
Paul Datlinger
@pauldatlinger
Scientist. Inventor of CROP-seq & scifi-RNA-seq. Associate Director at the Arc Institute | Virtual Cell Initiative
ID: 841731860547940352
14-03-2017 19:24:49
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Cell atlas projects and single-cell perturbation screens require cost-effective profiling for millions of cells or thousands of experimental conditions. Our new method scifi-RNA-seq supports projects of this scale. Article published today Nature Methods: nature.com/articles/s4159…

Compared to our preprint the article includes extensive benchmarking to 10x Genomics and plate-based combinatorial indexing. We included a CRISPR screen with scRNA-seq readout of T cell activation. Round1 barcodes are ideal for large perturbation screens with single-cell readout.







🧬Evo, the first foundation model trained at scale on DNA, is a Rosetta Stone for biology. DNA, RNA, and proteins are the fundamental molecules of life—and cracking the code of their complex language is an ongoing grand challenge. 🔬Today in Science Magazine, the labs of Arc


In collaboration with Reuben Saunders, Jonathan Weissman's Lab, and Xiaowei Zhuang, we are very excited to release Perturb-Multi: a platform for pooled multimodal genetic screens in intact mammalian tissue. Check it out! biorxiv.org/content/10.110…

Enjoyed writing this perspective on an AI-powered virtual cell with amazing collaborators But it’s been even more fun actually building it! 🔧 Check out open positions @ArcInstitute or find us NeurIPS Conference

Interested to screen T cells for Arc’s Virtual Cell Initiative and explore AI virtual cells in cancer immunotherapy? I’m looking for a talented Scientist to join my team at the Arc Institute in Palo Alto, California. Apply here! grnh.se/c565d05f4us

Today, Arc announced it is joining forces with NVIDIA Healthcare to develop powerful computational tools that will help researchers everywhere explore and understand living systems in ways previously impossible. arcinstitute.org/news/news/arc-…

Excited to partner with @Nvidia and NVIDIA Healthcare to scale the potential of AI models to design, predict, and understand biology. Our goal is to discover emergent properties of life, similar to those found in language, videos, and robotics arcinstitute.org/news/news/arc-…