scholarly journals Pediatric Single Cell Cancer Atlas: An Integrative Web-Based Resource for Single Cell Transcriptome Data from Pediatric Leukemias

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3488-3488
Author(s):  
Hope L Mumme ◽  
Swati S Bhasin ◽  
Beena E Thomas ◽  
Bhakti Dwivedi ◽  
Deborah DeRyckere ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Lymphoblastic Leukemia ◽  
Cell Cancer ◽  
Different Types ◽  
R Shiny ◽  
Cell Acute Lymphoblastic Leukemia ◽  
Equity Ownership ◽  
Cell Data

Abstract Introduction: The emergence and optimization of single cell profiling as a powerful tool to characterize the tumor microenvironment has revealed the heterogeneity of pediatric cancers, particularly different leukemia types/subtypes. Ease of access and analysis of the data from studies on different leukemia types is critical for improving diagnosis as well as therapy.Currently, there are no single cell data based resources available for pediatric leukemias. We have developed a comprehensive resource, Pediatric Single Cell Cancer Atlas (PedScAtlas), with the goal of developing a pan-leukemia genomics signature as well as highlighting the heterogeneity of different types of leukemia. This resource facilitates exploration and visualization of expression signatures in different leukemias without requiring extensive analysis and bioinformatics support. Methods: The PedScAtlas was built based on single cell data from various leukemias and normal bone marrow (BM) cells that have been pre-processed and analyzed using a uniform approach to generate normalized expression data (M. Bhasin et al. Blood 2020 (ASH), S. S. Bhasin et al. Blood 2020 (ASH), Panigraphy et al. JCI 2019, Stroopinsky et al. Haematologica 2021, Thomas et al. Blood 2020 (ASH)). The current version of PedScAtlas contains data from 30 local leukemia samples (Bhasin, et al. Blood 2020 (ASH), Thomas et al. Blood 2020 (ASH)) and those available on public resources such as GEO (Bailur et al. JCI Insight 2020). The PedScAtlas dataset and the Immune cell dataset each underwent quality control, integration, normalization, and dimensionality reduction using the Uniform Manifold Approximation and Projection (UMAP) method. Unsupervised UMAP analysis identified cellular clusters with similar transcriptome profiles that were annotated based on expression of cell-specific markers. Differential expression comparing different types of leukemia including acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell acute lymphoblastic leukemia (T-ALL), and mixed phenotype acute leukemia (MPAL) samples was performed. To further ascertain genes specifically expressed in malignant blasts, the atlas also contains data from normal BM samples (Bailur et al. JCI Insight 2020) and healthy immune cells from the census of Immune Cells by the Human Cell Atlas Project (https://data.humancellatlas.org/). The web resource source code is written in R programming language and the interactive webserver has been implemented using the R Shiny package (Fig 1). The tool has been extensively tested on multiple operating systems (Linux, Mac, Windows) and web-browsers (Chrome, FireFox, and Safari). The tool is currently hosted on a 64bit CentOS 6 backend server running the Shiny Server program designed to host R Shiny applications. Results: The PedScAtlas contains data that facilitate exploration of gene expression profiles across leukemia types/subtypes and tumor microenvironment (TME) cell types. The atlas includes data from 33,930 AML, 25,744 T-ALL, 13,404 MPAL, and 6,252 B-ALL blast cells. It also contains single cell profiles of healthy BM samples from publicly available studies. The user can select data sets from the 5 major types of leukemia and normal BM in any combination of their choice to explore the expression profile of a gene of interest. The data can be visualized as UMAP (Fig. 1), or violin plots with annotations based on cluster ID, cell type, disease type, sample ID, and future continuous remission or relapse outcome. The UMAP with gene expression analyses allows the user to visualize the distribution of cell expressing a given gene on the UMAP plot. The Biomarker tool shows expression of different leukemia biomarker gene sets in the entire leukemia dataset. The Immune Cell section contains BM data from the Human Cell Atlas Project; the purpose of this section is to validate leukemia biomarkers by checking that the gene does not have significant expression in the healthy immune microenvironment. Conclusions: The PedScAtlas resource provides a unique and straightforward tool for biomarker identification, analysis of leukemia subtype heterogeneity, and transcriptome profile of the immune cell microenvironment. The resource is available online at https://bhasinlab.bmi.emory.edu/PediatricSC/. Figure 1 Figure 1. Disclosures DeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Elizabeth Chavez ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Expression Patterns ◽  
The Other ◽  
Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

scholarly journals Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

2021 ◽  
Author(s):  
Zhibin Li ◽  
chengcheng Sun ◽  
Fei Wang ◽  
Xiran Wang ◽  
Jiacheng Zhu ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Evolutionary Biology ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Genetic Regulatory Networks ◽  
Peripheral Blood Mononuclear

Background: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. Methods: In this study, we compared the single-cell transcriptomes of 77 957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. Results: The results revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular cross-talks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. Conclusions: This study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders

scholarly journals Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Prashant Rajbhandari ◽  
Douglas Arneson ◽  
Sydney K Hart ◽  
In Sook Ahn ◽  
Graciel Diamante ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Metabolic Response ◽  
Cell Types ◽  
Specific Cell ◽  
Beneficial Effects ◽  
Thermogenic Adipocytes ◽  
And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

Abstract 15539: Single-cell Analysis of Aortic Tissues From Patients With Marfan Syndrome Reveals Changes in Smooth Muscle Cell Differentiation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Ashley Dawson ◽  
Yanming Li ◽  
Pingping Ren ◽  
Hernan Vasquez ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Smooth Muscle ◽  
Single Cell ◽  
Marfan Syndrome ◽  
Immune Cells ◽  
Matrix Protein ◽  
Immune Cell ◽  
Aortic Aneurysms ◽  
Extracellular Matrix Protein ◽  
Cardiac Transplant ◽  
Control Tissue

Background: Thoracic aortic aneurysms associated with Marfan syndrome (MFS) carry a high risk of mortality; however, the molecular and cellular processes leading to aortopathy in this population remain poorly understood. We aimed to use single-cell RNA (scRNA) sequencing to define the non-immune cell populations present within the aortic wall in MFS, hypothesizing that these would differ from those of non-aneurysmal control tissue. Methods: We performed scRNA sequencing of ascending aortic aneurysm tissues from MFS patients (n=3) undergoing aneurysm repair and of age-matched, non-aneurysmal control tissue from cardiac transplant donors and recipients (n=4). The Seurat package in R was used for analysis. Differentially expressed genes were identified using edgeR. Results: Eighteen non-immune cell clusters were identified, with conserved gene expression of the largest of the clusters consistent with smooth muscle cells (SMCs; n=6), fibroblasts (n=3), and endothelial cells (n=3). The SMCs and fibroblasts exhibited graded changes in their expression of contractile and extracellular matrix protein genes, supportive of a phenotypic continuum. Additionally, we identified differences in the proportions of non-immune cells in MFS tissues compared to controls. In control tissues, the most common non-immune cells expressed markers of contractile SMC maturity including CNN1 , MYH11 , and SMTN . In contrast, the largest clusters in MFS tissue were most closely related to SMCs on correlation analysis, but displayed increased expression of cyclin genes as well as immune, endothelial, and fibroblast genes indicative of de-differentiated, proliferative SMCs. Additionally, expression of genes associated with SMC phenotypic maturity, including MYH11 and MYOCD , were significantly downregulated in several of the MFS SMC clusters. Conclusion: Our data demonstrate a phenotypic continuum between fibroblasts and SMCs, with aortas from patients with MFS exhibiting an increased proportion of de-differentiated, proliferative SMCs compared to controls. Additionally, markers of SMC maturity were downregulated in SMCs in MFS compared to controls. This may be due to disruption of signaling pathways that promote differentiation.

scholarly journals Applications of Single-Cell Omics to Dissect Tumor Microenvironment

2020 ◽  
Vol 11 ◽  
Author(s):  
Tingting Guo ◽  
Weimin Li ◽  
Xuyu Cai
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Immune Checkpoint Blockade ◽  
Lineage Tracing ◽  
Great Success ◽  
Novel Technologies ◽  
Single Cell Sequencing

The recent technical and computational advances in single-cell sequencing technologies have significantly broaden our toolkit to study tumor microenvironment (TME) directly from human specimens. The TME is the complex and dynamic ecosystem composed of multiple cell types, including tumor cells, immune cells, stromal cells, endothelial cells, and other non-cellular components such as the extracellular matrix and secreted signaling molecules. The great success on immune checkpoint blockade therapy has highlighted the importance of TME on anti-tumor immunity and has made it a prime target for further immunotherapy strategies. Applications of single-cell transcriptomics on studying TME has yielded unprecedented resolution of the cellular and molecular complexity of the TME, accelerating our understanding of the heterogeneity, plasticity, and complex cross-interaction between different cell types within the TME. In this review, we discuss the recent advances by single-cell sequencing on understanding the diversity of TME and its functional impact on tumor progression and immunotherapy response driven by single-cell sequencing. We primarily focus on the major immune cell types infiltrated in the human TME, including T cells, dendritic cells, and macrophages. We further discuss the limitations of the existing methodologies and the prospects on future studies utilizing single-cell multi-omics technologies. Since immune cells undergo continuous activation and differentiation within the TME in response to various environmental cues, we highlight the importance of integrating multimodal datasets to enable retrospective lineage tracing and epigenetic profiling of the tumor infiltrating immune cells. These novel technologies enable better characterization of the developmental lineages and differentiation states that are critical for the understanding of the underlying mechanisms driving the functional diversity of immune cells within the TME. We envision that with the continued accumulation of single-cell omics datasets, single-cell sequencing will become an indispensable aspect of the immune-oncology experimental toolkit. It will continue to drive the scientific innovations in precision immunotherapy and will be ultimately adopted by routine clinical practice in the foreseeable future.

scholarly journals Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Guohe Song ◽  
Yang Shi ◽  
Meiying Zhang ◽  
Shyamal Goswami ◽  
Saifullah Afridi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
T Cell Subsets ◽  
M2 Macrophage ◽  
Advanced Hcc ◽  
Immune Cell Subsets

AbstractDiverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

scholarly journals Single Cell Viewer (SCV): An interactive visualization data portal for single cell RNA sequence data

2019 ◽  
Author(s):  
Shuoguo Wang ◽  
Constance Brett ◽  
Mohan Bolisetty ◽  
Ryan Golhar ◽  
Isaac Neuhaus ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Link Type ◽  
Technological Advances ◽  
R Shiny ◽  
Data Volume ◽  
Exploratory Data ◽  
Cell Data ◽  
Shiny Application

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

scholarly journals High Levels of Circulating Tumor Cells Are Associated with Increased Bone Marrow Proliferation in Newly Diagnosed Multiple Myeloma Patients

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1566-1566
Author(s):  
Cathelijne Fokkema ◽  
Madelon M.E. de Jong ◽  
Sabrin Tahri ◽  
Zoltan Kellermayer ◽  
Chelsea den Hollander ◽  
...  
Keyword(s):  
Bone Marrow ◽  
High Risk ◽  
Single Cell ◽  
Tumor Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Service Level Agreement ◽  
Service Level ◽  
Newly Diagnosed ◽  
Cell Data

Abstract Introduction The introduction of new treatment regimens has significantly increased the progression free survival (PFS) of newly diagnosed multiple myeloma (MM) patients. However, even with these novel treatments, for some the disease remains refractory, highlighting the need to identify the pathobiology of high-risk MM. In MM patients, high levels of circulating tumor cells (CTCs) is associated with an inferior prognosis independent of high-risk cytogenetics (Chakraborty et al., 2016), suggesting that CTC numbers are a relevant reflection of tumor cell biology. We hypothesized that high levels of CTCs in MM patients are either the result of a transcriptionally distinct tumor clone with enhanced migration capacities, or driven by transcriptional differences present in the bone marrow (BM) tumor cells. To test these hypotheses, we 1) compared MM cells from paired blood and BM samples, and 2) compared BM tumor cells of patients with high and low CTC levels, using single cell RNA-sequencing. Results We isolated plasma cell (PCs) from viably frozen mononuclear cells of paired peripheral blood (PB) and BM aspirates from five newly diagnosed MM patients (0.5%-8% CTCs) to determine the presence of a distinct CTC subclone. We generated single cell transcriptomes from 44,779 CTCs and 35,697 BM PCs. In the total 9 clusters common to BM PCs and CTCs were identified upon single cell data integration, but no cluster specific for either source was detected. Only 25 genes were significantly differential expressed between CTCs and BM PCs. The absence of transcriptional clusters unique to either CTCs or BM PCs, and the transcriptional similarity between these two anatomical sites makes it highly unlikely that CTC levels are driven by the presence of a transcriptionally-primed migratory clone. We next set out to identify possible transcriptional differences in BM PCs from eight patients with high (2-22%) versus thirteen patients with low (0.004%-0.08%) percentages of CTCs. Recurrent high-risk mutations were present in both groups. Single cell transcriptomes were generated from 74,830 BM PCs. Single cell data integration across all patients led to the identification of 8 distinct PC clusters, one of which was characterized by enhanced proliferation as defined by STMN1 and MKI67 transcription. Interestingly, this proliferative cluster was increased in patients with a high percentage of CTCs. Furthermore, cell cycle analyses based on canonical G2M and S phase markers revealed that actively cycling PCs were more frequent in the BM of patients with a high percentage of CTCs (64% versus 30%, p<0.001), irrespective of the transcriptional cluster of origin. We hypothesized that plasma cell-extrinsic cues from the bone marrow micro-environment might be driving tumor proliferation. In order to substantiate this, we isolated BM immune cells from the same 21 patients and generated a library of 301,045 single immune cell transcriptomes. This library contained all major immune cell subsets, including CD4 + and CD8 + T cells, NK cells, B cells and monocytes. Comparative analyses of these cell populations in patients with either high or low levels of CTC are ongoing. Conclusion Through single cell transcriptomic analyses, we demonstrate that CTCs and BM PCs are transcriptionally similar. Importantly, we identify increased BM PC proliferation as a significant difference between patients with high and low levels of CTCs, implicating an increased tumor proliferation as one of the potential mechanisms driving CTC levels and MM disease pathobiology. The relation of the BM immune micro-environment to this altered proliferative state is currently under investigation. Disclosures van der Velden: Janssen: Other: Service Level Agreement; BD Biosciences: Other: Service Level Agreement; Navigate: Other: Service Level Agreement; Agilent: Research Funding; EuroFlow: Other: Service Level Agreement, Patents & Royalties: for network, not personally. Sonneveld: SkylineDx: Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene/BMS: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding. Broyl: Sanofi: Honoraria; Janssen Pharmaceuticals: Honoraria; Celgene: Honoraria; Bristol-Meyer Squibb: Honoraria; Amgen: Honoraria.

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