scholarly journals Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

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.

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.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi104-vi104
Author(s):  
Bayli DiVita Dean ◽  
Tyler Wildes ◽  
Joseph Dean ◽  
David Shin ◽  
Connor Francis ◽  
...  

Abstract INTRODUCTION Bone marrow-derived hematopoietic stem and progenitor cells (HSPCs) give rise to the cellular components of the immune system. Unfortunately, immune reconstitution from HSPCs are negatively impacted by solid cancers, including high-grade gliomas. For example, an expansion of myeloid progenitor cells has been previously described across several cancers that originate outside the CNS. A similar expansion of MDSCs coupled with diminished T cell function has also been described in the peripheral blood of patients with newly-diagnosed GBM. Alterations in both lymphoid and myeloid compartments due to CNS malignancy led us to determine how intracranial gliomas impact HSPCs in both their capacity to reconstitute the immune compartment and in their cell fate determination. This is important to better understand the impact of gliomas on immunity and how we can leverage these findings to better develop cellular immunotherapeutics. METHODS HSPCs were isolated from bone marrow of C57BL/6 mice with orthotopic KR158B glioma, or age-matched naïve mice. Experiments were conducted to compare relative changes in: gene expression (RNA-sequencing), precursor frequencies, cell fate determination, and cellular function of cells derived from HSPCs of glioma-bearing mice. RESULTS RNA-sequencing revealed 700+ genes whose expression was significantly up- or downregulated in HSPCs from glioma-bearing mice, particularly those involved with stemness and metabolic activity. Importantly, HSPCs from glioma-bearing mice expressed upregulation of genes involved in myelopoiesis relative to naïve mice. This was coupled with an expansion of granulocyte macrophage precursors (GMPs), the progenitors to gMDSCs. Next, differentiation assays revealed that HSPCs from glioma-bearing mice had higher propensity of differentiating into MDSC under homeostatic conditions relative to controls both in vitro and in vivo. Furthermore, mice bearing intracranial gliomas possess an expansion of MDSCs which are more suppressive on T cell proliferation and hinders T cell-mediated tumor cell killing relative to MDSCs derived from naïve control mice.


eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Periklis Paganos ◽  
Danila Voronov ◽  
Jacob M Musser ◽  
Detlev Arendt ◽  
Maria Ina Arnone

Identifying the molecular fingerprint of organismal cell types is key for understanding their function and evolution. Here, we use single cell RNA sequencing (scRNA-seq) to survey the cell types of the sea urchin early pluteus larva, representing an important developmental transition from non-feeding to feeding larva. We identify 21 distinct cell clusters, representing cells of the digestive, skeletal, immune, and nervous systems. Further subclustering of these reveal a highly detailed portrait of cell diversity across the larva, including the identification of neuronal cell types. We then validate important gene regulatory networks driving sea urchin development and reveal new domains of activity within the larval body. Focusing on neurons that co-express Pdx-1 and Brn1/2/4, we identify an unprecedented number of genes shared by this population of neurons in sea urchin and vertebrate endocrine pancreatic cells. Using differential expression results from Pdx-1 knockdown experiments, we show that Pdx1 is necessary for the acquisition of the neuronal identity of these cells. We hypothesize that a network similar to the one orchestrated by Pdx1 in the sea urchin neurons was active in an ancestral cell type and then inherited by neuronal and pancreatic developmental lineages in sea urchins and vertebrates.


2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrick S. Stumpf ◽  
Xin Du ◽  
Haruka Imanishi ◽  
Yuya Kunisaki ◽  
Yuichiro Semba ◽  
...  

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.


2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Rabah Dabouz ◽  
Colin W. H. Cheng ◽  
Pénélope Abram ◽  
Samy Omri ◽  
Gael Cagnone ◽  
...  

Abstract Background Inflammation and particularly interleukin-1β (IL-1β), a pro-inflammatory cytokine highly secreted by activated immune cells during early AMD pathological events, contribute significantly to retinal neurodegeneration. Here, we identify specific cell types that generate IL-1β and harbor the IL-1 receptor (IL-1R) and pharmacologically validate IL-1β’s contribution to neuro-retinal degeneration using the IL-1R allosteric modulator composed of the amino acid sequence rytvela (as well as the orthosteric antagonist, Kineret) in a model of blue light–induced retinal degeneration. Methods Mice were exposed to blue light for 6 h and sacrificed 3 days later. Mice were intraperitoneally injected with rytvela, Kineret, or vehicle twice daily for 3 days. The inflammatory markers F4/80, NLRP3, caspase-1, and IL-1β were assessed in the retinas. Single-cell RNA sequencing was used to determine the cell-specific expression patterns of retinal Il1b and Il1r1. Macrophage-induced photoreceptor death was assessed ex vivo using retinal explants co-cultured with LPS-activated bone marrow–derived macrophages. Photoreceptor cell death was evaluated by the TUNEL assay. Retinal function was assessed by flash electroretinography. Results Blue light markedly increased the mononuclear phagocyte recruitment and levels of inflammatory markers associated with photoreceptor death. Co-localization of NLRP3, caspase-1, and IL-1β with F4/80+ mononuclear phagocytes was clearly detected in the subretinal space, suggesting that these inflammatory cells are the main source of IL-1β. Single-cell RNA sequencing confirmed the immune-specific expression of Il1b and notably perivascular macrophages in light-challenged mice, while Il1r1 expression was found primarily in astrocytes, bipolar, and vascular cells. Retinal explants co-cultured with LPS/ATP-activated bone marrow–derived macrophages displayed a high number of TUNEL-positive photoreceptors, which was abrogated by rytvela treatment. IL-1R antagonism significantly mitigated the inflammatory response triggered in vivo by blue light exposure, and rytvela was superior to Kineret in preserving photoreceptor density and retinal function. Conclusion These findings substantiate the importance of IL-1β in neuro-retinal degeneration and revealed specific sources of Il1b from perivascular MPs, with its receptor Ilr1 being separately expressed on surrounding neuro-vascular and astroglial cells. They also validate the efficacy of rytvela-induced IL-1R modulation in suppressing detrimental inflammatory responses and preserving photoreceptor density and function in these conditions, reinforcing the rationale for clinical translation.


IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S328
Author(s):  
Fengjiao Li ◽  
Mohammad Imam Hasan Bin Asad ◽  
Xiangshan Yuan ◽  
Weiwei Xian ◽  
Qiong Liu ◽  
...  

Nature ◽  
2013 ◽  
Vol 500 (7464) ◽  
pp. 593-597 ◽  
Author(s):  
Zhigang Xue ◽  
Kevin Huang ◽  
Chaochao Cai ◽  
Lingbo Cai ◽  
Chun-yan Jiang ◽  
...  

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.


Author(s):  
Zhirui Hu ◽  
Songpeng Zu ◽  
Jun S. Liu

AbstractA main challenge in analyzing single-cell RNA sequencing (scRNASeq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called “dropout”), the gene expression matrix has substantial zero read counts. Existing imputation methods either treat each cell or each gene identically and independently, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs, which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering. Optionally, SIMPLEs can integrate bulk RNASeq data for estimating dropout rates. In simulations, SIMPLEs performed significantly better than prevailing scRNASeq imputation methods by various metrics. By applying SIMPLEs to several real data sets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.


2021 ◽  
Vol 118 (19) ◽  
pp. e2102050118
Author(s):  
Abhijeet P. Deshmukh ◽  
Suhas V. Vasaikar ◽  
Katarzyna Tomczak ◽  
Shubham Tripathi ◽  
Petra den Hollander ◽  
...  

The epithelial-to-mesenchymal transition (EMT) plays a critical role during normal development and in cancer progression. EMT is induced by various signaling pathways, including TGF-β, BMP, Wnt–β-catenin, NOTCH, Shh, and receptor tyrosine kinases. In this study, we performed single-cell RNA sequencing on MCF10A cells undergoing EMT by TGF-β1 stimulation. Our comprehensive analysis revealed that cells progress through EMT at different paces. Using pseudotime clustering reconstruction of gene-expression profiles during EMT, we found sequential and parallel activation of EMT signaling pathways. We also observed various transitional cellular states during EMT. We identified regulatory signaling nodes that drive EMT with the expression of important microRNAs and transcription factors. Using a random circuit perturbation methodology, we demonstrate that the NOTCH signaling pathway acts as a key driver of TGF-β–induced EMT. Furthermore, we demonstrate that the gene signatures of pseudotime clusters corresponding to the intermediate hybrid EMT state are associated with poor patient outcome. Overall, this study provides insight into context-specific drivers of cancer progression and highlights the complexities of the EMT process.


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