clustifyr: an R package for automated single-cell RNA sequencing cluster classification

Activated polyreactive B cells are clonally expanded in autoantibody positive and patients with recent-onset type 1 diabetes

Autoreactive B cells play an important but ill-defined role in autoimmune type 1 diabetes (T1D). We isolated pancreatic islet antigen-reactive B cells from the peripheral blood of non-diabetic autoantibody-negative first-degree relatives, autoantibody-positive, and recent-onset T1D donors. Single-cell RNA sequencing analysis revealed that islet antigen-reactive B cells from autoantibody-positive and T1D donors had altered gene expression in pathways associated with B cell signaling and inflammation. Additionally, BCR sequencing uncovered a similar shift in islet antigen-reactive B cell repertoires among autoantibody-positive and T1D donors where greater clonal expansion was also observed. Notably, a substantial fraction of islet antigen-reactive B cells in autoantibody-positive and T1D donors appeared to be polyreactive, which was corroborated by analysis of recombinant monoclonal antibodies. These results expand our understanding of autoreactive B cell phenotypes during T1D and identify unique BCR repertoire changes that may serve as biomarkers for increased disease risk.

Transcriptomic and morphologic vascular aberrations underlying FCDIIb etiology

Focal cortical dysplasia type II (FCDII) is a major cause of drug-resistant epilepsy, but genetic factors explain only some cases, suggesting other mechanisms. In this study, we conduct a molecular analysis of brain lesions and adjacent areas in FCDIIb patients. By analyzing over 217,506 single-nucleus transcriptional profiles from 15 individuals, we find significant changes in smooth muscle cells (SMCs) and astrocytes. We identify abnormal vascular malformations and a unique type of SMC that we call "Firework cells", which migrate from blood vessels into the brain parenchyma and associate with VIM+ cells. These abnormalities create localized ischemic-hypoxic (I/H) microenvironments, as confirmed by clinical data, further impairing astrocyte function, activating the HIF-1α/mTOR/S6 pathway, and causing neuronal loss. Using zebrafish models, we demonstrate that vascular abnormalities resulting in I/H environments promote seizures. Our results highlight vascular malformations as a factor in FCDIIb pathogenesis, suggesting potential therapeutic avenues.

Multiomic profiling of T cell lymphoma after therapy with anti-BCMA CAR T cells and GPRC5D-directed bispecific antibody

Chimeric antigen receptor (CAR) T cells and bispecific T cell engagers have become integral components in the treatment of relapsed/refractory multiple myeloma. We report a 63-year-old male who received ciltacabtagene autoleucel CAR T cells and the GPRC5D × CD3 bispecific talquetamab for early relapse of his multiple myeloma. Nine months after CAR T therapy, he developed a symptomatic leukemic peripheral T cell lymphoma with cutaneous and intestinal involvement. Longitudinal single-cell RNA and T cell receptor sequencing of peripheral blood and bone marrow revealed two hyperexpanded CAR-carrying T cell clones. These expanded clones exhibited an exhausted effector-memory T cell transcriptional signature, and the neoplasm itself was sensitive to dexamethasone treatment. The immunophenotypic and transcriptional alterations of these abnormal T cells resembled those of T-large granular lymphocytic leukemia. Spatial transcriptomes of skin lesions confirmed the aberrant CAR-expressing T cells. Whole-genome sequencing revealed three distinct integration sites, within the introns of ZGPAT, KPNA4 and polycomb-associated noncoding RNAs. Before and after CAR T whole-genome analyses implicated clonal outgrowth of a TET2-mutated precursor propelled by additional subclone-specific loss of heterozygosity and other secondary mechanisms. This case highlights the evolution of a CAR-carrying peripheral T cell lymphoma following CAR T cell and bispecific T cell engager therapy, offering critical insights into the clonal evolution from a predisposed hematopoietic precursor to a mature neoplasm.

Single-cell transcriptomics of melanoma sentinel lymph nodes identifies immune cell signatures associated with metastasis

The sentinel lymph node (SLN) is the first lymph node encountered by a metastatic cancer cell and serves as a predictor of poor prognosis, as cases with clinically occult SLN metastases are classified as stage III with elevated rates of recurrence and diminished overall survival. However, the dynamics of immune infiltrates in SLNs remain poorly characterized. Here, using an unbiased cellular indexing of transcriptomes and epitopes by sequencing technique, we profiled 97,777 cells from SLN tissues obtained from patients with stages I/II and III cutaneous melanoma. We described the transcriptional programs of a multitude of T, B, and myeloid cell subtypes in SLNs. Based on the proportions of cell types, we determined that SLN subtypes stratified along a naive → activated axis; patients with a "high activated" signature score appeared to be undergoing a robust melanoma antigen-driven adaptive immune response and, thus, could be responsive to immunotherapy. Additionally, we identified transcriptomic signatures of SLN-infiltrating dendritic cell subsets that compromise antitumor immune responses. Our analyses provide valuable insights into tumor-driven immune changes in the SLN tissue, offering a powerful tool for the informed design of immune therapies for patients with high-risk melanoma.

CFTR represses a PDX1 axis to govern pancreatic ductal cell fate

Inflammation, acinar atrophy, and ductal hyperplasia drive pancreatic remodeling in newborn cystic fibrosis (CF) ferrets lacking a functional cystic fibrosis conductance regulator (CFTR) channel. These changes are associated with a transient phase of glucose intolerance that involves islet destruction and subsequent regeneration near hyperplastic ducts. The phenotypic changes in CF ductal epithelium and their impact on islet function are unknown. Using bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and assay for transposase-accessible chromatin using sequencing (ATAC-seq) on CF ferret models, we demonstrate that ductal CFTR protein constrains PDX1 expression by maintaining PTEN and GSK3β activation. In the absence of CFTR protein, centroacinar cells adopted a bipotent progenitor-like state associated with enhanced WNT/β-Catenin, transforming growth factor β (TGF-β), and AKT signaling. We show that the level of CFTR protein, not its channel function, regulates PDX1 expression. Thus, this study has discovered a cell-autonomous CFTR-dependent mechanism by which CFTR mutations that produced little to no protein could impact pancreatic exocrine/endocrine remodeling in people with CF.

A specific gene expression program underlies antigen archiving by lymphatic endothelial cells in mammalian lymph nodes

Lymph node (LN) lymphatic endothelial cells (LEC) actively acquire and archive foreign antigens. Here, we address questions of how LECs achieve durable antigen archiving and whether LECs with high levels of antigen express unique transcriptional programs. We used single cell sequencing in dissociated LN tissue and spatial transcriptomics to quantify antigen levels in LEC subsets and dendritic cell populations at multiple time points after immunization and determined that ceiling and floor LECs archive antigen for the longest duration. We identify, using spatial transcriptomics, antigen positive LEC-dendritic cell interactions. Using a prime-boost strategy we find increased antigen levels within LECs after a second immunization demonstrating that LEC antigen acquisition and archiving capacity can be improved over multiple exposures. Using machine learning we defined a unique transcriptional program within archiving LECs that predicted LEC archiving capacity in mouse and human independent data sets. We validated this modeling, showing we could predict lower levels of LEC antigen archiving in chikungunya virus-infected mice and demonstrated in vivo the accuracy of our prediction. Collectively, our findings establish unique properties of LECs and a defining transcriptional program for antigen archiving that can predict antigen archiving capacity in different disease states and organisms.

Airway-derived emphysema-specific alveolar type II cells exhibit impaired regenerative potential in COPD

Emphysema, the progressive destruction of gas exchange surfaces in the lungs, is a hallmark of COPD that is presently incurable. This therapeutic gap is largely due to a poor understanding of potential drivers of impaired tissue regeneration, such as abnormal lung epithelial progenitor cells, including alveolar type II (ATII) and airway club cells. We discovered an emphysema-specific subpopulation of ATII cells located in enlarged distal alveolar sacs, termed asATII cells. Single-cell RNA sequencing and in situ localisation revealed that asATII cells co-express the alveolar marker surfactant protein C and the club cell marker secretaglobin-3A2 (SCGB3A2). A similar ATII subpopulation derived from club cells was also identified in mouse COPD models using lineage labelling. Human and mouse ATII subpopulations formed 80-90% fewer alveolar organoids than healthy controls, indicating reduced progenitor function. Targeting asATII cells or their progenitor club cells could reveal novel COPD treatment strategies.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

eMCI: An Explainable Multimodal Correlation Integration Model for Unveiling Spatial Transcriptomics and Intercellular Signaling

Current integration methods for single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data are typically designed for specific tasks, such as deconvolution of cell types or spatial distribution prediction of RNA transcripts. These methods usually only offer a partial analysis of ST data, neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk. Here, we present eMCI, an explainable multimodal correlation integration model based on deep neural network framework. eMCI leverages the fusion of scRNA-seq and ST data using different spot-cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level. First, eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets. Second, eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication, by employing an attribution algorithm to dissect the visual input. Especially, eMCI has been applied to 3 cross-species datasets, including zebrafish melanomas, soybean nodule maturation, and human embryonic lung, which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context. In summary, eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference. This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell-cell communication specificity.

Genetic association between epilepsy and gliomas: Insights from Mendelian randomization and single-cell transcriptomic analyses

BACKGROUND

Seizures are prevalent in glioma patients, especially in those with low-grade gliomas. The interaction between gliomas and epilepsy involves complex biological mechanisms that are not fully understood.

METHODS

We collected Genome-Wide Association Study data for epilepsy and gliomas, performed differential expression analysis, and conducted Gene Ontology (GO) enrichment analysis on the identified genes. Single-cell RNA sequencing data (scRNA-seq) from GSE221534 dataset in Gene Expression Omnibus (GEO) were used to analyze cell-cell interactions within glioma samples from patients with and without epilepsy.

RESULTS

Mendelian Randomization (MR) analysis revealed significant associations between genetic variants related to epilepsy and glioma risk, suggesting a potential causal relationship, especially in astrocytomas. Differential expression analysis identified epilepsy-related genes that were significantly upregulated in astrocytoma tissues compared to normal brain tissues. GO enrichment analysis indicated that these genes are involved in critical biological processes such as neurogenesis and cellular signaling. The scRNA-seq analysis showed, compared to non-epileptic samples, glioma stem cells, microglia, and NK cells are increased in the core regions of astrocytomas in epileptic patients. Additionally, intercellular communication between tumor cells and other non-tumor cells is markedly enhanced in astrocytoma samples from epileptic patients.

CONCLUSION

This study provides evidence of a genetic association between epilepsy and gliomas and elucidates the biological mechanisms through which epilepsy may influence glioma progression.

Oxytocin receptor controls distinct components of pair bonding and development in prairie voles

Oxytocin receptor (Oxtr) signaling influences complex social behaviors in diverse species, including social monogamy in prairie voles. How Oxtr regulates specific components of social attachment behaviors and the neural mechanisms mediating them remains unknown. Here, we examine prairie voles lacking Oxtr and demonstrate that pair bonding comprises distinct behavioral modules: the preference for a bonded partner, and the rejection of novel potential mates. Our longitudinal study of social attachment shows that Oxtr sex-specifically influences early interactions between novel partners facilitating the formation of partner preference. Additionally, Oxtr suppresses promiscuity towards novel potential mates following pair bonding, contributing to rejection. Oxtr function regulates coordinated patterns of gene expression in regions implicated in attachment behaviors and regulates the expression of oxytocin in the paraventricular nucleus of the hypothalamus, a principal source of oxytocin. Thus, Oxtr controls genetically separable components of pair bonding behaviors and coordinates development of the neural substrates of attachment.

Mouse enteric neurons control intestinal plasmacytoid dendritic cell function via serotonin-HTR7 signaling

Serotonergic neurons in the central nervous system control behavior and mood, but knowledge of the roles of serotonergic circuits in the regulation of immune homeostasis is limited. Here, we employ mouse genetics to investigate the functions of enteric serotonergic neurons in the control of immune responses and find that these circuits regulate IgA induction and boost host defense against oral, but not systemic Salmonella Typhimurium infection. Enteric serotonergic neurons promote gut-homing, retention and activation of intestinal plasmacytoid dendritic cells (pDC). Mechanistically, this neuro-immune crosstalk is achieved through a serotonin-5-HT receptor 7 (HTR7) signaling axis that ultimately facilitates the pDC-mediated differentiation of IgA+ B cells from IgD+ precursors in the gut. Single-cell RNA-seq data further reveal novel patterns of bidirectional communication between specific subsets of enteric neurons and lamina propria DC. Our findings thus reveal a close interplay between enteric serotonergic neurons and gut immune homeostasis that enhances mucosal defense.

Assessment and Evaluation of Contemporary Approaches for Astrocyte Differentiation from hiPSCs: A Modeling Paradigm for Alzheimer's Disease

BACKGROUND

Astrocytes have recently gained attention as key players in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease. Numerous differentiation protocols have been developed to study human astrocytes in vitro. However, the properties of the resulting glia are inconsistent, making it difficult to select an appropriate method for a given research question. Therefore, we compared three approaches for the generation of iPSC-derived astrocytes. We performed a detailed analysis using a widely used long serum-free (LSFP) and short serum-free (SSFP) protocol, as well as a TUSP protocol using serum for a limited time of differentiation.

RESULTS

We used RNA sequencing and immunochemistry to characterize the cultures. Astrocytes generated by the LSFP and SSFP methods differed significantly in their characteristics from those generated by the TUSP method using serum. The TUSP astrocytes had a less neuronal pattern, showed a higher degree of extracellular matrix formation, and were more mature. The short-term presence of FBS in the medium facilitated the induction of astroglia characteristics but did not result in reactive astrocytes. Data from cell-type deconvolution analysis applied to bulk transcriptomes from the cultures assessed their similarity to primary and fetal human astrocytes.

CONCLUSIONS

Overall, our analyses highlight the need to consider the advantages and disadvantages of a given differentiation protocol for solving specific research tasks or drug discovery studies with iPSC-derived astrocytes.

Multiomic single cell sequencing identifies stemlike nature of mixed phenotype acute leukemia

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

Single-cell sequencing analysis of multiple myeloma heterogeneity and identification of new theranostic targets

Multiple myeloma (MM) is a heterogeneous and incurable tumor characterized by the malignant proliferation of plasma cells. It is necessary to clarify the heterogeneity of MM and identify new theranostic targets. We constructed a single-cell transcriptome profile of 48,293 bone marrow cells from MM patients and health donors (HDs) annotated with 7 continuous B lymphocyte lineages. Through CellChat, we discovered that the communication among B lymphocyte lineages between MM and HDs was disrupted, and unique signaling molecules were observed. Through pseudotime analysis, it was found that the differences between MM and HDs were mainly reflected in plasma cells. These differences are primarily related to various biological processes involving mitochondria. Then, we identified the key subpopulation associated with the malignant proliferation of plasma cells. This group of cells exhibited strong proliferation ability, high CNV scores, high expression of frequently mutated genes, and strong glucose metabolic activity. Furthermore, we demonstrated the therapeutic potential of WNK1 as a target. Our study provides new insights into the development of B cells and the heterogeneity of plasma cells in MM and suggests that WNK1 is a potential therapeutic target for MM.

Inflammation perturbs hematopoiesis by remodeling specific compartments of the bone marrow niche

Hematopoietic stem and progenitor cells (HSPC) are regulated by interactions with stromal cells in the bone marrow (BM) cavity, which can be segregated into two spatially defined central marrow (CM) and endosteal (Endo) compartments. However, the importance of this spatial compartmentalization for BM responses to inflammation and neoplasia remains largely unknown. Here, we extensively validate a combination of scRNA-seq profiling and matching flow cytometry isolation that reproducibly identifies 7 key CM and Endo populations across mouse strains and accurately surveys both niche locations. We demonstrate that different perturbations exert specific effects on different compartments, with type I interferon responses causing CM mesenchymal stromal cells to adopt an inflammatory phenotype associated with overproduction of chemokines modulating local monocyte dynamics in the surrounding microenvironment. Our results provide a comprehensive method for molecular and functional stromal characterization and highlight the importance of altered stomal cell activity in regulating hematopoietic responses to inflammatory challenges.

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma

Markers that predict response and resistance to chimeric antigen receptor (CAR) T cells in relapsed/refractory multiple myeloma are currently missing. We subjected mononuclear cells isolated from peripheral blood and bone marrow before and after the application of approved B cell maturation antigen-directed CAR T cells to single-cell multiomic analyses to identify markers associated with resistance and early relapse. Differences between responders and nonresponders were identified at the time of leukapheresis. Nonresponders showed an immunosuppressive microenvironment characterized by increased numbers of monocytes expressing the immune checkpoint molecule CD39 and suppressed CD8+ T cell and natural killer cell function. Analysis of CAR T cells showed cytotoxic and exhausted phenotypes in hyperexpanded clones compared to low/intermediate expanded clones. We identified potential immunotherapy targets on CAR T cells, like PD1, to improve their functionality and durability. Our work provides evidence that an immunosuppressive microenvironment causes resistance to CAR T cell therapies in multiple myeloma.

A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens

Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

Islet-antigen reactive B cells display a unique phenotype and BCR repertoire in autoantibody positive and recent-onset type 1 diabetes patients

Autoreactive B cells play an important but ill-defined role in autoimmune type 1 diabetes (T1D). To better understand their contribution, we performed single cell gene and BCR-seq analysis on pancreatic islet antigen-reactive (IAR) B cells from the peripheral blood of nondiabetic (ND), autoantibody positive prediabetic (AAB), and recent-onset T1D individuals. We found that the frequency of IAR B cells was increased in AAB and T1D. IAR B cells from these donors had altered expression of B cell signaling, pro-inflammatory, infection, and antigen processing and presentation genes. Both AAB and T1D donors demonstrated a significant increase in certain heavy and light chain V genes, and these V genes were enriched in islet-reactivity. Public clones of IAR B cells were restricted almost entirely to AAB and T1D donors. IAR B cells were clonally expanded in the autoimmune donors, particularly the AAB group. Notably, a substantial fraction of IAR B cells in AAB and T1D donors appeared to be polyreactive, which was corroborated by analysis of recombinant monoclonal antibodies. These results expand our understanding of autoreactive B cell activation during T1D and identify unique BCR repertoire changes that may serve as biomarkers for increased disease risk.

A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens

Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

A specific and portable gene expression program underlies antigen archiving by lymphatic endothelial cells

Antigens from protein subunit vaccination traffic from the tissue to the draining lymph node, either passively via the lymph or carried by dendritic cells at the local injection site. Lymph node (LN) lymphatic endothelial cells (LEC) actively acquire and archive foreign antigens, and archived antigen can be released during subsequent inflammatory stimulus to improve immune responses. Here, we answer questions about how LECs achieve durable antigen archiving and whether there are transcriptional signatures associated with LECs containing high levels of antigen. We used single cell sequencing in dissociated LN tissue to quantify antigen levels in LEC and dendritic cell populations at multiple timepoints after immunization, and used machine learning to define a unique transcriptional program within archiving LECs that can predict LEC archiving capacity in independent data sets. Finally, we validated this modeling, showing we could predict antigen archiving from a transcriptional dataset of CHIKV infected mice and demonstrated in vivo the accuracy of our prediction. Collectively, our findings establish a unique transcriptional program in LECs that promotes antigen archiving that can be translated to other systems.

Mitochondrial DNA mutations drive aerobic glycolysis to enhance checkpoint blockade response in melanoma

The mitochondrial genome (mtDNA) encodes essential machinery for oxidative phosphorylation and metabolic homeostasis. Tumor mtDNA is among the most somatically mutated regions of the cancer genome, but whether these mutations impact tumor biology is debated. We engineered truncating mutations of the mtDNA-encoded complex I gene, Mt-Nd5, into several murine models of melanoma. These mutations promoted a Warburg-like metabolic shift that reshaped tumor microenvironments in both mice and humans, consistently eliciting an anti-tumor immune response characterized by loss of resident neutrophils. Tumors bearing mtDNA mutations were sensitized to checkpoint blockade in a neutrophil-dependent manner, with induction of redox imbalance being sufficient to induce this effect in mtDNA wild-type tumors. Patient lesions bearing >50% mtDNA mutation heteroplasmy demonstrated a response rate to checkpoint blockade that was improved by ~2.5-fold over mtDNA wild-type cancer. These data nominate mtDNA mutations as functional regulators of cancer metabolism and tumor biology, with potential for therapeutic exploitation and treatment stratification.

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.

Plant biotechnology research with single-cell transcriptome: recent advancements and prospects

KEY MESSAGE

Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.

A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer

BACKGROUND

Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics.

METHODS

All patients of the FFPE biopsy cohort were treated by radical prostatectomy and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of 176 FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves and Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. In addition, we developed a prostate single-cell atlas of 41 PCa patients from 5 publicly available studies to analyze gene expression of ProstaTrend genes in different cell compartments.

RESULTS

Validation of the TRS using the original ProstaTrend signature in the cohort of FFPE biopsies revealed a relevant impact of FFPE-associated degradation on gene expression and consequently no significant association with prognosis (Cox-regression, p-value > 0.05) in FFPE tissue. However, the TRS based on the new version of the ProstaTrend-ffpe signature, which included 204 genes (of originally 1396 genes), was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value < 0.001) and retained prognostic relevance when adjusted for Gleason Grade Groups. We confirmed a significant association with BCR in 9 independent cohorts including 1109 patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that ProstaTrend-ffpe was among the best-ranked panels. We generated a PCa cell atlas to associate ProstaTrend genes with cell lineages or cell types. Tumor-specific luminal cells have a significantly higher TRS than normal luminal cells in all analyzed datasets. In addition, TRS of epithelial and luminal cells was correlated with increased Gleason score in 3 studies.

CONCLUSIONS

We developed a prognostic gene-expression signature for PCa that can be applied to FFPE biopsies and may be suitable to support clinical decision-making.

iPSC-derived models of PACS1 syndrome reveal transcriptional and functional deficits in neuron activity

PACS1 syndrome is a neurodevelopmental disorder characterized by intellectual disability and distinct craniofacial abnormalities resulting from a de novo p.R203W variant in phosphofurin acidic cluster sorting protein 1 (PACS1). PACS1 is known to have functions in the endosomal pathway and nucleus, but how the p.R203W variant affects developing neurons is not fully understood. Here we differentiated stem cells towards neuronal models including cortical organoids to investigate the impact of the PACS1 syndrome-causing variant on neurodevelopment. While few deleterious effects were detected in PACS1(+/R203W) neural precursors, mature PACS1(+/R203W) glutamatergic neurons exhibited impaired expression of genes involved in synaptic signaling processes. Subsequent characterization of neural activity using calcium imaging and multielectrode arrays revealed the p.R203W PACS1 variant leads to a prolonged neuronal network burst duration mediated by an increased interspike interval. These findings demonstrate the impact of the PACS1 p.R203W variant on developing human neural tissue and uncover putative electrophysiological underpinnings of disease.

Chikungunya virus infection disrupts lymph node lymphatic endothelial cell composition and function via MARCO

Infection with chikungunya virus (CHIKV) causes disruption of draining lymph node (dLN) organization, including paracortical relocalization of B cells, loss of the B cell-T cell border, and lymphocyte depletion that is associated with infiltration of the LN with inflammatory myeloid cells. Here, we found that, during the first 24 hours of infection, CHIKV RNA accumulated in MARCO-expressing lymphatic endothelial cells (LECs) in both the floor and medullary LN sinuses. The accumulation of viral RNA in the LN was associated with a switch to an antiviral and inflammatory gene expression program across LN stromal cells, and this inflammatory response - including recruitment of myeloid cells to the LN - was accelerated by CHIKV-MARCO interactions. As CHIKV infection progressed, both floor and medullary LECs diminished in number, suggesting further functional impairment of the LN by infection. Consistent with this idea, antigen acquisition by LECs, a key function of LN LECs during infection and immunization, was reduced during pathogenic CHIKV infection.

CellSTAR: a comprehensive resource for single-cell transcriptomic annotation

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.

Current annotation strategies for T cell phenotyping of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) has become a popular technique for interrogating the diversity and dynamic nature of cellular gene expression and has numerous advantages in immunology. For example, scRNA-seq, in contrast to bulk RNA sequencing, can discern cellular subtypes within a population, which is important for heterogenous populations such as T cells. Moreover, recent advancements in the technology allow the parallel capturing of the highly diverse T-cell receptor (TCR) sequence with the gene expression. However, the field of single-cell RNA sequencing data analysis is still hampered by a lack of gold-standard cell phenotype annotation. This problem is particularly evident in the case of T cells due to the heterogeneity in both their gene expression and their TCR. While current cell phenotype annotation tools can differentiate major cell populations from each other, labelling T-cell subtypes remains problematic. In this review, we identify the common automated strategy for annotating T cells and their subpopulations, and also describe what crucial information is still missing from these tools.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

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.

A roadmap for delivering a human musculoskeletal cell atlas

Advances in single-cell technologies have transformed the ability to identify the individual cell types present within tissues and organs. The musculoskeletal bionetwork, part of the wider Human Cell Atlas project, aims to create a detailed map of the healthy musculoskeletal system at a single-cell resolution throughout tissue development and across the human lifespan, with complementary generation of data from diseased tissues. Given the prevalence of musculoskeletal disorders, this detailed reference dataset will be critical to understanding normal musculoskeletal function in growth, homeostasis and ageing. The endeavour will also help to identify the cellular basis for disease and lay the foundations for novel therapeutic approaches to treating diseases of the joints, soft tissues and bone. Here, we present a Roadmap delineating the critical steps required to construct the first draft of a human musculoskeletal cell atlas. We describe the key challenges involved in mapping the extracellular matrix-rich, but cell-poor, tissues of the musculoskeletal system, outline early milestones that have been achieved and describe the vision and directions for a comprehensive musculoskeletal cell atlas. By embracing cutting-edge technologies, integrating diverse datasets and fostering international collaborations, this endeavour has the potential to drive transformative changes in musculoskeletal medicine.

Single-cell chromatin accessibility and transcriptomic characterization of Behcet's disease

Behect's disease is a chronic vasculitis characterized by complex multi-organ immune aberrations. However, a comprehensive understanding of the gene-regulatory profile of peripheral autoimmunity and the diverse immune responses across distinct cell types in Behcet's disease (BD) is still lacking. Here, we present a multi-omic single-cell study of 424,817 cells in BD patients and non-BD individuals. This study maps chromatin accessibility and gene expression in the same biological samples, unraveling vast cellular heterogeneity. We identify widespread cell-type-specific, disease-associated active and pro-inflammatory immunity in both transcript and epigenomic aspects. Notably, integrative multi-omic analysis reveals putative TF regulators that might contribute to chromatin accessibility and gene expression in BD. Moreover, we predicted gene-regulatory networks within nominated TF activators, including AP-1, NF-kB, and ETS transcript factor families, which may regulate cellular interaction and govern inflammation. Our study illustrates the epigenetic and transcriptional landscape in BD peripheral blood and expands understanding of potential epigenomic immunopathology in this disease.

Chikungunya virus infection disrupts lymph node lymphatic endothelial cell composition and function via MARCO

Infection with chikungunya virus (CHIKV) causes disruption of draining lymph node (dLN) organization, including paracortical relocalization of B cells, loss of the B cell-T cell border, and lymphocyte depletion that is associated with infiltration of the LN with inflammatory myeloid cells. Here, we find that during the first 24 h of infection, CHIKV RNA accumulates in MARCO-expressing lymphatic endothelial cells (LECs) in both the floor and medullary LN sinuses. The accumulation of viral RNA in the LN was associated with a switch to an antiviral and inflammatory gene expression program across LN stromal cells, and this inflammatory response, including recruitment of myeloid cells to the LN, was accelerated by CHIKV-MARCO interactions. As CHIKV infection progressed, both floor and medullary LECs diminished in number, suggesting further functional impairment of the LN by infection. Consistent with this idea, we find that antigen acquisition by LECs, a key function of LN LECs during infection and immunization, was reduced during pathogenic CHIKV infection.

A Novel Type of Monocytic Leukemia Stem Cell Revealed by the Clinical Use of Venetoclax-Based Therapy

The BCL2 inhibitor venetoclax has recently emerged as an important component of acute myeloid leukemia (AML) therapy. Notably, use of this agent has revealed a previously unrecognized form of pathogenesis characterized by monocytic disease progression. We demonstrate that this form of disease arises from a fundamentally different type of leukemia stem cell (LSC), which we designate as monocytic LSC (m-LSC), that is developmentally and clinically distinct from the more well-described primitive LSC (p-LSC). The m-LSC is distinguished by a unique immunophenotype (CD34-, CD4+, CD11b-, CD14-, CD36-), unique transcriptional state, reliance on purine metabolism, and selective sensitivity to cladribine. Critically, in some instances, m-LSC and p-LSC subtypes can co-reside in the same patient with AML and simultaneously contribute to overall tumor biology. Thus, our findings demonstrate that LSC heterogeneity has direct clinical significance and highlight the need to distinguish and target m-LSCs as a means to improve clinical outcomes with venetoclax-based regimens.

SIGNIFICANCE

These studies identify and characterize a new type of human acute myeloid LSC that is responsible for monocytic disease progression in patients with AML treated with venetoclax-based regimens. Our studies describe the phenotype, molecular properties, and drug sensitivities of this unique LSC subclass. This article is featured in Selected Articles from This Issue, p. 1949.

A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

Cancer initiation is orchestrated by an interplay between tumor-initiating cells and their stromal/immune environment. Here, by adapted single-cell RNA sequencing, we decipher the predicted signaling between tissue-resident hematopoietic stem/progenitor cells (HSPC) and their neoplastic counterparts with their native niches in the human bone marrow. LEPR+ stromal cells are identified as central regulators of hematopoiesis through predicted interactions with all cells in the marrow. Inflammatory niche remodeling and the resulting deprivation of critical HSPC regulatory factors are predicted to repress high-output hematopoietic stem cell subsets in NPM1-mutated acute myeloid leukemia (AML), with relative resistance of clonal cells. Stromal gene signatures reflective of niche remodeling are associated with reduced relapse rates and favorable outcomes after chemotherapy across all genetic risk categories. Elucidation of the intercellular signaling defining human AML, thus, predicts that inflammatory remodeling of stem cell niches drives tissue repression and clonal selection but may pose a vulnerability for relapse-initiating cells in the context of chemotherapeutic treatment.

SIGNIFICANCE

Tumor-promoting inflammation is considered an enabling characteristic of tumorigenesis, but mechanisms remain incompletely understood. By deciphering the predicted signaling between tissue-resident stem cells and their neoplastic counterparts with their environment, we identify inflammatory remodeling of stromal niches as a determinant of normal tissue repression and clinical outcomes in human AML. See related commentary by Lisi-Vega and Méndez-Ferrer, p. 349. This article is featured in Selected Articles from This Issue, p. 337.

COVID Variants, Villain and Victory: A Bioinformatics Perspective

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

Identification of an anergic BND cell-derived activated B cell population (BND2) in young-onset type 1 diabetes patients

Recent evidence suggests a role for B cells in the pathogenesis of young-onset type 1 diabetes (T1D), wherein rapid progression occurs. However, little is known regarding the specificity, phenotype, and function of B cells in young-onset T1D. We performed a cross-sectional analysis comparing insulin-reactive to tetanus-reactive B cells in the blood of T1D and controls using mass cytometry. Unsupervised clustering revealed the existence of a highly activated B cell subset we term BND2 that falls within the previously defined anergic BND subset. We found a specific increase in the frequency of insulin-reactive BND2 cells in the blood of young-onset T1D donors, which was further enriched in the pancreatic lymph nodes of T1D donors. The frequency of insulin-binding BND2 cells correlated with anti-insulin autoantibody levels. We demonstrate BND2 cells are pre-plasma cells and can likely act as APCs to T cells. These findings identify an antigen-specific B cell subset that may play a role in the rapid progression of young-onset T1D.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

scDual-Seq of Toxoplasma gondii-infected mouse BMDCs reveals heterogeneity and differential infection dynamics

Dendritic cells and macrophages are integral parts of the innate immune system and gatekeepers against infection. The protozoan pathogen, Toxoplasma gondii, is known to hijack host immune cells and modulate their immune response, making it a compelling model to study host-pathogen interactions. Here we utilize single cell Dual RNA-seq to parse out heterogeneous transcription of mouse bone marrow-derived dendritic cells (BMDCs) infected with two distinct genotypes of T. gondii parasites, over multiple time points post infection. We show that the BMDCs elicit differential responses towards T. gondii infection and that the two parasite lineages distinctly manipulate subpopulations of infected BMDCs. Co-expression networks define host and parasite genes, with implications for modulation of host immunity. Integrative analysis validates previously established immune pathways and additionally, suggests novel candidate genes involved in host-pathogen interactions. Altogether, this study provides a comprehensive resource for characterizing host-pathogen interplay at high-resolution.

SMARCB1 regulates a TFCP2L1-MYC transcriptional switch promoting renal medullary carcinoma transformation and ferroptosis resistance

Renal medullary carcinoma (RMC) is an aggressive tumour driven by bi-allelic loss of SMARCB1 and tightly associated with sickle cell trait. However, the cell-of-origin and oncogenic mechanism remain poorly understood. Using single-cell sequencing of human RMC, we defined transformation of thick ascending limb (TAL) cells into an epithelial-mesenchymal gradient of RMC cells associated with loss of renal epithelial transcription factors TFCP2L1, HOXB9 and MITF and gain of MYC and NFE2L2-associated oncogenic and ferroptosis resistance programs. We describe the molecular basis for this transcriptional switch that is reversed by SMARCB1 re-expression repressing the oncogenic and ferroptosis resistance programs leading to ferroptotic cell death. Ferroptosis resistance links TAL cell survival with the high extracellular medullar iron concentrations associated with sickle cell trait, an environment propitious to the mutagenic events associated with RMC development. This unique environment may explain why RMC is the only SMARCB1-deficient tumour arising from epithelial cells, differentiating RMC from rhabdoid tumours arising from neural crest cells.

Multiomic Single Cell Sequencing Identifies Stemlike Nature of Mixed Phenotype Acute Leukemia and Provides Novel Risk Stratification

Mixed phenotype acute leukemia (MPAL) is a leukemia whose biologic drivers are poorly understood, therapeutic strategy remains unclear, and prognosis is poor. We performed multiomic single cell (SC) profiling of 14 newly diagnosed adult MPAL patients to characterize the immunophenotypic, genetic, and transcriptional landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. However, progressive acquisition of mutations is associated with increased expression of immunophenotypic markers of immaturity. Using SC transcriptional profiling, we find that MPAL blasts express a stem cell-like transcriptional profile distinct from other acute leukemias and indicative of high differentiation potential. Further, patients with the highest differentiation potential demonstrated inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in this cohort, is applicable to bulk RNA sequencing data and was predictive of survival in an independent patient cohort, suggesting utility for clinical risk stratification.

Surface Lin28A expression consistent with cellular stress parallels indicators of senescence

AIMS

Declining cellular functional capacity resulting from stress or ageing is a primary contributor to impairment of myocardial performance. Molecular pathway regulation of biological processes in cardiac interstitial cells (CICs) is pivotal in stress and ageing responses. Altered localization of the RNA-binding protein Lin28A has been reported in response to environmental stress, but the role of Lin28A in response to stress in CICs has not been explored. Surface Lin28A redistribution is indicative of stress response in CIC associated with ageing and senescence.

METHODS AND RESULTS

Localization of Lin28A was assessed by multiple experimental analyses and treatment conditions and correlated to oxidative stress, senescence, and ploidy in adult murine CICs. Surface Lin28A expression is present on 5% of fresh CICs and maintained through Passage 2, increasing to 21% in hyperoxic conditions but lowered to 14% in physiologic normoxia. Surface Lin28A is coincident with elevated senescence marker p16 and beta-galactosidase (β-gal) expression in CICs expanded in hyperoxia, and also increases with polyploidization and binucleation of CICs regardless of oxygen culture. Transcriptional profiling of CICs using single-cell RNA-Seq reveals up-regulation of pathways associated with oxidative stress in CICs exhibiting surface Lin28A. Induction of surface Lin28A by oxidative stress is blunted by treatment of cells with the antioxidant Trolox in a dose-dependent manner, with 300 μM Trolox exposure maintaining characteristics of freshly isolated CICs possessing low expression of surface Lin28A and β-gal with predominantly diploid content.

CONCLUSION

Surface Lin28A is a marker of environmental oxidative stress in CICs and antioxidant treatment antagonizes this phenotype. The biological significance of Lin28 surface expression and consequences for myocardial responses may provide important insights regarding mitigation of cardiac stress and ageing.

Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing

Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.

Pioneer factor ASCL1 cooperates with the mSWI/SNF complex at distal regulatory elements to regulate human neural differentiation

Pioneer transcription factors are thought to play pivotal roles in developmental processes by binding nucleosomal DNA to activate gene expression, though mechanisms through which pioneer transcription factors remodel chromatin remain unclear. Here, using single-cell transcriptomics, we show that endogenous expression of neurogenic transcription factor ASCL1, considered a classical pioneer factor, defines a transient population of progenitors in human neural differentiation. Testing ASCL1's pioneer function using a knockout model to define the unbound state, we found that endogenous expression of ASCL1 drives progenitor differentiation by cis-regulation both as a classical pioneer factor and as a nonpioneer remodeler, where ASCL1 binds permissive chromatin to induce chromatin conformation changes. ASCL1 interacts with BAF SWI/SNF chromatin remodeling complexes, primarily at targets where it acts as a nonpioneer factor, and we provide evidence for codependent DNA binding and remodeling at a subset of ASCL1 and SWI/SNF cotargets. Our findings provide new insights into ASCL1 function regulating activation of long-range regulatory elements in human neurogenesis and uncover a novel mechanism of its chromatin remodeling function codependent on partner ATPase activity.

Single-cell transcriptome profiles of Drosophila fruitless-expressing neurons from both sexes

Drosophila melanogaster reproductive behaviors are orchestrated by fruitless neurons. We performed single-cell RNA-sequencing on pupal neurons that produce sex-specifically spliced fru transcripts, the fru P1-expressing neurons. Uniform Manifold Approximation and Projection (UMAP) with clustering generates an atlas containing 113 clusters. While the male and female neurons overlap in UMAP space, more than half the clusters have sex differences in neuron number, and nearly all clusters display sex-differential expression. Based on an examination of enriched marker genes, we annotate clusters as circadian clock neurons, mushroom body Kenyon cell neurons, neurotransmitter- and/or neuropeptide-producing, and those that express doublesex. Marker gene analyses also show that genes that encode members of the immunoglobulin superfamily of cell adhesion molecules, transcription factors, neuropeptides, neuropeptide receptors, and Wnts have unique patterns of enriched expression across the clusters. In vivo spatial gene expression links to the clusters are examined. A functional analysis of fru P1 circadian neurons shows they have dimorphic roles in activity and period length. Given that most clusters are comprised of male and female neurons indicates that the sexes have fru P1 neurons with common gene expression programs. Sex-specific expression is overlaid on this program, to build the potential for vastly different sex-specific behaviors.

Single-Nuclei RNA Sequencing of 5 Regions of the Human Prenatal Brain Implicates Developing Neuron Populations in Genetic Risk for Schizophrenia

BACKGROUND

While a variety of evidence supports a prenatal component in schizophrenia, there are few data regarding the cell populations involved. We sought to identify cells of the human prenatal brain mediating genetic risk for schizophrenia by integrating cell-specific gene expression measures generated through single-nuclei RNA sequencing with recent large-scale genome-wide association study (GWAS) and exome sequencing data for the condition.

METHODS

Single-nuclei RNA sequencing was performed on 5 brain regions (frontal cortex, ganglionic eminence, hippocampus, thalamus, and cerebellum) from 3 fetuses from the second trimester of gestation. Enrichment of schizophrenia common variant genetic liability and rare damaging coding variation was assessed in relation to gene expression specificity within each identified cell population.

RESULTS

Common risk variants were prominently enriched within genes with high expression specificity for developing neuron populations within the frontal cortex, ganglionic eminence, and hippocampus. Enrichments were largely independent of genes expressed in neuronal populations of the adult brain that have been implicated in schizophrenia through the same methods. Genes containing an excess of rare damaging variants in schizophrenia had higher expression specificity for developing glutamatergic neurons of the frontal cortex and hippocampus that were also enriched for common variant liability.

CONCLUSIONS

We found evidence for a distinct contribution of prenatal neuronal development to genetic risk for schizophrenia, involving specific populations of developing neurons within the second-trimester fetal brain. Our study significantly advances the understanding of the neurodevelopmental origins of schizophrenia and provides a resource with which to investigate the prenatal antecedents of other psychiatric and neurologic disorders.