scholarly journals Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Miranda V. Hunter ◽  
Reuben Moncada ◽  
Joshua M. Weiss ◽  
Itai Yanai ◽  
Richard M. White
Keyword(s):  
Tumor Microenvironment ◽  
Cell Types ◽  
Human Melanoma ◽  
Rna Seq ◽  
Zebrafish Model ◽  
Human Patient ◽  
Spatially Resolved ◽  
Interface Cell ◽  
Single Nucleus ◽  
Ets Family

AbstractDuring tumor progression, cancer cells come into contact with various non-tumor cell types, but it is unclear how tumors adapt to these new environments. Here, we integrate spatially resolved transcriptomics, single-cell RNA-seq, and single-nucleus RNA-seq to characterize tumor-microenvironment interactions at the tumor boundary. Using a zebrafish model of melanoma, we identify a distinct “interface” cell state where the tumor contacts neighboring tissues. This interface is composed of specialized tumor and microenvironment cells that upregulate a common set of cilia genes, and cilia proteins are enriched only where the tumor contacts the microenvironment. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress cilia genes outside of the interface. A cilia-enriched interface is conserved in human patient samples, suggesting it is a conserved feature of human melanoma. Our results demonstrate the power of spatially resolved transcriptomics in uncovering mechanisms that allow tumors to adapt to new environments.

scholarly journals Spatial transcriptomics reveals the architecture of the tumor/microenvironment interface

2020 ◽  
Author(s):  
Miranda V. Hunter ◽  
Reuben Moncada ◽  
Joshua M. Weiss ◽  
Itai Yanai ◽  
Richard M. White
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Tumor Microenvironment ◽  
Cancer Cells ◽  
Tumor Cells ◽  
Cell Types ◽  
Human Melanoma ◽  
Zebrafish Model ◽  
Interface Cell ◽  
Ets Family

SUMMARYDuring tumor progression, cancer cells come into contact with new cell types in the microenvironment, but it is unclear how tumor cells adapt to new environments. Here, we integrate spatial transcriptomics and scRNA-seq to characterize tumor/microenvironment interactions during the initial steps of invasion. Using a zebrafish model of melanoma, we identify a unique “interface” cell state at the tumor/microenvironment boundary. This interface is composed of specialized tumor and microenvironment cells that upregulate a common set of cilia genes, and cilia proteins are enriched only where the tumor contacts the microenvironment. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress cilia genes outside of the interface. An ETS-driven interface is conserved across ten patient samples, suggesting it is a conserved feature of human melanoma. Our results demonstrate the power of spatial transcriptomic approaches in uncovering mechanisms that allow tumors to invade into the microenvironment.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

2021 ◽  
Author(s):  
Peter Orchard ◽  
Nandini Manickam ◽  
Christa Ventresca ◽  
Swarooparani Vadlamudi ◽  
Arushi Varshney ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Cell ◽  
Muscle Fiber ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Skeletal Muscle Cell ◽  
Rna Seq ◽  
Rat Skeletal Muscle ◽  
Integrative Analyses ◽  
Single Nucleus

Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.

scholarly journals CellMap: Characterizing the types and composition of iPSC-derived cells from RNA-seq data

2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  
Keyword(s):  
Single Cell ◽  
Induced Pluripotent Stem Cell ◽  
Cell Types ◽  
Model Systems ◽  
Rna Seq ◽  
Cell Type ◽  
Fine Grained ◽  
Single Nucleus ◽  
Induced Pluripotent

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.

scholarly journals Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael J. Petrany ◽  
Casey O. Swoboda ◽  
Chengyi Sun ◽  
Kashish Chetal ◽  
Xiaoting Chen ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Neuromuscular Junction ◽  
Postnatal Development ◽  
Rna Sequencing ◽  
Cell Types ◽  
Myotendinous Junction ◽  
Rna Seq ◽  
Muscle Biology ◽  
Single Nucleus ◽  
Aging Muscle

AbstractWhile the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the presence of distinct myonuclear populations emerging in postnatal development as well as aging muscle. Our datasets also provided a platform for discovery of genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.

scholarly journals Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Early Stage ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Distinct Cell ◽  
Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

scholarly journals Different Glucose Metabolic Features According to Cancer and Immune Cells in the Tumor Microenvironment

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongyoon Choi ◽  
Kwon Joong Na
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Immune Cells ◽  
Human Cancer ◽  
Human Melanoma ◽  
The Cancer Genome Atlas ◽  
Enrichment Score ◽  
Metabolic Profiles ◽  
Rna Seq ◽  
Cancer Tissues

BackgroundA close metabolic interaction between cancer and immune cells in the tumor microenvironment (TME) plays a pivotal role in cancer immunity. Herein, we have comprehensively investigated the glucose metabolic features of the TME at the single-cell level to discover feasible metabolic targets for the tumor immune status.MethodsWe examined expression levels of glucose transporters (GLUTs) in various cancer types using The Cancer Genome Atlas (TCGA) data and single-cell RNA-seq (scRNA-seq) datasets of human cancer tissues including melanoma, head and neck, and breast cancer. In addition, scRNA-seq data of immune cells in the TME acquired from human melanoma after immune checkpoint inhibitors were analyzed to investigate the dynamics of glucose metabolic profiles of specific immune cells.ResultsPan-cancer bulk RNA-seq showed that the GLUT3-to-GLUT1 ratio was positively associated with immune cell enrichment score. The scRNA-seq datasets of various human cancer tissues showed that GLUT1 was highly expressed in cancer cells, while GLUT3 was highly expressed in immune cells in TME. The scRNA-seq data obtained from human melanoma tissues pre- and post-immunotherapy showed that glucose metabolism features of myeloid cells, particularly including GLUTs expression, markedly differed according to treatment response.ConclusionsDifferently expressed GLUTs in TME suggest that GLUT could be a good candidate a surrogate of tumor immune metabolic profiles and a target for adjunctive treatments for immunotherapy.

scholarly journals Comprehensive Analysis of Immune Cell Enrichment In The Tumor Microenvironment of Head And Neck Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Ikko Mito ◽  
Hideyuki Takahashi ◽  
Reika Kawabata-Iwakawa ◽  
Shota Ida ◽  
Hiroe Tada ◽  
...  
Keyword(s):  
T Cells ◽  
Tumor Microenvironment ◽  
Head And Neck ◽  
Immune Cell ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Immune Microenvironment ◽  
Immune Signatures ◽  
Cancer Tissues

Abstract Background: Head and neck squamous carcinoma (HNSCC) is highly infiltrated by immune cells, including tumor-infiltrating lymphocytes and myeloid lineage cells. In the tumor microenvironment, tumor cells orchestrate a highly immunosuppressive microenvironment by secreting immunosuppressive mediators, expressing immune checkpoint ligands, and downregulating human leukocyte antigen expression. In the present study, we aimed to comprehensively profile the immune microenvironment of HNSCC using RNA-sequencing (RNA-seq) data obtained from The Cancer Genome Atlas (TCGA) database.Methods: We calculated enrichment scores of 33 immune cell types based on RNA-seq data of HNSCC tissues and adjacent non-cancer tissues. Based on these scores, we performed non-supervised clustering and identified three immune signatures, i.e., cold, lymphocyte, and myeloid/dendritic cell (DC), using clustering results. We then compared the clinical and biological features of the three signatures.Results: Among HNSCC and non-cancer tissues, human papillomavirus (HPV)-positive HNSCCs exhibited the highest scores in various immune cell types, including CD4+ T cells, CD8+ T cells, B cells, plasma cells, basophils, and their subpopulations. Among the three immune signatures, the proportions of HPV-positive tumors, oropharyngeal cancers, early T tumors, and N factor positive cases were significantly higher in the lymphocyte signature than in other signatures. Among the three signatures, the lymphocyte signature showed the longest overall survival (OS), especially in HPV-positive patients, whereas the myeloid/DC signature demonstrated the shortest OS in these patients. Gene set enrichment analysis revealed the upregulation of several pathways related to inflammatory and proinflammatory responses in the lymphocyte signature. The expression of PRF1, IFNG, GZMB, PDCD1, LAG3, CTLA4, HAVCR2, and TIGIT was the highest in the lymphocyte signature. Meanwhile, the expression of PD-1 ligand genes CD274 and PDCD1LG2 was highest in the myeloid/DC signature. Conclusions: Herein, our findings revealed the transcriptomic landscape of the immune microenvironment that closely reflects the clinical and biological significance of HNSCC, indicating that molecular profiling of the immune microenvironment can be employed to develop novel biomarkers and precision immunotherapies for HNSCC.

scholarly journals Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing

2017 ◽  
Author(s):  
Trygve E. Bakken ◽  
Rebecca D. Hodge ◽  
Jeremy M. Miller ◽  
Zizhen Yao ◽  
Thuc N. Nguyen ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transcriptional Profiling ◽  
Neuronal Cell ◽  
Cell Types ◽  
Matched Pair ◽  
Rna Seq ◽  
Nuclear Rna ◽  
Single Nucleus

AbstractTranscriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (∼11,000 genes) than nuclei (∼7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

scholarly journals OTHR-14. An immunogenomic analysis of melanoma brain metastases (MBM) compared to extracranial metastases (ECM)

2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii17-iii17
Author(s):  
Lucy Boyce Kennedy ◽  
Amanda E D Van Swearingen ◽  
Jeff Sheng ◽  
Dadong Zhang ◽  
Xiaodi Qin ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Pathway Analysis ◽  
Immune Cell ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Molecular Profile ◽  
Cancer Center ◽  
Rna Seq

Abstract Background MBM have a unique molecular profile compared to ECM. Methods We analyzed a previously published dataset from MD Anderson Cancer Center, including RNA-seq on surgically resected, FFPE MBM and ECM from the same patients. STAR pipeline was used to estimate mRNA abundance. DESeq2 package was used to perform differential gene expression (DGE) analyses. Pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Paired DGE and GSEA compared MBM vs. lymph node (LN) metastases (n = 16) and MBM vs. skin mets (n = 10). CIBERSORTx estimated relative abundance of immune cell types in MBM and ECM. GATK Mutect2 pipeline was used to call somatic mutations using paired normal tumor samples. Mutations were annotated using the Ensembl Variant Effect Predictor and visualized using the Maftools package in R. RNA-seq was available on 54 human primary cutaneous melanomas (CM). Gene Ontology or KEGG Pathway analysis was performed using goana function of limma package in R. Results Paired GSEA found that autophagy pathways may be up-regulated in MBM vs. LN and MBM vs. skin mets. On a single-gene level, the most strongly up-regulated genes in autophagy pathways were GFAP and HBB. Fold changes in other autophagy-related genes were low and did not reach significance. Comparison between CM which recurred in brain vs. CM which did not recur identified up-regulation of autophagy pathways. CIBERSORTx identified an increased proportion of immune suppressive M2 macrophages compared to tumor suppressive M1 macrophages in MBMs and ECMs. Conclusion Up-regulation of autophagy pathways was observed in patient-matched MBM vs. LN and skin mets. This finding was driven by up-regulation of GFAP and HBB, which could reflect changes in the tumor microenvironment. Higher M2:M1 ratio may contribute to an immune suppressive tumor microenvironment and may be targetable. Validation of our findings in an independent Duke dataset is ongoing.

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