scholarly journals Analysis of Deficiency of Adenosine Deaminase 2 Pathogenesis Based on Single Cell RNA Sequencing of Monocytes

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2317-2317
Author(s):  
Naoki Watanabe ◽  
Shouguo Gao ◽  
Sachiko Kajigaya ◽  
Carrie Diamond ◽  
Lemlem Alemu ◽  
...  

Deficiency of adenosine deaminase 2 (DADA2) is a rare autosomal recessive disease caused by loss-of-function mutations in the ADA2 gene. DADA2 typically presents in childhood and is characterized by vasculopathy, stroke, inflammation, and immunodeficiency as well as hematologic manifestations, such as bone marrow failure and lymphoproliferation. The ADA2 protein is predominantly expressed in stimulated monocytes, dendritic cells and macrophages. ADA2 increases in the setting of inflammation and/or infection conditions. ADA2 has been reported to have a critical role in maintaining the balance between M1 (pro-inflammatory) and M2 (anti-inflammatory) macrophages. Macrophages of DADA2 patients are polarized towards M1 subset., DADA2 pathogenesis is not well characterized. To elucidate molecular mechanisms in DADA2 deficiency, we analyzed a gene expression profile of CD14+ monocytes derived from peripheral blood using single cell RNA sequencing (scRNA-seq). Blood was collected from DADA2 patients and age- and sex-matched healthy donors; all patients were studied in a registered research protocol (clinicaltrials.gov NCT00071045). Samples were obtained from 14 DADA2 patients and 6 healthy donors; median age of the DADA2 patients was 23 years old (range, 5 - 57 years). Among the 14 patients, 7 had hematological phenotypes: 5 lymphopenia, 3 neutropenia, 3 thrombocytopenia, and 2 with hypocellular bone marrow histology. Low serum immunoglobulins and cutaneous findings were frequent. Nine of the 14 patients had been treated with TNF inhibitors (etanercept and adalimumab). Mutations were distributed throughout the ADA2 gene; although two siblings had the same mutation, even they showed poor genotype-phenotype correlation. Monocytes were isolated by immunomagnetic positive selection with the EasySep™ positive CD14 selection kit Ⅱ, then subjected to scRNA-seq using Single Cell 3' Reagent Kits v2 (10X Genomics). Libraries for scRNA-seq were sequenced on the HiSeq-3000 instrument. Based on scRNA-seq data, we could classify monocytes into three populations by conventional flow cytometric criteria using cell surface protein expression imputed from scRNA-seq: CD14++CD16- classical, CD14++CD16+ intermediate, and CD14+CD16++ nonclassical monocytes (Figure A). CD16 expression was higher in DADA2 patients than in healthy donors (Figure B). A proportion of nonclassical monocytes among total monocytes were significantly higher in DADA2 patients compared to healthy donors (Figure C). On comparison of gene expression of each monocyte subtypes in DADA2 patients with that of healthy donors, there were 215, 237, and 267 differentially expressed upregulated genes in classical, intermediate, and nonclassical monocytes, respectively (at a threshold avg_logFC > 0.2). Approximately 35% of upregulated genes were overlapped among the three monocyte subtypes of DADA2 patients, including immune response genes such as IFITM1, IFITM2, IFITM3, and C3AR1 (Figure D). Common gene pathways were associated with immune function, such as interferon alpha/beta signaling and interferon gamma signaling. Specific genes to classical and intermediate monocytes were less than 10% of all the upregulated genes. Distinctively, the NF-κB pathway was upregulated in nonclassical monocytes, this might contribute to the pathogenesis of DADA2 as inflammatory disease. Overall, each monocyte subtype of DADA2 patients showed upregulation of immune response gene sets compared to controls. DADA2 patients have increased numbers of nonclassical monocytes which may contribute the immune dysregulation and increased inflammation observed in the disease. Figure Disclosures No relevant conflicts of interest to declare.

2018 ◽  
Author(s):  
Allegra A. Petti ◽  
Stephen R. Williams ◽  
Christopher A. Miller ◽  
Ian T. Fiddes ◽  
Sridhar N. Srivatsan ◽  
...  

AbstractVirtually all tumors are genetically heterogeneous, containing subclonal populations of cells that are defined by distinct mutations1. Subclones can have unique phenotypes that influence disease progression2, but these phenotypes are difficult to characterize: subclones usually cannot be physically purified, and bulk gene expression measurements obscure interclonal differences. Single-cell RNA-sequencing has revealed transcriptional heterogeneity within a variety of tumor types, but it is unclear how this expression heterogeneity relates to subclonal genetic events – for example, whether particular expression clusters correspond to mutationally defined subclones3,4,5,6-9. To address this question, we developed an approach that integrates enhanced whole genome sequencing (eWGS) with the 10x Genomics Chromium Single Cell 5’ Gene Expression workflow (scRNA-seq) to directly link expressed mutations with transcriptional profiles at single cell resolution. Using bone marrow samples from five cases of primary human Acute Myeloid Leukemia (AML), we generated WGS and scRNA-seq data for each case. Duplicate single cell libraries representing a median of 20,474 cells per case were generated from the bone marrow of each patient. Although the libraries were 5’ biased, we detected expressed mutations in cDNAs at distances up to 10 kbp from the 5’ ends of well-expressed genes, allowing us to identify hundreds to thousands of cells with AML-specific somatic mutations in every case. This data made it possible to distinguish AML cells (including normal-karyotype AML cells) from surrounding normal cells, to study tumor differentiation and intratumoral expression heterogeneity, to identify expression signatures associated with subclonal mutations, and to find cell surface markers that could be used to purify subclones for further study. The data also revealed transcriptional heterogeneity that occurred independently of subclonal mutations, suggesting that additional factors drive epigenetic heterogeneity. This integrative approach for connecting genotype to phenotype in AML cells is broadly applicable for analysis of any sample that is phenotypically and genetically heterogeneous.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1314-1314
Author(s):  
Allegra A Petti ◽  
Stephen R Williams ◽  
Christopher A Miller ◽  
Ian T Fiddes ◽  
David Chen ◽  
...  

Abstract Background. Acute Myeloid Leukemia (AML) is genetically and epigenetically heterogeneous. Most AML samples display clonal heterogeneity at presentation, which evolves with therapeutic interventions. To better understand the epigenetic consequences of clonal heterogeneity, we are using single-cell RNA-sequencing (scRNA-seq) to characterize expression heterogeneity in AML. To date, scRNA-seq has had limited utility in applications where it is essential to link transcriptional heterogeneity to genetic variation, because it has been difficult to identify specific mutations in individual cells using scRNA-seq data alone. To address this limitation, we developed an approach to use scRNA-seq data to identify expressed mutations in individual AML cells, and link these variants to the expression heterogeneity in the same samples. Methods. We generated duplicate cDNA libraries for each of 5 cryopreserved bone marrow samples from adult patients with de novo AML, using the 10x Genomics Chromium Single Cell 5' Gene Expression workflow for Single Cell RNA Sequencing. Single cell libraries were sequenced to yield a median of 20,474 cells per sample, and 192,427 reads per cell. Transcript alignment, counting, and inter-library normalization were performed using the Cell Ranger pipeline (10x Genomics). The Seurat R package was used for further normalization, filtering, principal component analysis, clustering, and t-SNE visualization. A nearest-neighbor algorithm was developed to assign each cell in the data set to the most transcriptionally similar hematopoietic lineage. For each case, we performed whole genome sequencing (WGS) to identify germline and somatic variants, and define clonal architecture. We then developed bioinformatic methods to determine which cells harbor these mutations, assign those cells to mutationally-defined subclones, and link mutations to defined expression clusters. Results. WGS identified 25-56 coding mutations per sample; we were able to identify 22%-46% of these mutations in at least one cell in the scRNA-seq data, including point mutations (e.g. DNMT3A, U2AF1, TP53, IDH1, IDH2, SRSF2, CEBPA, and others) and indels (e.g. FLT3-ITD, NPMc). Although the libraries were 5' biased, expressed mutations could be identified at long distances from the 5' end of transcripts; for example, an expressed DNMT3AR882H mutation (2.646 Kb from the initiating codon) was easily detected (Fig 1c). The frequency of detected mutations in the single-cell data varied widely (range: 1-1564 cells; median: 11 cells), and as expected, depended heavily on the expression level of the gene, and the size of the clone containing the mutation. Regardless, a median of 1378 cells (6.7%) had at least one identifiable mutation in the 5 samples. Using these data, we were able to 1) distinguish AML cells from normal cells in bone marrow samples (Fig 1a/b), 2) identify major subclones within the AML samples (Fig 1c/d), and 3) identify mutation-specific and subclone-specific expression profiles. In 2 samples with mutationally-defined subclones (one with a CEBPAR142fs mutation, and the other with a GATA2R361C mutation), subclone-specific gene expression profiles were clearly detected in the scRNA-seq data, and could be directly associated with cells containing the mutant transcription factors. In the case with the subclonal GATA2R361C mutation, cells with that mutation were restricted to a subset of expression clusters (Fig 1d). In this subset, we identified an expression signature that is supported by pre-existing knowledge of the GATA2/SPI1 transcriptional regulatory circuit. In addition, we observed that expression heterogeneity frequently occurs independent of mutations defined by specific subclones. For instance, the GATA2R361C subclone contained additional heterogeneity (5 independent expression clusters) that could not be accounted for by mutations (Fig 1a/d). Moreover, the other 3 cases exhibited extensive expression heterogeneity within the AML cells that was not explained by genetically defined subclones. In sum, scRNA-seq data, when adapted to detect mutations, has dramatically improved our understanding of the expression heterogeneity of AML, which arises from two main sources: 1) cell-type composition of the sample, and 2) expression variation among the AML cells themselves (caused by both mutation-associated and mutation-independent factors). Disclosures Williams: 10x Genomics: Employment, Equity Ownership. Fiddes:10x Genomics: Employment, Equity Ownership. Church:10x Genomics: Employment, Equity Ownership.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2603-2603 ◽  
Author(s):  
Oksana Zavidij ◽  
Nicholas Haradhvala ◽  
Tarek H Mouhieddine ◽  
Jihye Park ◽  
Romanos Pistofidis ◽  
...  

Abstract Introduction: In multiple myeloma (MM), despite well-characterized precursor states such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), there is a lack of sufficient biomarkers to predict mechanisms of disease progression. Most genomic analyses have sought biomarkers by study of the malignant plasma cells, however, cancers form a complex ecosystem with the immune and stromal microenvironment. Thus, to characterize the cellular composition and transcriptional programs of each component of the tumor and microenvironment at different stages of MM progression, we employed a single-cell RNA sequencing on a cohort of 22 patients and 9 healthy donors. Methods: We performed 10X droplet-based single-cell RNA sequencing using CD138-expressing plasma cells and microenvironmental populations isolated from bone marrow (BM) aspirates of patients with MGUS (n=6), low-risk SMM (n=3), high-risk SMM (n=13), newly diagnosed MM (n=8) and from 9 healthy donors (NBM). We collected a total of ~88.8K cells, comprising ~48K CD138+ cells (~36.4 from MM stages) and ~40.8K CD45+/CD138- cells (~30.8 from MM stages).Raw read data was processed using the Cell Ranger pipeline to obtain a gene-by-cell expression matrix, which was used to identify cell types and transcriptional programs by clustering and non-negative matrix factorization. Results: Expression profiles of plasma cells revealed clear tumor-specific differences including known oncogenic drivers in MM (MMSET/FGFR3, CCND1 and MAFB) as well as Lysosome-associated Membrane Protein 5 (LAMP5),Histone Cluster 1 H1 Family Member C (HIST1H1C) and Amphiregulin (AREG) distinguishing them from healthy plasma cells. We identified a subset of cycling plasma cells, observing a range of proliferative activity of the malignant fraction. Furthermore, our approach allowed a unique head-to-head comparison of gene expression changes in normal and malignant plasma cells in the MGUS and SMM patients within an individual, excluding inter-individual variation. We were able to discriminate malignant from non-malignant plasma cells and identify transcriptional alterations including known drivers, genes related to immune modulation (NKBIA) or controlling transcription and differentiation (EID1).Some alterations were patient-specific, while others, such as MHC I overexpression and CD27 loss, were recurrently observed across subsets of the cohort. Analysis of BM microenvironment in several stages of MM progression demonstrated a striking shift in the composition of immune cells with significant infiltration of natural killer cells, non-classical monocytes/macrophages, and T cells, enriched even in the earliest stages of the disease. Further investigation revealed significant upregulation of HLA expression at the mRNA level in CD14+ monocytes/macrophages. Intriguingly, comparison of healthy and patient samples by CyTOF showed downregulation of surface MHC II representation in the corresponding cell type, and moreover, co-culture with MM cell lines induced a sharp decrease of extracellular MHC II. This provided strong evidence for compromised antigen presentation by macrophages in the disease setting, hinting at a mechanism of immune evasion. Additionally, expression signatures in cytotoxic T-cells indicated a substantial skewing towards either granzyme B/H- or granzyme K-expressing memory cell-like transcriptional program. In a subgroup of patients, we found a strong simultaneous enrichment of the anti-viral/anti-bacterial gene expression signature for interferon type-1 activated genes in CD14+ monocytes/macrophages and T cells. Together, our results provide a comprehensive view at the complex interplay of the immune and malignant cells in different stages of the disease. We, for the first time, demonstrate the immune response beginning in premalignant conditions to be heterogeneous, including compromised antigen presentation as well as alterations in cellular composition and signaling. Consideration of the type of immunological response may prove valuable in determination of progression risk, as well as open up potential strategies for therapy. Disclosures Bustoros: Dava Oncology: Honoraria. Ghobrial:Celgene: Consultancy; Janssen: Consultancy; BMS: Consultancy; Takeda: Consultancy.


2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii110-ii110
Author(s):  
Christina Jackson ◽  
Christopher Cherry ◽  
Sadhana Bom ◽  
Hao Zhang ◽  
John Choi ◽  
...  

Abstract BACKGROUND Glioma associated myeloid cells (GAMs) can be induced to adopt an immunosuppressive phenotype that can lead to inhibition of anti-tumor responses in glioblastoma (GBM). Understanding the composition and phenotypes of GAMs is essential to modulating the myeloid compartment as a therapeutic adjunct to improve anti-tumor immune response. METHODS We performed single-cell RNA-sequencing (sc-RNAseq) of 435,400 myeloid and tumor cells to identify transcriptomic and phenotypic differences in GAMs across glioma grades. We further correlated the heterogeneity of the GAM landscape with tumor cell transcriptomics to investigate interactions between GAMs and tumor cells. RESULTS sc-RNAseq revealed a diverse landscape of myeloid-lineage cells in gliomas with an increase in preponderance of bone marrow derived myeloid cells (BMDMs) with increasing tumor grade. We identified two populations of BMDMs unique to GBMs; Mac-1and Mac-2. Mac-1 demonstrates upregulation of immature myeloid gene signature and altered metabolic pathways. Mac-2 is characterized by expression of scavenger receptor MARCO. Pseudotime and RNA velocity analysis revealed the ability of Mac-1 to transition and differentiate to Mac-2 and other GAM subtypes. We further found that the presence of these two populations of BMDMs are associated with the presence of tumor cells with stem cell and mesenchymal features. Bulk RNA-sequencing data demonstrates that gene signatures of these populations are associated with worse survival in GBM. CONCLUSION We used sc-RNAseq to identify a novel population of immature BMDMs that is associated with higher glioma grades. This population exhibited altered metabolic pathways and stem-like potentials to differentiate into other GAM populations including GAMs with upregulation of immunosuppressive pathways. Our results elucidate unique interactions between BMDMs and GBM tumor cells that potentially drives GBM progression and the more aggressive mesenchymal subtype. Our discovery of these novel BMDMs have implications in new therapeutic targets in improving the efficacy of immune-based therapies in GBM.


BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Xingmin Feng ◽  
Sachiko Kajigaya ◽  
Xujing Wang ◽  
...  

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.


iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  

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