Single-cell Transcriptomic Datasets Research Articles

Integrating transcriptomic datasets across neurological disease identifies unique myeloid subpopulations driving disease-specific signatures.

Microglia and bone marrow-derived monocytes are key elements of central nervous system (CNS) inflammation, both capable of enhancing and dampening immune-mediated pathology. However, the study-specific focus on individual cell types, disease models or experimental approaches has limited our ability to infer common and disease-specific responses. This meta-analysis integrates bulk and single-cell transcriptomic datasets of microglia and monocytes from disease models of autoimmunity, neurodegeneration, sterile injury, and infection to build a comprehensive resource connecting myeloid responses across CNS disease. We demonstrate that the bulk microglial and monocyte program is highly contingent on the disease environment, challenging the notion of a universal microglial disease signature. Integration of six single-cell RNA-sequencing datasets revealed that these disease-specific signatures are likely driven by differing proportions of unique myeloid subpopulations that were individually expanded in different disease settings. These subsets were functionally-defined as neurodegeneration-associated, inflammatory, interferon-responsive, phagocytic, antigen-presenting, and lipopolysaccharide-responsive cellular states, revealing a core set of myeloid responses at the single-cell level that are conserved across CNS pathology. Showcasing the predictive and practical value of this resource, we performed differential expression analysis on microglia and monocytes across disease and identified Cd81 as a new neuroinflammatory-stable gene that accurately identified microglia and distinguished them from monocyte-derived cells across all experimental models at both the bulk and single-cell level. Together, this resource dissects the influence of disease environment on shared immune response programmes to build a unified perspective of myeloid behavior across CNS pathology.

Development of a novel head and neck squamous cell carcinoma prognostic signature by bulk/single-cell sequencing data integration.

Identifying cell subpopulations conferring unfavorable prognosis in cancer holds clinical significance. Here, we sought to identify prognostic cell subsets and develop a novel, prognostic signature for head neck squamous cell carcinoma (HNSCC). Highly prognostic cell subpopulations in HNSCC were identified by integrating single-cell and bulk transcriptomic datasets. The prognostic signature and nomogram were developed by least absolute shrinkage and selection operator and multivariate Cox regression analyses based on significantly upregulated genes in this specific cell subpopulation, respectively. The qRT-PCR experiments were utilized for independent validation in our patient cohort. A specific cancer cell subset associated with unfavorable prognoses was identified. Functional dissections revealed that its transcriptional programs were significantly enriched in E2F, epithelial-to-mesenchymal transition, and glycolysis. A novel prognostic signature comprising six genes was developed and further validated. Risk scores based on qRT-PCR data robustly stratified patients into subgroups with distinct prognoses. A nomogram integrated from this signature and clinical stage had superior performance. Our model derived from integrative analyses of single-cell and bulk RNA-sequencing is a novel, robust prognostic biomarker for HNSCC.

Caspase-8 activation in neutrophils facilitates autoimmune kidney vasculitis through regulating CD4+ effector memory T cells.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) are closely associated with neutrophil recruitment and activation, but the impact of the neutrophil apoptosis process in autoimmune disease has been rarely explained. Here, by integrating and analyzing single-cell transcriptome datasets, we found that the caspase-8-associated pathway in neutrophils was highly activated in the kidney rather than in the blood. To verify the function of caspase-8 in neutrophils on AAVs progression, we constructed neutrophil-specific caspase-8 knockout mice combined with an AAVs model induced by human ANCA from AAVs patients, a rapid and powerful model developed in this study. Our results show that caspase-8 activation of neutrophils up-regulates the expression of several inflammatory and immunoregulatory factors, especially IL23A, regulating the activation and differentiation of tissue-resident CD4+ effector memory T cells. This study reveals that the activation of caspase-8 in neutrophils can worsen glomerulonephritis of AAVs by regulating inflammation and immunity.

Single-Cell Transcriptomic Analysis Reveals Reduction of Cytotoxic NK Cells in a Subset of Newly Diagnosed Multiple Myeloma Patients Impacting Outcome after Daratumumab Therapy

Introduction Anti-CD38 antibody-based therapies in multiple myeloma (MM) rely to a large degree on natural killer (NK) cell-mediated antibody-dependent cellular cytotoxicity (ADCC). Classically, NK cells are divided into cytotoxic CD56dim NK cells, important for ADCC, and cytokine-producing CD56bright NK cells. Accumulating evidence in other types of cancer suggests that alterations in the balance between CD56dim and CD56bright NK cells is associated with inferior clinical outcome. We hypothesized that changes in NK cell subset composition in the MM microenvironment would impact therapy-responses during NK cell-driven immunotherapies. Here, we used single-cell RNA sequencing and flow cytometry to investigate the heterogeneity of the NK cell compartment in the bone marrow (BM) of newly diagnosed MM (NDMM) patients, and the effects of altered NK cell subset composition on therapy response in patients undergoing first-line daratumumab-containing therapy. Results We performed single-cell RNA sequencing of the BM CD38+ immune cell compartment from 19 NDMM patients. NK cells were identified in silico resulting in a single-cell transcriptomic dataset of 24,664 NK cells. UMAP dimensionality reduction identified 5 transcriptomic distinct NK cell clusters, present in all NDMM patients. Conventional CD56bright and CD56dim NK cells were identified by increased transcription of GZMK or GZMB, respectively. To study the balance of cytotoxic vs cytokine-producing NK cells in MM we compared the relative abundance of cytokine-producing GZMK+CD56bright NK cells vs cytotoxic GZMB+CD56dim NK cells in individual patients in our cohort. The MM BM was characterized by heterogeneity in the ratio of cytotoxic vs cytokine-producing NK cell cluster composition, in which most patients had a predominance of cytotoxic NK cells, while a subset of patients presented with a relative decrease in cytotoxic NK cell clusters (Figure 1A). The reduction in cytotoxic NK cell subsets in this patient subgroup was reflected in an altered overall transcriptome of the total BM NK cell population, with enrichment for inhibitory receptors such as KLRB1 and KLRC1, and a relative loss of activating receptors such as FCGR3A (CD16), NCR3 (NKp30) and CD226 (DNAM-1), and cytotoxic effector genes including NKG7 and GNLY. To define the relative abundance of a reduced cytotoxic NK cell potential in NDMM patients, we performed flow cytometric analyses of 246 NDMM patients from 2 independent clinical cohorts. This revealed a reduced frequency of cytotoxic CD56dim NK cells, defined as < 90% CD56dim NK cells, in 17.9 % of 162 transplant-eligible treatment-naïve NDMM and in 28.6% of 84 treatment-naïve frail or unfit transplant ineligible MM patients. To test whether such relative decrease in cytotoxic NK cells would impact responses to daratumumab, we tested the association of BM NK cell composition with clinical outcome in the context of first-line daratumumab therapy in a cohort of frail NDMM patients ineligible for transplant from the HO143 trial (n=43). NDMM patients with reduced frequencies of BM cytotoxic NK cells presented with significantly shorter progression-free survival (PFS) (Figure 1B, PFS, p = 0.031, log-rank test), while OS was not impacted (p = 0.18, log-rank test). Univariate Cox regression analyses confirmed the correlation between reduced frequencies of CD56dim NK cells in the BM and significantly shorter PFS (hazard ratio (HR) 1.38; 95% confidence interval: 1.057 - 1.803; P = 0.018). Whether reduced frequencies of cytotoxic CD56dim NK cells affect outcome in transplant-eligible patients warrants additional study. Conclusion Here we show that about 20% of NDMM patients have an altered BM NK cell composition with a relative reduction in cytotoxic NK cells. Such altered NK cell composition correlates with significantly shorter PFS following daratumumab-containing therapy in a cohort of frail NDMM patients. Our data suggest that defining BM NK cell composition could stratify patients and identify a subgroup less likely to benefit from therapies driven by NK cell-mediated ADCC. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Repurposable Drugs That Interact with Steroid Responsive Gene Targets for Inner Ear Disease.

Corticosteroids, oral or transtympanic, remain the mainstay for inner ear diseases characterized by hearing fluctuation or sudden changes in hearing, including sudden sensorineural hearing loss (SSNHL), Meniere's disease (MD), and autoimmune inner ear disease (AIED). Despite their use across these diseases, the rate of complete recovery remains low, and results across the literature demonstrates significant heterogeneity with respect to the effect of corticosteroids, suggesting a need to identify more efficacious treatment options. Previously, our group has cross-referenced steroid-responsive genes in the cochlea with published single-cell and single-nucleus transcriptome datasets to demonstrate that steroid-responsive differentially regulated genes are expressed in spiral ganglion neurons (SGN) and stria vascularis (SV) cell types. These differentially regulated genes represent potential druggable gene targets. We utilized multiple gene target databases (DrugBank, Pharos, and LINCS) to identify orally administered, FDA approved medications that potentially target these genes. We identified 42 candidate drugs that have been shown to interact with these genes, with an emphasis on safety profile, and tolerability. This study utilizes multiple databases to identify drugs that can target a number of druggable genes in otologic disorders that are commonly treated with steroids, providing a basis for establishing novel repurposing treatment trials.

An Oxidative Stress-Related Gene Signature in Granulosa Cells Is Associated with Ovarian Aging.

Ovarian aging is associated with a decrease in fecundity. Increased oxidative stress of granulosa cells (GCs) is an important contributor. We thus asked whether there is an oxidative stress-related gene signature in GCs associated with ovarian aging. Public nonhuman primate (NHP) single-cell transcriptome was processed to identify GC cluster. Then, a GC signature for ovarian aging was established based on six oxidative stress-related differentially expressed genes (MAPK1, STK24, AREG, ATG7, ANXA1, and PON2). Receiver operating characteristic (ROC) analysis confirmed good discriminating capacity in both NHP single-cell and human bulk transcriptome datasets. Gene expression levels were investigated using qPCR in the human ovarian granulosa-like tumor cell line (KGN) and mouse GCs. In an oxidative stress model, KGN cells were treated with menadione (7.5 μM, 24 h) to induce oxidative stress, after which upregulation of MAPK1, STK24, ATG7, ANXA1, and PON2 and downregulation of AREG were observed (p < 0.05). In an aging model, KGN cells were continuously cultured for 3 months, leading to increased expressions of all genes (p < 0.05). In GCs of reproductively aged (8-month-old) Kunming mice, upregulated expression of Mapk1, Stk24, Atg7, and Pon2 and downregulated expression of Anxa1 and Areg were observed (p < 0.01). We therefore here identify a six-gene GC signature associated with oxidative stress and ovarian aging.

Integrative transcriptome analysis reveals TEKT2 and PIAS2 involvement in diabetic nephropathy.

Cell heterogeneity has impeded the accurate interpretation of the bulk transcriptome data from patients with diabetic nephropathy (DN). We performed an analysis by integrating bulk and single-cell transcriptome datasets to uncover novel mechanisms leading to DN, especially in the podocytes. Microdissected glomeruli and tubules transcriptome datasets were selected from Gene Expression Omnibus (GEO). Then the consistency between datasets was evaluated. The analysis of the bulk dataset and single-nucleus RNA dataset was integrated to reveal the cell type-specific responses to DN. The candidate genes were validated in kidney tissues from DN patients and diabetic mice. We compared 4 glomerular and 4 tubular datasets and found considerable discrepancies among datasets regarding the deferentially expressed genes (DEGs), involved signaling pathways, and the hallmark enrichment profiles. Deconvolution of the bulk data revealed that the variations in cell-type proportion contributed greatly to this discrepancy. The integrative analysis uncovered that the dysregulation of spermatogenesis-related genes, including TEKT2 and PIAS2, was involved in the development of DN. Importantly, the mRNA level of TEKT2 was negatively correlated with the mRNA levels of NPHS1 (r= -.66, p <.0001) and NPHS2 (r= -.85, p <.0001) in human diabetic glomeruli. Immunostaining confirmed that the expression of TEKT2 and PIAS2 were up-regulated in podocytes of DN patients and diabetic mice. Knocking down TEKT2 resisted high glucose-induced cytoskeletal remodeling and down-regulation of NPHS1 protein in the cultured podocyte. In conclusion, the integrative strategy can help us efficiently use the publicly available transcriptomics resources. Using this approach and combining it with classical research methods, we identified TEKT2 and PIAS2, two spermatogenesis-related genes involved in the pathogenesis of DN. Furthermore, TEKT2 is involved in this pathogenesis by regulating the podocyte cytoskeleton.

Molecular classification of human papillomavirus-positive cervical cancers based on immune signature enrichment.

BackgroundHuman papillomavirus-positive (HPV+) cervical cancers are highly heterogeneous in clinical and molecular characteristics. Thus, an investigation into their heterogeneous immunological profiles is meaningful in providing both biological and clinical insights into this disease.MethodsBased on the enrichment of 29 immune signatures, we discovered immune subtypes of HPV+ cervical cancers by hierarchical clustering. To explore whether this subtyping method is reproducible, we analyzed three bulk and one single cell transcriptomic datasets. We also compared clinical and molecular characteristics between the immune subtypes.ResultsClustering analysis identified two immune subtypes of HPV+ cervical cancers: Immunity-H and Immunity-L, consistent in the four datasets. In comparisons with Immunity-L, Immunity-H displayed stronger immunity, more stromal contents, lower tumor purity, proliferation potential, intratumor heterogeneity and stemness, higher tumor mutation burden, more neoantigens, lower levels of copy number alterations, lower DNA repair activity, as well as better overall survival prognosis. Certain genes, such as MUC17, PCLO, and GOLGB1, showed significantly higher mutation rates in Immunity-L than in Immunity-H. 16 proteins were significantly upregulated in Immunity-H vs. Immunity-L, including Caspase-7, PREX1, Lck, C-Raf, PI3K-p85, Syk, 14-3-3_epsilon, STAT5-α, GATA3, Src_pY416, NDRG1_pT346, Notch1, PDK1_pS241, Bim, NF-kB-p65_pS536, and p53. Pathway analysis identified numerous immune-related pathways more highly enriched in Immunity-H vs. Immunity-L, including cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, antigen processing and presentation, T/B cell receptor signaling, chemokine signaling, supporting the stronger antitumor immunity in Immunity-H vs. Immunity-L.ConclusionHPV+ cervical cancers are divided into two subgroups based on their immune signatures' enrichment. Both subgroups have markedly different tumor immunity, progression phenotypes, genomic features, and clinical outcomes. Our data offer novel perception in the tumor biology as well as clinical implications for HPV+ cervical cancer.

Dynamics of hepatocyte-cholangiocyte cell-fate decisions during liver development and regeneration

SummaryThe immense regenerative potential of the liver is attributed to the ability of its two key cell types – hepatocytes and cholangiocytes – to trans-differentiate to one another either directly or through intermediate progenitor states. However, the dynamic features of decision-making between these cell-fates during liver development and regeneration remains elusive. Here, we identify a core gene regulatory network comprising c/EBPα, TGFBR2, and SOX9 which is multistable in nature, enabling three distinct cell states – hepatocytes, cholangiocytes, and liver progenitor cells (hepatoblasts/oval cells) – and stochastic switching among them. Predicted expression signature for these three states are validated through multiple bulk and single-cell transcriptomic datasets collected across developmental stages and injury-induced liver repair. This network can also explain the experimentally observed spatial organization of phenotypes in liver parenchyma and predict strategies for efficient cellular reprogramming. Our analysis elucidates how the emergent dynamics of underlying regulatory networks drive diverse cell-fate decisions in liver development and regeneration.

In search of a Drosophila core cellular network with single-cell transcriptome data.

Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.

Multiscale Methods for Signal Selection in Single-Cell Data.

Analysis of single-cell transcriptomics often relies on clustering cells and then performing differential gene expression (DGE) to identify genes that vary between these clusters. These discrete analyses successfully determine cell types and markers; however, continuous variation within and between cell types may not be detected. We propose three topologically motivated mathematical methods for unsupervised feature selection that consider discrete and continuous transcriptional patterns on an equal footing across multiple scales simultaneously. Eigenscores () rank signals or genes based on their correspondence to low-frequency intrinsic patterning in the data using the spectral decomposition of the Laplacian graph. The multiscale Laplacian score (MLS) is an unsupervised method for locating relevant scales in data and selecting the genes that are coherently expressed at these respective scales. The persistent Rayleigh quotient (PRQ) takes data equipped with a filtration, allowing the separation of genes with different roles in a bifurcation process (e.g., pseudo-time). We demonstrate the utility of these techniques by applying them to published single-cell transcriptomics data sets. The methods validate previously identified genes and detect additional biologically meaningful genes with coherent expression patterns. By studying the interaction between gene signals and the geometry of the underlying space, the three methods give multidimensional rankings of the genes and visualisation of relationships between them.

Single-cell transcriptome and cell type-specific molecular pathways of human non-alcoholic steatohepatitis

The aim of this study is to characterize cell type-specific transcriptional signatures in non-alcoholic steatohepatitis (NASH) to improve our understanding of the disease. We performed single-cell RNA sequencing on liver biopsies from 10 patients with NASH. We applied weighted gene co-expression network analysis and validated our findings using a publicly available RNA sequencing data set derived from 160 patients with non-alcoholic fatty liver disease (NAFLD) and 24 controls with normal liver histology. Our study provides a comprehensive single-cell analysis of NASH pathology in humans, describing 19,627 single-cell transcriptomes from biopsy-proven NASH patients. Our data suggest that the previous notion of ”NASH-associated macrophages” can be explained by an up-regulation of normally existing subpopulations of liver macrophages. Similarly, we describe two distinct populations of activated hepatic stellate cells, associated with the level of fibrosis. Finally, we find that the expression of several circulating markers of NAFLD are co-regulated in hepatocytes together with predicted effector genes from NAFLD genome-wide association studies (GWAS), coupled to abnormalities in the complement system. In sum, our single-cell transcriptomic data set provides insights into novel cell type-specific and general biological processes associated with inflammation and fibrosis, emphasizing the importance of studying cell type-specific biological processes in human NASH.

ABC portal: a single-cell database and web server for blood cells.

ABC portal (http://abc.sklehabc.com) is a database and web portal containing 198 single-cell transcriptomic datasets of development, differentiation and disorder of blood/immune cells. All the datasets were re-annotated with a manually curated and unified single-cell reference, especially for the haematopoietic stem and progenitor cells. ABC portal provides web-based interactive analysis modules, especially a comprehensive cell-cell communication analysis and disease-related gene signature analysis. Importantly, ABC portal allows customized sample selection based on a combination of several metadata for downstream analysis and comparison analysis across datasets. ABC portal also allows users to select multiple cell types for analysis in the modules. Together, ABC portal provides an interactive interface of single-cell data exploration and re-analysis with customized analysis modules for the researchers and clinicians, and will facilitate understanding of haematopoiesis and blood/immune disorders.

Feature-scML: An Open-source Python Package for the Feature Importance Visualization of Single-Cell Omics with Machine Learning

Background: Inferring feature importance is both a promise and challenge in bioinformatics and computational biology. While multiple biological computation methods exist to identify decisive factors of single cell subpopulation, there is a need for a comprehensive toolkit that presents an intuitive and custom view of the feature importance. Objective: We developed a Feature-scML, a scalable and friendly toolkit that allows the users to visualize and reveal decisive factors for single cell omics analysis. Method: Feature-scML incorporates the following main three functions: (i) There are seven feature selection algorithms to comprehensively score and rank every feature. (ii) Four machine learning approaches and increment feature selection (IFS) strategy jointly determine the number of selected features. (iii) The Feature-scML supports the visualized feature importance, model performance evaluation, and model interpretation. The source code is available at https://github.com/liameihao/Feature-scML. Results: We systematically compared the performance of seven feature selection algorithms from Feature-scML on two single cell transcriptome datasets. It demonstrates the effectiveness and power of the Feature-scML. Conclusion: Feature-scML is effective for analyzing single-cell RNA omics datasets to automate the machine learning process and customize the visual analysis from the results.

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

GLOBE: a contrastive learning-based framework for integrating single-cell transcriptome datasets.

Integration of single-cell transcriptome datasets from multiple sources plays an important role in investigating complex biological systems. The key to integration of transcriptome datasets is batch effect removal. Recent methods attempt to apply a contrastive learning strategy to correct batch effects. Despite their encouraging performance, the optimal contrastive learning framework for batch effect removal is still under exploration. We develop an improved contrastive learning-based batch correction framework, GLOBE. GLOBE defines adaptive translation transformations for each cell to guarantee the stability of approximating batch effects. To enhance the consistency of representations alignment, GLOBE utilizes a loss function that is both hardness-aware and consistency-aware to learn batch effect-invariant representations. Moreover, GLOBE computes batch-corrected gene matrix in a transparent approach to support diverse downstream analysis. Benchmarking results on a wide spectrum of datasets show that GLOBE outperforms other state-of-the-art methods in terms of robust batch mixing and superior conservation of biological signals. We further apply GLOBE to integrate two developing mouse neocortex datasets and show GLOBE succeeds in removing batch effects while preserving the contiguous structure of cells in raw data. Finally, a comprehensive study is conducted to validate the effectiveness of GLOBE.

862 RashX: Immune single-cell transcriptional classification of human chronic inflammatory skin disease

We have developed one of the largest single-cell transcriptomic datasets of CD45+ immune cells in human inflammatory skin disease, including 13 psoriasis and 11 atopic dermatitis samples. Our analyses identify skin-resident memory T cells as containing the primary molecular signature differentiating psoriasis and atopic dermatitis, composed of 78 transcripts with disease specificity at a p value < 0.001, only 13 of which have been previously reported. Recomposition into pseudo-bulk transcriptomes diminishes discriminative significance of all but 2 of these genes, demonstrating the critical role of single-cell approaches in discriminating disease states.

P429: CLONALLY RESOLVED SINGLE-CELL MULTI-OMICS IDENTIFIES LEUKEMIA SURFACE ANTIGENS

Background: At the time of diagnosis, AML is a highly heterogeneous disease, usually consisting of various epigenetically and genetically distinct pre-leukemic, as well as leukemic subclones of various maturation stages that coexist in a competitive, or even cooperative manner. This clonal heterogeneity and, in particular, the strong resemblance of leukemic cells with their healthy counterparts presents a significant challenge in the identification of cancer specific gene expression and surface markers, both prerequisites for the development of targeted therapies. Existing approaches to add clonal resolution to droplet-based single-cell RNAseq data use qualitative information available from calls of individual mitochondrial or genomic single nucleotide variants as well as copy number variants. These single cell mutational calls, however, cannot be taken at face value, but need to be analysed through appropriate statistical methods, which are currently missing. Aims: Here we aimed at developing a novel method, CloneTracer, to add clonal resolution to new or existing human single-cell transcriptomic or multiomic datasets to ultimately allow identification of leukemia surface antigens through intra-sample comparison of cancerous and healthy cells of matched differentiation state. Methods: CloneTracer amplifies mitochondrial and selected nuclear SNVs, infers copy number variation, and estimates clonal hierarchies and identities through a Bayesian model that accounts for the noise properties of single-cell RNA-seq data. Results: Through combination of clonal identities with information on surface markers at single cell resolution, CloneTracer identified novel leukemia markers that enabled quantification of the leukemic burden in clinical specimens and enrichment for leukemic or healthy cells by FACS. Image:Summary/Conclusion: In summary, our data illustrate the benefits of clonally resolved single cell multi-omics for identifying cancer specific surface antigens and potential druggable targets by intra-patient comparisons.

DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data.

Single-cell RNA sequencing trades read-depth for dimensionality, often leading to loss of critical signaling gene information that is typically present in bulk data sets. We introduce DURIAN (Deconvolution and mUltitask-Regression-based ImputAtioN), an integrative method for recovery of gene expression in single-cell data. Through systematic benchmarking, we demonstrate the accuracy, robustness and empirical convergence of DURIAN using both synthetic and published data sets. We show that use of DURIAN improves single-cell clustering, low-dimensional embedding, and recovery of intercellular signaling networks. Our study resolves several inconsistent results of cell-cell communication analysis using single-cell or bulk data independently. The method has broad application in biomarker discovery and cell signaling analysis using single-cell transcriptomics data sets.

Abstract 2511: Metastatic lung colonization by osteosarcoma requires cooperation between tumor subpopulations with distinct phenotypes

Abstract Four decades of intense effort have not improved outcomes for patients with osteosarcoma. Even though 35-40% of young people so treated still die from the disease, usually from complications related to lung metastasis, our standard of care remains unchanged since 1982. While effective metastasis-targeting interventions could transform clinical care, our incomplete understanding of the mechanisms driving tumor spread have hampered efforts to develop rational, targeted therapeutic interventions. Working to address these gaps, our lab previously showed that concurrent inhibition of IL6 and CXCL8 could prevent metastases in up to 85% of osteosarcoma-bearing mice, though the mechanisms remain unclear. Seeking to understand the mechanism, we generated single-cell RNA-seq datasets from the lungs of osteosarcoma bearing mice collected at multiple time points during lung colonization. We employed niche labeling technology to isolate both tumor and tumor-associated cells and generated single cell transcriptomic datasets from the isolated cells. We used this data to perform inter-cellular signaling analyses and to characterize changes occurring in the composition and phenotype of cells occupying the metastatic niche over developmental time. Immunofluorescent staining of both murine and human metastatic lesions confirmed key findings from the scRNA-seq data. We found that a small subpopulation of osteosarcoma cells triggers an IL1-IL6 paracrine loop upon interaction with lung epithelial cells, driving IL6/CXCL8 production within the early metastatic niche. This subset of tumor cells is hypo-proliferative, hyper-secretory, p21+, and survives the stresses of dissemination to the lung. We call these metastasis initiating cells "anchor" cells and their rapidly proliferative counterparts "growth cells." Very early metastases show near-complete enrichment for anchor-phenotype osteosarcoma cells, while growth cells become dominant as lesions expand. Anchor derived signals can recruit circulating growth cells into a developing niche. However, it remains unclear whether this or state changes in anchor cell progeny are the primary mechanism for the appearance of growth cells within a lesion. Intriguingly, anchor cells appear resistant to chemotherapy, providing a potential mechanism for relapse after treatment. Taken together, the data suggest that the establishment of lung metastases arises through cooperation among tumor cells with distinct phenotypes. Anchor cells survive the initial stresses of the pulmonary environment and modify that environment. Proliferating growth cells are subsequently recruited to anchored niches to produce clinically relevant metastatic lesions. Effective treatment of metastases likely requires combinations of agents that target both osteosarcoma subtypes, which exhibit highly divergent sensitivity to conventional therapies. Citation Format: James B. Reinecke, John M. Hinckley, Sanjana Rajan, Amy C. Gross, Maren Cam, Ryan D. Roberts. Metastatic lung colonization by osteosarcoma requires cooperation between tumor subpopulations with distinct phenotypes [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 2511.