scholarly journals Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

2021 ◽  
Author(s):  
Hyobin Jeong ◽  
Karen Grimes ◽  
Peter-Martin Bruch ◽  
Tobias Rausch ◽  
Patrick Hasenfeld ◽  
...  
Keyword(s):  
Single Cell ◽  
Nucleosome Occupancy ◽  
Single Cells ◽  
Chromosomal Rearrangements ◽  
Regulatory Elements ◽  
Computational Method ◽  
Tumour Heterogeneity ◽  
Cancer Genomes ◽  
Functional Consequences ◽  
Oncogenic Transcription Factor

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

scholarly journals Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Tumour Heterogeneity ◽  
Fresh Tissue ◽  
Human Cancers ◽  
Cryopreserved Cell ◽  
Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

The 3D Genome Structure of Single Cells

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Tianming Zhou ◽  
Ruochi Zhang ◽  
Jian Ma
Keyword(s):  
Single Cell ◽  
Data Science ◽  
Spatial Organization ◽  
Method Development ◽  
Single Cells ◽  
Genome Structure ◽  
Computational Method ◽  
Publication Date ◽  
3D Genome ◽  
Genome Features

The spatial organization of the genome in the cell nucleus is pivotal to cell function. However, how the 3D genome organization and its dynamics influence cellular phenotypes remains poorly understood. The very recent development of single-cell technologies for probing the 3D genome, especially single-cell Hi-C (scHi-C), has ushered in a new era of unveiling cell-to-cell variability of 3D genome features at an unprecedented resolution. Here, we review recent developments in computational approaches to the analysis of scHi-C, including data processing, dimensionality reduction, imputation for enhancing data quality, and the revealing of 3D genome features at single-cell resolution. While much progress has been made in computational method development to analyze single-cell 3D genomes, substantial future work is needed to improve data interpretation and multimodal data integration, which are critical to reveal fundamental connections between genome structure and function among heterogeneous cell populations in various biological contexts. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

2018 ◽  
Author(s):  
Douglas Abrams ◽  
Parveen Kumar ◽  
R. Krishna Murthy Karuturi ◽  
Joshy George
Keyword(s):  
Experimental Design ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Number ◽  
Fold Change ◽  
Computational Method ◽  
Marker Genes ◽  
Cell Type ◽  
Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

scholarly journals Single-cell ATAC-seq Signal Extraction and Enhancement with SCATE

2019 ◽  
Author(s):  
Zhicheng Ji ◽  
Weiqiang Zhou ◽  
Hongkai Ji
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Single Cells ◽  
Regulatory Elements ◽  
Single Cell Sequencing ◽  
Statistical Framework ◽  
Genome Wide ◽  
Cell Subpopulations ◽  
Accessible Chromatin ◽  
Regulatory Landscape

AbstractSingle-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscape in single cells. Single-cell ATAC-seq data are sparse and noisy. Analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We show that using SCATE, one can better reconstruct the regulatory landscape of a heterogeneous sample.

scholarly journals Single-cell regulatory landscape and disease vulnerability map of adult Macaque cortex

2020 ◽  
Author(s):  
Ying Lei ◽  
Mengnan Cheng ◽  
Zihao Li ◽  
Zhenkun Zhuang ◽  
Liang Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Neurological Diseases ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Nucleotide Polymorphisms ◽  
Cell Type

Non-human primates (NHP) provide a unique opportunity to study human neurological diseases, yet detailed characterization of the cell types and transcriptional regulatory features in the NHP brain is lacking. We applied a combinatorial indexing assay, sci-ATAC-seq, as well as single-nuclei RNA-seq, to profile chromatin accessibility in 43,793 single cells and transcriptomics in 11,477 cells, respectively, from prefrontal cortex, primary motor cortex and the primary visual cortex of adult cynomolgus monkey Macaca fascularis. Integrative analysis of these two datasets, resolved regulatory elements and transcription factors that specify cell type distinctions, and discovered area-specific diversity in chromatin accessibility and gene expression within excitatory neurons. We also constructed the dynamic landscape of chromatin accessibility and gene expression of oligodendrocyte maturation to characterize adult remyelination. Furthermore, we identified cell type-specific enrichment of differentially spliced gene isoforms and disease-associated single nucleotide polymorphisms. Our datasets permit integrative exploration of complex regulatory dynamics in macaque brain tissue at single-cell resolution.

scholarly journals BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti
Keyword(s):  
Alternative Splicing ◽  
Single Cell ◽  
Rna Splicing ◽  
Single Cells ◽  
Computational Method ◽  
Cell Level ◽  
Transcriptomic Data ◽  
Alternative Transcripts ◽  
Computational Identification ◽  
Selection Of

AbstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

scholarly journals A combinatorial indexing strategy for epigenomic profiling of plant single cells

2021 ◽  
Author(s):  
Xiaoyu Tu ◽  
Alexandre P Marand ◽  
Robert J. Schmitz ◽  
Silin Zhong
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Arabidopsis Root ◽  
Indexing Method ◽  
Specialized Equipment ◽  
Cell Type Specific ◽  
Accessible Chromatin ◽  
Insight Into

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we profiled the Arabidopsis root single-cell epigenome using a combinatorial index and dual PCR barcode strategy without the need of any specialized equipment. We generated chromatin accessibility profiles for 13,576 Arabidopsis thaliana root nuclei with an average of 12,784 unique Tn5 integrations per cell and 85% of the Tn5 insertions localizing to discrete accessible chromatin regions. Comparison with data generated from a commercial microfluidic platform revealed that our method is capable of unbiased identification of cell type-specific chromatin accessibility with improved throughput, quality, and efficiency. We anticipate that by removing cost, instrument, and other technical obstacles, this combinatorial indexing method will be a valuable tool for routine investigation of single-cell epigenomes and usher new insight into plant growth, development and their interactions with the environment.

scholarly journals DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Wanwen Zeng ◽  
Xi Chen ◽  
Zhana Duren ◽  
Yong Wang ◽  
Rui Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Single Cells ◽  
Regulatory System ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cellular Level ◽  
Joint Analysis ◽  
Cell Assays ◽  
Cell Expression

Abstract Characterizing and interpreting heterogeneous mixtures at the cellular level is a critical problem in genomics. Single-cell assays offer an opportunity to resolve cellular level heterogeneity, e.g., scRNA-seq enables single-cell expression profiling, and scATAC-seq identifies active regulatory elements. Furthermore, while scHi-C can measure the chromatin contacts (i.e., loops) between active regulatory elements to target genes in single cells, bulk HiChIP can measure such contacts in a higher resolution. In this work, we introduce DC3 (De-Convolution and Coupled-Clustering) as a method for the joint analysis of various bulk and single-cell data such as HiChIP, RNA-seq and ATAC-seq from the same heterogeneous cell population. DC3 can simultaneously identify distinct subpopulations, assign single cells to the subpopulations (i.e., clustering) and de-convolve the bulk data into subpopulation-specific data. The subpopulation-specific profiles of gene expression, chromatin accessibility and enhancer-promoter contact obtained by DC3 provide a comprehensive characterization of the gene regulatory system in each subpopulation.

scholarly journals Applying Single-Cell Technology in Uveal Melanomas: Current Trends and Perspectives for Improving Uveal Melanoma Metastasis Surveillance and Tumor Profiling

2021 ◽  
Vol 7 ◽  
Author(s):  
Mona Meng Wang ◽  
Chuanfei Chen ◽  
Myoe Naing Lynn ◽  
Carlos R. Figueiredo ◽  
Wei Jian Tan ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Uveal Melanoma ◽  
Vision Loss ◽  
Single Cells ◽  
Chromosomal Rearrangements ◽  
Distant Metastases ◽  
Current Approach ◽  
Melanoma Metastasis ◽  
Molecular Features

Uveal melanoma (UM) is the most common primary adult intraocular malignancy. This rare but devastating cancer causes vision loss and confers a poor survival rate due to distant metastases. Identifying clinical and molecular features that portend a metastatic risk is an important part of UM workup and prognostication. Current UM prognostication tools are based on determining the tumor size, gene expression profile, and chromosomal rearrangements. Although we can predict the risk of metastasis fairly accurately, we cannot obtain preclinical evidence of metastasis or identify biomarkers that might form the basis of targeted therapy. These gaps in UM research might be addressed by single-cell research. Indeed, single-cell technologies are being increasingly used to identify circulating tumor cells and profile transcriptomic signatures in single, drug-resistant tumor cells. Such advances have led to the identification of suitable biomarkers for targeted treatment. Here, we review the approaches used in cutaneous melanomas and other cancers to isolate single cells and profile them at the transcriptomic and/or genomic level. We discuss how these approaches might enhance our current approach to UM management and review the emerging data from single-cell analyses in UM.

scholarly journals Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

2021 ◽  
Author(s):  
Diogo M Ribeiro ◽  
Chaymae Ziyani ◽  
Olivier Delaneau
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Regulatory Element ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Rna Seq ◽  
Regulatory Architecture ◽  
Gene Enhancer ◽  
Functional Relevance

Most human genes are co-expressed with a nearby gene. Yet, previous studies only reported this extensive local gene co-expression using bulk RNA-seq. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover thousands of co-expressed genes in two cell types and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally-related genes and (iii) provide evidence that these genes are transcribed synchronously and their co-expression is maintained up to the protein level. Finally, we identify gene-enhancer associations using multimodal single cell data, which reveal that >95% of co-expressed gene pairs share regulatory elements. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

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