Single Atlas Research Articles

Expression Atlas update: insights from sequencing data at both bulk and single cell level.

Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.

A single cell genomics atlas of the Drosophila larval eye reveals distinct photoreceptor developmental timelines

The Drosophila eye is a powerful model system to study the dynamics of cell differentiation, cell state transitions, cell maturation, and pattern formation. However, a high-resolution single cell genomics resource that accurately profiles all major cell types of the larval eye disc and their spatiotemporal relationships is lacking. Here, we report transcriptomic and chromatin accessibility data for all known cell types in the developing eye. Photoreceptors appear as strands of cells that represent their dynamic developmental timelines. As photoreceptor subtypes mature, they appear to assume a common transcriptomic profile that is dominated by genes involved in axon function. We identify cell type maturation genes, enhancers, and potential regulators, as well as genes with distinct R3 or R4 photoreceptor specific expression. Finally, we observe that the chromatin accessibility between cones and photoreceptors is distinct. These single cell genomics atlases will greatly enhance the power of the Drosophila eye as a model system.

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.

PSII-6 Computational Tools and Resources for Analysis and Exploration of Single-Cell Rnaseq Data in Agriculture

Abstract The agriculture genomics community has numerous data submission standards available but little experience describing and storing single-cell (e.g., scRNAseq) data. Other single-cell genomics infrastructure efforts, such as the Human Cell Atlas Data Coordination Platform (HCA DCP), have resources that could benefit our community. For example, the HCA DCP is integrated with Terra, a cloud-native workbench for computational biology developed by Broad, Verily, and Microsoft that houses tools for scGenomics analysis. We will describe a pilot-scale project to determine if our current metadata standards for livestock and crops can be used to ingest scRNAseq datasets in a manner consistent with HCA DCP standards and if established resources (e.g., Terra) can be used to analyze the ingested data. Currently, the most comprehensive data ingestion portal for high throughput sequencing datasets from plants, fungi, protists, and animals/humans is Annotare (located at EMBL-European Bioinformatics Institute), ensures that sufficient metadata are collected to enable re-analysis and dissemination via the Single Cell Expression Atlas (SCEA). Annotare supports user-directed annotation and processing of their data, as well as search tools via the SCEA and transferred to the Galaxy analysis space. For animal datasets, another EMBL-EBI portal, the FAANG portal, has been developed that provides bulk and scRNAseq data access. scRNAseq data/metadata can be submitted to FAANG using a semi-automated process. We have extended this tool for scRNAseq data so that files can be validated using the HCA DCP metadata and data validation service. These files are also incorporated using EMBL-EBI’s HCA DCP ingestion service and transferred to Terra for further analysis. Once incorporated, datasets will augment the DCP resource for the scientific community. In an extension of these efforts, we have also created a Shiny-based web application, called Shiny-PIGGI, for the single cell-level transcriptomic study of pig immune tissues and peripheral blood mononuclear cells, which will be an important resource for improved annotation of porcine immune genes and cell types. The Shiny-PIGGI (https://shinypiggi.ansci.iastate.edu) is implemented completely in R, runs on any modern web browser, and requires no programming. This tool thus increases accessibility through eliminating technical training requirements for using Seurat object and related R packages commonly used in scRNAseq analysis. Our main goal was to develop an interactive web application that allows users such as animal scientists and immunologists to visualize and analyze biological datasets. This tool will be demonstrated at the meeting. We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem to facilitate single cell-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

Parcellation of Brain Magnetic Resonance Images using Improved Atlases Selection Model

Introduction: Multiple atlas-based parcellation model has been demonstrated to perform better than single atlas-based parcellation model in terms of accuracy of the parcellation of human brain Magnetic Resonance Images (MRI). The weakness of the existing multiple atlas-based parcellation models is that the level of accuracy is limited if used for the ageing brain due to the presence of age-related changes such as atrophy. Aim: The aim of this study is to develop a novel multiple atlases selection model that ensures improved accuracy for the parcellation of the ageing brain by combining Cost function with the Similarity metric and Atrophy measure for atlases selection. This model is called COSA. Materials and Methods: A dataset with ten brain MRIs and ten atlases was used. A brain MRI was used one at a time as the target image while the remaining images constituted the source images. Using each target image, consensus atlases were obtained for COSA from the combination of a cost function, similarity index, and atrophy measure. These atlases were consequently used to parcellate the target image. Performance was assessed using Dice Coefficient and COSA was compared with existing atlases selection models. The existing atlases selection models investigated were Normalized Mutual Information (NMI), Mutual Information (MI), Correlation Ratio (CR), Normalized Correlation Ratio (NCR), and Least Square Error (LSE). Results: Mean of Dice Coefficient were: COSA = 0.7495196, NMI = 0.7479508, MI = 0.7473333, Jaccard Index = 0.7392522, CR = 0.7358384, NC = 0.7358043, Atrophy Measure = 0.7300867, LSE = 0.7299367, Single Atlas =0.6830223. Conclusion: Results show that COSA performs better than the existing multiple Atlas-based models.

Back to Basics in Cancer Research: A Message from the Incoming Editor-In-Chief.

Back to Basics in Cancer Research: A Message from the Incoming Editor-In-Chief.

Abstract LB176: Interrogating the single cell immune checkpoint inhibition atlas to decipher ICI response mechanisms

Abstract Introduction: Immune Checkpoint Inhibition (ICI) therapies have demonstrated significant clinical success in multiple cancer types. However, many patients do not develop durable responses to ICI treatments and the underlying mechanisms of action are not fully resolved. Recent single cell RNA-seq (scRNA-seq) studies provide a window into the complex interplay between cancer and heterogenous immune cell populations underlying ICI response. However, individual studies are typically focused on specific therapies in specific cancer types making it challenging to identify universal mechanisms and response markers. Approach: To facilitate a more comprehensive study of cell-type specific ICI mechanisms and response determinants, we developed the Single Cell Immune Checkpoint Inhibition Atlas (ICI Atlas) which integrates 27 scRNA-seq datasets (including 17 human and 10 mice datasets) spanning close to 1.9 million cells. Of the 27 datasets, 25 contain both pre- and post-treatment samples, one contains only pre-treatment samples and one contains only post-treatment samples. The Atlas spans over 30 cell types and 16 different cancer types as well as 20 treatment strategies spanning 11 ICI therapies and 28 other drugs. Results: To begin exploring the cell-type specific responses to ICI, we conducted comparative analyses of the ICI Atlas T cells in 4 groups: 1) responders vs. non-responders pre-treatment; 2) responders vs. non-responders post-treatment; 3) responders post-treatment vs. untreated; and 4) non-responders post-treatment vs. untreated leading to 62 pairwise comparisons across ICI Atlas datasets and treatment strategies. First, we found that T cells were either more abundant or more differentiated after ICI treatment and were more differentiated or more abundant in responders (post-treatment) compared to non-responders. We also found that genes previously identified in a meta-analysis of bulk mRNA datasets (Litchfield et al. 2021) as being effective predictors of ICI response are also more highly expressed in T cells of responders post-treatment compared to non-responders or compared to untreated subjects. Using an IFN-related gene signature, we found that post treatment as compared to baseline, IFN genes increased more substantially in responders than in non-responders. Across a large set of studies, we found that the IFN signature clusters T cells into IFN-effector/producers (effector/activated T cells) and IFN-insensitive cells (naive/memory T cells). Finally, using Atlas-derived ICI response signatures in T cells and data from a recently published study of mismatch repair deficient colorectal cancers (MMRd CRC, Pelka et al. 2021), we showed that T cells from MMRd CRC patients recapitulate signatures of ICI responders. Our results suggest that large-scale scRNA-seq atlases can serve as valuable tools for corroborating existing hypotheses, as well as providing key new insights into cell-type specific mechanisms underlying ICI therapy response. Citation Format: Piotr Topolewski, Aleksandra Możwiłło, Marlena Osipowicz, Roy Ronen, Janusz Dutkowski. Interrogating the single cell immune checkpoint inhibition atlas to decipher ICI response mechanisms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB176.

Abstract 4310: Ultra high-plex spatial proteogenomic investigation of giant cell glioblastoma multiforme immune infiltrates reveals distinct protein and RNA expression profiles

Abstract The advancement of spatially resolved, multiplex proteomic and transcriptomic technologies has revolutionized and redefined the approaches to complex biological questions pertaining to tissue heterogeneity, tumor microenvironments, cellular interactions, cellular diversity, and therapeutic response. Most spatial technologies yield single analyte proteomic or transcriptomic datasets from separate formalin-fixed paraffin-embedded (FFPE) tissues sections. Multiple studies have demonstrated a poor correlation between RNA expression and protein abundance owing to transcriptional and translational regulation, target turnover, and post-translational protein modifications. Therefore, a workflow that accurately measures RNA and protein simultaneously within a single tissue section with distinct spatial context is critical to a more complete biological understanding of cellular interactions and activities. Such multimodal omic datasets of protein and DNA or RNA have been termed “spatial proteogenomics”. Here we present a novel spatial proteogenomic (SPG) assay on the GeoMx® Digital Spatial Profiler platform with NGS readout that enables ultra high-plex digital quantitation of proteins (147-plex) and RNA (whole transcriptome, > 18,000-plex) from a single FFPE sample. We demonstrated high concordance, R > 0.85, and minor change in sensitivity (<11%) between the SPG assay and the single analyte GeoMx Whole Transcriptome Atlas and GeoMx NGS Protein assays. We used the SPG assay to interrogate 23 different glioblastoma multiforme samples across 4 pathologies. We observed clustering of both RNA and protein based on cancer pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme (gcGBM) revealed distinct protein and RNA expression profiles compared to that of glioblastoma multiforme (GBM). Spatial proteogenomics allowed simultaneous interrogation of critical protein post-translational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods. Within our dataset, we observed >2-fold higher protein expression levels of phospho-GSK3β (Ser9) in gcGBM compared to GBM. Inactivation of GSK3β through phosphorylation has been shown to enhance proliferation of GBM cells. We also observed differential protein expression phosphorylated Tau variants. Phospho-Thr231 Tau was >2-fold higher in GBM compared to gcGBM. Associated with neurodegenerative Alzheimer’s disease, changes in Tau expression and phosphorylation have also been observed in glioblastoma. Our study exemplifies the utility of the SPG assay in expanding our understanding of glioblastoma multiforme molecular pathology. Citation Format: Shilah Bonnett, Alyssa Rosenbloom, Giang Ong, Mark Conner, Daniel Newhouse, Felicia New, Hiromi Sato, Chi Pha, Saskia Ilcisin, John Lyssand, Gary Geiss, Joseph M. Beechem. Ultra high-plex spatial proteogenomic investigation of giant cell glioblastoma multiforme immune infiltrates reveals distinct protein and RNA expression profiles. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4310.

Abstract 1230: The pre-metastatic niche establishment through the induction of the epithelial-to-mesenchymal transition by circulating exosomes in lymphangioleiomyomatosis (LAM): implication of the complement cascade

Abstract Exosomes are a class of extra cellular vesicles (EVs), with a multi vesicular endosomal origin, that are released by all cell types, with sizes ranging from 30-150nm and a lipid bilayer membrane. They have been shown to play key roles in disease progression and have diagnostic and disease monitoring potential. This study aims to characterize exosomes from the serum of patients with Lymphangioleiomyomatosis (LAM), a rare, low-grade, metastasizing neoplasm that occurs predominantly in females. Using Nanoparticle Tracking Analysis (NTA), serum from LAM patients (n=19) have significantly increased numbers of exosomes compared to serum samples from healthy controls (n=20) (8.9x109 vs 13.8x109 particles/ml; p=0.024). Furthermore, particles counts are significantly negatively correlated with the forced expiratory volume (FEV1) (r=-0.4667; p-value=0.0295) in LAM patients. Metalloproteinase activity in exosomes was measured by zymography. Interestingly, these LAM-derived exosomes had significantly higher activity of Pro-MMP-9 (3-fold increase; p-value=0.0278). We analyzed the effects of serum-derived exosomes on cells from an A549 cell line. Increased expression of vimentin, a major marker of EMT, was observed in cells treated with LAM-derived exosomes compared to healthy controls (1.4-fold increase; p=0.0217). Moreover, we showed by a modified Boyden chamber assay that exosomes derived from serum treatment increases the invasive capacity of A549 cells (fold change of 2.2; p-value=0.0004). We interrogated the LAM Single Cell Atlas and identified increased expression of TWIST1, a key transcription factor for EMT. TWIST1 was increased 8-fold in the lung and was uniquely expressed in 'LAM cells', which had metastasized. Furthermore, proteomic analysis revealed high expression of C3 and C5 in LAM-derived exosomes (respectively 1.4- and 2.1-fold change; p=0.0276 and p=0.0034) suggesting the potential regulatory role of the compliment system in the EMT mediated by exosomes These preliminary results highlight the urge to study the specific mechanistic interplay between MMPs and EMT, mediated by exosomes, and whether exosomes are involved in the metastatic spread of LAM cells to the lung and their role in influencing innate and adaptive immune cell response to this invasion. Citation Format: Amina Jouida, Patrick McFadden, Grace Buckley, Evelyn Lynn, O'Callaghan Marissa, Yasuhito Sekimoto, Aurelie Fabre, Michael P. Keane, Cormac McCarthy. The pre-metastatic niche establishment through the induction of the epithelial-to-mesenchymal transition by circulating exosomes in lymphangioleiomyomatosis (LAM): implication of the complement cascade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1230.

Single cell multi-omic reference atlases of non-human primate immune tissues reveals CD102 as a biomarker for long-lived plasma cells

In response to infection or immunization, antibodies are produced that provide protection against re-exposure with the same pathogen. These antibodies can persist at high titers for decades and are maintained by bone marrow-resident long-lived plasma cells (LLPC). However, the durability of antibody responses to immunization varies amongst vaccines. It is unknown what factors contribute to the differential longevity of serum antibody responses and whether heterogeneity in LLPC contributes to this phenomenon. While LLPC differentiation has been studied extensively in mice, little is known about this population in humans or non-human primates (NHP). Here, we use multi-omic single-cell profiling to identify and characterize the LLPC compartment in NHP. We identify LLPC biomarkers including the marker CD102 and show that CD102 in combination with CD31 identifies LLPC in NHP bone marrow. Additionally, we find that CD102 is expressed by LLPC in mouse and humans. These results further our understanding of the LLPC compartment in NHP, identify biomarkers of LLPC, and provide tissue-specific single cell references for future studies.

A Single Cell Atlas and Interactive Web-Resource of Pediatric Cancers and Healthy Bone Marrow

Single-cell profiling allows for in-depth characterization of the tumor microenvironment and revealing the heterogeneity of pediatric cancers. Ease of access and analysis of the primary tumor single-cell data from studies on solid and blood pediatric tumors is essential for improving diagnostics and therapies. We have developed the Pediatric Single Cell Cancer Atlas (PedSCAtlas), with the goal of highlighting the heterogeneity of malignant and microenvironment cells across pediatric cancers. The atlas allows for quick exploration, comparative analysis, visualization, and prognostic assessment of genes/pathways across pediatric cancers without requiring bioinformatics analysis or computational support. The PedSCAtlas currently contains expression data of >1.2 million single cell (sc) and single nuclei (sn) from 134 pediatric hematological and solid-organ cancer samples and healthy bone marrow (BM). The hematological cancer samples are dominated by acute leukemias containing scRNA-seq data of acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell ALL (T-ALL), and mixed phenotype acute leukemia (MPAL). The scRNA-seq data are generated from the BM samples collected during the clinical diagnosis of the disease. The data for the atlas was either generated at the Bhasin lab or collected from public resources including the GEO database, ScPCA (https://scpca.alexslemonade.org/), and HCA (https://data.humancellatlas.org/). Additionally, the atlas also contains bulk RNA-sequencing data and clinical information retrieved from the TARGET Initiative (https://ocg.cancer.gov/programs/target). The single cell/nuclei datasets are processed using a uniform workflow that includes quality control and filtering, normalization, integration with batch correction (if needed), dimensionality reduction, clustering, and cellular annotations. The data normalization and integration are performed using sctransform (Hao and Hao et al, Cell 2021) and harmony (Korsunsky et al, Nature Methods 2019) algorithms respectively. The visualization of integrated pediatric cancers single cell was achieved using Uniform Manifold Approximations and Projection (UMAP) method (Becht et al, Nature Biotech 2018). The clusters were annotated based on the expression of cell-specific markers. Differential expression analysis was used to identify potential biomarker sets for malignant cells of disease subtypes. To further ascertain that these biomarkers were malignant cell (blast) specific in the acute leukemia subtypes, the genes were filtered to exclude those with consistent expression in healthy BM cell types. To compare the expression profile of genes between single cells and bulk data, the raw counts of bulk RNA-seq datasets were filtered and normalized using the Voom algorithm (Ritchie et al, Nucleic Acids Research 2015). The resource has modules for biomarker analysis, bulk RNA deconvolution, and survival analysis to facilitate easy analysis of single-cell data. Survival analysis is available for the bulkRNA-seq dataset; the survival is calculated using the overall survival values and gene expression/enrichment input by the user. The SingleCell module allows users to visualize the pediatric cancer dataset on a UMAP by selecting the aspect (i.e., cell type) to group the dataset by; it also allows users to enter a gene name or select a pathway of their interest and view its expression or enrichment in a feature or violin plot. The ImmuneCell module allows users to interact with the adult healthy BM dataset by inputting a gene and viewing its expression on a feature plot. The Biomarkers module contains pre-defined biomarker sets for each disease subtype in the selected cancer. The final module, BulkExpression, contains the bulk RNA-seq data for the selected cancer type and allows users to view gene expression or analyze the survival associations of an input gene or gene set. The web resource source code is written in R programming language and the interactive web server has been implemented using the R Shiny and ShinyDashboard packages. The PedScAtlas resource provides a unique and straightforward tool for biomarker identification, analysis of pediatric cancer subtype heterogeneity, and transcriptome profile of the tumor microenvironment. The resource is available online at https://bhasinlab.bmi.emory.edu/PediatricSC/. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Single-Cell Profiling of Acute Myeloid Leukemia Identified ARMH1, a Novel Protein Associated with Proliferation, Migration, and Drug Resistance

Despite advancements in treatments, half of pediatric acute myeloid leukemia (AML) patients relapse (Chen, J. & Glasser, C.L., 2020), making it pertinent to identify novel biomarkers that can be used for better stratification of patients post end of induction (EOI) therapy as well as to develop targeted therapies that could lead to higher clinical remission (CR) rates. Single-cell RNA sequencing (scRNAseq) analysis has revolutionized cancer research by identifying novel cell types, genes, and pathways. Our group used scRNAseq analysis of pediatric AML bone marrow (BM) samples collected at diagnosis (Dx) and EOI to identify the upregulation of multiple genes including a novel gene, Armadillo like helical domain containing 1 (ARMH1)/C1orf228, in cancerous blast cells (Thomas, B.E. et al., 2020). ARMH1 has not been previously associated with AML. Also, ARMH1 expression was observed to be significantly higher at Dx in samples from patients with relapse compared to samples from patients with CR (P<2.2e-16). Independent validation for ARMH1 blast-association using single cell and bulk transcriptome profiling revealed that ARMH1 expression was significantly higher in scRNAseq data of AML Dx blast cells compared to EOI non-blast cells (fold change, FC=1.8, P<2.2e-31) as well as in bulk RNAseq data of Dx samples with high blast% (>90%) compared to EOI samples (FC=1.8, P=.003) (Ulukaya, G. et al., 2021). Further analysis on other leukemias like T cell-acute lymphocytic leukemia (T-ALL) and mixed phenotypic acute leukemia (MPAL) also revealed higher expression of ARMH1 in the blast cells as compared to non-blast cells. Survival analysis on bulk RNAseq data using the survival genie platform (Dwivedi, B. et al., 2022) revealed that higher expression of ARMH1 was significantly associated with poor overall survival (OS) in both AML (P=.003) (Ulukaya, G. et al., 2021) and T-ALL (P=.014) datasets. Comparative gene expression analysis using the the Pediatric Single Cell Cancer Atlas - PedSCAtlas - tool (Mumme, H.L. et al., 2021), revealed negligible expression of ARMH1 in normal lymphoid, myeloid, and erythroid cell lineages, further confirming the leukemic blasts-specific over-expression of ARMH1. ARMH1 encodes a cytosolic protein of yet unknown function. The malignant blast specificity of ARMH1 suggested a role in leukemic cells' survival/maintenance. To ascertain its functional involvement in cancer cells, ARMH1 was silenced using siRNAs in both normal and AML cell lines. Through in vitro assays, we demonstrate that ARMH1 silencing significantly reduced leukemic cells' capacity to proliferate (P=.0041) and migrate (P=.0001). We also observed slightly increased sensitivity to chemotherapy drug, Cytarabine in the ARMH1 silenced cancer cells compared to control cancer cells in which the gene is fully functional. However, suppression of ARMH1 expression had no effect on cell growth in the normal HEK293t cell line (P=0.913). On the other hand, overexpression of ARMH1 via transient transfection of ARMH1 gene into the AML cell line, made the cells more resistant to Cytarabine and significantly increased both migration (P=.041) and cell proliferation (P=.032). In conclusion, we demonstrate for the first time that ARMH1, a still poorly understood protein, contributes to the increased proliferation and migration of malignant AML cells. With specific overexpression in blast cells of several pediatric leukemias and the newly discovered roles in proliferation, migration, and chemotherapy resistance, ARMH1 may be a good candidate for targeted treatment of pediatric leukemias.

A Multimodal Single Cell Atlas of Human Tonsils Reveals New Insights into T and B Cell Differentiation

Palatine tonsils are secondary lymphoid organs under constant exposure to antigens via the upper respiratory tract which makes them a compelling organ for adaptive immunity development. They represent an important niche for lymphocyte maturation, where they differentiate into a wide variety of functional states, tailoring immune responses to different insults. However, although some efforts have been made to characterise the lymphocyte maturation process in human tonsils at single cell resolution, these studies lacked cell numbers and multimodal, i.e multiomics, information to comprehensively capture the cellular heterogeneity of the process. Here we generated a comprehensive human tonsil atlas of >357,000 cells profiled across five different data modalities, such as transcriptome, epigenome, proteome, adaptive immune repertoire and spatial location. We identified 121 cell subtypes and states across lineages and characterised the maturation process of both B and T lymphocytes. The high number of profiled cells allowed the detection of rare cell subtypes, such as precursor B and T cells, the latter representing <0.01% of detected cells, supporting an ongoing maturation of lymphoid cells outside the bone marrow and thymus, respectively. Beyond these rare precursor lymphocytes, the tonsil is the niche for multiple steps of mature T and B cell differentiation. With regard to mature T cells, we described 37 different T cell (TC) subpopulations being CD4 TC the more representative population with 58,783 cells, which illustrated the maturation process of this lineage. We identified an early bifurcation following antigen recognition by naïve CD4 TC, resulting in two distinct precursor populations of T follicular helper (fh) and non-Tfh cells. Tfh precursor cells continued their differentiation trajectory towards terminally differentiated populations, with functions in the production of mature B cells (BC) and in the enhancement of antibody secretion. On the other branch of the CD4 TC differentiation trajectory, our data revealed three transcriptional Treg subtypes, with functions in the suppression of antitumor responses, COVID-19 and the control of BC antibody production, respectively. The multimodal study design further enabled the interrogation of regulatory circuits driving TC specialization, describing a potential BCL6 superenhancer specifically active in follicular TC. Intriguingly, the same region has been previously linked to BCL6 gene activation in Germinal Center (GC) BC, indicating its function as follicle-specific rather than cell type-specific cis-regulatory element. Mature B cells corresponded to the greatest population of the dataset, with >70% cells assigned to the BC lineage allowing the fine-grained description of the BC maturation process. We identified 8 distinct naïve BC (NBC) subpopulations and comprehensively characterized the activation pathway upon first antigen recognition, further expanding previous findings focused on MYC. Additionally, by the combination of gene expression and chromatin accessibility profiles we disentangled the transcription factor (TF) hierarchy associated with the Light zone (LZ) to Dark Zone (DZ) reentry. We identified the activity NF-kB family genes as one of the first determinants of the TF hierarchy that drives this specialization, which seems to be shared in LZ cells reentering the DZ as well as activated NBCs that enter the DZ for the first time. However, this regulation was not observed in LZ GCBCs that further differentiate into plasma cells (PCs). This final maturation step towards an antibody secreting phenotype was also characterized, identifying the first stages of PC precursor cells derived from either memory or LZ-GCBC and their transcriptional dynamics was also spatially mapped in the tonsillar tissue. Charting the regulatory landscape in PCs we validated the presence of described PC TFs, but we also discovered SIX5 as a novel potential TF associated with the maturation, but not the commitment, of PCs. Moreover, we identified a higher activity of the regulatory network of SIX5 in multiple myeloma. This study presents a multimodal single cell view of the TC and BC maturation process taking place in the tonsil. We not only present our analysis as a resource but we discover a yet unappreciated cellular heterogeneity as well as novel gene regulatory mechanisms driving naive towards highly specialized lymphoid cells.

TMIC-40. TRANSCRIPTOMIC CHARACTERIZATION OF PATIENT GLIOMAS AND DERIVED MODEL SYSTEMS REVEALS ENVIRONMENTAL INFLUENCE ON NEURODEVELOPMENTAL CELLULAR STATES

Abstract Bulk tumor and single-cell RNA sequencing have revealed the remarkable molecular heterogeneity and plasticity of gliomas, leading to the definition of tumor subtypes and cellular states describing inter- and intra-tumoral levels of heterogeneity respectively. While there has been great interest in creating and revising these classifications, the combination of selective pressures from molecular alterations within the tumor cell as well as extrinsic factors within the tumor microenvironment (TME) driving these levels of heterogeneity remain to be fully described. The brain TME is a complex, regionally heterogeneous ecosystem of communicating non-malignant and malignant cell types and scavenge-able nutrients and metabolites. We hypothesized that distinct environmental contexts within and across tumors may drive the intra- and inter-tumoral heterogeneity of gliomas. To identify tumorigenic programs impacted by environmental context, we performed bulk RNA sequencing of over 40 triplets of patient glioma samples and their matched derivative models established in direct orthotopic mouse xenografts and conventional gliomasphere cultures. Comparative analyses revealed environment-associated programs including in vivo immune and neuroglial signaling as well as altered in vitro lipid metabolism and upregulated cell migration. Next, to investigate associations between tumor cellular state and environment-associated programs we performed single-cell RNA sequencing of 4 patient and model system triplets. By annotating single cell profiles with previously defined single cell atlases of the developing and adult brain, 9 major states reflecting neurodevelopmental cell types were identified. Single cell profiling further revealed cell state-specific expression of environment-associated programs. We simultaneously observed the divergence of model cellular state compositions towards states expressing programs associated with their environment. We conclude that the transcriptional evolution of cellular states is connected to their ecological role within the TME. Consequently, we find grade- and diagnosis-associated composition differences between patient tumors predictive of their ability to establish a model in vitro.

Human Kidney Multimodal Single Cell and Spatial Transcriptomics Atlas Identify Key Cell Types and Pathways for Diabetic Kidney Disease

Human Kidney Multimodal Single Cell and Spatial Transcriptomics Atlas Identify Key Cell Types and Pathways for Diabetic Kidney Disease

Single Cell Chromatin Accessibility Atlas During Murine Kidney Injury and Repair

Single Cell Chromatin Accessibility Atlas During Murine Kidney Injury and Repair

Single cell atlas identifies lipid-processing and immunomodulatory endothelial cells in healthy and malignant breast

Since a detailed inventory of endothelial cell (EC) heterogeneity in breast cancer (BC) is lacking, here we perform single cell RNA-sequencing of 26,515 cells (including 8433 ECs) from 9 BC patients and compare them to published EC taxonomies from lung tumors. Angiogenic ECs are phenotypically similar, while other EC subtypes are different. Predictive interactome analysis reveals known but also previously unreported receptor-ligand interactions between ECs and immune cells, suggesting an involvement of breast EC subtypes in immune responses. We also identify a capillary EC subtype (LIPEC (Lipid Processing EC)), which expresses genes involved in lipid processing that are regulated by PPAR-γ and is more abundant in peri-tumoral breast tissue. Retrospective analysis of 4648 BC patients reveals that treatment with metformin (an indirect PPAR-γ signaling activator) provides long-lasting clinical benefit and is positively associated with LIPEC abundance. Our findings warrant further exploration of this LIPEC/PPAR-γ link for BC treatment.

Abstract 1221: Comprehensive cell-type classification of tumor and normal cells from single cell RNA sequencing in pan cancer settings

Abstract Single-cell RNA sequencing (scRNA-seq) allows for the study of the transcriptome at a cellular level, where populations of cells are annotated based on the expression of marker genes, providing a tool to gain cell-specific, subtle insights on cancer biology. However, precise annotation of cell type remains a challenge, hindering the efficiency of data interpretation. Several existing tools for cell-type annotation have been developed to improve resolution and reproducibility, yet their performance is reduced when the reference dataset contains many cell types, subclasses of similar cell types, or malignant cells. Interpatient malignant cell heterogeneity often leads to reduced accuracy when classifying cancer cells as most methods rely on correlations to a reference from a different source. Given the challenges in the annotation of scRNA-seq data of cancer and its high impact for elucidating mechanisms associated with tumor heterogeneity, pathogenesis, and treatment, we developed a comprehensive, hierarchically organized, multi-layered classifier spanning diverse malignant and normal cells of the tumor microenvironment. We found that performance improves when each layer focuses on a smaller number of classes and each cell sequentially moves down a series of classifiers with increased cell type resolution. When applied to an external validation dataset of over 300 primary solid tumor biopsies spanning diverse cancer types, the classifier accurately annotated the tissue of origin of malignant cells, and relevant subtypes of stromal and blood cells, with average F1 scores of 0.91, 0.95 and 0.99 respectively. Using confidence thresholds at each layer, the classifier abstains from classifying ambiguous cells. We applied 4 existing annotators provided with the same reference to the external test dataset and found that cancer cells are misclassified or unclassified, while the blood and stromal cells are accurately classified, highlighting our tool’s unique ability to classify cancer cells. Moreover, we applied our classifier’s to scRNA-seq data derived from breast cancer metastasis to the liver and were able to uncover the tissue of origin, demonstrating the potential use for determining the source of a metastatic tumour. Finally, given that our classifier is modular, we leveraged two recently published single cell breast cancer atlases to add a breast cancer subtype classification layer, that consistently identified the correct clinical subtype of single breast cancer cells in external data. This study provides a flexible model for the annotation of cells comprising the tumor microenvironment in pan cancer settings, while existing methods require tissue-specific references for every cancer type. Our classifier provides a powerful method for investigating intercellular communication pathways between tumor cells and non-malignant cells of the tumor microenvironment. Citation Format: Ido Nofech-Mozes, Philip Awadalla, Sagi Abelson. Comprehensive cell-type classification of tumor and normal cells from single cell RNA sequencing in pan cancer settings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1221.

A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

Cellular heterogeneity in growth and differentiation results in organ patterning. Single-cell transcriptomics allows characterization of gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during this methodology. To recover the original location of cells in the developing organ is essential to link gene activity with cellular identity and function in plants. Here, we propose a method to reconstruct genome-wide gene expression patterns of individual cells in a 3D flower meristem by combining single-nuclei RNA-seq with microcopy-based 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the method is used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com.