Luke Koblan

@LukeKoblan

K99/R00 fellow at @WhiteheadInst in @JswLab | Helen Hay Whitney Fellow | Chemical Biology PhD @Harvard in @liugroup

Joined January 2017

339 Following 754 Followers 188 Posts

Latest posts 5

Check out our new preprint led by @gokl_g @njwfish and @WilliamNColgan describing a new approach for inferring cell fate trajectories throughout mammalian development!

Jonathan Weissman's Lab @JswLab 3d

New preprint on generative modeling for mouse development! Rather than single cell trajectories, we set out to model embryos as trees.

Led by @gokl_g, @njwfish, @WilliamNColgan, w/ Philippe Rigollet, @stats_stephen, Zack Smith, @marinkazitnik, @lukekoblan and a wonderful team

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Luke Koblan @LukeKoblan 6d

@WilliamNColgan has put together a fantastic toolkit for analyzing lineage-tracing datasets! We hope this helps lay the groundwork for a unified analytical framework across these types of data. Check out his awesome work!

Jonathan Weissman's Lab @JswLab 6d

Excited to share Pycea, a new Python toolkit for lineage analysis and visualization developed by @WilliamNColgan!

Pycea brings lineage trees into the scverse ecosystem, enabling seamless analysis of lineage, gene expression, and spatial data.

biorxiv.org/content/10.648…

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Luke Koblan @LukeKoblan Oct 9

Exciting to have our work highlighted here!

nature Verified account @Nature Oct 9

‘Cell recorders’ based on gene editing have captured mouse development in unprecedented detail

go.nature.com/4sfdnON
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Luke Koblan @LukeKoblan Oct 8

Comprehensive fate mapping of C. elegans transformed our understanding of development by revealing how coordinated fate decisions generate a complex organism.

In our paper out today in @CellCellPress , we take a major step toward extending this vision to mammalian development.

sciencedirect.com/science/articl…

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Luke Koblan @LukeKoblan Jul 18

Such an awesome study from the Zhuang lab! Congrats to Limor and all of the co-authors!

ZhuangLab @ZhuangLab Jul 17

Excited to share our recent paper (doi.org/10.1016/j.cell…)! In this multi-year effort, we developed RT&T-AMP-MERFISH, enabling whole-transcriptome-scale, isoform-resolved single-cell spatial transcriptomics and imaging ~33,000 RNAs in the brain.

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