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Showing 1–8 of 8 results
Advanced filters: Author: Caleb Weinreb Clear advanced filters

  • Single-cell transcriptomics, fate assays and a computational theory enable prediction of cell fates during haematopoiesis, discovery of regulators of erythropoiesis and reveal coupling between the erythroid, basophil and mast cell fates.

    • Betsabeh Khoramian Tusi
    • Samuel L. Wolock
    • Merav Socolovsky
    Research
    Nature
    Volume: 555, P: 54-60

  • Keypoint-MoSeq is an unsupervised behavior segmentation algorithm that extracts behavioral modules from keypoint tracking data acquired with diverse algorithms, as demonstrated in mice, rats and fruit flies. The extracted modules faithfully reflect human-annotated behaviors even though they are obtained in an unsupervised fashion.

    • Caleb Weinreb
    • Jonah E. Pearl
    • Sandeep Robert Datta
    ResearchOpen Access
    Nature Methods
    Volume: 21, P: 1329-1339

  • Photometric recordings and optogenetic manipulation show that dopamine fluctuations in the dorsolateral striatum in mice modulate the use, sequencing and vigour of behavioural modules during spontaneous behaviour.

    • Jeffrey E. Markowitz
    • Winthrop F. Gillis
    • Sandeep Robert Datta
    ResearchOpen Access
    Nature
    Volume: 614, P: 108-117

  • In situ barcoding and fate mapping in mice reveals that an early wave of progenitor specification, driven by embryonic multipotent progenitor cells, gives rise to adult blood independently of haematopoietic stem cells.

    • Sachin H. Patel
    • Constantina Christodoulou
    • Fernando D. Camargo
    Research
    Nature
    Volume: 606, P: 747-753

  • Transposon tagging to clonally trace progenitors and stem cells provides evidence for a substantially revised roadmap for unperturbed haematopoiesis, and highlights unique properties of multipotent progenitors and haematopoietic stem cells in situ.

    • Alejo E. Rodriguez-Fraticelli
    • Samuel L. Wolock
    • Fernando D. Camargo
    Research
    Nature
    Volume: 553, P: 212-216

  • Motion Sequencing uses three-dimensional machine vision and unsupervised machine learning on depth videos to decompose spontaneous mouse behavior into a series of elemental modules called ‘syllables’, revealing how often syllables are used and how they transition over time.

    • Sherry Lin
    • Winthrop F. Gillis
    • Sandeep Robert Datta
    Protocols
    Nature Protocols
    Volume: 19, P: 3242-3291