scholarly journals A fast variational algorithm to detect the clonal copy number substructure of tumors from single-cell data

2021 ◽  
Author(s):  
Antonio De Falco ◽  
Francesca P Caruso ◽  
Xiao Dong Su ◽  
Antonio Iavarone ◽  
Michele Ceccarelli
Keyword(s):  
Brain Tumors ◽  
Single Cell ◽  
Expression Profile ◽  
Copy Number ◽  
Individual Cell ◽  
Segmentation Algorithm ◽  
Intratumor Heterogeneity ◽  
Copy Number Profile ◽  
Malignant Cells ◽  
Cell Data

Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clonal substructure of tumors from single cell data. It uses a multichannel segmentation algorithm exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN can automatically and accurately discriminate between malignant and non-malignant cells, resulting in a practical framework to analyze tumors and their microenvironment. We apply SCEVAN to several datasets encompassing 106 samples and 93,322 cells from different tumors types and technologies. We demonstrate its application to characterize the intratumor heterogeneity and geographic evolution of malignant brain tumors.

scholarly journals Molecular Cross-Validation for Single-Cell RNA-seq

2019 ◽  
Author(s):  
Joshua Batson ◽  
Loïc Royer ◽  
James Webber
Keyword(s):  
Single Cell ◽  
Cross Validation ◽  
Individual Cell ◽  
Principal Component ◽  
Ground Truth ◽  
Rna Seq ◽  
Optimal Parameters ◽  
Denoising Method ◽  
Data Driven Approach ◽  
Cell Data

Single-cell RNA sequencing enables researchers to study the gene expression of individual cells. However, in high-throughput methods the portrait of each individual cell is noisy, representing thousands of the hundreds of thousands of mRNA molecules originally present. While many methods for denoising single-cell data have been proposed, a principled procedure for selecting and calibrating the best method for a given dataset has been lacking. We present “molecular cross-validation,” a statistically principled and data-driven approach for estimating the accuracy of any denoising method without the need for ground-truth. We validate this approach for three denoising methods—principal component analysis, network diffusion, and a deep autoencoder—on a dataset of deeply-sequenced neurons. We show that molecular cross-validation correctly selects the optimal parameters for each method and identifies the best method for the dataset.

Local progenitor cells shape the neoplastic vasculature and promote brain tumor growth.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14044-e14044
Author(s):  
Roland Kälin ◽  
Linzhi Cai ◽  
Dongxu Zhao ◽  
Huabin Zhang ◽  
Wenlong Zhang ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Single Cell ◽  
Progenitor Cells ◽  
Cell Population ◽  
Expression Profile ◽  
Progenitor Cell ◽  
Myeloid Cells ◽  
Lineage Tracing

e14044 Background: Aggressive brain tumors like glioblastoma depend on support by their local environment. While the role of tumor-associated myeloid cells on glioblastoma progression is well-documented, we have only partial knowledge of the pathological impact of glioblastoma -parenchymal progenitor cells. Methods: We investigated the glioblastoma microenvironment with transgenic lineage-tracing models ( nestin-creER2, R26-tdTomato and sox2-creER2,R26-tdTomato), intravital imaging, single-cell transcriptomics, immunofluorescence and flow-cytometry as well as histopathology and characterized a previously unknown tumor-associated progenitor cell. In functional experiments, we studied the knockout of the transcription factor SOX2 in these tumor-associated cells. Results: Lineage-traced cells from mouse glioblastoma were obtained by flow-cytometry and single cell transcriptomes compared to established gene expression data from brain tumor parenchymal cells. The traced tumor-associated cells had a transcriptomic profile largely resembling myeloid cells and expressed microglia-/macrophage-markers on the protein-level. However, transgenic models and bone-marrow chimera revealed that the traced cells were clearly distinct from microglia or macrophages. The traced tumor associated cells with a myeloid expression profile derived from a SOX2-dependent progenitor cell. Consequently, conditional Sox2-knockout ablated the entire myeloid-like cell population. Remarkably, this tumor-associated cell population had a large impact on disease-progression causing significant reduction of glioblastoma –vascularization to 53%, changing vascular function and leading to a decrease in tumor volume to 42% as compared to controls. The myeloid-like progenitor cells were identified in human brain tumors by immunofluorescence and in scRNA-seq data. Conclusions: We identified a previously unacknowledged population of tumor-associated progenitor cells with a myeloid-like expression profile that transiently appeared during glioblastoma growth. These progenitors have strong impact on glioblastoma progression and point towards a new and promising therapeutic target in order to support anti-angiogenic regimen in glioblastoma.

scholarly journals Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) inference based on single cell copy number profile v1 (protocols.io.bfhpjj5n)

protocols.io ◽  
2020 ◽  
Author(s):  
Fang Wang ◽  
Qihan Wang ◽  
Vakul Mohanty ◽  
Shaoheng Liang ◽  
Jinzhuang Dou ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
Copy Number Profile ◽  
Lineage Tree

scholarly journals Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Meghan C. Ferrall-Fairbanks ◽  
Markus Ball ◽  
Eric Padron ◽  
Philipp M. Altrock
Keyword(s):  
Acute Myeloid Leukemia ◽  
Targeted Therapy ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Diversity Index ◽  
Cellular Heterogeneity ◽  
Intratumor Heterogeneity ◽  
Disease Evolution ◽  
Cell Data ◽  
Acute Myeloid

PURPOSE Many cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from pre-existence or by evolving new genotypes and traits. Intratumor heterogeneity serves as a reservoir for resistance, which often occurs as a result of the selection of minor cellular subclones. On the level of gene expression, clonal heterogeneity can only be revealed using high-dimensional single-cell methods. We propose using a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution. MATERIALS AND METHODS We focused on individual patient samples that were probed with single-cell RNA (scRNA) sequencing to describe heterogeneity. We developed a pipeline to analyze single-cell data via sample normalization, clustering, and mathematical interpretation using a generalized diversity measure, as well as to exemplify the utility of this platform using single-cell data. RESULTS We focused on three sources of patient scRNA sequencing data: two healthy bone marrow (BM) donors, two patients with acute myeloid leukemia—each sampled before and after BM transplantation, four samples of presorted lineages—and six patients with lung carcinoma with multiregion sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified the ways in which these samples differed. Whereas a widely used Shannon diversity index sometimes reveals fewer differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparison of pre– and post–BM transplantation acute myeloid leukemia samples did not reveal differences in heterogeneity, although biological differences can exist. CONCLUSION GDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach can be widely applied to quantify heterogeneity across samples and conditions.

scholarly journals SCYN: single cell CNV profiling method using dynamic programming

BMC Genomics ◽  
2021 ◽  
Vol 22 (S5) ◽  
Author(s):  
Xikang Feng ◽  
Lingxi Chen ◽  
Yuhao Qing ◽  
Ruikang Li ◽  
Chaohui Li ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Single Cell ◽  
Copy Number ◽  
Ground Truth ◽  
Intratumor Heterogeneity ◽  
Comparative Genomic ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Gastric Cancer Cells ◽  
Complex Disorders

Abstract Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Results Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on in silico dataset. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. Conclusions SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN.

scholarly journals Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

2019 ◽  
Author(s):  
Haoyun Lei ◽  
Bochuan Lyu ◽  
E. Michael Gertz ◽  
Alejandro A. Schäffer ◽  
Xulian Shi ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
Simulated Data ◽  
Mixed Integer ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Minimum Evolution ◽  
Promising Alternative ◽  
Single Cell Sequencing

AbstractCharacterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations — e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets — have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

scholarly journals SCYN: Single cell CNV profiling method using dynamic programming

2020 ◽  
Author(s):  
Xikang Feng ◽  
Lingxi Chen ◽  
Yuhao Qing ◽  
Ruikang Li ◽  
Chaohui Li ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Single Cell ◽  
Copy Number ◽  
Intratumor Heterogeneity ◽  
Comparative Genomic ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Gastric Cancer Cells ◽  
Complex Disorders ◽  
Link Type

Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on two in silico datasets. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN. The visualization tools are hosted on https://sc.deepomics.org/.

scholarly journals Alleloscope: Integrative single cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

2020 ◽  
Author(s):  
Chi-Yun Wu ◽  
Billy T. Lau ◽  
Heonseok Kim ◽  
Anuja Sathe ◽  
Susan M. Grimes ◽  
...  
Keyword(s):  
Single Cell ◽  
Chromatin Remodeling ◽  
Copy Number ◽  
Gastric Cancer Cell Line ◽  
Chromatin Accessibility ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Copy Number Aberrations ◽  
Allele Specific ◽  
Allelic Copy Number

AbstractCancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single cell DNA and ATAC sequencing data, either separately or in combination. This approach allows for integrative multi-omic analysis of allele-specific copy number and chromatin accessibility on the same cell. On scDNA-seq data from gastric, colorectal, and breast cancer samples, with extensive validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multi-allelic copy number aberrations, where cells that carry varying allelic configurations adding to the same total copy number co-evolve within a tumor. The contributions of such allele-specific events to intratumor heterogeneity have been under-reported and under-studied due to the lack of methods for their detection. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detects multi-allelic copy number events and copy neutral loss-of-heterozygosity, enabling the dissection of the contributions of chromosomal instability and chromatin remodeling in tumor evolution.

scholarly journals Alleloscope: Integrative single cell analysis of allele-specific copy number alterations and chromatin accessibility in cancer

2020 ◽  
Author(s):  
Chi-Yun Wu ◽  
Billy Lau ◽  
Heonseok Kim ◽  
Anuja Sathe ◽  
Susan M Grimes ◽  
...  
Keyword(s):  
Single Cell ◽  
Chromatin Remodeling ◽  
Copy Number ◽  
Gastric Cancer Cell Line ◽  
Chromatin Accessibility ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Copy Number Aberrations ◽  
Allele Specific ◽  
Allelic Copy Number

Abstract Cancer progression is driven by both somatic copy number aberrations (CNAs) and chromatin remodeling, yet little is known about the interplay between these two classes of events in shaping the clonal diversity of cancers. We present Alleloscope, a method for allele-specific copy number estimation that can be applied to single cell DNA and ATAC sequencing data, either separately or in combination. This approach allows for integrative multi-omic analysis of allele-specific copy number and chromatin accessibility on the same cell. On scDNA-seq data from gastric, colorectal, and breast cancer samples, with extensive validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multi-allelic copy number aberrations, where cells that carry varying allelic configurations adding to the same total copy number co-evolve within a tumor. The contributions of such allele-specific events to intratumor heterogeneity have been under-reported and under-studied due to the lack of methods for their detection. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detects multi-allelic copy number events and copy neutral loss-of-heterozygosity, enabling the dissection of the contributions of chromosomal instability and chromatin remodeling in tumor evolution.

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