Xiaojing Gao

We previously built programmable RNA sensors based on editing by housekeeping ADAR enzymes. But they can't sense arbitrary sequences due to design constraints (analogous to PAM for CRISPR). With our new "modulADAR", we overcome that constraint by leveraging ADAR's modularity.

Engineering the next frontier of mammalian systems. Accepting abstracts through April 15 - submit yours now: bit.ly/4jQmrFj #mSBW2026 #SyntheticBiology #MammalianSystems #CallForAbstracts

@StevenMBanik @noScandineHere Congrats, Steven, Robert, and team! We resonate very much with your philosophy and can't wait to see what ALTER can achieve! By the way, I'm sure @DrBinSquared and @AshAlizadeh are happy to see MARIA put to another exciting use.

Excited to share our latest preprint on work led by postdoc Robert Lusi (@noScandineHere)! Introducing ALTER (AGO-Led Targeted Editing of RNA) for non-downregulatory RNA manipulation by repurposing hAGO2, non-immunogenic and capitalizing on evolution. biorxiv.org/content/10.648…

@NornGroup @impetusgrants @Stanford Grateful for the support that makes it possible for us to launch some of the wilder ideas. Stay tuned for the urinary peptide piece :)

Tracking aging is hard because we don’t yet know what blood markers capture signals. This prevents us from measuring aging over time, or test ways to change it. Through @impetusgrants, we funded @SynBioGaoLab at @Stanford to change this. With his project on protein circuits to enable urinal monitoring of aging, his lab aims to engineer biomolecular circuits that detect internal aging hallmarks and convert them into reporter peptides that can be measured in a simple urine sample. The lab has been prolific, with three papers across Nature Chemical Biology @nchembio and Cell Systems @CellSystemsCP establishing the building blocks for this system: a synthetic receptor platform (LIDAR), a platform to control enzyme activity using human-derived proteins (hDIRECT), and a machine-learning workflow to predict whether the body accepts these synthetic tools (“computational deimmunization”). Congratulations to the Gao lab! More to come in 2026.

New preprint from the lab led by the amazing Jack Bryant! # tunable genetic medicines, # endogenous ADARs "Regulatable In Vivo Gene Expression via Adaptamers" biorxiv.org/content/10.648…

Important paper from Jason Chin's and Julian Sale's labs realizing key operations needed for building synthetic human genomes. My repeated reaction reading this paper was "um, I didn't know you could just do that." science.org/doi/abs/10.112…

If anyone wants to kill a few minutes during their (pre-)Thanksgiving travel: gaolab.blog/2025/11/12/ind…

Have you ever wondered what it would've been like to live in a different kind of society? After a record number of rejections, I'm self-archiving my first attempt at... flash fiction, featuring "Hemingway-esque economy" and "Ishiguro's measured revelation" (if you ask Claude).

Thrilled to share our new paper in Cell! We show that phase separation can buffer growth-mediated dilution in synthetic circuits by stabilizing gene expression under dynamic conditions, bringing spatial control into the genetic design toolbox Proud of our amazing team & collaborators! cell.com/cell/fulltext/…

I am delighted to share this work, led by Miguel Alcantar and done in collaboration with Amgen, on the OrthoRep-driven evolution of computationally designed minibinders. Here, we focus not only on getting to high affinity, but also on mapping sequence-affinity landscapes of diverse evolutionary outcomes. One way we arrive at diverse evolutionary outcomes is through "neutral drift" where we take one binder and diverge it into many by selecting for the maintenance of (but not improvement of) binding. This is very easy to do with OrthoRep because rapid mutation of the binder occurs autonomously. The result is a rich sequence-affinity dataset spanning both a range of sequences and affinities. We are now scaling these approaches, including to antibodies and a broad array of targets, to get the right distribution and volume of data for training generative models for binder design. biorxiv.org/content/10.110…

It’s back! 🎉 The Stanford–UCSC Advanced Techniques in Neuroimaging Workshop returns April 13–17, 2026 🧠💡 Learn from experts, hands on experience. Free and housing included! Spots are limited — apply now and spread the word! #Neuroimaging #Zuolab med.stanford.edu/beckman/ATNWor…

Our RAEFISH spatial transcriptomics technology is now published in Cell @CellCellPress! RAEFISH enables sequencing-free whole genome spatial transcriptomics at single molecule resolution. This work represents the first time that transcripts from more than 23,000 genes were directly probed and imaged in situ with any technology, and the first time numerous different gRNAs were directly probed and distinguished by imaging in a high-content CRISPR screen. The challenge: Recent breakthroughs in spatial transcriptomic technologies, from us and others, have greatly improved our ability to profile cell types, states, cellular interactions, and the underlying gene programs within the native tissue contexts. However, these technologies have limitations. Methods based on 2D-array-capture/tagging and ex situ sequencing offer genome-scale coverage, but lack the resolution needed to accurately study fine spatial organization. In contrast, image-based methods that rely on highly multiplexed fluorescence in situ hybridization or in situ sequencing provide single-molecule resolution and resolve fine spatial organization, but require pre-selecting a limited set of target genes (typically hundreds to a few thousand genes), which limits discovery and sometimes leads to only validations of prior knowledge due to the pre-selected targets being well studied in the context. The solution: RAEFISH, our lab's new flagship image-based spatial transcriptomics technology, simultaneously enables single-molecule spatial resolution and whole-genome level coverage of long and short, endogenous and engineered RNA species in cell cultures and intact tissues. The results: 🔥 We performed RAEFISH targeting 23,312 human genes in cell cultures, and demonstrated hypothesis-free discovery of cell cycle associated genes and subcellular localization patterns of transcripts, including nearly the entire protein coding transcriptome and additional long noncoding RNAs. 🔥 We performed RAEFISH targeting 21,955 mouse genes in mouse liver, placenta, and lymph node tissues. Our analyses on immediately neighboring cells uncovered intriguing cell-cell interactions and previously unknown gene expression programs underlying the interactions, such as those between cholangiocytes and immune cells. 🔥 Finally, we further developed RAEFISH to directly read out guide RNAs (gRNAs), demonstrating Perturb-RAEFISH in an image-based high-content CRISPR screen. The capacity of Perturb-RAEFISH to directly read out gRNAs addresses a crucial limitation of previous techniques that read out a barcode/identifier sequence paired with each gRNA species, as the pairing can be shuffled due to RNA recombination intrinsic to lentivirus used in such screens, which limits screen sensitivity and accuracy. In summary, RAEFISH provides the biomedical research community with a generalizable research tool, which will bring more spatial and mechanistic insights across health and disease. This work was co-led by my postdocs Drs. @ChengYubao, Shengyuan Dang, and Yuan Zhang, and was supported by the @NIH, @genome_gov, @sennetresearch, and @psscra. I would like to thank our co-authors, funding agencies, editor, reviewers, and my whole lab @YaleGenetics @YaleCellBio @YaleCancer @YaleMed @Yale. Link to paper: cell.com/cell/fulltext/…

@ReggieVolley @GallowayLabMIT The tweeps have to take the thread from here. I've played all my cards.

@GallowayLabMIT @SynBioGaoLab Karaoke night, cool! But that will cost a lot of Money! Money! Money!

but there's not a soul out there... no one to hear my prayer....

@GallowayLabMIT 😅

@SynBioGaoLab they'll take a chance on me? 😂

Super excited to share what we’ve been working on in collaboration with @SynBioGaoLab over the past few months on de novo antibody design. Check out this great thread by our team lead @santimillef highlighting the technical aspects of the pipeline!

Not only can we now build AI-generated bacteriophages... we can design de novo antibodies to bind them! With Germinal, a new pipeline for de novo antibody design, we move closer to a world where antibodies can be designed against virtually any protein, on demand. Check it out:

Entering my PhD, de novo antibody design was a grand challenge I thought would not be solved without huge increases in affinity data and Ab-Ag structure. Only 2 years later, we provide the first open-source recipe to get antibody binders, almost magically, out of a computer (1/3)

Today, we report Germinal, a method for efficient de novo antibody design, with @santimillef and @SynBioGaoLab. Germinal achieves success rates of 4-22% across diverse epitopes. We make the work fully open, without doing lame things like posting a preprint without methods. 🧵

@santimillef @_jnwang @driscoll_cl @talaldotpdb @Xiaowei0402 @BrianHie Image credit: DALL-E. Not long ago I wouldn't have imagined my lab making our own nanobodies, the same way that I wouldn't have imagined myself making any image worth posting. Grateful to stand on the shoulders of so many protein designers.

doi.org/10.1101/2025.0… Spearheaded by @santimillef, it’s a tremendous team effort with @_jnwang, @driscoll_cl, Haoyu, @talaldotpdb, and @Xiaowei0402, and a close collaboration between @BrianHie’s and my labs. Can’t wait to see what binders everyone will design with it!

Having often dealt with binder-limited projects, we sought a more accessible source for nanobodies than yeast display or llama. Here we introduce Germinal, computationally designing antibody-like binders with such a hit rate that only tens need to be screened for each target.