Cai Lab

The Cai Lab at Caltech. We developed sequential FISH (seqFISH), seqFISH+, MEMOIR, and RNA SPOTs, apply them to study various biological questions.

[Cai Lab @LongCai_Lab]

Quality of the data matters! at least for predicting glioma progression from a patient's initial biopsy before any progression has occurred. Excited to share our new pre-print, authored by Michal Polonsky, Jonathan Fox, Yunrui Lu and Sheel Shah. (1/7)

https://t.co/sXX4NyxPJW

[Cai Lab @LongCai_Lab]

Thus, quality is much more important than quantity! In glioma tumor progression, high-sensitivity and multiple-modalities spatial approaches can identify markers to risk-stratify patients, and elucidate potential targets for therapeutic interventions. (7/7)

[Cai Lab @LongCai_Lab]

Thrilled to share our @CellCellPress paper with Shosei Yoshida & Ben Simons, led by @ChakraArun:
1) spatial geometry can resolve temporal dynamics
2) Sertoli cells run an intrinsic cycle coupled to germline, suggesting oscillator coupling as a principle of tissue organization

[Arun Chakravorty @ChakraArun]

Our paper is finally out in Cell! Years of work on one of biology's most beautiful tissue clocks, and finding an intrinsic oscillator that helps organize tissue dynamics. From my PhD work at @LongCai_Lab, with Shosei Yoshida and Ben Simons!
🧵(1/10)
https://t.co/fRFxG3rbo2

[Cai Lab @LongCai_Lab]

Check out the preprint! (10/10) https://t.co/S5hhzqMi3g

[Cai Lab @LongCai_Lab]

Our new preprint with S.Yoshida and B.Simons! Led by @ChakraArun, we leverage spatial information to resolve temporal dynamics in spermatogenesis!
https://t.co/S5hhzqMi3g

Also check out a complementary preprint by S.Y. and B.S using live imaging! https://t.co/rrbxyFYylk

(1/10)

[Cai Lab @LongCai_Lab]

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)

[ElowitzLab @ElowitzLab]

Just posted a preprint on our new spatial genomic recording system, baseMEMOIR.
https://t.co/ef5i8FnIOw
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.