Spatiotemporal dissection of the cell cycle with single-cell proteogenomics

Mahdessian, Diana; Cesnik, Anthony J.; Gnann, Christian; Danielsson, Frida; Stenström, Lovisa; Arif, Muhammad; Zhang, Cheng Cheng; Le, Trang; Johansson, Fredric; Shutten, Rutger; Bäckström, Anna; Axelsson, Ulrika; Thul, Peter; Cho, Nathan; Carja, Oana; Uhlén, Mathias; Mardinoğlu, Adil; Stadler, Charlotte; Lindskog, Cecilia; Ayoglu, Burcu; Leonetti, Manuel D.; Pontén, Fredrik; Sullivan, Devin P.; Lundberg, Emma

doi:10.1038/s41586-021-03232-9

Cited by 130 publications

(160 citation statements)

References 91 publications

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“…In contrast to FACS, FUCCI systems, combined with an appropriate quantification method, make it possible to continuously measure cell cycle progression by placing the 2 protein measurements in a 2-dimensional space. Cell cycle pseudotime needs to be inferred from these 2-dimensional measurements, which is usually done by a variant of polar angle (Hsiao et al, 2020;Mahdessian et al, 2021). Mahdessian et al (2021) measured human U-2 OS cells to derive a FUCCI-based pseudo-time scoring.…”

Section: Cell Cycle Position Estimation On Gold-standard Datasetsmentioning

confidence: 99%

“…Hsiao et al (2020) used FUCCI on human induced pluripotent stem cells (iPSC) followed by scRNA sequencing using Fluidigm C1. While the Mahdessian et al (2021) data are used to estimate a continuous cell cycle position (which we term "FUCCI pseudotime") based on polar angle of the FUCCI scores. Compared with the data in Mahdessian et al (2021), there are larger differences between FUCCI pseudotime and tricycle cell cycle position.…”

Section: Cell Cycle Position Estimation On Gold-standard Datasetsmentioning

confidence: 99%

“…While the Mahdessian et al (2021) data are used to estimate a continuous cell cycle position (which we term "FUCCI pseudotime") based on polar angle of the FUCCI scores. Compared with the data in Mahdessian et al (2021), there are larger differences between FUCCI pseudotime and tricycle cell cycle position. However, we can directly compare the associated expression dynamics of key cell cycle genes (Figure 5 for TOP2A, Supplementary Figure S13 for 8 additional genes).…”

Section: Cell Cycle Position Estimation On Gold-standard Datasetsmentioning

confidence: 99%

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Universal prediction of cell cycle position using transfer learning

Stein-O’Brien

Augustin²,

Slosberg³

et al. 2021

Preprint

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The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle as both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data. Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the ubiquitous applicability of transfer learning. We show that tricycle can predict any cell’s position in the cell cycle regardless of the cell type, species of origin, and even sequencing assay. The accuracy of tricycle compares favorably to gold-standard experimental assays which generally require specialized measurements in specifically constructed in vitro systems. Unlike gold-standard assays, tricycle is easily applicable to any single-cell RNA-seq dataset. Tricycle is highly scalable, universally accurate, and eminently pertinent for atlas-level data.

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Section: Cell Cycle Position Estimation On Gold-standard Datasetsmentioning

confidence: 99%

Section: Cell Cycle Position Estimation On Gold-standard Datasetsmentioning

confidence: 99%

Section: Cell Cycle Position Estimation On Gold-standard Datasetsmentioning

confidence: 99%

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Universal prediction of cell cycle position using transfer learning

Stein-O’Brien

Augustin²,

Slosberg³

et al. 2021

Preprint

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“…Calculating sample size requirements for temporal dynamics in single cell proteomics Introduction Individual cells express a unique proteome; this is true for cells in a complex environment as well as cells in a laboratory-controlled cell culture experiment. These differences arise from both intrinsic and extrinsic factors, such as access to nutrients, spatial relationships to other cells or cell cycle status 1 . Tissues in multicellular organisms often contain a variety of discrete cell types, each of which expresses a unique proteome, and the combination of these functional cell states gives rise to the overall tissue function.…”

mentioning

confidence: 99%

“…This is true both for bulk measurements and also for single cells [10][11][12] . A recent investigation of proteome dynamics during the cell cycle found only 15% of mitotic cycling proteins had coordinated cycling mRNA transcripts 1 . Thus, to identify dynamic proteome responses at the single cell level, proteomic measurements are necessary.…”

mentioning

confidence: 99%

Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics

Boekweg

Guise

Plowey

et al. 2021

Molecular & Cellular Proteomics

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Quantifying Landscape‐Flux via Single‐Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle

Zhu,

Wang

2024

Advanced Science

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Recent developments in single‐cell sequencing technology enable the acquisition of entire transcriptome data. Understanding the underlying mechanism and identifying the driving force of transcriptional regulation governing cell function directly from these data remains challenging. This study reconstructs a continuous vector field of the cell cycle based on discrete single‐cell RNA velocity to quantify the single‐cell global nonequilibrium dynamic landscape‐flux. It reveals that large fluctuations disrupt the global landscape and genetic perturbations alter landscape‐flux, thus identifying key genes in maintaining cell cycle dynamics and predicting associated functional effects. Additionally, it quantifies the fundamental energy cost of the cell cycle initiation and unveils that sustaining the cell cycle requires curl flux and dissipation to maintain the oscillatory phase coherence. This study enables the inference of the cell cycle gene regulatory networks directly from the single‐cell transcriptomic data, including the feedback mechanisms and interaction intensity. This provides a golden opportunity to experimentally verify the landscape‐flux theory and also obtain its associated quantifications. It also offers a unique framework for combining the landscape‐flux theory and single‐cell high‐through sequencing experiments for understanding the underlying mechanisms of the cell cycle and can be extended to other nonequilibrium biological processes, such as differentiation development and disease pathogenesis.

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Spatiotemporal dissection of the cell cycle with single-cell proteogenomics

Cited by 130 publications

References 91 publications

Universal prediction of cell cycle position using transfer learning

Universal prediction of cell cycle position using transfer learning

Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics

Quantifying Landscape‐Flux via Single‐Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle

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