scholarly journals ATRT-13. DIFFERENT CELLS OF ORIGIN PAVE THE WAY FOR MOLECULAR HETEROGENEITY IN RHABDOID TUMORS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii278-iii278
Author(s):  
Monika Graf ◽  
Marta Interlandi ◽  
Natalia Moreno ◽  
Dörthe Holdhof ◽  
Viktoria Melcher ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Time Frame ◽  
Precursor Cells ◽  
Genetically Engineered ◽  
Molecular Heterogeneity ◽  
Loss Of Function ◽  
Cellular Origin ◽  
Cells Of Origin ◽  
Rhabdoid Tumors

Abstract Rhabdoid tumors (RT) are rare but highly aggressive pediatric neoplasms. These tumors carry homozygous loss-of-function alterations of SMARCB1 in almost all cases with an otherwise low mutational load. RT arise at different intracranial (ATRT) as well as extracranial (MRT) anatomical sites. Three main molecular subgroups (ATRT-SHH, ATRT-TYR, ATRT-MYC) have been characterized for ATRT which are epigenetically and clinically diverse, while MRT show remarkable similarities with ATRT-MYC distinct from ATRT-SHH and ATRT-TYR. Even though there are hypotheses about various cells of origin among RT subgroups, precursor cells of RT have not yet been identified. Previous studies on the temporal control of SMARCB1 knockout in genetically engineered mouse models have unveiled a tight vulnerable time frame during embryogenesis with regard to the susceptibility of precursor cells to result in RT. In this study, we employed single-cell RNA sequencing to describe the intra- and intertumoral heterogeneity of murine ATRT-SHH and -MYC as well as extracranial MYC tumor cells. We defined subgroup-specific tumor markers for all RT classes but also observed a notable overlap of gene expression patterns in all MYC subgroups. By comparing these single-cell transcriptomes with available single-cell maps of early embryogenesis, we gained first insights into the cellular origin of RT. Finally, unsupervised clustering of published human RT methylation data and healthy control tissues confirmed the existence of different cells of origin for intracranial SHH tumors and MYC tumors independent of their anatomical localizations.

scholarly journals ETMR-06. DISSECTING THE MOLECULAR AND DEVELOPMENTAL BASIS OF PINEOBLASTOMA THROUGH GENOMICS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii323-iii324
Author(s):  
Brian Gudenas ◽  
Bernhard Englinger ◽  
Anthony P Y Liu ◽  
Yiai Tong ◽  
David Meredith ◽  
...  
Keyword(s):  
Pineal Gland ◽  
Single Cell ◽  
Outcome Data ◽  
Genetically Engineered ◽  
Developmental Trajectory ◽  
Transcriptional Analysis ◽  
Molecular Heterogeneity ◽  
Developmental Origins ◽  
Cell Of Origin ◽  
Molecular Features

Abstract Pineoblastoma (PB) is an aggressive embryonal brain tumor comprising 1% of pediatric CNS tumors. The clinico-molecular heterogeneity and developmental origins underlying PB are poorly understood; therefore, we have assembled a molecular cohort of histologically defined PBs (n=43) with corresponding outcome data. Methylation profiling revealed four molecularly and clinically distinct PB subgroups, including two novel entities. Mutational and transcriptional analysis identified characteristic molecular features of each subgroup, such as mutations in the miRNA processing pathway or FOXR2 proto-oncogene overexpression. Furthermore, subgroups exhibited differences in propensity for metastasis, cytogenetics, and clinical outcomes. To dissect PB developmental origins and resolve PB subgroup biology, we have employed a combination of single-cell genomics and genetically engineered mouse modeling. We created a single-cell transcriptional atlas of the developing murine pineal gland across 11 timepoints and are currently integrating these data with single nuclei RNA-seq data of human PB (n=25). Single-cell analysis of the developing pineal gland revealed three distinct populations of pinealocytes, referred to as early, mid and late pinealocytes, which segregate by developmental stage yet lie along a single developmental trajectory. Preliminary results implicate significant associations between PBs and the early pinealocyte population as well as subgroup-specific differences in intratumoral heterogeneity. Furthermore, this knowledge has informed the downstream generation of biologically faithful disease models, including a transgenic mouse model of the PB-RB subgroup. Remarkably, this model shows up-regulation of key markers of PB such as Crx, Asmt and Otx2 and substantiates early pinealocytes as the probable cell-of-origin for this PB subgroup.

scholarly journals Time-resolved single-cell analysis of Brca1 associated mammary tumourigenesis reveals aberrant differentiation of luminal progenitors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Karsten Bach ◽  
Sara Pensa ◽  
Marija Zarocsinceva ◽  
Katarzyna Kania ◽  
Julie Stockis ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Genetically Engineered ◽  
Cell Of Origin ◽  
Loss Of Function ◽  
Time Resolved ◽  
Genetically Engineered Mouse Models ◽  
Tumour Formation ◽  
Luminal Progenitor Cell ◽  
Single Cell Profiling

AbstractIt is unclear how genetic aberrations impact the state of nascent tumour cells and their microenvironment. BRCA1 driven triple negative breast cancer (TNBC) has been shown to arise from luminal progenitors yet little is known about how BRCA1 loss-of-function (LOF) and concomitant mutations affect the luminal progenitor cell state. Here we demonstrate how time-resolved single-cell profiling of genetically engineered mouse models before tumour formation can address this challenge. We found that perturbing Brca1/p53 in luminal progenitors induces aberrant alveolar differentiation pre-malignancy accompanied by pro-tumourigenic changes in the immune compartment. Unlike alveolar differentiation during gestation, this process is cell autonomous and characterised by the dysregulation of transcription factors driving alveologenesis. Based on our data we propose a model where Brca1/p53 LOF inadvertently promotes a differentiation program hardwired in luminal progenitors, highlighting the deterministic role of the cell-of-origin and offering a potential explanation for the tissue specificity of BRCA1 tumours.

scholarly journals Distinct Expression Patterns of Carbonic Anhydrase IX in Clear Cell, Microcystic, and Angiomatous Meningiomas

2019 ◽  
Vol 78 (12) ◽  
pp. 1081-1088
Author(s):  
Rati Chkheidze ◽  
Patrick J Cimino ◽  
Kimmo J Hatanpaa ◽  
Charles L White ◽  
Manuel Ferreira ◽  
...  
Keyword(s):  
Carbonic Anhydrase ◽  
Clear Cell ◽  
Expression Patterns ◽  
Carbonic Anhydrase Ix ◽  
World Health ◽  
Loss Of Function ◽  
Ca Ix ◽  
Hypoxia Marker ◽  
Other World ◽  
Health Organization

Abstract Clear cell, microcytic, and angiomatous meningiomas are 3 vasculature-rich variants with overlapping morphological features but different prognostic and treatment implications. Distinction between them is not always straightforward. We compared the expression patterns of the hypoxia marker carbonic anhydrase IX (CA-IX) in meningiomas with predominant clear cell (n = 15), microcystic (n = 9), or angiomatous (n = 11) morphologies, as well as 117 cases of other World Health Organization recognized histological meningioma variants. Immunostaining for SMARCE1 protein, whose loss-of-function has been associated with clear cell meningiomas, was performed on all clear cell meningiomas, and selected variants of meningiomas as controls. All clear cell meningiomas showed absence of CA-IX expression and loss of nuclear SMARCE1 expression. All microcystic and angiomatous meningiomas showed diffuse CA-IX immunoreactivity and retained nuclear SMARCE1 expression. In other meningioma variants, CA-IX was expressed in a hypoxia-restricted pattern and was highly associated with atypical features such as necrosis, small cell change, and focal clear cell change. In conclusion, CA-IX may serve as a useful diagnostic marker in differentiating clear cell, microcystic, and angiomatous meningiomas.

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.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals MBRS-46. CHARTING NEOPLASTIC AND IMMUNE CELL HETEROGENEITY IN HUMAN AND GEM MODELS OF MEDULLOBLASTOMA USING scRNAseq

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii406-iii406
Author(s):  
Andrew Donson ◽  
Kent Riemondy ◽  
Sujatha Venkataraman ◽  
Ahmed Gilani ◽  
Bridget Sanford ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Cell Heterogeneity ◽  
Transcriptomic Data ◽  
Single Cell Rna Sequencing ◽  
Transcript Profiles

Abstract We explored cellular heterogeneity in medulloblastoma using single-cell RNA sequencing (scRNAseq), immunohistochemistry and deconvolution of bulk transcriptomic data. Over 45,000 cells from 31 patients from all main subgroups of medulloblastoma (2 WNT, 10 SHH, 9 GP3, 11 GP4 and 1 GP3/4) were clustered using Harmony alignment to identify conserved subpopulations. Each subgroup contained subpopulations exhibiting mitotic, undifferentiated and neuronal differentiated transcript profiles, corroborating other recent medulloblastoma scRNAseq studies. The magnitude of our present study builds on the findings of existing studies, providing further characterization of conserved neoplastic subpopulations, including identification of a photoreceptor-differentiated subpopulation that was predominantly, but not exclusively, found in GP3 medulloblastoma. Deconvolution of MAGIC transcriptomic cohort data showed that neoplastic subpopulations are associated with major and minor subgroup subdivisions, for example, photoreceptor subpopulation cells are more abundant in GP3-alpha. In both GP3 and GP4, higher proportions of undifferentiated subpopulations is associated with shorter survival and conversely, differentiated subpopulation is associated with longer survival. This scRNAseq dataset also afforded unique insights into the immune landscape of medulloblastoma, and revealed an M2-polarized myeloid subpopulation that was restricted to SHH medulloblastoma. Additionally, we performed scRNAseq on 16,000 cells from genetically engineered mouse (GEM) models of GP3 and SHH medulloblastoma. These models showed a level of fidelity with corresponding human subgroup-specific neoplastic and immune subpopulations. Collectively, our findings advance our understanding of the neoplastic and immune landscape of the main medulloblastoma subgroups in both humans and GEM models.

scholarly journals Longitudinal single-cell profiling reveals molecular heterogeneity and tumor-immune evolution in refractory mantle cell lymphoma

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shaojun Zhang ◽  
Vivian Changying Jiang ◽  
Guangchun Han ◽  
Dapeng Hao ◽  
Junwei Lian ◽  
...  
Keyword(s):  
Mantle Cell Lymphoma ◽  
Single Cell ◽  
Communication Networks ◽  
Immune Cell ◽  
Cell Lymphoma ◽  
Survivin Expression ◽  
Therapy Resistance ◽  
Molecular Heterogeneity ◽  
Multiple Cancer ◽  
Mantle Cell

AbstractThe mechanisms driving therapeutic resistance and poor outcomes of mantle cell lymphoma (MCL) are incompletely understood. We characterize the cellular and molecular heterogeneity within and across patients and delineate the dynamic evolution of tumor and immune cell compartments at single cell resolution in longitudinal specimens from ibrutinib-sensitive patients and non-responders. Temporal activation of multiple cancer hallmark pathways and acquisition of 17q are observed in a refractory MCL. Multi-platform validation is performed at genomic and cellular levels in PDX models and larger patient cohorts. We demonstrate that due to 17q gain, BIRC5/survivin expression is upregulated in resistant MCL tumor cells and targeting BIRC5 results in marked tumor inhibition in preclinical models. In addition, we discover notable differences in the tumor microenvironment including progressive dampening of CD8+ T cells and aberrant cell-to-cell communication networks in refractory MCLs. This study reveals diverse and dynamic tumor and immune programs underlying therapy resistance in MCL.

scholarly journals Comprehensive analysis of single cell ATAC-seq data with SnapATAC

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rongxin Fang ◽  
Sebastian Preissl ◽  
Yang Li ◽  
Xiaomeng Hou ◽  
Jacinta Lucero ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Open Chromatin ◽  
Cell Type ◽  
Process Data ◽  
Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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