Massively multiplex single-cell Hi-C

Mapping Intimacies ◽

10.1101/065052 ◽

2016 ◽

Cited By ~ 4

Author(s):

Vijay Ramani ◽

Xinxian Deng ◽

Kevin L Gunderson ◽

Frank J Steemers ◽

Christine M Disteche ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Heterogeneity ◽

Proof Of Concept ◽

Chromosome Conformation ◽

Large Numbers ◽

Conformational Properties ◽

Novel Method ◽

Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

Nucleic Acids Research ◽

10.1093/nar/gkab795 ◽

2021 ◽

Author(s):

Sheng Zhu ◽

Qiwei Lian ◽

Wenbin Ye ◽

Wei Qin ◽

Zhe Wu ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Single Cell Level ◽

Cell Heterogeneity ◽

Rna Seq ◽

Cell Level ◽

Eukaryotic Gene ◽

User Friendly ◽

Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

Computational approaches towards reducing contamination in single-cell RNA-seq data

10.1101/2020.07.15.205062 ◽

2020 ◽

Author(s):

Siamak Yousefi ◽

Hao Chen ◽

Jesse F. Ingels ◽

Melinda S. McCarty ◽

Arthur G. Centeno ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Real Life ◽

Cell Types ◽

Cell Capture ◽

Rna Seq ◽

Sequence Analyses ◽

Cell Functions ◽

Biological Interpretation ◽

Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

Mapping multicellular programs from single-cell profiles

10.1101/2020.08.11.245472 ◽

2020 ◽

Author(s):

Livnat Jerby-Arnon ◽

Aviv Regev

Keyword(s):

Single Cell ◽

Spatial Data ◽

Spatial Information ◽

Single Cells ◽

Cell Types ◽

Risk Genes ◽

Health And Disease ◽

Different Cell Types ◽

Cellular Components ◽

Acting In Concert

ABSTRACTTissue homeostasis relies on orchestrated multicellular circuits, where interactions between different cell types dynamically balance tissue function. While single-cell genomics identifies tissues’ cellular components, deciphering their coordinated action remains a major challenge. Here, we tackle this problem through a new framework of multicellular programs: combinations of distinct cellular programs in different cell types that are coordinated together in the tissue, thus forming a higher order functional unit at the tissue, rather than only cell, level. We develop the open-access DIALOGUE algorithm to systematically uncover such multi-cellular programs not only from spatial data, but even from tissue dissociated and profiled as single cells, e.g., by single-cell RNA-Seq. Tested on spatial transcriptomes from the mouse hypothalamus, DIALOGUE recovered spatial information, predicted the properties of a cell’s environment only based on its transcriptome, and identified multicellular programs that mark animal behavior. Applied to brain samples and colon biopsies profiled by scRNA-Seq, DIALOGUE identified multicellular configurations that mark Alzheimer’s disease and ulcerative colitis (UC), including a program spanning five cell types that is predictive of response to anti-TNF therapy in UC patients and enriched for UC risk genes from GWAS, each acting in different cell types, but all cells acting in concert. Taken together, our study provides a novel conceptual and methodological framework to unravel multicellular regulation in health and disease.

Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

10.1101/205971 ◽

2017 ◽

Cited By ~ 6

Author(s):

Bastiaan Spanjaard ◽

Bo Hu ◽

Nina Mitic ◽

Jan Philipp Junker

Keyword(s):

Single Cell ◽

Computational Analysis ◽

Systematic Approach ◽

Single Cells ◽

Cell Lineage ◽

Transcriptome Profiling ◽

Cell Types ◽

Lineage Tracing ◽

Lineage Trees ◽

Different Cell Types

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner 1–3. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

10.1101/2021.09.13.460162 ◽

2021 ◽

Author(s):

Wenxuan Deng ◽

Biqing Zhu ◽

Seyoung Park ◽

Tomokazu S. Sumida ◽

Avraham Unterman ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Earth Metal ◽

Cell Types ◽

Specific Cell ◽

Cell Type ◽

Proteomics Data ◽

Data Annotation ◽

Different Cell Types ◽

Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing

10.1101/157230 ◽

2017 ◽

Cited By ~ 6

Author(s):

Ryan M. Mulqueen ◽

Dmitry Pokholok ◽

Steve Norberg ◽

Andrew J. Fields ◽

Duanchen Sun ◽

...

Keyword(s):

Single Cell ◽

Bisulfite Sequencing ◽

Single Cells ◽

Human Cell Lines ◽

Proof Of Concept ◽

Methylation Analysis ◽

Genome Methylation ◽

Novel Method ◽

Cellular Identity ◽

Methylation Profiling

AbstractHere we present a novel method: single-cell combinatorial indexing for methylation analysis (sci-MET), which is the first highly scalable assay for whole genome methylation profiling of single cells. We use sci-MET to produce 2,697 total single-cell bisulfite sequencing libraries and achieve read alignment rates of 69 ± 7%, comparable to those of bulk cell methods. As a proof of concept, we applied sci-MET to successfully deconvolve the cellular identity of a mixture of three human cell lines.

Joint profiling of chromatin accessibility and gene expression in thousands of single cells

Science ◽

10.1126/science.aau0730 ◽

2018 ◽

Vol 361 (6409) ◽

pp. 1380-1385 ◽

Cited By ~ 211

Author(s):

Junyue Cao ◽

Darren A. Cusanovich ◽

Vijay Ramani ◽

Delasa Aghamirzaie ◽

Hannah A. Pliner ◽

...

Keyword(s):

Gene Expression ◽

Target Genes ◽

Adult Mouse ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Cellular Biology ◽

Proof Of Concept ◽

Large Numbers ◽

Regulatory Sites

Although we can increasingly measure transcription, chromatin, methylation, and other aspects of molecular biology at single-cell resolution, most assays survey only one aspect of cellular biology. Here we describe sci-CAR, a combinatorial indexing–based coassay that jointly profiles chromatin accessibility and mRNA (CAR) in each of thousands of single cells. As a proof of concept, we apply sci-CAR to 4825 cells, including a time series of dexamethasone treatment, as well as to 11,296 cells from the adult mouse kidney. With the resulting data, we compare the pseudotemporal dynamics of chromatin accessibility and gene expression, reconstruct the chromatin accessibility profiles of cell types defined by RNA profiles, and link cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells.

Advantages of using graph databases to explore chromatin conformation capture experiments

BMC Bioinformatics ◽

10.1186/s12859-020-03937-0 ◽

2021 ◽

Vol 22 (S2) ◽

Author(s):

Daniele D’Agostino ◽

Pietro Liò ◽

Marco Aldinucci ◽

Ivan Merelli

Keyword(s):

Web Application ◽

High Throughput Sequencing ◽

Cell Types ◽

Graph Database ◽

Graph Databases ◽

Sources Of Information ◽

Chromosome Conformation ◽

Wide Scale ◽

User Friendly ◽

Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

Multiplexed profiling of single-cell extracellular vesicles secretion

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1814348116 ◽

2019 ◽

Vol 116 (13) ◽

pp. 5979-5984 ◽

Cited By ~ 22

Author(s):

Yahui Ji ◽

Dongyuan Qi ◽

Linmei Li ◽

Haoran Su ◽

Xiaojie Li ◽

...

Keyword(s):

Single Cell ◽

Cell Lines ◽

Extracellular Vesicles ◽

Single Cell Analysis ◽

Comprehensive Evaluation ◽

Single Cells ◽

Deep Understanding ◽

Principal Component ◽

Cell Heterogeneity ◽

Indispensable Tool

Extracellular vesicles (EVs) are important intercellular mediators regulating health and diseases. Conventional methods for EV surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EV secretion. Herein, by using spatially patterned antibody barcodes, we realized multiplexed profiling of single-cell EV secretion from more than 1,000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to a deep understanding of previously undifferentiated single-cell heterogeneity underlying EV secretion. Notably, we observed that the decrement of certain EV phenotypes (e.g.,CD63+EV) was associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EV secretion and cytokines secretion simultaneously from the same single cells to investigate the multidimensional spectrum of cellular communications, from which we resolved tiered functional subgroups with distinct secretion profiles by visualized clustering and principal component analysis. In particular, we found that different cell subgroups dominated EV secretion and cytokine secretion. The technology introduced here enables a comprehensive evaluation of EV secretion heterogeneity at single-cell level, which may become an indispensable tool to complement current single-cell analysis and EV research.

HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

10.1101/2021.09.01.458575 ◽

2021 ◽

Author(s):

Qing Xie ◽

Chengong Han ◽

Victor Jin ◽

Shili Lin

Keyword(s):

Quality Improvement ◽

Data Quality ◽

Single Cell ◽

Single Cells ◽

High Sensitivity ◽

Real Data ◽

Cell Types ◽

Sequencing Depth ◽

2D Data ◽

Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.