Alexandre Marand

🎉Congrats to Eckerlab alumni José Alonso @AlonsoStepanova @NCState & Bob Schmitz @Schmitz_Lab @universityofga on their election to @theNASciences today! Working with you both at @salkinstitute was a true privilege - this honor is so well deserved. So proud! 🌟#NAS163 #NASmembers

We have been cooking up this story for a while and we are excited to finally be able to share! Read on if you're interested in whole plant regeneration WITHOUT the application of hormones!

@JoeEcker @NaturePlants What a cool study! Congrats to Joseph and the whole team

🌿 New paper from the lab out in @NaturePlants! We built a single-cell atlas of Arabidopsis leaf development to ask: how does drought reshape leaf gene expression at the cellular level? doi.org/10.1038/s41477… 🧵

New preprint! The same TFs can drive distinct regulatory programs depending on where they bind. TSS → rapid stress responses Intronic & upstream → cell-type programs Enhancer-like CRMs → embryo/meristem programs Coding-sequence binding → repression biorxiv.org/content/10.648…

🧬 Another super exciting new work from the lab: Decoding the gene regulatory landscape through multimodal learning of protein–DNA interactions. 👉 Link to preprint: biorxiv.org/content/10.110… In this work, we introduce Chromnitron, a biologically grounded multimodal foundation model that decodes the global gene regulatory landscape by hundreds to thousands of proteins binding in the genome. ⁉️ Why is this protein-centered view of regulatory genomics so important? DNA sequence 🧬provides the same genetic blueprint, but the regulatory logic that gives rise to diverse cell types and states – in both health and disease conditions – is executed by the 100s-1000s of chromatin proteins that differentially read the DNA sequences to establish cell-type-specific gene expression programs. 🤖 Chromnitron is specifically developed to decode this cell-type-specific regulatory logic. Designed to learn from the first principles that determines protein–DNA interactions in the cell, Chromnitron integrates 1) genomic DNA sequence, 2) cell-type-specific chromatin states, and importantly, 3) protein sequence-encoded structural features. 👨🏻‍💻 Through large-scale pre-training & fine-tuning with a finely curated atlas of high-quality datasets, Chromnitron learns the shared and protein-specific mechanisms of protein–DNA interactions, and thus can accurately predict the binding landscapes across hundreds of proteins in unseen cell types. 💡Chromnitron isn’t just accurate and cell-type-specific prediction — it is a discovery engine! With Chromnitron: we identified novel regulators of T cell exhaustion (ZNF865 & ZNF766); we reconstructed the dynamic regulatory landscapes of how global chromatin proteins orchestrate neurodevelopment. 🔮 Chromnitron represents a significant step toward predictive, interpretable, and mechanistic understanding of gene regulation and cell fate determination. We expect Chromnitron to accelerate discovery and engineering in regulatory genomics, particularly in human biology, and empower therapeutic opportunities. 🙏🏻 Last, this is another wonderful collaboration with Dr. Aristotelis Tsirigos @artsinyc, and led by the brilliant Drs. Jimin Tan @tan_jimin, Xi Fu @alexanderfuxi, and Xinyu Ling @Dennislxy, and many more co-authors and friends who helped make this work truly possible! 👉 Find out more about the model and discoveries (e.g. architecture, interpretability, benchmarking, generalizability, etc): biorxiv.org/content/10.110… #MultimodalAI #GeneRegulation #TranscriptionFactor #RegulatoryGenomics #CellFate #AIinBiology

Next-generation genome editing: no transgene, no tissue culture dlvr.it/TQ9HMS #plantscience

A New Year, a new paper out on Jan 1 in @ScienceMagazine ! Congratulations to @SiddhantUJain and all our co-authors and collaborators! This study answers a longstanding question in our field regarding recruitment of chromatin regulatory machinery and provides a powerful new framework for development of precision medicines in #cancer and beyond! @kadochlab @alex2110x @hhmi_science @DanaFarberNews @DanaFarber science.org/doi/10.1126/sc… #generegulation #chromatin #cancer #therapeutics #basicscience #BAFcomplex #mSWISNF #chromatinremodeling

I'm delighted to announce our new preprint on genome-scale perturb-seq in CD4 T cells. We learned general lessons about the power of perturb-seq, and specific lessons in T cell biology Led by amazing postdocs Ronghui Zhu and Emma Dann with my wonderful collaborator Alex Marson

scRNA-seqデータから「細胞状態の加速度」と「ポテンシャル地形」を再構成する新手法『ddHodge』を発表しました。筆頭著者は前原一満さんです。従来のVelocity解析を超え、ホッジ分解を用いて細胞運命決定の力学を解明します。nature.com/articles/s4146…) 1/5

The plant ontology of cell types #comment #MolecularPlant cell.com/molecular-plan…

Modern GWAS can identify 1000s of significant hits but it can be hard to turn this into biological insight. I'm excited to share our new work combining genetic associations and Perturb-seq to build interpretable causal graphs, out today in @Nature:

Our lab's new paper is out! We introduce ENTRAP-seq, a high-throughput in planta assay for multiplexed profiling of protein-coding libraries. Using ENTRAP-seq and machine learning, we discovered transcriptional regulators from ~1,500 plant viral genomes. go.nature.com/4pw5cL1

Our new review in Science @ScienceMagazine is out with @jennyrussinova and @NemanjaVukas. We discuss how precise modulation of brassinosteroid signaling can enable more targeted, predictable improvements in plant growth and resilience. science.org/doi/10.1126/sc…

Dr. Torii’s commentary published @CurrentBiology “Plant regeneration: Breaking the silence of totipotency by reprogramming a stomatal precursor cell”🌱🧬 authors.elsevier.com/a/1lzT-3QW8SA4…

We @uga_genetics are hiring a TT assistant professor in Human Genetics to investigate how genetic makeup influences an individual’s risks for diseases or responses to drugs and therapies. All research approaches will be considered. Please RT. ugajobsearch.com/postings/456597

Which mutations rewire function of regulatory DNA? Excited to share SEAM: Systematic Explanation of Attribtuion-based Mechanisms. SEAM is an explainable AI method that dissects cis-regulatory mechanisms learned by seq2fun genomic deep learning models. Led by @EESetiz 1/N 🧵👇

🌱Tenure-track Assistant Professor Position OPEN🌱 -Plant Interkingdom/Environment Interactions & Plant Immunity- Now ads out in Science Careers! Please retweet and APPLY🤠🤘 jobs.sciencecareers.org/job/675377/ass…

Congratulations @JoeEcker for this well-deserved recognition. A true pioneer! Salk scientist Joseph Ecker awarded McClintock Prize for Plant Genetics and Genome Studies salk.edu/news-release/s…

Hybrids reveal accessible chromatin trans genetic associations biorxiv.org/content/10.110… #biorxiv_genetic