Faculty Opinions recommendation of Single-cell analysis of transcription kinetics across the cell cycle.

ABSTRACTViral infection and replication are affected by host cell heterogeneity, but the mechanisms underlying the effects remain unclear. Using single-cell analysis, we investigated the effects of host cell heterogeneity, including cell size, inclusion, and cell cycle, on foot-and-mouth disease virus (FMDV) infection (acute and persistent infections) and replication. We detected various viral genome replication levels in FMDV-infected cells. Large cells and cells with a high number of inclusions generated more viral RNA copies and viral protein and a higher proportion of infectious cells than other cells. Additionally, we found that the viral titer was 10- to 100-fold higher in cells in G2/M than those in other cell cycle phases and identified a strong correlation between cell size, inclusion, and cell cycle heterogeneity, which all affected the infection and replication of FMDV. Furthermore, we demonstrated that host cell heterogeneity influenced the adsorption of FMDV due to differences in the levels of FMDV integrin receptors expression. Collectively, these results further our understanding of the evolution of a virus in a single host cell.IMPORTANCEIt is important to understand how host cell heterogeneity affects viral infection and replication. Using single-cell analysis, we found that viral genome replication levels exhibited dramatic variability in foot-and-mouth disease virus (FMDV)-infected cells. We also found a strong correlation between heterogeneity in cell size, inclusion number, and cell cycle status and that all of these characteristics affect the infection and replication of FMDV. Moreover, we found that host cell heterogeneity influenced the viral adsorption as differences in the levels of FMDV integrin receptors' expression. This study provided new ideas for the studies of correlation between FMDV infection mechanisms and host cells.

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Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

Physiological Genomics ◽

10.1152/physiolgenomics.00065.2020 ◽

2020 ◽

Vol 52 (10) ◽

pp. 468-477

Author(s):

Alexander C. Zambon ◽

Tom Hsu ◽

Seunghee Erin Kim ◽

Miranda Klinck ◽

Jennifer Stowe ◽

...

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Single Cell Analysis ◽

Imaging System ◽

Single Cells ◽

Cell Types ◽

Cell Analysis ◽

Mcf7 Cells ◽

Genomic Studies ◽

Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

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Decision letter: Single-cell analysis of transcription kinetics across the cell cycle

10.7554/elife.12175.020 ◽

2015 ◽

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Single Cell Analysis ◽

Cell Analysis

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Statistical single-cell analysis of cell cycle-dependent quantum dot cytotoxicity and cellular uptake using a microfluidic system

RSC Advances ◽

10.1039/c4ra01665c ◽

2014 ◽

Vol 4 (47) ◽

pp. 24929-24934 ◽

Cited By ~ 14

Author(s):

Jing Wu ◽

Haifang Li ◽

Qiushui Chen ◽

Xuexia Lin ◽

Wu Liu ◽

...

Keyword(s):

Cell Cycle ◽

Quantum Dot ◽

Single Cell ◽

Cellular Uptake ◽

Single Cell Analysis ◽

Single Cells ◽

Microfluidic System ◽

Cell Analysis ◽

System P ◽

Cycle Dependent

The response of single cells in different cell cycle phases to QD cytotoxicity studied on a microfluidic device.

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The Expression and Prognostic Value of SUMO1-Activating Enzyme Subunit 1 and Its Potential Mechanism in Triple-Negative Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.729211 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qingshui Wang ◽

Wenting Zhong ◽

Lin Deng ◽

Qili Lin ◽

Youyu Lin ◽

...

Keyword(s):

Breast Cancer ◽

Cell Cycle ◽

Protein Expression ◽

Single Cell ◽

Prognostic Model ◽

Triple Negative ◽

Single Cell Analysis ◽

Cell Analysis ◽

Mrna And Protein Expression

Background: Triple-negative breast cancer (TNBC) is the most invasive and metastatic subtype of breast cancer. SUMO1-activating enzyme subunit 1 (SAE1), an E1-activating enzyme, is indispensable for protein SUMOylation. SAE1 has been found to be a relevant biomarker for progression and prognosis in several tumor types. However, the role of SAE1 in TNBC remains to be elucidated.Methods: In the research, the mRNA expression of SAE1 was analyzed via the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database. Cistrome DB Toolkit was used to predict which transcription factors (TFs) are most likely to increase SAE1 expression in TNBC. The correlation between the expression of SAE1 and the methylation of SAE1 or quantity of tumor-infiltrating immune cells was further invested. Single-cell analysis, using CancerSEA, was performed to query which functional states are associated with SAE1 in different cancers in breast cancer at the single-cell level. Next, weighted gene coexpression network (WGCNA) was applied to reveal the highly correlated genes and coexpression networks of SAE1 in TNBC patients, and a prognostic model containing SAE1 and correlated genes was constructed. Finally, we also examined SAE1 protein expression of 207 TNBC tissues using immunohistochemical (IHC) staining.Results: The mRNA and protein expression of SAE1 were increased in TNBC tissues compared with adjacent normal tissues, and the protein expression of SAE1 was significantly associated with overall survival (OS) and disease-free survival (DFS). Correlation analyses revealed that SAE1 expression was positively correlated with forkhead box M1 (FOXM1) TFs and negatively correlated with SAE1 methylation site (cg14042711) level. WGCNA indicated that the genes coexpressed with SAE1 belonged to the green module containing 1,176 genes. Through pathway enrichment analysis of the module, 1,176 genes were found enriched in cell cycle and DNA repair. Single-cell analysis indicated that SAE1 and its coexpression genes were associated with cell cycle, DNA damage, DNA repair, and cell proliferation. Using the LASSO COX regression, a prognostic model including SAE1 and polo-like kinase 1 (PLK1) was built to accurately predict the likelihood of DFS in TNBC patients.Conclusion: In conclusion, we comprehensively analyzed the mRNA and protein expression, prognosis, and interaction genes of SAE1 in TNBC and constructed a prognostic model including SAE1 and PLK1. These results might be important for better understanding of the role of SAE1 in TNBC. In addition, DNA methyltransferase and TFs inhibitor treatments targeting SAE1 might improve the survival of TNBC patients.

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