iiinova — where your digital self keeps evolving

Reposted by @cxqiu.bsky.social

Junhong Choi @choijunhong.bsky.social · 11/08/2026

Excited to share the first preprint from our lab! We combined a DNA Typewriter lineage recorder with clone-resolved founder profiling to find what makes metastatic potential heritable in lung cancer xenografts. 1/13 www.biorxiv.org/content/10.6...

biorxiv.org

Time-resolved lineage recording reveals a pre-existing, heritable cell state underlying metastatic potential

Metastasis causes most cancer deaths, yet no recurrent mutation specifically drives it, raising the possibility that metastatic potential is a non-genetic yet heritable cell state. Classic experiments...

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Reposted by @cxqiu.bsky.social

Jay Shendure @jshendure.bsky.social · 31/07/2026

Thrilled to post thread re: new single-cell lineage of mouse embryo reconstructed w/ DNA Typewriter. One animal, zygote to late organogenesis (E13.5). Tree has 1,340,794 transcriptionally profiled, annotated tips (cells), 1,142,588 dated internal nodes, rooted at zygote 1/n

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Reposted by @cxqiu.bsky.social

Junyue Cao @junyuecao.bsky.social · 16/04/2026

Excited to share our new paper @nature.com! We developed PerturbFate, a scalable single-cell platform to discover how diverse genetic perturbations converge on a shared drug-resistant cell state, and key programs driving it, led by our incredible Zihan Xu from @rockefeller.edu rdcu.be/fdC14

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Reposted by @cxqiu.bsky.social

Niklas Kempynck @niklaskemp.bsky.social · 08/04/2026

CREsted is finally out! You can find the article, together with a summarizing Research Briefing, in thread. 🦎

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Reposted by @cxqiu.bsky.social

Jay Shendure @jshendure.bsky.social · 10/04/2026

Latest from Shendure & Qiu labs (@cxqiu.bsky.social) )! We combined a new 4M cell mouse whole embryo scATAC-seq atlas (E10-P0), millions of 'evolutionarily coherent' orthologs from 241 mammalian genomes (Zoonomia), and the CREsted CNN framework (@steinaerts.bsky.social).

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

We thank Q-T-π (Canis familiaris), Tater and Tot (Feline catus) for inspiration. Nothing in biology makes sense except in the light of evolution — apparently including AI.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Huge team effort led by CX Qiu, Riza Daza, and Ian Welsh. Jay Shendure supervised the project. Key contributions from Rupali Patwardhan, @niklaskemp.bsky.social & @steinaerts.bsky.social (CREsted), built on our mouse timelapse with @coletrapnell.bsky.social. Grateful to the whole team and Zoonomia.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Everything is open: interactive preprint, count matrices, models, all 7,712 prediction tracks, code & reproducible figures → doi.org/10.62329/hxkk6249. Raw data: GEO GSE325776. Code: github.com/ChengxiangQiu/jax-atac-code

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Limitations we're upfront about: promoter suppression is still heuristic, some species bias remains, and all labels derive from mouse. Matched atlases in a few more species would help a lot. This is v1 — substantial headroom remains.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Model organisms aren't just for cataloging biology — they're training substrates for AI models of human biology. Mouse experimental depth + mammalian sequence diversity = virtual access to human regulatory landscapes we can't profile directly.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

We applied STEAM to all 241 Zoonomia genomes: 32 × 241 = 7,712 genome-wide enhancer tracks. HumMus for human + mouse. BabaGanoush for the full spread!

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Some favorites — human enhancers with no mouse ortholog, validated by fetal accessibility: > FECH intron 1 (erythroid, heme biosynthesis) > upstream of TFRC (erythroid, iron uptake) > upstream of APOB (hepatocyte, LDL cholesterol) > upstream of CYP2C19 (hepatocyte, drug metabolism)

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Even for human enhancers with NO mouse ortholog at all, STEAM predicts the right cell class. Hepatocyte-predicted elements are more accessible in human hepatoblasts; erythroid-predicted elements in erythroblasts. 7× difference in the expected direction.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Key validation: human-only predicted enhancers are 8–9× more accessible than mouse-only predictions in the corresponding human fetal cell type — using Domcke et al. human fetal accessibility data the model never saw. Evolutionary transfer learning works.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

We apply STEAM genome-wide to human + mouse: ~340K enhancers per species across 32 cell classes. Jaccard co-occurrence of enhancer predictions recovers nearly identical lineage structure in both species — regulatory logic is deeply shared.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

STEAM resolves 11 synteny groups of hepatocyte enhancers at this locus — orthologous enhancer families with shared ancestry but divergent sequences. Some are deeply conserved, others lineage-restricted (e.g. one group found only in Old World monkeys).

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

The payoff: at the Afp locus, hepatocyte enhancer predictions jump from 1.2/species to 4.6/species across 136 mammals. Signal-to-noise goes from 3× to 15×. The Mus-restricted bias largely disappears.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Performance scales with phylogenetic breadth, plateauing ~32 species — but even partial inclusion accelerates convergence dramatically.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Enter STEAM: we augment training with syntenic enhancer orthologs from up to 241 Zoonomia genomes — a ~200× expansion in sequence diversity, preserving cell-class labels. Orthologous enhancers are nature's data augmentation: divergent sequences, shared function.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

But the evolution-aware model doesn't transfer across species. At the Afp locus, hepatocyte predictions light up in Mus and go dark elsewhere. The culprit: insufficient sequence diversity from training on one genome.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

These predicted enhancers inform gene expression: stronger enhancer predictions near a gene → higher cell-class-specific expression, with clear distance-dependent decay. This holds across all 32 developmental lineages.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Compare the Alb/Afp locus: the evolution-naive model predicts promoters, tandem-repeat artifacts, and real enhancers. The evolution-aware model distills this to six clean hepatocyte-specific elements, trimmed to core regions of 154–474 bp.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Filtering yields 32 cell-class-specific enhancer clusters that map one-to-one onto developmental lineages, plus one large promoter cluster — cleanly separated. The evolution-aware model trained on these eliminates both failure modes.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Step three: use evolution to clean up. Real enhancers should be syntenically retained across mammals AND show coherent predicted activity across orthologs. Both filters yield clean bimodal distributions — nature's quality control.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

But when we tile the WHOLE genome? Two failure modes: tandem repeats generate massive false positives (24× enrichment), and promoter grammar contaminates distal enhancer predictions. Strong performance on peaks ≠ reliable genome-wide inference.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Step two: train CREsted to predict cell-class-specific accessibility from DNA sequence. Strong performance on held-out peaks (r = 0.74), with lineage structure clearly recovered.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

The atlas integrates tightly with our matched scRNA-seq timelapse (11M cells, same embryo cohort). Nearest neighbors in the co-embedding land right on matching timepoints.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

Step one: the atlas. 3.9M nuclei by sci-ATAC-seq3 from 36 whole mouse embryos, one per 6-hour bin, E10 to P0. No dissection. We resolve 13 lineages → 36 cell classes → 140 cell types across the full arc of organogenesis.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

The core idea: cis-regulatory sequences evolve fast, but the trans-acting programs that read them evolve slowly. This mismatch — the same principle that powered AlphaFold — means models trained on mouse enhancers should generalize to orthologous cell types across Mammalia.

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cxqiu.bsky.social @cxqiu.bsky.social · 09/04/2026

New preprint @cxqiu.bsky.social @jshendure.bsky.social ! Can we learn regulatory grammars of human cell types — by training on mouse development and transferring across 241 mammalian genomes? Introducing STEAM & a whole-organism scATAC-seq atlas from E10 to birth. www.biorxiv.org/content/10.6...

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cxqiu.bsky.social @cxqiu.bsky.social · 04/11/2025

Grateful to my mentors @jshendure.bsky.social, @coletrapnell.bsky.social, @cbmoens.bsky.social, @wnoble.bsky.social, Bob Waterston, @ksusztak.bsky.social , and Qinghua Cui for their guidance and support along the way 🙏 !!

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cxqiu.bsky.social @cxqiu.bsky.social · 04/11/2025

Thrilled to share I’ve started my lab at Dartmouth’s Geisel School of Medicine! We focus on mapping cellular trajectories & TF networks in development and Mendelian disorders, exploring new therapies. Join us—postdocs, grads, and scientists welcome! sites.dartmouth.edu/qiulab/

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Reposted by @cxqiu.bsky.social

Nobu Hamazaki @nobuhamazaki.bsky.social · 26/09/2025

New paper from my lab and @jshendure.bsky.social lab! Led by the brilliant @zukailiu.bsky.social and @cxqiu.bsky.social. We tackled how anterior and posterior progenitor cells cooperate to self-organize into an embryonic structure (termed AP-gastruloid). (1/n) www.biorxiv.org/content/10.1...

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Reposted by @cxqiu.bsky.social

Silvia Domcke @sdomcke.bsky.social · 25/05/2025

Expanding Perturb-seq-like screens to multicellular systems also increases the complexity of confounding effects. We encountered this when we perturbed all TFs in mosaic embryoid bodies (EBs) and develop a scalable solution by barcoding monoclonal individuals. 🧵 www.biorxiv.org/content/10.1...

biorxiv.org

Barcoded monoclonal embryoids are a potential solution to confounding bottlenecks in mosaic organoid screens

Genetic screens in organoids hold tremendous promise for accelerating discoveries at the intersection of genomics and developmental biology. Embryoid bodies (EBs) are self-organizing multicellular str...

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