scholarly journals PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marilisa Montemurro ◽  
Elena Grassi ◽  
Carmelo Gabriele Pizzino ◽  
Andrea Bertotti ◽  
Elisa Ficarra ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Third Party ◽  
Sequencing Technology ◽  
Reduction Methods ◽  
Visualization Tools ◽  
Cell Subpopulations ◽  
Multiple Samples ◽  
Analytical Approaches

Abstract Background Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

scholarly journals Cell “hashing” with barcoded antibodies enables multiplexing and doublet detection for single cell genomics

2017 ◽  
Author(s):  
Marlon Stoeckius ◽  
Shiwei Zheng ◽  
Brian Houck-Loomis ◽  
Stephanie Hao ◽  
Bertrand Z. Yeung ◽  
...  
Keyword(s):  
Single Cell ◽  
Surface Proteins ◽  
Batch Effects ◽  
Sequencing Technology ◽  
Massive Datasets ◽  
Human Pbmc ◽  
Single Cell Sequencing ◽  
The Cost ◽  
Multiple Samples ◽  
Cellular Transcriptome

ABSTRACTDespite rapid developments in single cell sequencing technology, sample-specific batch effects, detection of cell doublets, and the cost of generating massive datasets remain outstanding challenges. Here, we introduce cell “hashing”, where oligo-tagged antibodies against ubiquitously expressed surface proteins are used to uniquely label cells from distinct samples, which can be subsequently pooled. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its sample of origin, and robustly identify doublets originating from multiple samples. We demonstrate our approach by pooling eight human PBMC samples on a single run of the 10x Chromium system, substantially reducing our per-cell costs for library generation. Cell “hashing” is inspired by, and complementary to, elegant multiplexing strategies based on genetic variation, which we also leverage to validate our results. We therefore envision that our approach will help to generalize the benefits of single cell multiplexing to diverse samples and experimental designs.

scholarly journals On the discovery of subpopulation-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data

2019 ◽  
Author(s):  
Helena L. Crowell ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Daniela Calini ◽  
Ludovic Collin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
R Package ◽  
State Transitions ◽  
Sequencing Data ◽  
Statistical Framework ◽  
Single Cell Rna Sequencing ◽  
Cell Subpopulations ◽  
Multiple Samples

AbstractSingle-cell RNA sequencing (scRNA-seq) has quickly become an empowering technology to profile the transcriptomes of individual cells on a large scale. Many early analyses of differential expression have aimed at identifying differences between subpopulations, and thus are focused on finding subpopulation markers either in a single sample or across multiple samples. More generally, such methods can compare expression levels in multiple sets of cells, thus leading to cross-condition analyses. However, given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis. For example, one could investigate the condition-specific responses of cell subpopulations measured from patients from each condition; however, it is not clear which statistical framework best handles this situation. In this work, we surveyed the methods available to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated “pseudobulk” data. We developed a flexible simulation platform that mimics both single and multi-sample scRNA-seq data and provide robust tools for multi-condition analysis within the muscat R package.

scholarly journals Single-Cell Analysis Reveals Spatial Heterogeneity of Immune Cells in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Youyu Wang ◽  
Xiaohua Li ◽  
Shengkun Peng ◽  
Honglin Hu ◽  
Yuntao Wang ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Growth Factor ◽  
Lung Adenocarcinoma ◽  
Spatial Heterogeneity ◽  
Single Cell ◽  
Immune Cells ◽  
Cell Activity ◽  
Gene Set Enrichment Analysis ◽  
Cell Subpopulations

The impacts of the tumor microenvironment (TME) on tumor evolvability remain unclear. A challenge for nearly all cancer types is spatial heterogeneity, providing substrates for the emergence and evolvability of drug resistance and leading to unfavorable prognosis. Understanding TME heterogeneity among different tumor sites would provide deeper insights into personalized therapy. We found 9,992 cell profiles of the TME in human lung adenocarcinoma (LUAD) samples at a single-cell resolution. By comparing different tumor sites, we discovered high TME heterogeneity. Single-sample gene set enrichment analysis (ssGSEA) was utilized to explore functional differences between cell subpopulations and between the core, middle and edge of tumors. We identified 8 main cell types and 27 cell subtypes of T cells, B cells, fibroblasts and myeloid cells. We revealed CD4+ naive T cells in the tumor core that express high levels of immune checkpoint molecules and have a higher activity of immune-exhaustion signaling. CD8+ T cell subpopulations in the tumor core correlate with the upregulated activity of transforming growth factor-β (TGF-β) and fibroblast growth factor receptor (FGFR) signaling and downregulated T cell activity. B cell subtypes in the tumor core downregulate cytokine production. In this study, we revealed that there was immunological heterogeneity in the TME of patients with LUAD that have different ratios of immune cells and stromal cells, different functions, and various degrees of activation of immune-related pathways in different tumor parts. Therefore, clarifying the spatial heterogeneity of the tumor in the immune microenvironment can help clinicians design personalized treatments.

Single-cell sequencing technology in tumor research

2021 ◽  
Author(s):  
Xue Bai ◽  
Yuxuan Li ◽  
Xuemei Zeng ◽  
Qiang Zhao ◽  
Zhiwei Zhang
Keyword(s):  
Single Cell ◽  
Sequencing Technology ◽  
Single Cell Sequencing ◽  
Tumor Research

scholarly journals Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Luca Alessandri ◽  
Francesca Cordero ◽  
Marco Beccuti ◽  
Nicola Licheri ◽  
Maddalena Arigoni ◽  
...  
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Medical Personnel ◽  
Cellular Heterogeneity ◽  
Biological Information ◽  
Cell Clusters ◽  
The Neural Network ◽  
Rnaseq Data ◽  
Functional Features ◽  
Cell Subpopulations

AbstractSingle-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

scholarly journals EPEN-31. SINGLE-CELL RNAseq OF CHILDHOOD EPENDYMOMA REVEALS DISTINCT NEOPLASTIC CELL SUBPOPULATIONS THAT IMPACT ETIOLOGY, MOLECULAR CLASSIFICATION AND OUTCOME

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii314-iii314
Author(s):  
Andrew Donson ◽  
Austin Gillen ◽  
Riemondy Kent ◽  
Ahmed Gilani ◽  
Sujatha Venkataraman ◽  
...  
Keyword(s):  
Single Cell ◽  
Histological Analysis ◽  
Molecular Classification ◽  
Neoplastic Cell ◽  
Good Prognosis ◽  
Cellular Heterogeneity ◽  
Mesenchymal Phenotype ◽  
Ependymal Differentiation ◽  
Cell Subpopulations ◽  
Cell Niche

Abstract Ependymoma (EPN) is a brain tumor commonly presenting in childhood that remains fatal in the majority of children. Intra-tumoral cellular heterogeneity in bulk-tumor samples significantly confounds our understanding of EPN biology, impeding development of effective therapy. We therefore used single-cell RNA sequencing to catalog cellular heterogeneity of 26 childhood EPN, predominantly from ST-RELA, PFA1 and PFA2 subgroups. ST-RELA and PFA subgroups clustered separately, with ST-RELA clustering largely according to individual sample-of-origin. PFA1 and PFA2 subgroup EPNs cells were intermixed and revealed 4 major subpopulations – 2 with characteristics of ependymal differentiation (transporter and ciliated phenotype subpopulations), an undifferentiated subpopulation and a mesenchymal phenotype. Pseudotime analysis showed the undifferentiated progenitor subpopulation either differentiating into ependymal differentiation subpopulations or transitioning into the mesenchymal subpopulation. Histological analysis revealed that undifferentiated and mesenchymal subpopulations cells colocalized to perinecrotic/perivascular zones, the putative ependymoma stem cell niche. Deconvolution of PFA bulk transcriptome data showed that undifferentiated and mesenchymal subpopulations were associated with a poor prognosis; whereas the ciliated ependymal cell-differentiated subpopulation was associated with a good prognosis. In conflict with current distinct classification paradigms, the ratio of mesenchymal and ciliated subpopulations determined bulk-tumor subgroups assignment to PFA1 and PFA2 respectively. This atlas of EPN cellular heterogeneity provides an important advance in our understanding of EPN biology, identifying high-risk associated subpopulations for therapeutic targeting.

EPCO-06. AGE- AND REGION-SPECIFIC MULTI-OMIC CHARACTERIZATION OF H3-K27M MUTANT DIFFUSE MIDLINE GLIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi2-vi2
Author(s):  
Ilon Liu ◽  
Jiang Li ◽  
Daeun Jeong ◽  
Olivia A Hack ◽  
McKenzie Shaw ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Age Groups ◽  
Mesenchymal Cell ◽  
Cell Compartments ◽  
Histone 3 ◽  
Age At Surgery ◽  
Single Nucleus ◽  
Pre Treatment ◽  
Cell Subpopulations

Abstract Diffuse midline gliomas driven by lysine27-to-methionine mutations in histone 3 (H3-K27M DMGs) are among the most fatal brain tumors. Molecular studies including single cell RNA-sequencing (scRNA-seq) of pediatric and predominantly pontine H3-K27M DMGs have shown that the H3-K27M oncohistone keeps glioma cells locked in a stem-like oligodendrocyte precursor cell (OPC) state that is capable of self-renewal and tumor-initiation. However, a comprehensive dissection of the cellular architecture of H3-K27M DMGs across different midline regions and age groups is required to better understand the cell-intrinsic and contextual regulation of H3-K27M DMG cell identities. In particular, the more recently described group of adult H3-K27M DMGs remains understudied. Here, we have collected and characterized 45 H3-K27M mutant patient tumors, spanning pontine (n=26), thalamic (n=17), and spinal (n=2) locations. Median age at surgery was 12 (2-68) years, encompassing 21 early childhood (0-10 years), 12 adolescent (11-20 years), and 12 adult (≥ 21 years) tumors. The majority of samples were obtained pre-treatment (n=28), as opposed to post-treatment or at autopsy (n=17). We profiled all 45 tumors by single cell/single nucleus RNA-seq and selected tumors were further characterized by the single cell assay for transposase-accessible chromatin (scATAC-seq). Our integrated analyses highlight the predominance of transcriptionally and epigenetically defined OPC-like tumor cells as the main cell population of H3-K27M DMGs across all age groups and locations. We further identify distinct age- and location-specific OPC-like cell subpopulations. Comparison of pediatric and adult tumors further demonstrates a significant increase of mesenchymal cell states in adult H3-K27M DMGs, which we link to differences in glioma-associated immune cell compartments between age groups. Together, this study sheds light on the effects of age- and region-dependent microenvironments in shaping cellular identities in H3-K27M DMGs.

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