The asynchrony in the exit from naive pluripotency cannot be explained by differences in the cell cycle phase

Jayaram, Swathi; Romeike, Merrit; Buecker, Christa

doi:10.1101/2023.09.15.557731

Cited by 2 publications

(3 citation statements)

References 61 publications

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“…Mouse ESCs (R1) were cultured as previously described [43]. Briefly, the base medium is HyClone DMEM/F12 base medium without Hepes (Cytiva), with 4mg/mL AlbuMAX™ II Lipid-Rich Bovine Serum Albumin (GIBCO™), 1× MACS NeuroBrew-21 with Vitamin A (Miltenyi Biotec), 1× MEM NEAA (GIBCO™), 50U/mL Penicillin–Streptomycin (GIBCO™), 1mM Sodium Pyruvate (GIBCO™), and 1×2-Mercaptoethanol (GIBCO™).…”

Section: Methodsmentioning

confidence: 99%

“…5.2 scRNA-seq data processing and analysis 5.2.1 The dataset cDNA reads were mapped to the mm10 reference genome and demultiplexed into droplets by using Cellranger as described in [43]. MULTISeq barcodes were mapped to droplets and counted using CITE-Seq-Count and the Seurat vignette "Demultiplexing with Hashtag oligos" was used to perform cell classification.…”

Section: X Genomics Scrna-seq With Multi-seq Barcoding and Validationmentioning

confidence: 99%

“…10X Genomics scRNA-seq was performed as described in [43]. Cells were harvested via trypsinization for 10 mins at 37.…”

Section: X Genomics Scrna-seq With Multi-seq Barcoding and Validationmentioning

confidence: 99%

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Unveiling Gene Perturbation Effects through Gene Regulatory Networks Inference from single-cell transcriptomic data

Corridori,

Romeike,

Nicoletti

et al. 2024

Preprint

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Physiological and pathological processes are governed by a network of genes called gene regulatory networks (GRNs). By reconstructing GRNs, we can accurately model how cells behave in their natural state and predict how genetic changes will affect them. Transcriptomic data of single cells are now available for a wide range of cellular processes in multiple species. Thus, a method building predictive GRNs from single-cell RNA sequencing (scRNA-seq) data, without any additional prior knowledge, could have a great impact on our understanding of biological processes and the genes playing a key role in them. To this aim, we developed IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework designed to infer directed, weighted, and signed GRNs directly from unperturbed single-cell RNA sequencing data. IGNITE uses the GRNs to generate gene expression data upon single and multiple genetic perturbations. IGNITE is based on the inverse problem for a kinetic Ising model, a model from statistical physics that has been successfully applied to biological networks. We tested IGNITE on murine pluripotent stem cells (PSCs) transitioning from the naive to formative states. Using as input only scRNA-seq data of unperturbed PSCs, IGNITE simulated single and triple gene knockouts. Comparison with experimental data revealed high accuracy, up to 74%, outperforming currently available methods. In sum, IGNITE identifies predictive GRNs from scRNA-seq data without additional prior knowledge and faithfully simulates single and multiple gene perturbations. Applications of IGNITE range from studying cell differentiation to identifying genes specifically active under pathological conditions.

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Section: Methodsmentioning

confidence: 99%

Section: X Genomics Scrna-seq With Multi-seq Barcoding and Validationmentioning

confidence: 99%

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Unveiling Gene Perturbation Effects through Gene Regulatory Networks Inference from single-cell transcriptomic data

Corridori,

Romeike,

Nicoletti

et al. 2024

Preprint

Self Cite

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PUFFFIN: an ultra-bright, customisable, single-plasmid system for labelling cell neighbourhoods

Lebek,

Malaguti,

Boezio

et al. 2024

EMBO J

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Cell communication coordinates developmental processes, maintains homeostasis, and contributes to disease. Therefore, understanding the relationship between cells in a shared environment is crucial. Here we introduce Positive Ultra-bright Fluorescent Fusion For Identifying Neighbours (PUFFFIN), a cell neighbour-labelling system based upon secretion and uptake of positively supercharged fluorescent protein s36GFP. We fused s36GFP to mNeonGreen or to a HaloTag, facilitating ultra-bright, sensitive, colour-of-choice labelling. Secretor cells transfer PUFFFIN to neighbours while retaining nuclear mCherry, making identification, isolation, and investigation of live neighbours straightforward. PUFFFIN can be delivered to cells, tissues, or embryos on a customisable single-plasmid construct composed of interchangeable components with the option to incorporate any transgene. This versatility enables the manipulation of cell properties, while simultaneously labelling surrounding cells, in cell culture or in vivo. We use PUFFFIN to ask whether pluripotent cells adjust the pace of differentiation to synchronise with their neighbours during exit from naïve pluripotency. PUFFFIN offers a simple, sensitive, customisable approach to profile non-cell-autonomous responses to natural or induced changes in cell identity or behaviour.

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The asynchrony in the exit from naive pluripotency cannot be explained by differences in the cell cycle phase

Cited by 2 publications

References 61 publications

Unveiling Gene Perturbation Effects through Gene Regulatory Networks Inference from single-cell transcriptomic data

Unveiling Gene Perturbation Effects through Gene Regulatory Networks Inference from single-cell transcriptomic data

PUFFFIN: an ultra-bright, customisable, single-plasmid system for labelling cell neighbourhoods

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