Sudarshan Pinglay

Now out in @ScienceMagazine we present 'Genome-shuffle-seq': a method to shuffle mammalian genomes and characterize the impact of structural variants (SVs) with single-cell resolution in one experiment. science.org/doi/10.1126/sc…

@BrianHie @pdhsu @Nature This would not have been possible without @AnsaBio providing the synthetic DNA. Their up to 50kb clonal DNA without any sequence restrictions is an absolute game changer!

Thrilled to see the Evo2 paper led by @BrianHie, @pdhsu & team out in @Nature! We helped bring long (20kb!) AI-designed DNA to life in cells. Seeing experiments match the designs was wild-biological abstraction is starting to feel real. Join us → pinglay-lab.com

We are hiring a staff member to support our projects - please reach out with any questions! wd5.myworkdaysite.com/recruiting/uw/…

if you are excited about these areas, come join our lab @uwgenome and the Seattle Hub for Synthetic Biology! We are hiring at all levels. pinglay-lab.com

We also discuss current bottlenecks for mammalian genome writing (mainly throughput and size), mechanisms to address them, and future directions for the technology. We think it is particularly poised to revolutionize generating training data for genomic AI models.

Happy to share that our review on mammalian genome writing with @atwater @BroshRan @JefBoeke @JShendure and Matt Maurano is now out in @CellPressNews! sciencedirect.com/science/articl…

@anshulkundaje What do you think are the types of tech dev/data that are most limiting?

This is obviously true. Can't comment on all areas of AI 4 science but for bio, we are in some ways overindexing on AI (for now) when key bottlenecks continue to be novel techdev, data & data engineering (silo-ing/privacy vs access) & the optimal expt. design strategies. 1/

We are hiring a scientist to lead our 'Build' team at the Seattle Hub for Synthetic Biology. @alleninstitute.org Job posting below: alleninstitute.org/careers/jobs/?… Please reach out if you have any questions!

Here's a link to the preprint if you made it this far: biorxiv.org/content/10.110… any feedback would be highly appreciated!

A big thank you to all co-authors - looking forward to seeing where we can take this approach. If you are interested in joining us on this effort, check out our website: pinglay-lab.com

We then used SGE to engineer CHO cells to grow without isoleucine, a feat we could not achieve via rational design and delivery of entire synthetic pathways. Again, mitochondrial localization was favored, with individual clones reflecting ~40-50kb of integrated DNA!

Using SGE, we screened millions of pathway combinations in a single experiment to engineer CHO cells that grew at WT rate (~1.1 day/doubling) in valine-free medium. Intriguingly, the best clones all employed mitochondrial localization of pathway components.

Excited to share our work with @julie_trolle and @JefBoeke We developed a 'shotgun' method to screen millions of synthetic metabolic pathways to enable mammalian cells to grow without two essential nutrients for the first time in >500 million years! biorxiv.org/content/10.110…

In collaboration with Harris Wang’s lab, we previously engineered cells to grow without valine by importing 4 genes from E.coli. However, the cells grew 4x slower than normal - and we could not extend this strategy to enable any other amino acids. doi.org/10.7554/eLife.…

As a test case, we used SGE to engineer essential amino acid prototrophy in mammalian cells, a behavior last seen over 500 million years ago. Unlike E. coli, which can make all 20 amino acids, mammals lack the pathways for 9 “essential” ones that we obtain via our diet.

In SGE, we clone and randomly deliver a barcoded TU library at high MOI so that each cell represents a unique synthetic metabolic pathway experiment. Cells with the desired phenotype (e.g. viability) are selected, and TU barcodes are sequenced to identify functional combinations.

We developed Shotgun Genetic Engineering (SGE), which exploits the fact that building and delivering many small, transcription units - each with a gene, promoter and localization signal - is exponentially easier than delivering a single large construct to a mammalian cell.

Mammalian metabolic engineering is key to advancing bioproduction, cell therapy, and rejuvenation. But as pathway complexity grows, so does the combinatorial design space! But delivering large DNA constructs to mammalian cells is inefficient, making large screens intractable.