Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Riba, Andrea; Oravecz, Attila; Durik, Matej; Jiménez, Sara; Alunni, Violaine; Cerciat, Marie; Jung, Matthieu; Keime, Céline; Keyes, William M.; Molina, Nacho

doi:10.1038/s41467-022-30545-8

Cited by 34 publications

(67 citation statements)

References 92 publications

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“…2D). We confirmed an increase in expression of two major ABC transporters, including ABCB1, and ABCG2, and a decrease in PGR, another marker previously shown downregulated in the SP + compared to the SPof human myometrium cells (16) (37)(38)(39) were determined by the cell cycle score and the velocity of the scRNA-seq data, respectively. We identified a small group of cells within the vascular myocyte cluster in the G1/G0 phase (Fig 4A) using a computational assignment of cell-cycle stage, as described by Scialdone et al (40).…”

Section: Myometrium Side Population Cells Are Not Enriched In Msc Mar...supporting

confidence: 82%

“…Similar clusters were reported in a scRNA-seq comparison of fibroids and myometrium (38). The depth of sequencing for each cell was close to saturation allowing us to identify a small cell population with stem cell characteristics, including the expression of MSC markers SUSD2, MCAM, PDGFRβ, and CSPG4, a quiescent (G0) cell cycle state (41) and low transcriptomic activity/low RNA velocity (41)(42)(43).…”

Section: Discussionsupporting

confidence: 63%

“…Known MSC properties such as quiescence (G0) and the low gene regulation dynamics (41)(42)(43) were determined by the cell cycle score and the velocity of the scRNA-seq data, respectively. We identified a small group of cells within the vascular myocyte cluster in the G1/G0 phase (Fig 4A) using a computational assignment of cellcycle stage (44).…”

Section: E)mentioning

confidence: 99%

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Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

Paul

Carpenter

Fitch

et al. 2023

Preprint

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Uterine fibroids are benign tumors that develop in the myometria of most women with unknown etiology. Myometrial stem/progenitor cells (MyoSPCs) have been proposed as the cells of origin for fibroids because uterine fibroids are almost always clonal. We previously identified SUSD2 as a possible MyoSPC marker, but the relatively poor enrichment in stem cell characteristics of SUSD2+ over SUSD2- cells compelled us to find better discerning markers for more rigorous analysis. We combined bulk RNA-seq of SUSD2+/- cells with single cell RNA-seq to identify markers capable of further enriching for MyoSPCs. We observed seven distinct cell clusters within the myometrium, with the vascular myocyte cluster most highly enriched for MyoSPC characteristics and markers, including SUSD2. CRIP1 expression was found highly upregulated in both techniques and was used as a marker to sort CRIP1+/PECAM1- cells that were enriched for colony forming units and were able to differentiate into mesenchymal lineages, suggesting that CRIP1+/PECAM1- cells could be used to better understand the etiology of uterine fibroids.

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Section: Myometrium Side Population Cells Are Not Enriched In Msc Mar...supporting

confidence: 82%

Section: Discussionsupporting

confidence: 63%

See 1 more Smart Citation

Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

Paul

Carpenter

Fitch

et al. 2023

Preprint

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“…13d). We also plotted genes that are important for the cell cycle 44 and observed an upregulation of these genes at 12 hpf, suggesting an increase in proliferation at this stage (Supp. Fig.…”

Section: Inter-individual Transcriptomic Variance Between Sibling Emb...mentioning

confidence: 99%

Zebrahub – Multimodal Zebrafish Developmental Atlas Reveals the State-Transition Dynamics of Late-Vertebrate Pluripotent Axial Progenitors

Lange

Granados

Kumar

et al. 2023

Preprint

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Elucidating the developmental process of an organism will require the complete cartography of cellular lineages in the spatial, temporal, and molecular domains. We present Zebrahub, a comprehensive dynamic atlas of zebrafish embryonic development that combines single-cell sequencing time course data with light-sheet microscopy-based lineage reconstructions. Zebrahub is a foundational resource to study developmental processes at both transcriptional and spatiotemporal levels. It is publicly accessible as a web-based resource, providing an open-access collection of datasets and tools. Using this resource we shed new light on the pluripotency of Neuro-Mesodermal Progenitors (NMPs). We find that NMPs are pluripotent only during early axis elongation before becoming exclusively mesodermal progenitors. We attribute this restriction in NMP cell fate to emerging morphodynamic features that compartmentalize tissue motion.

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“…Firstly, any discussion about sequencing read quality checks, read alignment, or quality checks for the alignment have been excluded, as deep learning is not involved in these procedures. Secondly, there is no discussion of RNA velocity-based downstream analyses, which involve identifying developmental transitions between cell types, including approaches such as DeepCycle [13] and VeloAE [14] . Since the input to the RNA velocity differs from that of standard scRNA-seq data analysis, which requires splicing information, this topic has been excluded.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Brendel

Bai

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

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Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Cited by 34 publications

References 92 publications

Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

Cysteine-Rich Intestinal Protein 1 is a Novel Surface Marker for Myometrial Stem/Progenitor Cells

Zebrahub – Multimodal Zebrafish Developmental Atlas Reveals the State-Transition Dynamics of Late-Vertebrate Pluripotent Axial Progenitors

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

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