scholarly journals Kronos scRT: a uniform framework for single-cell replication timing analysis

2021 ◽  
Author(s):  
Stefano Gnan ◽  
Joseph M. Josephides ◽  
Xia Wu ◽  
Manuela Spagnuolo ◽  
Dalila Saulebekova ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Timing Analysis ◽  
Replication Timing ◽  
Genomic Region ◽  
Cell Replication ◽  
Specific Order ◽  
Mammalian Genomes ◽  
A Cell ◽  
Cell Type Specific

Mammalian genomes are replicated in a cell-type specific order and in coordination with transcription and chromatin organization. Although the field of replication is also entering the single-cell era, current studies require cell sorting, individual cell processing and have yielded a limited number (<100) of cells. Here, we have developed Kronos scRT (https://github.com/CL-CHEN-Lab/Kronos scRT), a software for single-cell Replication Timing (scRT) analysis. Kronos scRT does not require a specific platform nor cell sorting, allowing the investigation of large datasets obtained from asynchronous cells. Analysis of published available data and droplet-based scWGS data generated in the current study, allows exploitation of scRT data from thousands of cells for different mouse and human cell lines. Our results demonstrate that, although most cells replicate within a close timing range for a given genomic region, replication can also occur stochastically throughout S phase. Altogether, Kronos scRT allows investigating the RT program at a single-cell resolution for both homogeneous and heterogeneous cell populations in a fast and comprehensive manner.

scholarly journals Single-cell replication profiling reveals stochastic regulation of the mammalian replication-timing program

2017 ◽  
Author(s):  
Vishnu Dileep ◽  
David M. Gilbert
Keyword(s):  
Single Cell ◽  
Mammalian Cells ◽  
Replication Timing ◽  
Chromatin Interaction ◽  
Similar Degree ◽  
Cell Replication ◽  
Stochastic Variation ◽  
Cell Cycles ◽  
And Function ◽  
Cell Variation

AbstractIn mammalian cells, distinct replication domains (RDs), corresponding to structural units of chromosomes called topologically-associating domains (TADs), replicate at different times during S-phase1–4. Further, early/late replication of RDs corresponds to active/inactive chromatin interaction compartments5,6. Although replication origins are selected stochastically, such that each cell is using a different cohort of origins to replicate their genomes7–12, replication-timing is regulated independently and upstream of origin selection13 and evidence suggests that replication timing is conserved in consecutive cell cycles14. Hence, quantifying the extent of cell-to-cell variation in replication timing is central to studies of chromosome structure and function. Here we devise a strategy to measure variation in single-cell replication timing using DNA copy number. We find that borders between replicated and un-replicated DNA are highly conserved between cells, demarcating active and inactive compartments of the nucleus. Nonetheless, measurable variation was evident. Surprisingly, we detected a similar degree of variation in replication timing from cell-to-cell, between homologues within cells, and between all domains genome-wide regardless of their replication timing. These results demonstrate that stochastic variation in replication timing is independent of elements that dictate timing or extrinsic environmental variation.

scholarly journals Automated population identification and sorting algorithms for high-dimensional single-cell data

2016 ◽  
Author(s):  
Benedict Anchang ◽  
Sylvia K. Plevritis
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Intracellular Signaling ◽  
Expert Knowledge ◽  
Single Cells ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Type Specific ◽  
Cell Subpopulations ◽  
Cell Data

AbstractCell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

scholarly journals ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics

2020 ◽  
Author(s):  
Helena García-Castro ◽  
Nathan J Kenny ◽  
Patricia Álvarez-Campos ◽  
Vincent Mason ◽  
Anna Schönauer ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Cell Types ◽  
Cell Dissociation ◽  
Rna Integrity ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
A Cell ◽  
Dissociated Cells ◽  
Cell Fixation

AbstractSingle-cell sequencing technologies are revolutionizing biology, but are limited by the need to dissociate fresh samples that can only be fixed at later stages. We present ACME (ACetic-MEthanol) dissociation, a cell dissociation approach that fixes cells as they are being dissociated. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, can be sorted by Fluorescence-Activated Cell Sorting (FACS) and are permeable, enabling combinatorial single-cell transcriptomic approaches. As a proof of principle, we have performed SPLiT-seq with ACME cells to obtain around ∼34K single cell transcriptomes from two planarian species and identified all previously described cell types in similar proportions. ACME is based on affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.

scholarly journals Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Elior Rahmani ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
Lisa F. Barcellos ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Cell Biology ◽  
Cell Type ◽  
Cell Type Specific

scholarly journals The Protecting Role of Dormant Origins in Response to Replicative Stress

2018 ◽  
Author(s):  
Lilas Courtot ◽  
Jean-Sébastien Hoffmann ◽  
Valérie Bergoglio
Keyword(s):  
Single Cell ◽  
Replication Fork ◽  
Replication Timing ◽  
G1 Phase ◽  
Human Chromosomes ◽  
Entire Genome ◽  
Replicative Stress ◽  
Origin Activation ◽  
A Cell

Maintenance of the human chromosomes stability requires a tight regulation of DNA replication to duplicate once and only once the entire genome of a single cell. In mammalians cells, origin activation is controlled in space and time by a cell specific and robust program called replication timing. About 100 000 of potential origins are loaded onto the chromatin at the G1 phase but only 20-30% are selected and active during the replication of a given cell. When the replication fork is slowed down by exogenous or endogenous sources, the cell need to activate more origins to complete the replication on time. Thus, the large choice of origins that can be activated may be a key player in the protection of the genome. The aim of this review is to discuss about the role of these dormant origins as housekeepers of the human genome in response to replicative stress.

scholarly journals Alignment of single-cell RNA-seq samples without over-correction using kernel density matching

2020 ◽  
Author(s):  
Mengjie Chen ◽  
Qi Zhan ◽  
Zepeng Mu ◽  
Lili Wang ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Kernel Density ◽  
Cell Types ◽  
Clinical Samples ◽  
Cell Type ◽  
Inflammatory Monocytes ◽  
A Cell ◽  
Cell Type Specific

AbstractSingle-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for most biological and medical applications as it allows users to measure gene expression levels in a cell-type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell-type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq datasets with cell-types that may overlap only partially, and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering, and in terms of avoiding over-correction. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell-type-specific differential gene expression comparisons across biopsy sites and disease conditions, and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online (https://qzhan321.github.io/dmatch/).

scholarly journals Discovering Novel Cell Types across Heterogeneous Single-cell Experiments

2020 ◽  
Author(s):  
Maria Brbić ◽  
Marinka Zitnik ◽  
Sheng Wang ◽  
Angela O. Pisco ◽  
Russ B. Altman ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Learning To Learn ◽  
Multiple Datasets ◽  
Temporal Relationships ◽  
Meta Learning ◽  
A Cell ◽  
Cell Type Specific

Although tremendous effort has been put into cell type annotation and classification, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as novel cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method annotates cells by probabilistically defining a cell type based on nearest landmarks in the embedding space. MARS has a unique ability to discover cell types that have never been seen before and annotate experiments that are yet unannotated. We apply MARS to a large aging cell atlas of 23 tissues covering the life span of a mouse. MARS accurately identifies cell types, even when it has never seen them before. Further, the method automatically generates interpretable names for novel cell types. Remarkably, MARS estimates meaningful cell-type-specific signatures of aging and visualizes them as trajectories reflecting temporal relationships of cells in a tissue.

scholarly journals Single Cell Molecular Alterations Reveal Pathogenesis and Targets of Concussive Brain Injury

2018 ◽  
Author(s):  
Douglas Arneson ◽  
Yumei Zhuang ◽  
Hyae Ran Byun ◽  
In Sook Ahn ◽  
Zhe Ying ◽  
...  
Keyword(s):  
Brain Injury ◽  
Single Cell ◽  
Cell Types ◽  
Single Cell Sequencing ◽  
Molecular Alterations ◽  
Hippocampal Dysfunction ◽  
Behavioral Abnormalities ◽  
Sequencing Method ◽  
A Cell ◽  
Cell Type Specific

ABSTRACTThe complex neuropathology of traumatic brain injury (TBI) is difficult to dissect in the hippocampus considering the convoluted hippocampal cytoarchitecture. As a major casualty of TBI, hippocampal dysfunction results in cognitive decline that may escalate to other neurological disorders, and the molecular basis is hidden in the genomic programs of individual hippocampal cells. Using the unbiased single cell sequencing method Drop-seq, we uncovered the hippocampal cell types most sensitive to concussive mild TBI (mTBI) as well as the vulnerable genes, pathways and cell-cell interactions predictive of disease pathogenesis in a cell-type specific manner, revealing hidden pathogenic mechanisms and potential targets. Targeting Ttr, encoding the thyroid hormone T4 transporter transthyretin, mitigated the genomic and behavioral abnormalities associated with mTBI. Single cell genomics provides unique evidence about altered circuits and pathogenic pathways, and pinpoints new targets amenable to therapeutics in mTBI and related disorders.

scholarly journals Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology

2018 ◽  
Author(s):  
Elior Rahmani ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
Lisa F. Barcellos ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Cell Biology ◽  
Large Scale ◽  
Tissue Level ◽  
Cell Type ◽  
Validation Data ◽  
Cell Type Specific ◽  
Disease Associated Genes ◽  
The Individual

AbstractHigh costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types. Corresponding software is available from: https://github.com/cozygene/TCA.

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