocle trajectory analysis, and single-cell regulatory network inference and clustering (SCENIC) to uncover the underlying mechanisms. In silico gene perturbation and subsequent experiments confirm NFATc2 regulation of type I interferon response and neuroinflammation. Our analysis revealed that microglia, endothelial cells, and pericytes exhibited the highest activity of IFN-I. Furthermore, single-cell trajectory detection demonstrated that microglia in the midbrain of PD patients were in a pro-inflammatory activation state, which was validated in the 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model as well. We identified transcription factors NFATc2, which was significantly up-regulated and involved in the expression of ISGs and activation of microglia in PD. In the 1-Methyl-4-phenylpyridinium (MPP+)-induced BV2 cell model, the suppression of NFATc2 resulted in a reduction in IFN-β levels, impeding the phosphorylation of STAT1, and attenuating the activation of the NF-κB pathway. Furthermore, the downregulation of NFATc2 mitigated the detrimental effects on SH-SY5Y cells co-cultured in conditioned medium. Our study highlights the critical role of microglia in type I interferon responses in PD. Additionally, we identified transcription factors NFATc2 as key regulators of aberrant type I interferon responses and microglial pro-inflammatory activation in PD. These findings provide new insights into the pathogenesis of PD and may have implications for the development of novel therapeutic strategies.

CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast

Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.

Single-cell atlas of the small intestine throughout the human lifespan demonstrates unique features of fetal immune cells

Proper development of mucosal immunity is critical for human health. Over the past decade, it has become evident that in humans, this process begins in utero. However, there are limited data on the unique features and functions of fetal mucosal immune cells. To address this gap, we integrated several single-cell ribonucleic acid sequencing datasets of the human small intestine (SI) to create an SI transcriptional atlas throughout the human life span, ranging from the first trimester to adulthood, with a focus on immune cells. Fetal SI displayed a complex immune landscape comprising innate and adaptive immune cells that exhibited distinct transcriptional programs from postnatal samples, especially compared with pediatric and adult samples. We identified shifts in myeloid populations across gestation and progression of memory T-cell states throughout the human lifespan. In particular, there was a marked shift of memory T cells from those with stem-like properties in the fetal samples to fully differentiated cells with a high expression of activation and effector function genes in adult samples, with neonatal samples containing both features. Finally, we demonstrate that the SI developmental atlas can be used to elucidate improper trajectories linked to mucosal diseases by implicating developmental abnormalities underlying necrotizing enterocolitis, a severe intestinal complication of prematurity. Collectively, our data provide valuable resources and important insights into intestinal immunity that will facilitate regenerative medicine and disease understanding.

Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference

The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.

Chemotherapy induces myeloid-driven spatial T-cell exhaustion in ovarian cancer

To uncover the intricate, chemotherapy-induced spatiotemporal remodeling of the tumor microenvironment, we conducted integrative spatial and molecular characterization of 97 high-grade serous ovarian cancer (HGSC) samples collected before and after chemotherapy. Using single-cell and spatial analyses, we identify increasingly versatile immune cell states, which form spatiotemporally dynamic microcommunities at the tumor-stroma interface. We demonstrate that chemotherapy triggers spatial redistribution and exhaustion of CD8+ T cells due to prolonged antigen presentation by macrophages, both within interconnected myeloid networks termed "Myelonets" and at the tumor stroma interface. Single-cell and spatial transcriptomics identifies prominent TIGIT-NECTIN2 ligand-receptor interactions induced by chemotherapy. Using a functional patient-derived immuno-oncology platform, we show that CD8+T-cell activity can be boosted by combining immune checkpoint blockade with chemotherapy. Our discovery of chemotherapy-induced myeloid-driven spatial T-cell exhaustion paves the way for novel immunotherapeutic strategies to unleash CD8+ T-cell-mediated anti-tumor immunity in HGSC.

Long-term effects of Omicron BA.2 breakthrough infection on immunity-metabolism balance: a 6-month prospective study

There have been reports of long coronavirus disease (long COVID) and breakthrough infections (BTIs); however, the mechanisms and pathological features of long COVID after Omicron BTIs remain unclear. Assessing long-term effects of COVID-19 and immune recovery after Omicron BTIs is crucial for understanding the disease and managing new-generation vaccines. Here, we followed up mild BA.2 BTI convalescents for six-month with routine blood tests, proteomic analysis and single-cell RNA sequencing (scRNA-seq). We found that major organs exhibited ephemeral dysfunction and recovered to normal in approximately six-month after BA.2 BTI. We also observed durable and potent levels of neutralizing antibodies against major circulating sub-variants, indicating that hybrid humoral immunity stays active. However, platelets may take longer to recover based on proteomic analyses, which also shows coagulation disorder and an imbalance between anti-pathogen immunity and metabolism six-month after BA.2 BTI. The immunity-metabolism imbalance was then confirmed with retrospective analysis of abnormal levels of hormones, low blood glucose level and coagulation profile. The long-term malfunctional coagulation and imbalance in the material metabolism and immunity may contribute to the development of long COVID and act as useful indicator for assessing recovery and the long-term impacts after Omicron sub-variant BTIs.

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.

CD4+ T cell activation distinguishes response to anti-PD-L1+anti-CTLA4 therapy from anti-PD-L1 monotherapy

Cancer patients often receive a combination of antibodies targeting programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen-4 (CTLA4). We conducted a window-of-opportunity study in head and neck squamous cell carcinoma (HNSCC) to examine the contribution of anti-CTLA4 to anti-PD-L1 therapy. Single-cell profiling of on- versus pre-treatment biopsies identified T cell expansion as an early response marker. In tumors, anti-PD-L1 triggered the expansion of mostly CD8+ T cells, whereas combination therapy expanded both CD4+ and CD8+ T cells. Such CD4+ T cells exhibited an activated T helper 1 (Th1) phenotype. CD4+ and CD8+ T cells co-localized with and were surrounded by dendritic cells expressing T cell homing factors or antibody-producing plasma cells. T cell receptor tracing suggests that anti-CTLA4, but not anti-PD-L1, triggers the trafficking of CD4+ naive/central-memory T cells from tumor-draining lymph nodes (tdLNs), via blood, to the tumor wherein T cells acquire a Th1 phenotype. Thus, CD4+ T cell activation and recruitment from tdLNs are hallmarks of early response to anti-PD-L1 plus anti-CTLA4 in HNSCC.

Single-Cell CD4 and CD8 T-Cell Secretome Profiling Reveals Temporal and Niche Differences in Acute Myeloid Leukemia Following Immune Checkpoint Blockade Therapy

Acute myeloid leukemia (AML) is a heterogeneous malignancy of the blood primarily treated with intensive chemotherapy. The allogeneic T-cell antileukemic activity via donor lymphocyte infusions and stem cell transplantation suggests a potential role for checkpoint blockade therapy in AML. While clinical trials employing these treatments have fallen short of expected results, a deeper exploration into the functional states of T cells in AML could bridge this knowledge gap. In this study, we analyzed the polyfunctional activity of T cells in a cohort of patients with relapsed/refractory (RelRef) AML treated on the clinical trial (ClinicalTrials.gov identifier: NCT02397720) of combination therapy using azacitidine and nivolumab (Aza/Nivo). We utilized the single-cell polyfunctional multiplexed immune assay IsoPlexis to evaluate the CD4 and CD8 T cells in peripheral blood and bone marrow samples collected before and after immunotherapy. This revealed at a pseudobulk level that the CD4 T cells exhibited higher functional activity post-immunotherapy (post-IO), suggesting that CD4-directed therapies may play a role in RelRef AML. Additional single-cell analysis revealed significant differences in baseline polyfunctionality in bone marrows of responders as compared with nonresponders for both CD4 and CD8 T cells. Overall, this study highlights the impact of polyfunctional assessment in understanding CD4 and CD8 dynamics in contexts of therapy in AML.

SIGNIFICANCE

We found T-cell polyfunctionality differs between local and systemic microenvironments. Enhanced variability in proteomic profiles of bone marrow CD4 T cells post-IO suggests their pivotal role in AML treatment response. Single-cell analysis identified novel CD4 and CD8 T-cell functional groups linked to immunotherapy response within the bone marrow.

scPerturb: harmonized single-cell perturbation data

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.

Single-cell time series analysis reveals the dynamics of HSPC response to inflammation

Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.

IL-23 stabilizes an effector Treg cell program in the tumor microenvironment

Interleukin-23 (IL-23) is a proinflammatory cytokine mainly produced by myeloid cells that promotes tumor growth in various preclinical cancer models and correlates with adverse outcomes. However, as to how IL-23 fuels tumor growth is unclear. Here, we found tumor-associated macrophages to be the main source of IL-23 in mouse and human tumor microenvironments. Among IL-23-sensing cells, we identified a subset of tumor-infiltrating regulatory T (Treg) cells that display a highly suppressive phenotype across mouse and human tumors. The use of three preclinical models of solid cancer in combination with genetic ablation of Il23r in Treg cells revealed that they are responsible for the tumor-promoting effect of IL-23. Mechanistically, we found that IL-23 sensing represents a crucial signal driving the maintenance and stabilization of effector Treg cells involving the transcription factor Foxp3. Our data support that targeting the IL-23/IL-23R axis in cancer may represent a means of eliciting antitumor immunity.

Single cell-resolved study of advanced murine MASH reveals a homeostatic pericyte signaling module

BACKGROUND & AIMS

Metabolic dysfunction-associated steatohepatitis (MASH) is linked to insulin resistance and type 2 diabetes and marked by hepatic inflammation, microvascular dysfunction, and fibrosis, impairing liver function and aggravating metabolic derangements. The liver homeostatic interactions disrupted in MASH are still poorly understood. We aimed to elucidate the plasticity and changing interactions of non-parenchymal cells associated with advanced MASH.

METHODS

We characterized a diet-induced mouse model of advanced MASH at single-cell resolution and validated findings by assaying chromatin accessibility, bioimaging murine and human livers, and via functional experiments in vivo and in vitro.

RESULTS

The fibrogenic activation of hepatic stellate cells (HSCs) led to deterioration of a signaling module consisting of the bile acid receptor NR1H4/FXR and HSC-specific GS-protein-coupled receptors (GSPCRs) capable of preserving stellate cell quiescence. Accompanying HSC activation, we further observed the attenuation of HSC Gdf2 expression, and a MASH-associated expansion of a CD207-positive macrophage population likely derived from both incoming monocytes and Kupffer cells.

CONCLUSION

We conclude that HSC-expressed NR1H4 and GSPCRs of the healthy liver integrate postprandial cues, which sustain HSC quiescence and, through paracrine signals, overall sinusoidal health. Hence HSC activation in MASH not only drives fibrogenesis but may desensitize the hepatic sinusoid to liver homeostatic signals.

IMPACT AND IMPLICATIONS

Homeostatic interactions between hepatic cell types and their deterioration in metabolic dysfunction-associated steatohepatitis are poorly characterized. In our current single cell-resolved study of advanced murine metabolic dysfunction-associated steatohepatitis, we identified a quiescence-associated hepatic stellate cell-signaling module with potential to preserve normal sinusoid function. As expression levels of its constituents are conserved in the human liver, stimulation of the identified signaling module is a promising therapeutic strategy to restore sinusoid function in chronic liver disease.

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.

Paired single-B-cell transcriptomics and receptor sequencing reveal activation states and clonal signatures that characterize B cells in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is associated with a dismal prognosis. Immune checkpoint blockade (ICB) to induce antitumor activity in AML patients has yielded mixed results. Despite the pivotal role of B cells in antitumor immunity, a comprehensive assessment of B lymphocytes within AML's immunological microenvironment along with their interaction with ICB remains rather constrained.

METHODS

We performed an extensive analysis that involved paired single-cell RNA and B-cell receptor (BCR) sequencing on 52 bone marrow aspirate samples. These samples included 6 from healthy bone marrow donors (normal), 24 from newly diagnosed AML patients (NewlyDx), and 22 from 8 relapsed or refractory AML patients (RelRef), who underwent assessment both before and after azacitidine/nivolumab treatment.

RESULTS

We delineated nine distinct subtypes of B cell lineage in the bone marrow. AML patients exhibited reduced nascent B cell subgroups but increased differentiated B cells compared with healthy controls. The limited diversity of BCR profiles and extensive somatic hypermutation indicated antigen-driven affinity maturation within the tumor microenvironment of RelRef patients. We established a strong connection between the activation or stress status of naïve and memory B cells, as indicated by AP-1 activity, and their differentiation state. Remarkably, atypical memory B cells functioned as specialized antigen-presenting cells closely interacting with AML malignant cells, correlating with AML stemness and worse clinical outcomes. In the AML microenvironment, plasma cells demonstrated advanced differentiation and heightened activity. Notably, the clinical response to ICB was associated with B cell clonal expansion and plasma cell function.

CONCLUSIONS

Our findings establish a comprehensive framework for profiling the phenotypic diversity of the B cell lineage in AML patients, while also assessing the implications of immunotherapy. This will serve as a valuable guide for future inquiries into AML treatment strategies.

Fructose regulates the pentose phosphate pathway and induces an inflammatory and resolution phenotype in Kupffer cells

Over-consumption of fructose in adults and children has been linked to increased risk of non-alcoholic fatty liver disease (NAFLD). Recent studies have highlighted the effect of fructose on liver inflammation, fibrosis, and immune cell activation. However, little work summarizes the direct impact of fructose on macrophage infiltration, phenotype, and function within the liver. We demonstrate that chronic fructose diet decreased Kupffer cell populations while increasing transitioning monocytes. In addition, fructose increased fibrotic gene expression of collagen 1 alpha 1 (Col1a1) and tissue metallopeptidase inhibitor 1 (Timp1) as well as inflammatory gene expression of tumor necrosis factor alpha (Tnfa) and expression of transmembrane glycoprotein NMB (Gpnmb) in liver tissue compared to glucose and control diets. Single cell RNA sequencing (scRNAseq) revealed fructose elevated expression of matrix metallopeptidase 12 (Mmp12), interleukin 1 receptor antagonist (Il1rn), and radical S-adenosyl methionine domain (Rsad2) in liver and hepatic macrophages. In vitro studies using IMKC and J774.1 cells demonstrated decreased viability when exposed to fructose. Additionally, fructose increased Gpnmb, Tnfa, Mmp12, Il1rn, and Rsad2 in unpolarized IMKC. By mass spectrometry, C13 fructose tracing detected fructose metabolites in glycolysis and the pentose phosphate pathway (PPP). Inhibition of the PPP further increased fructose induced Il6, Gpnmb, Mmp12, Il1rn, and Rsad2 in nonpolarized IMKC. Taken together, fructose decreases cell viability while upregulating resolution and anti-inflammatory associated genes in Kupffer cells.

Comparison of the single-cell and single-nucleus hepatic myeloid landscape within decompensated cirrhosis patients

Background and aims

A complete understanding of disease pathophysiology in advanced liver disease is hampered by the challenges posed by clinical specimen collection. Notably, in these patients, a transjugular liver biopsy (TJB) is the only safe way to obtain liver tissue. However, it remains unclear whether successful sequencing of this extremely small and fragile tissue can be achieved for downstream characterization of the hepatic landscape.

Methods

Here we leveraged in-house available single-cell RNA-sequencing (scRNA-seq) and single-nucleus (snRNA-seq) technologies and accompanying tissue processing protocols and performed an in-patient comparison on TJB's from decompensated cirrhosis patients (n = 3).

Results

We confirmed a high concordance between nuclear and whole cell transcriptomes and captured 31,410 single nuclei and 6,152 single cells, respectively. The two platforms revealed similar diversity since all 8 major cell types could be identified, albeit with different cellular proportions thereof. Most importantly, hepatocytes were most abundant in snRNA-seq, while lymphocyte frequencies were elevated in scRNA-seq. We next focused our attention on hepatic myeloid cells due to their key role in injury and repair during chronic liver disease. Comparison of their transcriptional signatures indicated that these were largely overlapping between the two platforms. However, the scRNA-seq platform failed to recover sufficient Kupffer cell numbers, and other monocytes/macrophages featured elevated expression of stress-related parameters.

Conclusion

Our results indicate that single-nucleus transcriptome sequencing provides an effective means to overcome complications associated with clinical specimen collection and could sufficiently profile all major hepatic cell types including all myeloid cell subsets.

Multimodal profiling reveals site-specific adaptation and tissue residency hallmarks of γδ T cells across organs in mice

γδ T cells perform heterogeneous functions in homeostasis and disease across tissues. However, it is unclear whether these roles correspond to distinct γδ subsets or to a homogeneous population of cells exerting context-dependent functions. Here, by cross-organ multimodal single-cell profiling, we reveal that various mouse tissues harbor unique site-adapted γδ subsets. Epidermal and intestinal intraepithelial γδ T cells are transcriptionally homogeneous and exhibit epigenetic hallmarks of functional diversity. Through parabiosis experiments, we uncovered cellular states associated with cytotoxicity, innate-like rapid interferon-γ production and tissue repair functions displaying tissue residency hallmarks. Notably, our observations add nuance to the link between interleukin-17-producing γδ T cells and tissue residency. Moreover, transcriptional programs associated with tissue-resident γδ T cells are analogous to those of CD8+ tissue-resident memory T cells. Altogether, this study provides a multimodal landscape of tissue-adapted γδ T cells, revealing heterogeneity, lineage relationships and their tissue residency program.

Human uterine natural killer cells regulate differentiation of extravillous trophoblast early in pregnancy

In humans, balanced invasion of trophoblast cells into the uterine mucosa, the decidua, is critical for successful pregnancy. Evidence suggests that this process is regulated by uterine natural killer (uNK) cells, but how they influence reproductive outcomes is unclear. Here, we used our trophoblast organoids and primary tissue samples to determine how uNK cells affect placentation. By locating potential interaction axes between trophoblast and uNK cells using single-cell transcriptomics and in vitro modeling of these interactions in organoids, we identify a uNK cell-derived cytokine signal that promotes trophoblast differentiation at the late stage of the invasive pathway. Moreover, it affects transcriptional programs involved in regulating blood flow, nutrients, and inflammatory and adaptive immune responses, as well as gene signatures associated with disorders of pregnancy such as pre-eclampsia. Our findings suggest mechanisms on how optimal immunological interactions between uNK cells and trophoblast enhance reproductive success.

Stabilized mosaic single-cell data integration using unshared features

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.

Effect of aging on the human myometrium at single-cell resolution

Age-associated myometrial dysfunction can prompt complications during pregnancy and labor, which is one of the factors contributing to the 7.8-fold increase in maternal mortality in women over 40. Using single-cell/single-nucleus RNA sequencing and spatial transcriptomics, we have constructed a cellular atlas of the aging myometrium from 186,120 cells across twenty perimenopausal and postmenopausal women. We identify 23 myometrial cell subpopulations, including contractile and venous capillary cells as well as immune-modulated fibroblasts. Myometrial aging leads to fewer contractile capillary cells, a reduced level of ion channel expression in smooth muscle cells, and impaired gene expression in endothelial, smooth muscle, fibroblast, perivascular, and immune cells. We observe altered myometrial cell-to-cell communication as an aging hallmark, which associated with the loss of 25 signaling pathways, including those related to angiogenesis, tissue repair, contractility, immunity, and nervous system regulation. These insights may contribute to a better understanding of the complications faced by older individuals during pregnancy and labor.

Activated fibroblasts drive cellular interactions in end-stage pediatric hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a relatively rare but debilitating diagnosis in the pediatric population and patients with end-stage HCM require heart transplantation. In this study, we performed single-nucleus RNA sequencing on pediatric HCM and control myocardium. We identified distinct underling cellular processes in pediatric, end-stage HCM in cardiomyocytes, fibroblasts, endothelial cells, and myeloid cells, compared to controls. Pediatric HCM was enriched in cardiomyocytes exhibiting "stressed" myocardium gene signatures and underlying pathways associated with cardiac hypertrophy. Cardiac fibroblasts exhibited clear activation signatures and heightened downstream processes associated with fibrosis, more so than adult counterparts. There was notable depletion of tissue-resident macrophages, and increased vascular remodeling in endothelial cells. Our analysis provides the first single nuclei analysis focused on end-stage pediatric HCM.

Trajectory inference across multiple conditions with condiments

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell's conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

Tumour-retained activated CCR7+ dendritic cells are heterogeneous and regulate local anti-tumour cytolytic activity

Tumour dendritic cells (DCs) internalise antigen and upregulate CCR7, which directs their migration to tumour-draining lymph nodes (dLN). CCR7 expression is coupled to an activation programme enriched in regulatory molecule expression, including PD-L1. However, the spatio-temporal dynamics of CCR7+ DCs in anti-tumour immune responses remain unclear. Here, we use photoconvertible mice to precisely track DC migration. We report that CCR7+ DCs are the dominant DC population that migrate to the dLN, but a subset remains tumour-resident despite CCR7 expression. These tumour-retained CCR7+ DCs are phenotypically and transcriptionally distinct from their dLN counterparts and heterogeneous. Moreover, they progressively downregulate the expression of antigen presentation and pro-inflammatory transcripts with more prolonged tumour dwell-time. Tumour-residing CCR7+ DCs co-localise with PD-1+CD8+ T cells in human and murine solid tumours, and following anti-PD-L1 treatment, upregulate stimulatory molecules including OX40L, thereby augmenting anti-tumour cytolytic activity. Altogether, these data uncover previously unappreciated heterogeneity in CCR7+ DCs that may underpin a variable capacity to support intratumoural cytotoxic T cells.

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks

With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

Single-cell profiling of the microenvironment in human bone metastatic renal cell carcinoma

Bone metastasis is of common occurrence in renal cell carcinoma with poor prognosis, but no optimal treatment approach has been established for bone metastatic renal cell carcinoma. To explore the potential therapeutic targets for bone metastatic renal cell carcinoma, we profile single cell transcriptomes of 6 primary renal cell carcinoma and 9 bone metastatic renal cell carcinoma. We also include scRNA-seq data of early-stage renal cell carcinoma, late-stage renal cell carcinoma, normal kidneys and healthy bone marrow samples in the study to better understand the bone metastasis niche. The molecular properties and dynamic changes of major cell lineages in bone metastatic environment of renal cell carcinoma are characterized. Bone metastatic renal cell carcinoma is associated with multifaceted immune deficiency together with cancer-associated fibroblasts, specifically appearance of macrophages exhibiting malignant and pro-angiogenic features. We also reveal the dominance of immune inhibitory T cells in the bone metastatic renal cell carcinoma which can be partially restored by the treatment. Trajectory analysis showes that myeloid-derived suppressor cells are progenitors of macrophages in the bone metastatic renal cell carcinoma while monocytes are their progenitors in primary tumors and healthy bone marrows. Additionally, the infiltration of immune inhibitory CD47+ T cells is observed in bone metastatic tumors, which may be a result of reduced phagocytosis by SIRPA-expressing macrophages in the bone microenvironment. Together, our results provide a systematic view of various cell types in bone metastatic renal cell carcinoma and suggest avenues for therapeutic solutions.

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model's capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study's findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention.