Single-cell RNA Sequencing Studies Research Articles

Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis

Acinar cells produce digestive enzymes that impede transcriptomic characterization of the exocrine pancreas. Thus, single-cell RNA-sequencing studies of the pancreas underrepresent acinar cells relative to histological expectations, and a robust approach to capture pancreatic cell responses in disease states is needed. We sought to innovate a method that overcomes these challenges to accelerate study of the pancreas in health and disease. We leverage FixNCut, a single-cell RNA-sequencing approach in which tissue is reversibly fixed with dithiobis(succinimidyl propionate) before dissociation and single-cell preparation. We apply FixNCut to an established mouse model of acute pancreatitis, validate findings using GeoMx whole transcriptome atlas profiling, and integrate our data with prior studies to compare our method in both mouse and human pancreas datasets. FixNCut achieves unprecedented definition of challenging pancreatic cells, including acinar and immune populations in homeostasis and acute pancreatitis, and identifies changes in all major cell types during injury and recovery. We define the acinar transcriptome during homeostasis and acinar-to-ductal metaplasia and establish a unique gene set to measure deviation from normal acinar identity. We characterize pancreatic immune cells, and analysis of T-cell subsets reveals a polarization of the homeostatic pancreas toward type-2 immunity. We report immune responses during acute pancreatitis and recovery, including early neutrophil infiltration, expansion of dendritic cell subsets, and a substantial shift in the transcriptome of macrophages due to both resident macrophage activation and monocyte infiltration. FixNCut preserves pancreatic transcriptomes to uncover novel cell states during homeostasis and following pancreatitis, establishing a broadly applicable approach and reference atlas for study of pancreas biology and disease.

Macrophage profiling in atherosclerosis: understanding the unstable plaque.

The development and rupture of atherosclerotic plaques is a major contributor to myocardial infarctions and ischemic strokes. The dynamic evolution of the plaque is largely attributed to monocyte/macrophage functions, which respond to various stimuli in the plaque microenvironment. To this end, macrophages play a central role in atherosclerotic lesions through the uptake of oxidized low-density lipoprotein that gets trapped in the artery wall, and the induction of an inflammatory response that can differentially affect the stability of the plaque in men and women. In this environment, macrophages can polarize towards pro-inflammatory M1 or anti-inflammatory M2 phenotypes, which represent the extremes of the polarization spectrum that include Mhem, M(Hb), Mox, and M4 populations. However, this traditional macrophage model paradigm has been redefined to include numerous immune and nonimmune cell clusters based on in-depth unbiased single-cell approaches. The goal of this review is to highlight (1) the phenotypic and functional properties of monocyte subsets in the circulation, and macrophage populations in atherosclerotic plaques, as well as their contribution towards stable or unstable phenotypes in men and women, and (2) single-cell RNA sequencing studies that have advanced our knowledge of immune, particularly macrophage signatures present in the atherosclerotic niche. We discuss the importance of performing high-dimensional approaches to facilitate the development of novel sex-specific immunotherapies that aim to reduce the risk of cardiovascular events.

Single-cell analysis of human PBMCs in healthy and type 2 diabetes populations: dysregulated immune networks in type 2 diabetes unveiled through single-cell profiling.

Abnormalities in glucose metabolism that precede the onset of type 2 diabetes (T2D) activate immune cells, leading to elevated inflammatory factors and chronic inflammation. However, no single-cell RNA sequencing (scRNA-seq) studies have characterized the properties and networks of individual immune cells in T2D. Here, we analyzed peripheral blood mononuclear cells (PBMCs) from non-diabetes and T2D patients by scRNA-seq. We found that CD14 monocytes in T2D patients were in a pro-inflammatory state and intermediate monocytes expressed more MHC class II genes. In T2D patients, cytotoxic CD4 T cells, effector memory CD8 T cells, and γδ T cells have increased cytotoxicity and clonal expansion. B cells were characterized by increased differentiation into intermediate B cells, plasma cells, and isotype class switching with increased expression of soluble antibody genes. These results suggest that monocytes, T cells, and B cells could interact to induce chronic inflammation in T2D patients with pro-inflammatory characteristics.

An advanced comprehensive muti-cell-type-specific model for predicting anti-PD-1 therapeutic effect in melanoma.

Rationale: Immune checkpoint inhibitors targeting the programmed cell death (PD)-1/PD-L1 pathway have promise in patients with advanced melanoma. However, drug resistance usually results in limited patient benefits. Recent single-cell RNA sequencing studies have elucidated that MM patients display distinctive transcriptional features of tumor cells, immune cells and interstitial cells, including loss of antigen presentation function of tumor cells, exhaustion of CD8+T and extracellular matrix secreted by fibroblasts to prevents immune infiltration, which leads to a poor response to immune checkpoint inhibitors (ICIs). However, cell subgroups beneficial to anti-tumor immunity and the model developed by them remain to be further identified. Methods: In this clinical study of neoadjuvant therapy with anti-PD-1 in advanced melanoma, tumor tissues were collected before and after treatment for single-nucleus sequencing, and the results were verified using multicolor immunofluorescence staining and public datasets. Results: This study describes four cell subgroups which are closely associated with the effectiveness of anti-PD-1 treatment. It also describes a cell-cell communication network, in which the interaction of the four cell subgroups contributes to anti-tumor immunity. Furthermore, we discuss a newly developed predictive model based on these four subgroups that holds significant potential for assessing the efficacy of anti-PD-1 treatment. Conclusions: These findings elucidate the primary mechanism of anti-PD-1 resistance and offer guidance for clinical drug administration for melanoma.

Comprehensive Integration of Multiple Single-Cell Transcriptomic Data Sets Defines Distinct Cell Populations and Their Phenotypic Changes in Murine Atherosclerosis.

The application of single-cell transcriptomic (single-cell RNA sequencing) analysis to the study of atherosclerosis has provided unique insights into the molecular and genetic mechanisms that mediate disease risk and pathophysiology. However, nonstandardized methodologies and relatively high costs associated with the technique have limited the size and replication of existing data sets and created disparate or contradictory findings that have fostered misunderstanding and controversy. To address these uncertainties, we have performed a conservative integration of multiple published single-cell RNA sequencing data sets into a single meta-analysis, performed extended analysis of native resident vascular cells, and used in situ hybridization to map the disease anatomic location of the identified cluster cells. To investigate the transdifferentiation of smooth muscle cells to macrophage phenotype, we have developed a classifying algorithm based on the quantification of reporter transgene expression. The reporter gene expression tool indicates that within the experimental limits of the examined studies, transdifferentiation of smooth muscle cell to the macrophage lineage is extremely rare. Validated transition smooth muscle cell phenotypes were defined by clustering, and the location of these cells was mapped to lesion anatomy with in situ hybridization. We have also characterized 5 endothelial cell phenotypes and linked these cellular species to different vascular structures and functions. Finally, we have identified a transcriptomically unique cellular phenotype that constitutes the aortic valve. Taken together, these analyses resolve a number of outstanding issues related to differing results reported with vascular disease single-cell RNA sequencing studies, and significantly extend our understanding of the role of resident vascular cells in anatomy and disease.

Contribution of collagen XIII to lung function and development of pulmonary fibrosis

BackgroundCollagen XIII is a transmembrane collagen associated with neuromuscular junction development, and in humans its deficiency results in congenital myasthenic syndrome type 19 (CMS19), which leads to breathing difficulties. CMS19...

Natural Killer Lymphocytes Mediate Renal Fibrosis Due to Acute Cardiorenal Syndrome.

The acute kidney injury (AKI) to chronic kidney disease (CKD) transition presents an opportunity for intervention to prevent CKD. Our lab developed a novel murine model of AKI-CKD transition, cardiac arrest and cardiopulmonary resuscitation (CA/CPR), in which all animals develop CKD at 7 weeks. The purpose of this study was to identify potential immune drivers of fibrosis after CA/CPR. Cardiac arrest was induced by potassium chloride and mice were resuscitated with chest compressions and epinephrine. The kidney immune landscape after CA/CPR was profiled using 11-color flow cytometry analysis and immunofluorescence. Immune cell-derived mediators of fibrosis were identified by analyzing data from three previously published single cell or single nuclear RNA sequencing (sc/snRNASeq) studies. NRK49F fibroblasts were treated with granzyme A (GzA) in vitro, then cell proliferation was quantified using 5-ethynyl-2'-deoxyuridine. GzA was pharmacologically inhibited both in vitro and in vivo. Immune cells infiltrated the kidney after CA/CPR, consisting primarily of innate immune cells, including monocytes/macrophages, neutrophils, and natural killer (NK) cells. NK cell infiltration immediately preceded mesenchymal cell expansion, which occurred starting 7 days after CA/CPR. Immune cells colocalized with mesenchymal cells, accumulating in areas of fibrosis. Analysis of previously published sc/snRNASeq data revealed GzA as a potential mediator of immune to mesenchymal communication. GzA administration to fibroblasts in vitro induced cell growth and proliferation. Pharmacologic blockade of GzA signaling in vivo attenuated fibrosis and improved renal function after CA/CPR. Renal inflammation occurs during cardiorenal syndrome, which correlates with mesenchymal cell expansion. GzA, produced by NK cells, presents a novel therapeutic target to prevent the transition to CKD after AKI.

Mechanisms of Aging Vary with Developmental Stages in the Human Hematopoietic Trajectory

Background : Hematopoietic stem and progenitor cells (HSPCs) undergo functional changes with age. An unresolved issue is whether aging is induced by a shift in intrinsic state in which all HSPCs undergo coordinated changes in functional potential, or by clonal evolution of a senescent population.Identification of senescent cells in human hematopoiesis poses another challenge. In this study, we have identified lineage-specific, senescence-associated pathways at different developmental stages of HSPCs and have developed an “aging signature HSC” for non-primed human HSCs. Methods: HSPCs (CD34+ cells) were isolated from the bone marrow of healthy human subjects (old = >60 years; young = 21 to <35 years). Single-cell RNA-sequencing (scRNAseq) studies and downstream analysis were performed using different R packages for normalization, dimension reduction, clustering, classification, pseudotime ordering, and developmental trajectory analyses. Gene Set Enrichment Analysis, GSEA (version 4.3.2., Broad Institute, Inc) was applied to identify pathways and processes that were coordinately up- or down-regulated with aging. Results: HSPCs (CD34+ cells) were derived from 13 healthy human subjects (old subjects n = 6; young subjects n = 7). There were 26,076 genes identified. The HSPCs were ordered and classified along their developmental trajectory during differentiation processes from (1) hematopoietic stem cells (HSC) to (2) myeloid-lymphoid progenitors (MLP), (3) megakaryocyte-erythrocyte progenitors (MEP), (4) granulocytic-monocytic progenitors (GMP), (5) granulocyte- (G), and (6) monocyte- (Mono) progenitors, (7) dendritic cell (DC), (8) erythroid progenitors (E), as well as various subsets of lymphocytes (Ly1 to Ly4). The results are depicted in Figure 1A. Remarkable are the increases in HSCs (p < 0.05) and in Ly4 (p < 0.001) in old subjects. The Kolmogorov-Smirnov test showed a significant delay in the developmental “pseudotimes” of HSCs (p < 0.001) and of Ly4 (p < 0.05). We then compared the differences in GSEA between old and young subjects in each of the following clusters: (A) whole HSPCs; (B) HSPCs along myeloid pathway (mye-HSPC); (C) HSC-MLP-MEP; (D) HSC. The major findings relevant to aging are summarized in Fig. 1B. Pathways that are significantly increased in the old subjects and throughout all comparisons include: “Formation of senescence associated heterochromatin foci”, “Aging signature HSC”, “DNA damage telomere stress induced senescence”, and “TNFR1 induced NFKB signaling pathway”. “Hypoxia”, “P53 pathway”, “IL2 signaling”, “TNFA signaling via NFKB” were elevated upon enrichment of the primitive HSC population, while other pathways decreased: “Senescence associated secretory phenotype (SASP)”, “DNA methylation”. “Cell cycle” and “DNA repair” are consistently more abundant in young subjects. We analyzed the 512 cells in the HSC cluster with higher resolution and were able to identify a sub-cluster “MPP2A” that is significantly increased in old subjects (4.5% of all HSPCs and 28.5% of HSCs, versus 1.9% and 12.9% respectively in young subjects; p < 0.005). The results are depicted in the highlighted circles in Fig. 1A. This population is quiescent and is highly enriched in “aging signature HSC” in old subjects (p <0.0001). Prominent examples of these signature genes within this list are shown in Fig. 1C). Further along the trajectory, a unique population of B cells “Ly4” was found predominantly in old subjects. The transcriptome profile Ly4 is similar to immature and age-associated B cells (ABCs). This cluster of lymphoid cells may represent the precursors of ABCs described thus far only in peripheral blood, providing another evidence that the aging mechanisms at various differentiation stages are different. Conclusions: In this study, we have defined specific pathways that distinctly separate aging HSCPs from young ones, and shown that the definition of “aging signature” varies with developmental stages. The accumulation a small cluster in the non-primed HSC compartment, with “aging signature HSC” and cell cycle arrest in old subjects indicates that aging of human hematopoiesis is induced by clonal evolution. The abundance of a unique population among lymphoid precursors with expression profiles similar to ABCs demonstrates that lymphoid and myeloid progenitors age differently.

TMIC-63. SINGLE CELL SEQUENCING OF RECURRENT GLIOBLASTOMA DEFINES CELLULAR STATES ASSOCIATED WITH THERAPY RESISTANCE

Abstract Glioblastoma (GBM), an aggressive, primary tumor is composed of multiple clonal populations. Resistance to therapy is a hallmark of GBM, making it a deadly disease. Upon therapeutic stress, tumor heterogeneity and glial stem cell plasticity within the primary tumor contributes to reprogramming of the transcriptional activity. This alters the tumor landscape and generates therapy resistant clonal populations. Single cell RNA sequencing studies of primary GBM have defined various tumor cell states namely, the NPC-like (Neuronal precursor-like), OPC-like (oligodendrocyte precursor-like) and MES-like (mesenchymal-like) cellular states. These studies have also defined the immune components and the normal brain cells associated with primary GBM. Yet, there are no studies focused on the single cell analysis of recurrent GBM. To define the tumor landscape at recurrence, we performed single cell RNA sequencing of 180K cells from excised tumor tissue obtained from 11 patients after failure of therapeutic interventions. A corresponding cohort of 240K cells were sequenced from primary tumors obtained from 13 patients. We performed cellular state analyses of tumor cells in both the primary (~70K) and recurrent (~10K) samples. Primary GBM showed higher fractions of AC-like (0.55), compared to NPC-like (0.10), OPC-like (0.20) and MES-like (0.15). On the other hand, recurrent tissues have very low AC-like (0.09) but elevated NPC-like (0.23), OPC-like (0.40) and MES-like (0.27). This data shows a significant decrease in AC-like cells and corresponding increase in MES-like, OPC-like, NPC-like cells after recurrence. Within this dataset we also have matched longitudinal samples for analysis. We are currently performing trajectory analysis to identify genes and transcription programs activated during tumor recurrence. Experimentally, using known markers for the different cellular states we have also performed immunofluorescence analyses to validate the different cell states altered during recurrence. Our efforts to generate comprehensive recurrent GBM atlas will prove to be valuable to the GBM community.

IMMU-11. NLRP3+ TUMOR-ASSOCIATED MYELOID CELLS ARE ENRICHED IN ASTROCYTIC GLIOMAS AND REPRESENT A THERAPEUTIC VULNERABILITY

Abstract BACKGROUND Tumor-associated myeloid cells (TAMs) are known drivers of immunosuppression and glioma progression. Hence novel approaches for repolarizing TAMs from a protumorigenic to an antitumorigenic phenotype is an active area of investigation. While single-cell RNA sequencing (scRNA-seq) studies have characterized glioma TAMs in great detail, their uniqueness or similarity to TAMs in other tumors remains unknown. Cross-comparison of glioma TAMs with TAM subsets from other tumor types will aid in identifying novel TAM-targeted immunotherapies. METHODS We performed scRNA-seq of ~26K myeloid cells from nine patients with high-grade astrocytoma (n=3) and oligodendroglioma (n=6). The scRNA-seq findings were validated using TCGA RNA-seq data, public scRNA-Seq data, and immunohistochemistry (IHC) analysis. RESULTS The TAM clusters in gliomas predominantly exhibited a microglia-specific gene signature, while macrophage and monocyte clusters were also identified. Utilizing canonical TAM markers, we identified three major TAM subtypes, SPP1+ regulatory, FOLR2+ tissue-resident, and NLRP3+ inflammatory, enriched in astrocytomas. A gene set enrichment analysis of the three TAM subtype genes confirmed their enrichment in astrocytomas (IDHMut-non-codel) (P=0.0005; normalized enrichment score [NES]=1.79) in the TCGA bulk RNAseq data from IDH1-mutant LGGs. Investigation of the three TAM subtype markers in public scRNA-seq data confirmed their enrichment in immune/myeloid clusters across multiple glioma types (gangliogliomas, pilocytic astrocytomas, astrocytomas, diffuse midline gliomas, and glioblastomas). Finally, IHC analysis of 14 astrocytomas demonstrated regional variation in NLRP3 and FOLR2 expression in all cases, with a trend toward an inverse association between NLRP3 and FOLR2 expression. CONCLUSIONS 1) Astrocytomas (IDHMut-non-codel) comprise TAM subsets common across multiple tumor types. 2) The FOLR2+ TAMs predict favorable outcomes, while the NLRP3+ TAMs are immunosuppressive and predict adverse outcomes in breast cancer. Given the inverse expression of NLRP3 and FOLR2 in astrocytomas, we hypothesize that NLRP3 TAMs+ are immunosuppressive and a potential immunotherapeutic target in astrocytomas.

STEM-22. SINGLE-CELL LINEAGE TRACING IN PRIMARY GLIOBLASTOMA REVEALS DISTINCT PROGENITOR SUBTYPES DRIVING INTRATUMORAL HETEROGENEITY

Abstract Glioblastoma (GBM) is the most common type of adult primary brain tumor with nearly a 100% rate of recurrence. One of the key clinical challenges is the vast degree of inter- and intra-tumoral variation in cell types and cell states, which change throughout the course of disease. Single-cell RNA-sequencing (scRNA-seq) studies in GBM samples have illuminated the complex landscape of cell type composition in primary and recurrent GBM, however these studies do not address how this diverse array of cell types developed or which cell populations are principally responsible for generating the heterogeneity and treatment resistance in the tumor. In this study, we implement a scRNA-seq compatible lineage tracing strategy, CellTagging, to track the behavior of individual tumor cells over time. Human GBM samples are dissociated immediately upon resection and infected with the CellTag DNA barcode library. The labeled cells are transplanted onto human cortical organoids, which facilitate the crucial normal-tumor cell interactions that enable GBM growth and invasion. After a period of proliferation, tumor cells are dissociated from the organoid and a fraction are harvested for scRNA-seq for transcriptomic and DNA barcode analysis. The remaining tumor cells are re-transplanted onto new organoids and harvested for scRNA-seq at later timepoints. Clonal relationships are determined by identifying cells with the same DNA barcode profile, and cell type characterizations are assigned via transcriptomic analysis. Transcriptomic profiling demonstrates the existence of multiple subtypes of tumor progenitors across timepoints. Strikingly, clonal analysis indicates that each progenitor subtype gives rise to numerous mature tumor cell types, suggesting multiple distinct lineages that contribute to tumor heterogeneity. Through differential expression analysis, we can predict which progenitors are most likely to drive which cell states. By introducing a treatment paradigm into our lineage tracing protocol, we identify tumor progenitor populations that contribute to treatment resistance.

Organotypic heterogeneity in microvascular endothelial cell responses in sepsis-a molecular treasure trove and pharmacological Gordian knot.

In the last decades, it has become evident that endothelial cells (ECs) in the microvasculature play an important role in the pathophysiology of sepsis-associated multiple organ dysfunction syndrome (MODS). Studies on how ECs orchestrate leukocyte recruitment, control microvascular integrity and permeability, and regulate the haemostatic balance have provided a wealth of knowledge and potential molecular targets that could be considered for pharmacological intervention in sepsis. Yet, this information has not been translated into effective treatments. As MODS affects specific vascular beds, (organotypic) endothelial heterogeneity may be an important contributing factor to this lack of success. On the other hand, given the involvement of ECs in sepsis, this heterogeneity could also be leveraged for therapeutic gain to target specific sites of the vasculature given its full accessibility to drugs. In this review, we describe current knowledge that defines heterogeneity of organ-specific microvascular ECs at the molecular level and elaborate on studies that have reported EC responses across organ systems in sepsis patients and animal models of sepsis. We discuss hypothesis-driven, single-molecule studies that have formed the basis of our understanding of endothelial cell engagement in sepsis pathophysiology, and include recent studies employing high-throughput technologies. The latter deliver comprehensive data sets to describe molecular signatures for organotypic ECs that could lead to new hypotheses and form the foundation for rational pharmacological intervention and biomarker panel development. Particularly results from single cell RNA sequencing and spatial transcriptomics studies are eagerly awaited as they are expected to unveil the full spatiotemporal signature of EC responses to sepsis. With increasing awareness of the existence of distinct sepsis subphenotypes, and the need to develop new drug regimen and companion diagnostics, a better understanding of the molecular pathways exploited by ECs in sepsis pathophysiology will be a cornerstone to halt the detrimental processes that lead to MODS.

Sctensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data

BackgroundComplex biological systems are described as a multitude of cell–cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand–receptor (L–R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. ResultsIn this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L–R pairs.ConclusionsThrough extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

A Multimodal Single Cell Atlas of Adult Murine Haematopoiesis Reveals Nine Differentiation Trajectories and Epigenetic Regulatory Programs

Single cell genomics has revolutionised our understanding of differentiating systems and led to a reinterpretation of haematopoiesis, moving from a step-wise hierarchical tree model towards more continous differentiation landscapes. Recent single cell RNA sequencing (scRNA-seq) studies of Lineage- ckit+ (LK) mouse bone marrow progenitors have defined a highly granular landscape of differentiation from multipotent stem cells to committed progenitors in 8 different lineages and have served as a valuable reference landscape for comparisons with perturbation states. While these landscapes have permitted exploration and interrogation of early haematopoietic progenitors, as yet, no map of mouse total bone marrow haematopoiesis exists at single cell resolution, limiting efforts to fully understand the gene programs which specify and the cell surface phenotypes which identify maturing haematopoietic lineages nor the connection between early bifurcation decisions in progenitor cells and mature cellular outputs. To answer these questions, we performed droplet-based scRNA-seq and cell surface proteomics on whole bone marrow from 4 male and 4 female 12-week old C57Bl6 mice, using a panel of 138 antibodies against cell surface antigens. To increase representation of rare progenitor populations, we complemented total bone marrow mononuclear cells with c-kit enrichment and FACS sorted LK and LK sca1-positive (LSK) populations. After quality control filtering the single cell transcriptomes and proteomes, over 198,000 single cells were analysed separately before multimodal integration into a single UMAP embedding. Addition of cell surface proteome information to the scRNA-seq data through multimodal integration produced a representation with greater resolution of haematopoietic cell types, including improved resolution of T-cell subtypes and separation of a rare population of eosinophil progenitors which were previously indistinguishable from basophil progenitors. This integrated landscape of mouse haematopoiesis was then clustered and annotated though an iterative process of label transfer and manual annotation into 41 cell type states (Figure 1). Using cell fate analysis, which utilises transcriptional similarity and pseudotemporal ordering to estimate the probability of a single cell's commitment to defined terminal states, we were able to resolve cell fate probabilities towards nine haematopoietic lineages (erythroid, neutrophil, megakaryocyte, lymphoid, monocyte, dendritic cell (DC), plasmacytoid dendritic cell (pDC), basophil and eosinophil (Figure 2)). From these probabilities we defined nine trajectories from the earliest uncommitted progenitors to terminally differentiated populations, including a pDC trajectory involving both lymphoid and common DC progenitors. Analysis of trajectory-based gene expression trends provides a framework for discovery of lineage drivers as well as shared gene expression programs across multiple lineages. Leveraging cell surface proteome data permits the use of ‘in-silico-FACS’ to describe phenotypes of cell populations which exist at cell fate branch-points, for further in-vitro characterisation. To demonstrate the utility of our atlas as a reference, we projected perturbation datasets from 7 pre-leukaemic mouse models onto the atlas, allowing quantitation of mutation-associated cellular and tissue-scale alterations. Finally, we have leveraged the transcription-start site enrichment of 5' scRNA-seq to compute enhancer RNA (eRNA) expression in our atlas, allowing the identification of dynamic usage of specific enhancer elements during haematopoietic specification and thus providing a novel framework for correlation of eRNA expression (as a proxy for active distal chromatin regions) with gene expression trends along specific haematopoietic lineages.

Combining single-cell RNA sequencing and population-based studies reveals hand osteoarthritis-associated chondrocyte subpopulations and pathways

Hand osteoarthritis is a common heterogeneous joint disorder with unclear molecular mechanisms and no disease-modifying drugs. In this study, we performed single-cell RNA sequencing analysis to compare the cellular composition and subpopulation-specific gene expression between cartilage with macroscopically confirmed osteoarthritis (n = 5) and cartilage without osteoarthritis (n = 5) from the interphalangeal joints of five donors. Of 105 142 cells, we identified 13 subpopulations, including a novel subpopulation with inflammation-modulating potential annotated as inflammatory chondrocytes. Fibrocartilage chondrocytes exhibited extensive alteration of gene expression patterns in osteoarthritic cartilage compared with nonosteoarthritic cartilage. Both inflammatory chondrocytes and fibrocartilage chondrocytes showed a trend toward increased numbers in osteoarthritic cartilage. In these two subpopulations from osteoarthritic cartilage, the ferroptosis pathway was enriched, and expression of iron overload-related genes, e.g., FTH1, was elevated. To verify these findings, we conducted a Mendelian randomization study using UK Biobank and a population-based cross-sectional study using data collected from Xiangya Osteoarthritis Study. Genetic predisposition toward higher expression of FTH1 mRNA significantly increased the risk of hand osteoarthritis (odds ratio = 1.07, 95% confidence interval: 1.02–1.11) among participants (n = 332 668) in UK Biobank. High levels of serum ferritin (encoded by FTH1), a biomarker of body iron overload, were significantly associated with a high prevalence of hand osteoarthritis among participants (n = 1 241) of Xiangya Osteoarthritis Study (P-for-trend = 0.037). In conclusion, our findings indicate that inflammatory and fibrocartilage chondrocytes are key subpopulations and that ferroptosis may be a key pathway in hand osteoarthritis, providing new insights into the pathophysiology and potential therapeutic targets of hand osteoarthritis.

Genome-Scale High-Resolution Spatial Mapping of the Pro-Tumorigenic Cellular Niche in Classic Hodgkin Lymphoma

Introduction: A fundamental hallmark of cancer is that tumor cells repurpose the tissue microenvironment to promote their own survival. An increased understanding of these mechanisms may lead to improved microenvironment-directed therapies, particularly in lymphoid malignancies. In classic Hodgkin lymphoma (cHL), the rare malignant Hodgkin Reed Sternberg (HRS) cells are surrounded by a CD4+ T-cell and macrophage-rich inflammatory infiltrate. Recent multiplexed immunofluorescence studies suggest that the micron-scale niche around HRS cells is composed of distinct populations of PD-L1+ macrophages and CD4+ T cells, including regulatory CTLA4+ and LAG3+ subsets (Carey et al. Blood 2017, Patel et al. Blood 2019 and Aoki et al. Cancer Discov 2020). However, the topography of the intact tumor microenvironment of cHL requires further definition. Recent single-cell RNA sequencing studies have led to important insights into the biology of cHL; however, they do not adequately capture myeloid cells, fibroblasts, and HRS cells, likely due to the relative fragility of these cells in conventional tissue dissociation protocols. In this study, we use tandem single nucleus and spatially resolved RNA sequencing to systematically dissect the pro-tumorigenic cellular niche of cHL to define potentially targetable microenvironmental dependencies. Methods: Using single nucleus (10X Chromium V3.1 gene expression) and spatially resolved RNA sequencing (Slide-seqV2 and 10X Visium), we generated a dataset of 12 newly diagnosed cHL and 7 reactive lymphoid tissues comprising over 324,855 nuclei and 2.5 million spatially resolved gene expression profiles to construct a genome-scale single cell and spatially resolved atlas of cHL. To validate the expression of ligands/receptors on proximal cell types within the HRS cell niche at single-cell resolution, we also generated an orthogonal 1000-plex image-based spatial transcriptomic dataset (CosMx Spatial Molecular Imager). Additionally, we developed a novel Bayesian consensus tensor factorization approach (C-ZIPTF) to infer shared cancer-associated immune signatures and ligand-receptor interactions. Results: Single nucleus RNA sequencing captures a diversity of cell types and cell states in cHL, including distinct myeloid and stromal cell subsets and HRS cells. Copy number inference analysis identifies the HRS cells, and unsupervised consensus tensor factorization reveals recurrent HRS cell transcriptional states, including aberrant myeloid, stromal, neuronal, and secretory/cytokine programs. Of all clinical and histological parameters, EBV infection status showed the strongest association with HRS cell transcriptional state. Regulatory T cells, macrophages, and fibroblasts are quantitatively increased in cHL compared to reactive lymphoid tissues and additionally exhibit qualitatively altered expression programs. Using C-ZIPTF, we identify disease-defining and prognostically relevant immunosuppressive regulatory T cell and myeloid cell programs and an activated pro-fibrotic myofibroblast-like fibroblast signature. Spatial analysis provides strong evidence for a “niche” model, i.e., the spatial organization of cell types and states around HRS cells. Specifically, CD4+ T cells and myeloid cells are spatially enriched in the immediate proximity of HRS cells and, plasma cells are depleted. Moreover, the CD4+ T cells and myeloid cells exhibit a specialized pro-tumorigenic gene expression program with increased expression of multiple immunosuppressive cytokines, checkpoint molecules, and putative growth factors for HRS cells. Spatially-aware ligand-receptor interaction analysis enables systematic identification and prioritization of novel growth factors for HRS cells. Functional testing of these putative growth factors using an ex vivo 3D cell culture system confirms their growth-promoting effect on primary HRS cells. Conclusions: The HRS cells in cHL are surrounded by a distinctive pro-tumorigenic cellular niche that not only mediates immune evasion but may also directly promote tumor cell growth. These observations provide a rich set of hypotheses for preclinical model development and microenvironment-directed therapies. More broadly, our study demonstrates the power of spatial genomic approaches to deconvolute the molecular architecture of pro-tumorigenic cellular niches in tumor ecosystems.

Dynamic scRNA-seq of live human pancreatic slices reveals functional endocrine cell neogenesis through an intermediate ducto-acinar stage

Human pancreatic plasticity is implied from multiple single-cell RNA sequencing (scRNA-seq) studies. However, these have been invariably based on static datasets from which fate trajectories can only be inferred using pseudotemporal estimations. Furthermore, the analysis of isolated islets has resulted in a drastic underrepresentation of other cell types, hindering our ability to interrogate exocrine-endocrine interactions. The long-term culture of human pancreatic slices (HPSs) has presented the field with an opportunity to dynamically track tissue plasticity at the single-cell level. Combining datasets from same-donor HPSs at different time points, with or without a known regenerative stimulus (BMP signaling), led to integrated single-cell datasets storing true temporal or treatment-dependent information. This integration revealed population shifts consistent with ductal progenitor activation, blurring of ductal/acinar boundaries, formation of ducto-acinar-endocrine differentiation axes, and detection of transitional insulin-producing cells. This study provides the first longitudinal scRNA-seq analysis of whole human pancreatic tissue, confirming its plasticity in a dynamic fashion.

The structural and functional complexity of the integrative hypothalamus.

The hypothalamus ("hypo" meaning below, and "thalamus" meaning bed) consists of regulatory circuits that support basic life functions that ensure survival. Sitting at the interface between peripheral, environmental, and neural inputs, the hypothalamus integrates these sensory inputs to influence a range of physiologies and behaviors. Unlike the neocortex, in which a stereotyped cytoarchitecture mediates complex functions across a comparatively small number of neuronal fates, the hypothalamus comprises upwards of thousands of distinct cell types that form redundant yet functionally discrete circuits. With single-cell RNA sequencing studies revealing further cellular heterogeneity and modern photonic tools enabling high-resolution dissection of complex circuitry, a new era of hypothalamic mapping has begun. Here, we provide a general overview of mammalian hypothalamic organization, development, and connectivity to help welcome newcomers into this exciting field.

On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.

Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.

Identification of Clinically Relevant Brain Endothelial Cell Biomarkers in Plasma.

Proteins expressed by brain endothelial cells (BECs), the primary cell type of the blood-brain barrier, may serve as sensitive plasma biomarkers for neurological and neurovascular conditions, including cerebral small vessel disease. Using data from the BLSA (Baltimore Longitudinal Study of Aging; n=886; 2009-2020), BEC-enriched proteins were identified among 7268 plasma proteins (measured with SomaScanv4.1) using an automated annotation algorithm that filtered endothelial cell transcripts followed by cross-referencing with BEC-specific transcripts reported in single-cell RNA-sequencing studies. To identify BEC-enriched proteins in plasma most relevant to the maintenance of neurological and neurovascular health, we selected proteins significantly associated with 3T magnetic resonance imaging-defined white matter lesion volumes. We then examined how these candidate BEC biomarkers related to white matter lesion volumes, cerebral microhemorrhages, and lacunar infarcts in the ARIC study (Atherosclerosis Risk in Communities; US multisite; 1990-2017). Finally, we determined whether these candidate BEC biomarkers, when measured during midlife, were related to dementia risk over a 25-year follow-up period. Of the 28 proteins identified as BEC-enriched, 4 were significantly associated with white matter lesion volumes (CDH5 [cadherin 5], CD93 [cluster of differentiation 93], ICAM2 [intracellular adhesion molecule 2], GP1BB [glycoprotein 1b platelet subunit beta]), while another approached significance (RSPO3 [R-Spondin 3]). A composite score based on 3 of these BEC proteins accounted for 11% of variation in white matter lesion volumes in BLSA participants. We replicated the associations between the BEC composite score, CDH5, and RSPO3 with white matter lesion volumes in ARIC, and further demonstrated that the BEC composite score and RSPO3 were associated with the presence of ≥1 cerebral microhemorrhages. We also showed that the BEC composite score, CDH5, and RSPO3 were associated with 25-year dementia risk. In addition to identifying BEC proteins in plasma that relate to cerebral small vessel disease and dementia risk, we developed a composite score of plasma BEC proteins that may be used to estimate blood-brain barrier integrity and risk for adverse neurovascular outcomes.