Multiple Single Cell Research Articles

Cell Type Diversity Statistic: An Entropy-Based Metric to Compare Overall Cell Type Composition Across Samples.

Changes of cell type composition across samples can carry biological significance and provide insight into disease and other conditions. Single cell transcriptomics has made it possible to study cell type composition at a fine resolution. Most single cell studies investigate compositional changes between samples for each cell type independently, not accounting for the fixed number of cells per sample in sequencing data. Here, we provide a metric of the distribution of cell type proportions in a sample that can be used to compare the overall distribution of cell types across multiple samples and biological conditions. This is the first method to measure overall cell type composition at the single cell level. We use the method to assess compositional changes in peripheral blood mononuclear cells (PBMCs) related to aging and extreme old age using multiple single cell datasets from individuals of four age groups across the human lifespan.

Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment

ABSTRACT The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterogeneity is the key for developing immunotherapy. However, the available methods can only partially portray the TME heterogeneity with a small number of cell types. Here, we developed a deep learning-based frame with a design visible, DCNet, that embeds the relationships between cells and their marker genes in the neural network, and can infer the cell landscape with more than 400 cell types based on bulk RNA-seq data. DCNet accurately recapitulated the cell landscape of multiple single cell RNA-seq datasets, which showed better robustness and stability. Based on the cell landscape of TCGA patients, which was built with DCNet, the patients were divided into two groups with significant differences in survival time and distinct cell-type populations. DCNet provides a foundation for decoding TME heterogeneity. The source code of DCNet can be found on GitHub: https://github.com/xindd/DCNet.

DevKidCC allows for robust classification and direct comparisons of kidney organoid datasets

BackgroundWhile single-cell transcriptional profiling has greatly increased our capacity to interrogate biology, accurate cell classification within and between datasets is a key challenge. This is particularly so in pluripotent stem cell-derived organoids which represent a model of a developmental system. Here, clustering algorithms and selected marker genes can fail to accurately classify cellular identity while variation in analyses makes it difficult to meaningfully compare datasets. Kidney organoids provide a valuable resource to understand kidney development and disease. However, direct comparison of relative cellular composition between protocols has proved challenging. Hence, an unbiased approach for classifying cell identity is required.MethodsThe R package, scPred, was trained on multiple single cell RNA-seq datasets of human fetal kidney. A hierarchical model classified cellular subtypes into nephron, stroma and ureteric epithelial elements. This model, provided in the R package DevKidCC (github.com/KidneyRegeneration/DevKidCC), was then used to predict relative cell identity within published kidney organoid datasets generated using distinct cell lines and differentiation protocols, interrogating the impact of such variations. The package contains custom functions for the display of differential gene expression within cellular subtypes.ResultsDevKidCC was used to directly compare between distinct kidney organoid protocols, identifying differences in relative proportions of cell types at all hierarchical levels of the model and highlighting variations in stromal and unassigned cell types, nephron progenitor prevalence and relative maturation of individual epithelial segments. Of note, DevKidCC was able to distinguish distal nephron from ureteric epithelium, cell types with overlapping profiles that have previously confounded analyses. When applied to a variation in protocol via the addition of retinoic acid, DevKidCC identified a consequential depletion of nephron progenitors.ConclusionsThe application of DevKidCC to kidney organoids reproducibly classifies component cellular identity within distinct single-cell datasets. The application of the tool is summarised in an interactive Shiny application, as are examples of the utility of in-built functions for data presentation. This tool will enable the consistent and rapid comparison of kidney organoid protocols, driving improvements in patterning to kidney endpoints and validating new approaches.

Single-Cell and Bulk RNASeq Profiling of COVID-19 Patients Reveal Immune and Inflammatory Mechanisms of Infection-Induced Organ Damage.

The SARS-CoV-2 virus’s ability to induce hypercytokinemia and cause multiple organ failure makes it imperative to find effective treatments. To understand the mechanism of viral infection and its effects on organ tissues, we analyzed multiple single-cell and bulk RNAseq data from COVID-19 patients’ organ samples. Various levels of severity of infection were accounted for, with comparative analyses between mild, moderate, and severely infected patients. Our analysis uncovered an upregulation of the innate immune response via several inflammatory genes, IL-2, IL-6, IL-8, IL-17A, and NF-κB. Consequently, we found that the upregulation of these downstream effects can lead to organ injury. The downregulated pathways such as eukaryotic initiation factor 2 (eIF2) and eIF4-mediated host translation, were found to lead to an increased viral translation. We also found that the loss of inhibitory peptides can suppress an overactive innate immune response via NF-κB and interleukin-mediated pathways. Investigation of viral-host protein mapping showed that the interaction of viral proteins with host proteins correlated with the down- and upregulation of host pathways such as decreased eIF2-mediated host translation and increased hypertrophy and fibrosis. Inflammation was increased via the stimulation of pro-inflammatory cytokines and suppression of host translation pathways that led to reduced inflammatory inhibitors. Cardiac hypertrophy and organ fibrosis were the results of increased inflammation in organs of severe and critical patients. Finally, we identified potential therapeutic targets for the treatment of COVID-19 and its deleterious effects on organs. Further experimental investigation would conclusively determine the effects of COVID-19 infection on organs other than the lungs and the effectiveness of the proposed therapeutic targets.

Microfluidic technology for multiple single-cell capture.

Microfluidic devices are widely used in single-cell capture and for pairing single cells or groups of cells for cell-cell interaction analysis; these advances have improved drug screening and cell signal transduction analysis. The complex in vivo environment involves interactions between two cells and among multiple cells of the same or different phenotypes. This study reviewed the core principles and performance of several microfluidic multiple- and single-cell capture methods, namely, the microwell, valve, trap, and droplet methods. The advantages and disadvantages of the methods were compared, and suggestions regarding their application to multiple-cell capture were provided. The results may serve as a reference for research on microfluidic multiple single-cell coculture technology.

Data-driven comparison of multiple high-dimensional single-cell expression profiles

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

P-061: Single-cell whole-exome DNA sequencing traces clonal trajectory in paired evolution of MGUS to multiple myeloma

<h3>Background</h3> Accurate measurement of heterogeneity and reconstruction of evolutionary pathways are key to decipher clonal dynamics in tumour origins. Although bulk population genetic sequencing data has been used extensively to understand the life history of tumours, sequencing single cell DNA is likely to provide a much higher resolution of clonal dynamics and yield new insights into competing cellular populations with different genotypes. This is of significant importance when clonal evolution from a benign state to malignant disease is being considered, such as in the clonal evolution of MGUS to multiple myeloma (MM). <h3>Methods</h3> To this end, we further refined a multiple displacement amplification-based single cell whole-exome sequencing (scWES) protocol and applied it to examine MM cells (2 cases), and in one of these cases, probed evolution from a pre-existing MGUS stage in a paired setting. Single tumour cells were isolated from CD138+CD38+ fluorescence-activated cell sorted populations and CD3+ for T cells as germline controls. We developed a novel computational pipeline to accurately call somatic SNVs/indels and copy number aberration (CNA) events in the form of loss-of-heterozygosity (LOH) and gains in each single cell, identified drivers at the genome-wide level and reconstructed the tumour phylogeny while estimating and accounting for allelic dropout (N=176 single cells, 25× each cell). <h3>Results</h3> By focusing on the patient that allowed comparison of single cells at pre- and post-malignancy states, we show that pre-malignant subclones can be readily identified, and strikingly that malignant cells arise and expand from one of the MGUS subclones in the combined tumour phylogeny. <h3>Conclusions</h3> These results suggest that this scWES based method is a pragmatic approach to investigate clonal dynamics by allowing: 1) the analysis of genetic heterogeneity at the genome-wide level (much higher resolution than targeted panels), and 2) at low sequencing costs across large set of cells when compared with whole genome sequencing-based methods.

Increased Autotaxin Levels in Severe COVID-19, Correlating with IL-6 Levels, Endothelial Dysfunction Biomarkers, and Impaired Functions of Dendritic Cells.

Autotaxin (ATX; ENPP2) is a secreted lysophospholipase D catalyzing the extracellular production of lysophosphatidic acid (LPA), a pleiotropic signaling phospholipid. Genetic and pharmacologic studies have previously established a pathologic role for ATX and LPA signaling in pulmonary injury, inflammation, and fibrosis. Here, increased ENPP2 mRNA levels were detected in immune cells from nasopharyngeal swab samples of COVID-19 patients, and increased ATX serum levels were found in severe COVID-19 patients. ATX serum levels correlated with the corresponding increased serum levels of IL-6 and endothelial damage biomarkers, suggesting an interplay of the ATX/LPA axis with hyperinflammation and the associated vascular dysfunction in COVID-19. Accordingly, dexamethasone (Dex) treatment of mechanically ventilated patients reduced ATX levels, as shown in two independent cohorts, indicating that the therapeutic benefits of Dex include the suppression of ATX. Moreover, large scale analysis of multiple single cell RNA sequencing datasets revealed the expression landscape of ENPP2 in COVID-19 and further suggested a role for ATX in the homeostasis of dendritic cells, which exhibit both numerical and functional deficits in COVID-19. Therefore, ATX has likely a multifunctional role in COVID-19 pathogenesis, suggesting that its pharmacological targeting might represent an additional therapeutic option, both during and after hospitalization.

Identification of novel GPCR partners of the central melanocortin signaling

ObjectiveHomo- or heterodimerization of G protein–coupled receptors (GPCRs) generally alters the normal functioning of these receptors and mediates their responses to a variety of physiological stimuli in vivo. It is well known that melanocortin-3 receptor (MC3R) and melanocortin-4 receptor (MC4R) are key regulators of appetite and energy homeostasis in the central nervous system (CNS). However, the GPCR partners of MC3R and MC4R are not well understood. Our objective is to analyze single cell RNA-seq datasets of the hypothalamus to explore and identify novel GPCR partners of MC3R and MC4R and examine the pharmacological effect on the downstream signal transduction and membrane translocation of melanocortin receptors. MethodsWe conducted an integrative analysis of multiple single cell RNA-seq datasets to reveal the expression pattern and correlation of GPCR families in the mouse hypothalamus. The emerging GPCRs with important metabolic functions were selected for cloning and co-immunoprecipitation validation. The positive GPCR partners were then tested for the pharmacological activation, competitive binding assay and surface translocation ELISA experiments. ResultsBased on the expression pattern of GPCRs and their function enrichment results, we narrowed down the range of potential GPCR interaction with MC3R and MC4R for further confirmation. Co-immunoprecipitation assay verified 23 and 32 novel GPCR partners that interacted with MC3R and MC4R in vitro. The presence of these GPCR partners exhibited different effects in the physiological regulation and signal transduction of MC3R and MC4R. ConclusionsThis work represented the first large-scale screen for the functional GPCR complex of central melanocortin receptors and defined a composite metabolic regulatory GPCR network of the hypothalamic nucleuses.

Abstract 198: Quantifying miRNA activity in single cell clusters

Abstract Background: MicroRNAs are small noncoding RNAs that mediate gene regulation at the post-transcriptional level via multiple mechanisms such as mRNA degradation, translational inhibition, and mRNA stabilization. They are involved in several cellular processes from development to homeostasis, and their deregulation is implicated in several diseases, including cancer. Since miRNA lacks the polyA tail, the standard single cell RNAseq protocols do not capture miRNAs, thus severely limiting our understanding of miRNA functions at cellular resolution. To overcome this limitation, we develop a novel machine learning method to infer the miRNA activity in a sample given its RNAseq profile. Methods: We develop a model using XGBoost, to predict miRNA profile in a sample from its global mRNA profile. We train and test the model using cross validation in the CCLE collection, as well as a number of healthy and cancer human tissue data obtained from GTEx and TCGA. We quantify the method's performance as the correlation between actual and predicted miRNA expression values across the test samples. We validate our model in multiple single cell datasets where miRNA and mRNA profiles are available for the same cell types by assessing the model's ability to identify cell type specific miRNAs based on a model trained on independent bulk datasets. Results: First, we show in CCLE collection, and multiple TCGA tissues, that a model based on all genes was far more accurate than the model based only on known targets. Our mean cross validation model accuracy across 10 tissues having greater than 100 paired miRNA and mRNA samples (in terms of Spearman correlation between predicted and actual expression of a miRNA) is 0.45 (min 0.39 in Pancreas to a max of 0.51 in Brain). In comparison to the normal tissues in GTEx, in the malignant counterpart in TCGA, due to greater heterogeneity, therefore greater variability in gene expression, our model performs significantly better (average cross validation accuracy improvement of 0.19). We have validated our model in independent single cell data. Using a model trained in bulk tissue data, we predict microRNA expression levels in a single cell based on the single cell RNA and compare our predicted fold difference by a miRNA's expression between two cell types with the actual fold difference. We quantify the prediction accuracy as the correlation between the predicted and actual fold differences across all miRNAs. In a total of 4 cell type pair comparisons (different sets of kidney, brain, breast, and skin), our model achieves an average accuracy of 0.81 (ranging from 0.73 to 0.89), thus strongly validating our model. Our next step is to apply our model to study miRNA activities during T cell development, Pancreatic Ductal Adenocarcinoma, and Glioblastoma, in collaboration with experimentalists. Conclusions: Our method addresses a major bottleneck in studying miRNA activities at a cellular resolution and can be applied to any scRNA data to infer miRNA activity. Citation Format: Gulden Olgun, Vishaka Gopalan, Sridhar Hannenhalli. Quantifying miRNA activity in single cell clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 198.

Interim analysis for window of opportunity trial of single dose preoperative durvalumab (D) with or without tremelimumab (T) for operable head and neck squamous cell carcinoma (HNSCC).

e18043 Background: We aim to evaluate the efficacy and safety for single dose of preoperative D with or without T in patients with operable HNSCC with immune correlates. Methods: Patients with histologically confirmed, potentially resectable, stage II, III, and IVA HNSCC were eligible. Enrolled patients were randomized into D or D+T, stratified by primary site and human papilloma (HPV) infection status assessed by p16 expression. A single dose of preoperative D (1500mg) or D+T (1500mg+75mg) was administered and followed 2 to 8 weeks later by surgical tumor resection with curative intent. Postoperative (chemo) radiation (PO-CRT) was planned based on standard guidelines, followed by maintenance with D every 4 weeks for one year. Pathology response was quantified as the proportion of the resection bed with viable tumor, tumor necrosis, fibrosis, and inflammation. The primary objective was to determine the local recurrence rate. Secondary endpoints included pathologic response, safety and tolerability, disease-free survival, and immune dynamics incorporating multiple immunofluorescence and single cell transcriptomic analysis. A total 48 patients are planned to be enrolled. Results: As of Jan. 31, 2021, a total 36 patients were enrolled and received surgical resections with available data for pathologic response (D: 16 patients, D+T: 20 patients). Patient characteristics of both arms were well-balanced: hypopharynx/larynx/oral cavity as primary site (12.5%/12.5%/18.8%/56.3% in D, 20.0%/10.0%/10.0%/60.0% in D+T); HPV positivity (50.0% in D, 50.0% in D+T); cT1/T2/T3/T4 (6.3%/37.5%/31.3%/25.0% in D, 10.0%/25.0%/40.0%/25.0% in D+T); cN0/N1/N2/N3 (25.0%/18.8%/50.0%/6.3% in D, 30.0%/30.0%/40.0%/0% in D+T). Significant tumor shrinkage defined as more than 30% of size reduction was observed in 43.7% (7/16), including 2 patients with complete response in D and 20.0% (4/20) in D+T. During 128 days of median follow-ups, local recurrence was observed in 2 patients of D+T arm (5.6%). HPV positivity was numerically associated with favorable treatment response (average tumor shrinkage; -21.8% in p16-positive vs. -6.4% in p16-negative). Meaningful pathologic response was achieved in terms of tumor necrosis (average 10.3% in D, 5.5% in D+T), fibrosis (average 13.4% in D, 10.3% in D+T), and inflammation (average 15.0% in D, 5.8% in D+T), without significant differences according to HPV positivity. After preoperative D or D+T, serious adverse events and unexpected surgical complications did not occur. Conclusions: These early data suggested that preoperative D or D+T in patients with operable HNSCC was safe and feasible and had the potential to provide clinical benefits. The trial is ongoing and the updated outcomes with immune correlates including immunofluorescence and single cell transcriptomic analysis will be presented. Clinical trial information: NCT03737968.

Profiling single-cell histone modifications using indexing chromatin immunocleavage sequencing.

Recently, multiple single-cell assays were developed for detecting histone marks at the single-cell level. These techniques are either limited by the low cell throughput or sparse reads which limit their applications. To address these problems, we introduce indexing single-cell immunocleavage sequencing (iscChIC-seq), a multiplex indexing method based on TdT terminal transferase and T4 DNA ligase-mediated barcoding strategy and single-cell ChIC-seq, which is capable of readily analyzing histone modifications across tens of thousands of single cells in one experiment. Application of iscChIC-seq to profiling H3K4me3 and H3K27me3 in human white blood cells (WBCs) enabled successful detection of more than 10,000 single cells for each histone modification with 11 K and 45 K nonredundant reads per cell, respectively. Cluster analysis of these data allowed identification of monocytes, T cells, B cells, and NK cells from WBCs. The cell types annotated from H3K4me3 single-cell data are specifically correlated with the cell types annotated from H3K27me3 single-cell data. Our data indicate that iscChIC-seq is a reliable technique for profiling histone modifications in a large number of single cells, which may find broad applications in studying cellular heterogeneity and differentiation status in complex developmental and disease systems.

ScMC learns biological variation through the alignment of multiple single-cell genomics datasets

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.

Mesothelial Cell-Derived Antigen-Presenting Cancer-Associated Fibroblasts Induce Expansion of Regulatory T Cells in Pancreatic Cancer

Multiple recent single cell RNA sequencing (scRNA-seq) studies have identified a unique cancer-associated fibroblast (CAF) population in pancreatic ductal adenocarcinoma (PDA) called antigen-presenting CAFs (apCAFs). apCAFs are characterized by the expression of MHC II molecules, suggesting a function in regulating tumor immunity. Here we integrated multiple scRNA-seq studies and found that apCAFs are derived from mesothelial cells. Our data show that during PDA progression, mesothelial cells form apCAFs by down-regulating mesothelial features and gaining the fibroblastic features, a process induced by IL-1 and TGFβ. Moreover, apCAFs directly ligate and induce naive CD4+ T cells into regulatory T cells (Tregs) in an antigen-specific manner. Our study elucidates an important but neglected cell type in the regulation of PDA immunity and may lead to targeted therapeutic strategies that can overcome immune evasion in PDA.

Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer's disease

AbstractBackgroundAlzheimer’s disease (AD) is an incurable neurodegenerative disorder in which neuroinflammation plays a critical function. Yet, little is known about the contribution of the adaptive immune response in AD. Here we used integrated analyses of multiple cohorts to identify peripheral and central adaptive immune changes in AD.MethodFirst, we performed mass cytometry of peripheral blood mononuclear cells and discovered an immune signature of AD consisting of increased numbers of CD8+ T effector memory CD45RA+ (TEMRA) cells.ResultIn a second cohort, we found that CD8+ TEMRA cells were negatively associated with cognition. Furthermore, single cell RNA sequencing revealed their enhanced T cell receptor (TCR) signaling. Notably, by utilizing multiple single cell TCR sequencing strategies on a third cohort, we discovered clonally expanded CD8+ TEMRA cells in patient cerebrospinal fluid (CSF). Finally, we used machine learning, cloning, and peptide screens to demonstrate specificity of clonally expanded AD CSF TCRs to two separate Epstein‐Barr virus antigens.ConclusionThese results reveal a novel blood‐CSF adaptive immune response in AD and provide evidence of clonal, antigen‐experienced T cells patrolling the intrathecal space of brains affected by age‐related neurodegeneration.

GCAnno: a graph-based single cell type annotation method

BackgroundCurrent single cell analysis methods annotate cell types at cluster-level rather than ideally at single cell level. Multiple exchangeable clustering methods and many tunable parameters have a substantial impact on the clustering outcome, often leading to incorrect cluster-level annotation or multiple runs of subsequent clustering steps. To address these limitations, methods based on well-annotated reference atlas has been proposed. However, these methods are currently not robust enough to handle datasets with different noise levels or from different platforms.ResultsHere, we present gCAnno, a graph-based Cell type Annotation method. First, gCAnno constructs cell type-gene bipartite graph and adopts graph embedding to obtain cell type specific genes. Then, naïve Bayes (gCAnno-Bayes) and SVM (gCAnno-SVM) classifiers are built for annotation. We compared the performance of gCAnno to other state-of-art methods on multiple single cell datasets, either with various noise levels or from different platforms. The results showed that gCAnno outperforms other state-of-art methods with higher accuracy and robustness.ConclusionsgCAnno is a robust and accurate cell type annotation tool for single cell RNA analysis. The source code of gCAnno is publicly available at https://github.com/xjtu-omics/gCAnno.

Abstract 865: Using machine learning to detect heterogeneity in single cell multi-omics datasets

Abstract Background: While scRNA-seq helps in obtaining high-resolution views of single-cell heterogeneity through characterization of the functional state of cells, our understanding of the cellular properties and population architectures of heterogeneous tissues will be greatly advanced by the multi-omics investigation of single cells. Recently, several computational methods have been developed to integrate data from multiple different single cell experiments measuring individual analytes. But unambiguous inference that a cellular phenotype is caused by a genotype can only be achieved by their measurement from the same single cell. To address this gap, we have developed the Tapestri multi-omics workflow to analyze the RNA and DNA information from the same cell. Methods: After pre-processing of the reads, cell calling is done by identifying the good barcodes using both DNA and RNA reads. DNA reads are processed to identify genetic variants in each cell. The variant cell matrix is then filtered for data completeness to ensure only high-quality data is used in downstream processing. Ploidy is estimated using by normalizing DNA reads, genetic variants and ploidy information together is used to identify subclones. The RNA reads are log normalized and scaled within each cell. Next we set the mean expression of each transcript to 0 and scale the variance to 1. This avoids downstream analysis being skewed by high expressors. We then trained a random forest classifier to identify significantly differentially expressed transcripts across subclones which were identified using genetic variants and ploidy information. Results: Using the top differentially expressed transcripts we performed dimensionality reduction followed by clustering of cell types. The resulting visualization showed how well the genotypic and transcriptomic datasets integrated with one another. We tested this method on a model system with Raji and KG1 cell lines titrated at 50:50 ratio and were clearly able to associate the transcriptional variation with the genotypic variation of the 2 different cell lines. The method was also validated on a PBMC sample to ensure robustness of methods. We were able to identify the different cell types present the sample and were able to overlay that information with genetic variants to identify sub-clones in the identified cell types. Citation Format: Saurabh Gulati, Shu Wang, Saurabh Parikh, Ben Liu, Kaustubh Gokhale, Manimozhi Manivannan, Sombeet Sahu, Dong Kim, Anup Parikh. Using machine learning to detect heterogeneity in single cell multi-omics datasets [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 865.

Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics.

Similarities between stem cells and cancer cells have implicated mammary stem cells in breast carcinogenesis. Recent evidence suggests that normal breast stem cells exist in multiple phenotypic states: epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M). Hybrid E/M cells in particular have been implicated in breast cancer metastasis and poor prognosis. Mounting evidence also suggests that stem cell phenotypes change throughout the life course, for example, through embryonic development and pregnancy. The goal of this study was to use single cell RNA-sequencing to quantify cell state distributions of the normal mammary (NM) gland throughout developmental stages and when perturbed into a stem-like state in vitro using conditional reprogramming (CR). Using machine learning based dataset alignment, we integrate multiple mammary gland single cell RNA-seq datasets from human and mouse, along with bulk RNA-seq data from breast tumors in the Cancer Genome Atlas (TCGA), to interrogate hybrid stem cell states in the normal mammary gland and cancer. CR of human mammary cells induces an expanded stem cell state, characterized by increased expression of embryonic stem cell associated genes. Alignment to a mouse single-cell transcriptome atlas spanning mammary gland development from in utero to adulthood revealed that NM cells align to adult mouse cells and CR cells align across the pseudotime trajectory with a stem-like population aligning to the embryonic mouse cells. Three hybrid populations emerge after CR that are rare in NM: KRT18+/KRT14+ (hybrid luminal/basal), EPCAM+/VIM+ (hybrid E/M), and a quadruple positive population, expressing all four markers. Pseudotime analysis and alignment to the mouse developmental trajectory revealed that E/M hybrids are the most developmentally immature. Analyses of single cell mouse mammary RNA-seq throughout pregnancy show that during gestation, there is an enrichment of hybrid E/M cells, suggesting that these cells play an important role in mammary morphogenesis during lactation. Finally, pseudotime analysis and alignment of TCGA breast cancer expression data revealed that breast cancer subtypes express distinct developmental signatures, with basal tumors representing the most “developmentally immature” phenotype. These results highlight phenotypic plasticity of normal mammary stem cells and provide insight into the relationship between hybrid cell populations, stemness, and cancer.

Illuminating the onco-GPCRome: targeting the prostaglandin receptor-Gαs signaling axis as a novel immune checkpoint in cancer

Abstract Recent advances in checkpoint blockade immunotherapy (CBI) have revolutionized cancer treatment, but the limited response rates in most cancers suggest that new approaches and targets are clearly needed to fully elucidate the underlying biology of dysfunctional CD8 T cells and achieve durable responses. G-protein coupled receptors (GPCRs) are the most intensively studied drug targets as they play key roles in many physiological processes, and they have remained longstanding favorable pharmacological targets. Using novel computational approaches integrating multiple single cell RNAseq databases, we first deconvoluted tumor-infiltrating T cell heterogeneity to generate a transcriptomic GPCR signature based on coupling specificity and receptor family. Preliminary data shows an upregulation of prostaglandin receptors, PTGER2 and PTGER4, on exhausted T cells, suggesting that these GPCRs, coupled to the Gαs G protein α subunit, may contribute to diminishing anti-tumor cytotoxicity. Expression and stimulation of EP2 and EP4 on highly activated CD8 T cells and subsequent Gαs signaling decreases cytokine secretion and CXCR3-mediated migration. Additionally, blockade of EP2 and EP4 significantly decreases tumor growth in multiple immune competent, syngeneic tumor models. Altogether, we hypothesize that activation of Gαs downstream signaling cascades may be dampening the anti-tumor cytotoxicity of CD8 T cells, thereby limiting the effectiveness of PD-1 and CTLA-4 blockade. EP2 and EP4 and Gαs signaling may represent promising candidates as immune checkpoints that can be targeted in combination with CBI as part of novel multimodality precision immunotherapy approaches to reactivate the immune system to destroy tumors.

Clonally expanded CD8 T cells patrol Alzheimer’s cerebrospinal fluid

Abstract Alzheimer’s disease (AD) is an incurable neurodegenerative disorder in which neuroinflammation plays a critical function. Yet, little is known about the contribution of the adaptive immune response in AD. Here we used integrated analyses of multiple cohorts to identify peripheral and central adaptive immune changes in AD. First, we performed mass cytometry of peripheral blood mononuclear cells and discovered an immune signature of AD consisting of increased numbers of CD8+ T effector memory CD45RA+ (TEMRA) cells. In a second cohort, we found that CD8+ TEMRA cells were negatively associated with cognition. Furthermore, single cell RNA sequencing revealed their enhanced T cell receptor (TCR) signaling. Notably, by utilizing multiple single cell TCR sequencing strategies on a third cohort, we discovered clonally expanded CD8+ TEMRA cells in patient cerebrospinal fluid (CSF). Finally, we used machine learning, cloning, and peptide screens to demonstrate specificity of clonally expanded AD CSF TCRs to two separate Epstein-Barr virus antigens. These results reveal a novel blood-CSF adaptive immune response in AD and provide evidence of clonal, antigen-experienced T cells patrolling the intrathecal space of brains affected by age-related neurodegeneration.