scholarly journals TAD-like single-cell domain structures exist on both active and inactive X chromosomes and persist under epigenetic perturbations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yubao Cheng ◽  
Miao Liu ◽  
Mengwei Hu ◽  
Siyuan Wang

Abstract Background Topologically associating domains (TADs) are important building blocks of three-dimensional genome architectures. The formation of TADs has been shown to depend on cohesin in a loop-extrusion mechanism. Recently, advances in an image-based spatial genomics technique known as chromatin tracing lead to the discovery of cohesin-independent TAD-like structures, also known as single-cell domains, which are highly variant self-interacting chromatin domains with boundaries that occasionally overlap with TAD boundaries but tend to differ among single cells and among single chromosome copies. Recent computational modeling studies suggest that epigenetic interactions may underlie the formation of the single-cell domains. Results Here we use chromatin tracing to visualize in female human cells the fine-scale chromatin folding of inactive and active X chromosomes, which are known to have distinct global epigenetic landscapes and distinct population-averaged TAD profiles, with inactive X chromosomes largely devoid of TADs and cohesin. We show that both inactive and active X chromosomes possess highly variant single-cell domains across the same genomic region despite the fact that only active X chromosomes show clear TAD structures at the population level. These X chromosome single-cell domains exist in distinct cell lines. Perturbations of major epigenetic components and transcription mostly do not affect the frequency or strength of the single-cell domains. Increased chromatin compaction of inactive X chromosomes occurs at a length scale above that of the single-cell domains. Conclusions In sum, this study suggests that single-cell domains are genome architecture building blocks independent of the tested major epigenetic components.

2021 ◽  
Author(s):  
Yubao Cheng ◽  
Miao Liu ◽  
Mengwei Hu ◽  
Siyuan Wang

Background: Topologically associating domains (TADs) are important building blocks of three-dimensional genome architectures. The formation of TADs was shown to depend on cohesin in a loop-extrusion mechanism. Recently, advances in an image-based spatial genomics technique known as chromatin tracing led to the discovery of cohesin-independent TAD-like structures, also known as single-cell domains - highly variant self-interacting chromatin domains with boundaries that occasionally overlap with TAD boundaries but tend to differ among single cells and among single chromosome copies. Several recent computational modeling studies suggest that single-cell variations of epigenetic profiles may underlie the formation of the single-cell domains. Results: Here we use chromatin tracing to visualize in female human cells the fine-scale chromatin folding of inactive and active X chromosomes, which are known to have distinct global epigenetic landscapes and distinct population-averaged TAD profiles, with inactive X chromosomes largely devoid of TADs and cohesin. We show that both inactive and active X chromosomes possess highly variant single-cell domains across the same genomic region despite the fact that only active X chromosomes show clear TAD structures at the population level. These X chromosome single-cell domains exist in distinct cell lines. Perturbations of major epigenetic components did not significantly affect the frequency or strength of the single-cell domains. Increased chromatin compaction of inactive X chromosomes occurs at a length scale above that of the single-cell domains. Conclusions: In sum, this study suggests that single-cell domains are genome architecture building blocks independent of variations in major epigenetic landscapes.


2021 ◽  
Author(s):  
Yodai Takei ◽  
Shiwei Zheng ◽  
Jina Yun ◽  
Sheel Shah ◽  
Nico Pierson ◽  
...  

AbstractNuclear architecture in tissues can arise from cell-type specific organization of nuclear bodies, chromatin states and chromosome structures. However, the lack of genome-wide measurements to interrelate such modalities within single cells limits our overall understanding of nuclear architecture. Here, we demonstrate integrated spatial genomics in the mouse brain cortex, imaging thousands of genomic loci along with RNAs and subnuclear markers simultaneously in individual cells. We revealed chromatin fixed points, combined with cell-type specific organization of nuclear bodies, arrange the interchromosomal organization and radial positioning of chromosomes in diverse cell types. At the sub-megabase level, we uncovered a collection of single-cell chromosome domain structures, including those for the active and inactive X chromosomes. These results advance our understanding of single-cell nuclear architecture in complex tissues.


Author(s):  
Martin Philpott ◽  
Jonathan Watson ◽  
Anjan Thakurta ◽  
Tom Brown ◽  
Tom Brown ◽  
...  

AbstractHere we describe single-cell corrected long-read sequencing (scCOLOR-seq), which enables error correction of barcode and unique molecular identifier oligonucleotide sequences and permits standalone cDNA nanopore sequencing of single cells. Barcodes and unique molecular identifiers are synthesized using dimeric nucleotide building blocks that allow error detection. We illustrate the use of the method for evaluating barcode assignment accuracy, differential isoform usage in myeloma cell lines, and fusion transcript detection in a sarcoma cell line.


2019 ◽  
Vol 85 (18) ◽  
Author(s):  
Yutaka Yawata ◽  
Tatsunori Kiyokawa ◽  
Yuhki Kawamura ◽  
Tomohiro Hirayama ◽  
Kyosuke Takabe ◽  
...  

ABSTRACT Here we analyzed the innate fluorescence signature of the single microbial cell, within both clonal and mixed populations of microorganisms. We found that even very similarly shaped cells differ noticeably in their autofluorescence features and that the innate fluorescence signatures change dynamically with growth phases. We demonstrated that machine learning models can be trained with a data set of single-cell innate fluorescence signatures to annotate cells according to their phenotypes and physiological status, for example, distinguishing a wild-type Aspergillus nidulans cell from its nitrogen metabolism mutant counterpart and log-phase cells from stationary-phase cells of Pseudomonas putida. We developed a minimally invasive method (confocal reflection microscopy-assisted single-cell innate fluorescence [CRIF] analysis) to optically extract and catalog the innate cellular fluorescence signatures of each of the individual live microbial cells in a three-dimensional space. This technique represents a step forward from traditional techniques which analyze the innate fluorescence signatures at the population level and necessitate a clonal culture. Since the fluorescence signature is an innate property of a cell, our technique allows the prediction of the types or physiological status of intact and tag-free single cells, within a cell population distributed in a three-dimensional space. Our study presents a blueprint for a streamlined cell analysis where one can directly assess the potential phenotype of each single cell in a heterogenous population by its autofluorescence signature under a microscope, without cell tagging. IMPORTANCE A cell’s innate fluorescence signature is an assemblage of fluorescence signals emitted by diverse biomolecules within a cell. It is known that the innate fluoresce signature reflects various cellular properties and physiological statuses; thus, they can serve as a rich source of information in cell characterization as well as cell identification. However, conventional techniques focus on the analysis of the innate fluorescence signatures at the population level but not at the single-cell level and thus necessitate a clonal culture. In the present study, we developed a technique to analyze the innate fluorescence signature of a single microbial cell. Using this novel method, we found that even very similarly shaped cells differ noticeably in their autofluorescence features, and the innate fluorescence signature changes dynamically with growth phases. We also demonstrated that the different cell types can be classified accurately within a mixed population under a microscope at the resolution of a single cell, depending solely on the innate fluorescence signature information. We suggest that single-cell autofluoresce signature analysis is a promising tool to directly assess the taxonomic or physiological heterogeneity within a microbial population, without cell tagging.


2021 ◽  
Author(s):  
Kailing Tu ◽  
Keying Lu ◽  
Qilin Zhang ◽  
Wei Huang ◽  
Dan Xie

Abstract Single-nucleotide variant (SNV) detection in the genome of single cells is affected by DNA amplification artefacts, including imbalanced alleles and early PCR errors. Existing single-cell genotyper accuracy often depends on the quality and coordination of both the target single-cell and external data, such as heterozygous profiles determined by bulk data. In most single-cell studies, information from different sources is not perfectly matched. High-accuracy SNV detection with a limited single data source remains a challenge. We developed a new variant detection method, SCOUT (Single Cell Genotyper Utilizing Information from Local Genome Territory), the greatest advantage of which is not requiring external data while base calling. By leveraging base count information from the adjacent genomic region, SCOUT classifies all candidate SNVs into homozygous, heterozygous, intermediate and low major allele SNVs according to the highest likelihood score. Compared with other genotypers, SCOUT improves the variant detection performance by 2.0–77.5% in real and simulated single-cell datasets. Furthermore, the running time of SCOUT increases linearly with sequence length; as a result, it shows 400% average acceleration in operating efficiency compared with other methods.


Science ◽  
2018 ◽  
Vol 361 (6405) ◽  
pp. 924-928 ◽  
Author(s):  
Longzhi Tan ◽  
Dong Xing ◽  
Chi-Han Chang ◽  
Heng Li ◽  
X. Sunney Xie

Three-dimensional genome structures play a key role in gene regulation and cell functions. Characterization of genome structures necessitates single-cell measurements. This has been achieved for haploid cells but has remained a challenge for diploid cells. We developed a single-cell chromatin conformation capture method, termed Dip-C, that combines a transposon-based whole-genome amplification method to detect many chromatin contacts, called META (multiplex end-tagging amplification), and an algorithm to impute the two chromosome haplotypes linked by each contact. We reconstructed the genome structures of single diploid human cells from a lymphoblastoid cell line and from primary blood cells with high spatial resolution, locating specific single-nucleotide and copy number variations in the nucleus. The two alleles of imprinted loci and the two X chromosomes were structurally different. Cells of different types displayed statistically distinct genome structures. Such structural cell typing is crucial for understanding cell functions.


Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 845
Author(s):  
Esra Sengul ◽  
Meltem Elitas

Integration of microfabricated, single-cell resolution and traditional, population-level biological assays will be the future of modern techniques in biology that will enroll in the evolution of biology into a precision scientific discipline. In this study, we developed a microfabricated cell culture platform to investigate the indirect influence of macrophages on glioma cell behavior. We quantified proliferation, morphology, motility, migration, and deformation properties of glioma cells at single-cell level and compared these results with population-level data. Our results showed that glioma cells obtained slightly slower proliferation, higher motility, and extremely significant deformation capability when cultured with 50% regular growth medium and 50% macrophage-depleted medium. When the expression levels of E-cadherin and Vimentin proteins were measured, it was verified that observed mechanophenotypic alterations in glioma cells were not due to epithelium to mesenchymal transition. Our results were consistent with previously reported enormous heterogeneity of U87 glioma cell line. Herein, for the first time, we quantified the change of deformation indexes of U87 glioma cells using microfluidic devices for single-cells analysis.


2016 ◽  
Vol 113 (12) ◽  
pp. 3251-3256 ◽  
Author(s):  
Mikihiro Hashimoto ◽  
Takashi Nozoe ◽  
Hidenori Nakaoka ◽  
Reiko Okura ◽  
Sayo Akiyoshi ◽  
...  

Cellular populations in both nature and the laboratory are composed of phenotypically heterogeneous individuals that compete with each other resulting in complex population dynamics. Predicting population growth characteristics based on knowledge of heterogeneous single-cell dynamics remains challenging. By observing groups of cells for hundreds of generations at single-cell resolution, we reveal that growth noise causes clonal populations of Escherichia coli to double faster than the mean doubling time of their constituent single cells across a broad set of balanced-growth conditions. We show that the population-level growth rate gain as well as age structures of populations and of cell lineages in competition are predictable. Furthermore, we theoretically reveal that the growth rate gain can be linked with the relative entropy of lineage generation time distributions. Unexpectedly, we find an empirical linear relation between the means and the variances of generation times across conditions, which provides a general constraint on maximal growth rates. Together, these results demonstrate a fundamental benefit of noise for population growth, and identify a growth law that sets a “speed limit” for proliferation.


2021 ◽  
Author(s):  
Julia S Spear ◽  
Katharine A White

Transient changes in intracellular pH (pHi) have been shown to regulate normal cell behaviors like migration and cell-cycle progression, while dysregulated pHi dynamics are a hallmark of cancer. However, little is known about how pHi heterogeneity and dynamics influence population-level measurements or single-cell behaviors. Here, we present and characterize single-cell pHi heterogeneity distributions in both normal and cancer cells and measure dynamic pHi increases in single cells in response to growth factor signaling. Next, we measure pHi dynamics in single cells during cell cycle progression. We determined that single-cell pHi is significantly decreased at the G1/S boundary, increases from S phase to the G2/M transition, rapidly acidifies during mitosis, and recovers in daughter cells. This sinusoidal pattern of pHi dynamics was linked to cell cycle timing regardless of synchronization method. This work confirms prior work at the population level and reveals distinct advantages of single-cell pHi measurements in capturing pHi heterogeneity across a population and dynamics within single cells.


2020 ◽  
Author(s):  
Andrian Yang ◽  
Yu Yao ◽  
Xiunan Fang ◽  
Jianfu Li ◽  
Yongyan Xia ◽  
...  

AbstractMotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful.ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms.Availability and implementationstarmapVR is freely accessible at https://holab-hku.github.io/starmapVR, with the corresponding source code available at https://github.com/holab-hku/starmapVR under the open source MIT license.Supplementary InformationSupplementary data are available at Bioinformatics online.


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