Cai Lab

Our new preprint with S.Yoshida and B.Simons! Led by @ChakraArun, we leverage spatial information to resolve temporal dynamics in spermatogenesis! biorxiv.org/content/10.110… Also check out a complementary preprint by S.Y. and B.S using live imaging! biorxiv.org/content/10.110… (1/10)

Check out the preprint! (10/10) biorxiv.org/content/10.110…

Using gene regulatory network inference we also identified a network of transcription factors with a cyclic topology and identify germ cell signals that interact with this network, illuminating how Sertoli cells can coordinate with germ-cell development. (9/10)

Our new preprint with S.Yoshida and B.Simons! Led by @ChakraArun, we leverage spatial information to resolve temporal dynamics in spermatogenesis! biorxiv.org/content/10.110… Also check out a complementary preprint by S.Y. and B.S using live imaging! biorxiv.org/content/10.110… (1/10)

Just posted a preprint on our new spatial genomic recording system, baseMEMOIR. biorxiv.org/content/10.110… Motivation: Cells divide, differentiate, and migrate to form exquisitely organized tissues. Reconstructing the dynamic histories of individual cells, including their lineage relationships and ancestral states, is essential for understanding how intrinsic and extrinsic signals generate tissues during development and in regenerative medicine. Engineered genomic recording systems can reconstruct cell lineage histories from endpoint measurements. However, existing methods either require sequencing (disrupting spatial organization) or have been limited in memory size and scalability. To address this need, @ChadlyDuncan, Kirsten Frieda, and others in the lab created a high memory capacity image-readable recording system termed baseMEMOIR.

Check out Giotto Suite — our latest tool for spatial multiomics analysis. This is a complete overhaul of the original Giotto package, a result from years of making. Great collaboration with @RnDries team. Kudos to @jiaji_g_chen @josschavezf1 and the entire Giotto team!

Explore our data at: woldlab.caltech.edu/celltiles/mous…

Spatial data needs to be taken into account to discover certain functionally distinct cellular states, as dimension reduction of gene expression data alone will not project on all functionally distinct cell types. We will have more examples of this in different systems.

Why spatial? What can we learn from spatial transcriptomic analysis that we can't learn otherwise? Excited to present our collaboration with @AndyMcMahonLab led by @MichalPolonsky and @GerhardtLouisa : doi.org/10.1101/2023.1…