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In vivo developmental atlases provide a crucial reference for the new class of stem-cell-derived human embryo models, helping accelerate insights into the mechanisms of human development.
DARLIN enables the generation of a massive diversity of barcodes for in vivo lineage tracing and the combination with single-cell multi-omics measurements.
Advancements in methods that enable in vitro culture of mammalian embryos have become an essential way of investigating mammalian early embryonic development and modeling developmental and pregnancy-related disorders. Here, we discuss the recent method development in this space and analyze current challenges and future directions.
The creation of multiple stem-cell-derived models of mammalian embryogenesis is opening many new doors to study human development and brings a need for scientists to demonstrate responsible dialog over the associated ethical issues.
Research with human embryos and embryo models, this year’s Method of the Year, can be fraught. In contrast, digital embryos could be studied, even perturbed, in computational what-happens-when experiments.
Increasingly advanced in vitro stem-cell-derived human embryo models raise novel ethical questions and shed a light on long-standing questions regarding research on human embryos.
Recent methodological advances in measurements of geometry and forces in the early embryo and its models are enabling a deeper understanding of the complex interplay of genetics, mechanics and geometry during development.
Tracking cells is a time-consuming part of biological image analysis, and traditional manual annotation methods are prohibitively laborious for tracking neurons in the deforming and moving Caenorhabditis elegans brain. By leveraging machine learning to develop a ‘targeted augmentation’ method, we substantially reduced the number of labeled images required for tracking.
Targettrack is a deep-learning-based pipeline for automatic tracking of neurons within freely moving C. elegans. Using targeted augmentation, the pipeline has a reduced need for manually annotated training data.
We developed MAbID, a method for combined genomic profiling of histone modifications and chromatin-binding proteins in single cells, enabling researchers to study the interconnectivity between gene-regulatory mechanisms. We demonstrated MAbID’s implementation in profiling multifactorial changes in chromatin signatures during in vitro neural differentiation and in primary mouse bone marrow tissue.
Although single-cell RNA-sequencing has revolutionized biomedical research, exploring cell states from an extracellular vesicle viewpoint has remained elusive. We present an algorithm, SEVtras, that accurately captures signals from small extracellular vesicles and determines source cell-type secretion activity. SEVtras unlocks an extracellular dimension for single-cell analysis with diagnostic potential.