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GEMLI: Gene Expression Memory-Based Lineage Inference from Single-Cell RNA-Sequencing Datasets.
Methods Mol Biol
January 2025
Ecole Polytechnique Fédérale de Lausanne, School of Life Sciences, Institute of Bioengineering, Lausanne, Switzerland.
Gene expression memory-based lineage inference (GEMLI) is a computational tool allowing to predict cell lineages solely from single-cell RNA-sequencing (scRNA-seq) datasets and is publicly available as an R package on GitHub. GEMLI is based on the occurrence of gene expression memory, i.e.
View Article and Find Full Text PDFReconstructing Progenitor State Hierarchy and Dynamics Using Lineage Barcoding Data.
Methods Mol Biol
January 2025
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm.
View Article and Find Full Text PDFLineage Tracing by Light-Sheet Microscopy and Computational Reconstruction.
Methods Mol Biol
January 2025
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Crete, Greece.
Lineage tracing based on modern live imaging approaches enables to visualize, reconstruct, and analyze the developmental history, fate, and dynamic behaviors of cells in vivo in a direct, comprehensive, and quantitative manner. Light-sheet fluorescence microscopy (LSFM) has greatly boosted lineage tracing efforts, because fluorescently labeled specimens can be imaged in their entirety, over long periods of time, with high spatiotemporal resolution and minimal photodamage. In addition, an increasing arsenal of commercial and open-source software solutions for cell and nuclei segmentation and tracking can be employed to convert data from pixel-based to object-based representations, and to reconstruct the lineages of cells in their native context as they organize in tissues, organs, and whole organisms.
View Article and Find Full Text PDFBayesian Phylogenetic Lineage Reconstruction with Loss of Heterozygosity Mutations Derived from Single-Cell RNA Sequencing.
Methods Mol Biol
January 2025
Allen Discovery Center for Lineage Tracing and Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
Mutations are acquired frequently, such t`hat each cell's genome inscribes its history of cell divisions. Loss of heterozygosity (LOH) accumulates throughout the genome, offering large encoding capacity for phylogenetic inference of cell lineage.In this chapter, we demonstrate a method, using single-cell RNA sequencing, for reconstructing cell lineages from inferred LOH events in a Bayesian manner, annotating the lineage with cell phenotypes, and marking developmental time points based on X-chromosome inactivation.
View Article and Find Full Text PDFExtensor tendon injuries are commonly encountered after trauma. These injuries often result in acute weakness and have widely differing treatment options depending on the severity and the location of the injury within the upper extremity. Zone I injuries can often be treated nonoperatively with the potential for pinning of any large bony avulsion fragments.
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