scholarly journals Multidimensional single-cell modeling of cellular signaling

2020 ◽  
Author(s):  
James D. Wade ◽  
Xiao-Kang Lun ◽  
Bernd Bodenmiller ◽  
Eberhard O. Voit
Keyword(s):  
Single Cell ◽  
Time Lapse ◽  
Computational Method ◽  
Erk Signaling ◽  
Erk Pathway ◽  
Signaling Systems ◽  
Multiple Pathway ◽  
Cell Modeling ◽  
Cell Variation ◽  
Complex Signaling

AbstractCell-to-cell differences in signaling components can lead to qualitatively different responses to stimuli. Understanding this heterogeneity in signaling response is limited by the inability of time-lapse methods to measure multiple pathway components simultaneously in situ. Here, we present Distribution-Independent Single-Cell ODE modeling (DISCO), a computational method for inference of continuous single-cell signaling dynamics from multiplexed snapshot data. We used DISCO to analyze signaling in the MAPK/ERK pathway of HEK293T cells stimulated with the growth factor EGF. Our model recapitulates known features of the ERK signaling response and enables the detection of hidden cell-to-cell variation in seemingly homogeneous samples. Further, DISCO analysis suggested that the MAPK/ERK pathway transmits signal duration rather than amplitude, and that cell-to-cell variation in MAPK/ERK signaling response depends primarily on initial cell states. Finally, we applied an extended version of DISCO to explain changes in signaling kinetics due to overexpression of a disease-relevant protein. Overall, DISCO enables a deeper understanding of how single-cell variation affects cellular responses in complex signaling systems.

scholarly journals Development of single-cell-level microfluidic technology for long-term growth visualization of living cultures of Mycobacterium smegmatis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Han Wang ◽  
Gloria M. Conover ◽  
Song-I Han ◽  
James C. Sacchettini ◽  
Arum Han
Keyword(s):  
Drug Treatment ◽  
Single Cell ◽  
Mycobacterium Smegmatis ◽  
Culture Media ◽  
Low Cost ◽  
Bacterial Pathogen ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Phenotype

AbstractAnalysis of growth and death kinetics at single-cell resolution is a key step in understanding the complexity of the nonreplicating growth phenotype of the bacterial pathogen Mycobacterium tuberculosis. Here, we developed a single-cell-resolution microfluidic mycobacterial culture device that allows time-lapse microscopy-based long-term phenotypic visualization of the live replication dynamics of mycobacteria. This technology was successfully applied to monitor the real-time growth dynamics of the fast-growing model strain Mycobacterium smegmatis (M. smegmatis) while subjected to drug treatment regimens during continuous culture for 48 h inside the microfluidic device. A clear morphological change leading to significant swelling at the poles of the bacterial membrane was observed during drug treatment. In addition, a small subpopulation of cells surviving treatment by frontline antibiotics was observed to recover and achieve robust replicative growth once regular culture media was provided, suggesting the possibility of identifying and isolating nonreplicative mycobacteria. This device is a simple, easy-to-use, and low-cost solution for studying the single-cell phenotype and growth dynamics of mycobacteria, especially during drug treatment.

scholarly journals High-resolution single-cell 3D-models of chromatin ensembles during Drosophila embryogenesis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qiu Sun ◽  
Alan Perez-Rathke ◽  
Daniel M. Czajkowsky ◽  
Zhifeng Shao ◽  
Jie Liang
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
3D Models ◽  
Computational Method ◽  
Midblastula Transition ◽  
Genome Wide ◽  
Large Ensembles ◽  
Chromatin Folding ◽  
Function Relationship ◽  
Relationship Of

AbstractSingle-cell chromatin studies provide insights into how chromatin structure relates to functions of individual cells. However, balancing high-resolution and genome wide-coverage remains challenging. We describe a computational method for the reconstruction of large 3D-ensembles of single-cell (sc) chromatin conformations from population Hi-C that we apply to study embryogenesis in Drosophila. With minimal assumptions of physical properties and without adjustable parameters, our method generates large ensembles of chromatin conformations via deep-sampling. Our method identifies specific interactions, which constitute 5–6% of Hi-C frequencies, but surprisingly are sufficient to drive chromatin folding, giving rise to the observed Hi-C patterns. Modeled sc-chromatins quantify chromatin heterogeneity, revealing significant changes during embryogenesis. Furthermore, >50% of modeled sc-chromatin maintain topologically associating domains (TADs) in early embryos, when no population TADs are perceptible. Domain boundaries become fixated during development, with strong preference at binding-sites of insulator-complexes upon the midblastula transition. Overall, high-resolution 3D-ensembles of sc-chromatin conformations enable further in-depth interpretation of population Hi-C, improving understanding of the structure-function relationship of genome organization.

scholarly journals Deciphering the Receptor Repertoire Encoding Specific Odorants by Time-Lapse Single-Cell Array Cytometry

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Masato Suzuki ◽  
Nobuo Yoshimoto ◽  
Ken Shimono ◽  
Shun’ichi Kuroda
Keyword(s):  
Single Cell ◽  
Time Lapse ◽  
Cell Array ◽  
Receptor Repertoire

MAAPER: model-based analysis of alternative polyadenylation using 3' end-linked reads

2021 ◽  
Author(s):  
Wei Vivian Li ◽  
Dinghai Zheng ◽  
Ruijia Wang ◽  
Bin Tian
Keyword(s):  
Single Cell ◽  
Robust Statistics ◽  
Single Cell Analysis ◽  
Alternative Polyadenylation ◽  
Computational Method ◽  
Model Based ◽  
Eukaryotic Genes ◽  
Counting Strategy ◽  
Cleavage And Polyadenylation ◽  
Model Based Analysis

Most eukaryotic genes harbor multiple cleavage and polyadenylation sites (PASs), leading to expression of alternative polyadenylation (APA) isoforms. APA regulation has been implicated in a diverse array of physiological and pathological conditions. While RNA sequencing tools that generate reads containing the PAS, named onSite reads, have been instrumental in identifying PASs, they have not been widely used. By contrast, a growing number of methods generate reads that are close to the PAS, named nearSite reads, including the 3' end counting strategy commonly used in single cell analysis. How these nearSite reads can be used for APA analysis, however, is poorly studied. Here, we present a computational method, named model-based analysis of alternative polyadenylation using 3' end-linked reads (MAAPER), to examine APA using nearSite reads. MAAPER uses a probabilistic model to predict PASs for nearSite reads with high accuracy and sensitivity, and examines different types of APA events, including those in 3'UTRs and introns, with robust statistics. We show MAAPER's accuracy with data from both bulk and single cell RNA samples and its applicability in unpaired or paired experimental designs. Our result also highlights the importance of using well annotated PASs for nearSite read analysis.

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 Live-cell time-lapse imaging and single-cell tracking of in vitro cultured neural stem cells – Tools for analyzing dynamics of cell cycle, migration, and lineage selection

Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Cell Tracking ◽  
Time Lapse ◽  
Live Cell ◽  
Time Lapse Imaging

scholarly journals Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

2020 ◽  
Author(s):  
Nadia M. V. Sampaio ◽  
Caroline M. Blassick ◽  
Jean-Baptiste Lugagne ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Stress Response ◽  
Single Cell ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Heterogeneity ◽  
Temporal Expression ◽  
Functional Consequences

AbstractCell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival.

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 Exploring the Changing Landscape of Cell-to-Cell Variation After CTCF Knockdown via Single Cell RNA-seq

2019 ◽  
Author(s):  
Wei Wang ◽  
Gang Ren ◽  
Ni Hong ◽  
Wenfei Jin
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Single Cell ◽  
Zinc Finger ◽  
Ctcf Binding ◽  
Rna Seq ◽  
Expression Noise ◽  
Genome Wide ◽  
Cell Variation ◽  
Variable Genes

Abstract Background: CCCTC-Binding Factor (CTCF), also known as 11-zinc finger protein, participates in many cellular processes, including insulator activity, transcriptional regulation and organization of chromatin architecture. Based on single cell flow cytometry and single cell RNA-FISH analyses, our previous study showed that deletion of CTCF binding site led to a significantly increase of cellular variation of its target gene. However, the effect of CTCF on genome-wide landscape of cell-to-cell variation is unclear. Results: We knocked down CTCF in EL4 cells using shRNA, and conducted single cell RNA-seq on both wild type (WT) cells and CTCF-Knockdown (CTCF-KD) cells using Fluidigm C1 system. Principal component analysis of single cell RNA-seq data showed that WT and CTCF-KD cells concentrated in two different clusters on PC1, indicating gene expression profiles of WT and CTCF-KD cells were systematically different. Interestingly, GO terms including regulation of transcription, DNA binding, Zinc finger and transcription factor binding were significantly enriched in CTCF-KD-specific highly variable genes, indicating tissue-specific genes such as transcription factors were highly sensitive to CTCF level. The dysregulation of transcription factors potentially explain why knockdown of CTCF lead to systematic change of gene expression. In contrast, housekeeping genes such as rRNA processing, DNA repair and tRNA processing were significantly enriched in WT-specific highly variable genes, potentially due to a higher cellular variation of cell activity in WT cells compared to CTCF-KD cells. We further found cellular variation-increased genes were significantly enriched in down-regulated genes, indicating CTCF knockdown simultaneously reduced the expression levels and increased the expression noise of its regulated genes. Conclusions: To our knowledge, this is the first attempt to explore genome-wide landscape of cellular variation after CTCF knockdown. Our study not only advances our understanding of CTCF function in maintaining gene expression and reducing expression noise, but also provides a framework for examining gene function.

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.

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