scholarly journals Quantitative assessment of cell population diversity in single-cell landscapes

PLoS Biology ◽  
2018 ◽  
Vol 16 (10) ◽  
pp. e2006687 ◽  
Author(s):  
Qi Liu ◽  
Charles A. Herring ◽  
Quanhu Sheng ◽  
Jie Ping ◽  
Alan J. Simmons ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Quantitative Assessment ◽  
Population Diversity

scholarly journals Quantitative assessment of cell population diversity in single-cell landscapes

2018 ◽  
Author(s):  
Qi Liu ◽  
Charles A. Herring ◽  
Quanhu Sheng ◽  
Jie Ping ◽  
Alan J. Simmons ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Systematic Investigation ◽  
Population Diversity ◽  
Cell Populations ◽  
Batch Correction ◽  
Population Structures ◽  
Altered Cell ◽  
Cell Transcriptome ◽  
Compositional Diversity

AbstractSingle-cell RNA-sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such expansion or shrinkage, or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes, identification and definition of altered cell populations, and benchmarking batch correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions, and has broad utility for gaining insight on how cell populations respond to perturbations.

scholarly journals Resolution of DNA damage at the single-cell level shows largely different actions of X-rays and bleomycin.

1995 ◽  
Vol 43 (2) ◽  
pp. 229-235 ◽  
Author(s):  
M I Affentranger ◽  
W Burkart
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Large Cell ◽  
Dna Strand Breaks ◽  
Single Cell Level ◽  
X Rays ◽  
Cell Level ◽  
Strand Breaks ◽  
Two Agents ◽  
Dna Strand

Both X-rays and the radiomimetic agent bleomycin (BLM) induce DNA strand breaks, predominantly via reactive radicals. To compare the induction of breaks with the two agents in Chinese hamster (CHO-K1) cells, two different alkaline unwinding methods, a 3H tracer-based analysis of large cell populations and an optical adaption allowing measurement of single cells, were applied. Radiation and BLM show qualitatively similar dose responses when the average number of DNA strand breaks is measured in a large cell population. However, the breakage pattern at the single-cell level indicates large discrepancies between the actions of the two agents. Irradiated cells show a uniform distribution of DNA strand breaks over the cell population. Effects of treatment with 30 micrograms x ml-1 BLM for 2 hr vary from practically zero in some cells to high levels of DNA strand breakage in others. Unlike the repair of radiation-induced DNA breaks, the repair efficiency of BLM-induced DNA strand breaks, as measured at the single-cell level, varies strongly among cells of the same population. Such heterogeneity at the cellular level potentially reduces BLM's usefulness for tumor therapy because the appearance of BLM-resistant subpopulations may critically impair treatment outcome.

scholarly journals Quantitative assessment of single-cell RNA-sequencing methods

2013 ◽  
Vol 11 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Angela R Wu ◽  
Norma F Neff ◽  
Tomer Kalisky ◽  
Piero Dalerba ◽  
Barbara Treutlein ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Quantitative Assessment ◽  
Single Cell Rna Sequencing

scholarly journals Diversity in ATP concentrations in a single bacterial cell population revealed by quantitative single-cell imaging

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Hideyuki Yaginuma ◽  
Shinnosuke Kawai ◽  
Kazuhito V. Tabata ◽  
Keisuke Tomiyama ◽  
Akira Kakizuka ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Bacterial Cell ◽  
Cell Imaging ◽  
Single Cell Imaging

scholarly journals IKAP - Identifying K mAjor cell Population groups in single-cell RNA-seq analysis

2019 ◽  
Author(s):  
Yun-Ching Chen ◽  
Abhilash Suresh ◽  
Chingiz Underbayev ◽  
Clare Sun ◽  
Komudi Singh ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Population ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Population Groups ◽  
Tuning Parameters ◽  
Multiple Datasets ◽  
Cell Groups ◽  
Cell Ontology

AbstractIn single-cell RNA-seq analysis, clustering cells into groups and differentiating cell groups by marker genes are two separate steps for investigating cell identity. However, results in clustering greatly affect the ability to differentiate between cell groups. We develop IKAP – an algorithm identifying major cell groups that improves differentiating by tuning parameters for clustering. Using multiple datasets, we demonstrate IKAP improves identification of major cell types and facilitates cell ontology curation.

scholarly journals Assessment of Heterogeneity of Cytochrome P450 Activity in Cancer-Cell Population by Cytometry of Reaction Rate Constant is Robust to Variation in Substrate Concentration

2020 ◽  
Author(s):  
Mariana Bleker de Oliveira ◽  
Vasilij Koshkin ◽  
Christopher G. R. Perry ◽  
Sergey N. Krylov
Keyword(s):  
Cytochrome P450 ◽  
Single Cell ◽  
Cell Population ◽  
Rate Constant ◽  
Substrate Concentration ◽  
Reaction Rate ◽  
Reaction Rate Constant ◽  
Single Cell Level ◽  
Cell Level ◽  
Key Players

ABSTRACTEnzymes of the cytochrome P450 (CYP) family catalyze the metabolism of chemotherapeutic agents and are among the key players in primary and acquired chemoresistance of cancer. The activity of CYP is heterogeneous in tumor tissues, and the quantitative characteristics of this heterogeneity can be used to predict chemoresistance. Cytometry of reaction rate constant (CRRC) is a kinetic approach to assess cell population heterogeneity by measuring rates of processes at the single-cell level via time-lapse imaging. CRRC was shown to be an accurate and robust method for assessing the heterogeneity of drug-extrusion activity catalyzed by ABC transporters, which are also key players in cancer chemoresistance. We hypothesized that CRRC is also a reliable method for assessing the heterogeneity of CYP activity. Here, we evaluated the robustness of assessing the heterogeneity of CYP activity by CRRC with respect to controlled variation in the concentration of a CYP substrate by comparing CRRC with non-kinetic approaches. We found that changing the substrate concentration by 20% resulted only in minimal changes in the position, width, and asymmetry of the peak in the CRRC histogram, while these parameters varied greatly in the non-kinetic histograms. Moreover, the Kolmogorov-Smirnov statistical test showed that the distribution of the cell population in CRRC histograms was not significantly different; the result was opposite for non-kinetic histograms. In conclusion, we were able to demonstrate the robustness of CRRC with respect to changes in substrate concentration when evaluating CYP activity at the single-cell level.

scholarly journals Spatiotemporal transcriptome at single-cell resolution reveals key radial glial cell population in axolotl telencephalon development and regeneration

2021 ◽  
Author(s):  
Xiaoyu Wei ◽  
Sulei Fu ◽  
Hanbo Li ◽  
Yang Liu ◽  
Shuai Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Glial Cell ◽  
Cell Population ◽  
Cell Types ◽  
Mammalian Brain ◽  
Regeneration Ability ◽  
Radial Glial Cell ◽  
Cell Dynamics ◽  
Brain Regeneration ◽  
Radial Glial

Brain regeneration requires a precise coordination of complex responses in a time- and region-specific manner. Identifying key cell types and molecules that direct brain regeneration would provide potential targets for the advance of regenerative medicine. However, progress in the field has been hampered largely due to very limited regeneration capacity of the mammalian brain and understanding of the regeneration process at both cellular and molecular level. Here, using axolotl brain with astonishing regeneration ability upon injury, and the Stereo-seq (SpaTial Enhanced REsolution Omics-sequencing), we reconstruct the first architecture of axolotl telencephalon with gene expression profiling at single-cell resolution, and fine cell dynamics maps throughout development and regeneration. Intriguingly, we discover a marked heterogeneity of radial glial cell (RGC) types with distinct behaviors. Of note, one subtype of RGCs is activated since early regeneration stages and proliferates while other RGCs remain dormant. Such RGC subtype appears to be the major cell population involved in early wound healing response and gradually covers the injured area before presumably transformed into the lost neurons. Altogether, our work systematically decodes the complex cellular and molecular dynamics of axolotl telencephalon in development and regeneration, laying the foundation for studying the regulatory mechanism of brain regeneration in future.

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