Charlotte Bunne’s Post

🎉 Excited to share another publication from my postdoc at USC, now live on biorXiv! https://lnkd.in/guydpaBR 🧬 We share how we can control the shape of mammalian cell aggregates with synthetic gene networks. 🏐 ➡️ 🥖 As this is was a yet unreached milestone, we started "easy" by focusing on a simple morphogenetic operation: axial elongation. To do so, we combined 2 main modules: - a circuit triggering cell activation as a travelling wave (that we had described here: https://lnkd.in/gtxi38ii) - a series of genetic effectors controlling tissue physics (growth and viscosity) placed under the control of cell activation. 🚀 To strongly reduce the time necessary to explore the design space, we used a computational 3D tissue model. This allowed us to identify the main design principles to implement them in vitro. ⏳ Jumping to the conclusion, we identified several circuits yielding tissue elongation! 🔍 There is of course still a lot of room for improvement, but we believe this work is important for the community for 2 main reasons: 1) Engineering self-organized tissues of controlled shapes will be huge for many future achievements in 🤟 regenerative medicine, 🫀 organoid reproducibility, 🤖 biobots, 🔬 self-organized microengineering... and this is a significant step in this direction. 2) Our work highlights the need for 💻 tissue simulation tools quickly explore the design space (a euphemism: in vitro work takes time). However, we need better ones to more faithfully simulate ⚛️ tissue physics, while remaining accessible in terms of computation time. A conendrum to be solved with AI? 💡 What I think will unlock the next level of synthetic tissue elongation. First, co-induction of multiple converging effectors, but we need better methods for stably engineering cell lines with large packages. Second, the adhesion code is an extremely powerful method to stabilize large-scale tissue shapes. Last, there might be better chassis cell lines than fibroblasts. 👏 Many thanks to the team behind the work: Leonardo Morsut, Christian Chung, NAISARGEE JAIN, Catcher Salazar, Steffen Grosser, Neo Phuchane and Calvin Lam!

Sunbelt Spatial Omics Seminars! Highlighting star researchers in Spatial Omics... The first speaker of this Fall 2024 series: Prof. Savas Tay University of Chicago September 20, 2 pm EST Title: Spatially resolved analysis of proteins, protein complexes and mRNA by proximity sequencing Abstract: In this talk I will describe proximity sequencing (Prox-seq), a new single cell method that enables simultaneous measurement of proteins, protein complexes and mRNA in thousands of individual cells [1, 2], and analysis of these molecular species in tissues in a spatially resolved manner. Prox-seq combines proximity ligation assay with single-cell sequencing to measure proteins and their complexes from all pairwise combinations of targeted proteins, providing quadratically-scaled multiplexing. Prox-seq provides access to an untapped measurement modality for single-cells and for intact tissues, is practical and broadly applicable, and can discover new protein interactions in different cell types. I will also describe our recent progress in spatially-resolved analysis of human tonsils and B cell germinal centers using spatial prox-seq. 1. L. Vistain, H. V. Phan, B. Keisham, C. Jordi, M. Chen, S. Reddy, S. Tay. Quantification of extracellular proteins, protein complexes and mRNA in single cells by proximity sequencing. Nature Methods 19, 1578 (2022) 2. J. Xia, H. V. Phan, L. Vistain, M. Chen, A. Khan, S. Tay. Computational prediction of protein interactions on single cells by proximity sequencing. https://lnkd.in/edjmaJxA (2023) Register with the QR code below and this link: https://lnkd.in/eDH5kM4s Co-organized with Sunil Badve (Winship/Emory), Ahmet F Coskun (GaTech), Hector Torres (Program Coordinator), Yesim Gokmen-Polar, PhD (Winship/Emory) (Inspired and Advised by Rong Fan)

Please check our recent work, a review in the Biophysical Journal ! In this paper, Shangying W. and I discuss the application of Machine Learning methods in biophysics and specifically in cellular engineering and reprogramming. https://lnkd.in/db9R8z9Y

𝗖𝗼𝗱𝗶𝗻𝗴 𝗰𝗹𝗶𝗻𝗶𝗰 👨🏽💻 The wealth of data stored in electronic medical records has long been considered a veritable treasure trove for scientists able to properly plumb its depths.  Emerging computational techniques and data management technologies are making this more possible, while also addressing complicated clinical research challenges, such as optimizing the design of clinical trials and quickly matching eligible patients most likely to benefit.  Scientists are also using new methods to find meaning in previously published studies and creating even larger, more accessible datasets. “While we are deep in the hype cycle of artificial intelligence [AI] right now, the more important topic is data,” says Sanju Sinha, PhD, an assistant professor in the Cancer Molecular Therapeutics Program at Sanford Burnham Prebys. “Integrating data together in a clear, structured format and making it accessible to everyone is crucial to new discoveries in basic and clinical biomedical research.” Read more: https://bit.ly/3SEO1t3 #artificialintelligence #computationalbiology #data #datascience #datasets

🌟 I am thrilled to share that our perspective article on Molecular Connectomics, co-authored with nadav yayon, Dr Kerstin Meyer, and Sarah Teichmann FMedSci FRS, has been accepted for publication in PLOS Biology. In this piece, we explore important questions: What experimental data and machine learning techniques are needed to capture human biology across scales in 3D? What are the connections between emergent properties in biology (like how morphology arises from molecular interactions) and emergent abilities in foundation models (such as zero-shot learning)? Our article outlines a vision for linking molecular and morphological cell features in three dimensions across scales. By leveraging machine learning and artificial intelligence, we aim to reveal the complex properties of biological systems. To capture these emergent phenomena, we need datasets that span different scales—just as Molecular Connectomics does, probing from the μm to the cm scale. These emergent biological phenomena could provide new zero-shot tasks for foundation models, enabling them to study molecular connectomics data. In turn, molecular connectomics can be used to benchmark the zero-shot capabilities of these models. I want to thank the Wellcome Sanger Institute, University of Cambridge, Cambridge Stem Cell Institute for all the support! https://lnkd.in/dnaZS9Gx #MolecularConnectomics #PLOSBiology #FoundationModels #AI #ML #Genomics #imaging #SpatialTranscriptomics #ZeroShotLearning

Boltz-1: Pioneering Open-Source Excellence in Biomolecular Modeling MIT’s Jameel Clinic unveils Boltz-1, a groundbreaking AI model democratizing access to biomolecular interaction research. With Boltz-1, researchers can now accelerate drug discovery and molecular engineering, breaking barriers in computational biology. Why Boltz-1 stands out: Impressive Results: ➡️ LDDT-PLI: 65% (vs. Chai-1's 40%) ➡️ DockQ Success Rate: 83% (exceeding Chai-1's 76%) ➡️ Improved accuracy in protein-ligand complex predictions Key Features: ➡️Fully open-source under the MIT license ➡️Matches AlphaFold3-level accuracy ➡️Advanced MSA pairing algorithms ➡️Enhanced confidence modeling With an 83% success rate in structure prediction, Boltz-1 is set to revolutionize drug discovery and protein design research, accelerating innovation like never before. Explore Boltz-1 and its transformative potential! #Biotechnology #OpenSource #DrugDiscovery #AIinBiology #Innovation

Why do we use imaging data to understand biology? In a new video, Imran Haque, SVP of AI and Digital Sciences, walks us through the treasure trove of data found in images, explaining how we use algorithms to structure pixels, how we are leading discovery in the field, and the massive advances that have been made in training foundation models to make new predictions of promising therapeutic targets. 🔹 Some highlights: ◾ Imaging and algorithms can provide as much or more information than RNA sequencing and cost far less money, so you can run more experiments – and you don’t have to kill the cells to do it.  ◾ With ML foundation models acting as a lever, we only need a small amount of data to perform as well as a much larger set. With areas as vast as chemistry and biology, you want to build as much as you can to break these scale barriers.  ◾ The models – called masked autoencoders – can reconstruct some 75-90% of the images of cells that have been masked. ◾ Once trained, we use these models to turn an unmasked image into a list of numbers and then match these to the database of images of all the cells we’ve taken in the past to see how similar or different the biology is that is created by different conditions (or perturbations) for those cells..  ◾ This technique – called similarity comparison – is central to our digital maps of biology. It’s how we discovered RBM39 provided a new means to inhibit CDK12, a promising therapeutic target for cancer. Full video here: https://lnkd.in/ebdHGfZG #ai #ml #tech #techbio #chemistry #biology #models

Recent advancements in artificial intelligence and extensive biological data have created a unique opportunity to develop the first AI-driven virtual human cell. This initiative aims to simulate the behavior of human biomolecules, cells, and potentially tissues and organs. The virtual cell could revolutionize biological research by enabling in silico experimentation, enhancing our understanding of cellular processes, and accelerating the development of personalized medicine. Achieving this ambitious goal will require a global, collaborative effort across various scientific disciplines and sectors, ensuring open access to resulting models for the scientific community. The potential impact on healthcare and research is significant.

It's easy to imagine how these developments will lead to medical and health infrastructure orders of magnitude better than humans have ever enjoyed. From assuming 220 types of cells to now realising there are over 5000 types of cells. Science is wondrous, provided we don't destroy ourselves through war or environment. "An adult human body consists of some 37trn cells. Not so long ago, these were thought to come in 220 different types. That number, the product of painstaking decades spent peering through microscopes at slides bearing tissue sections coloured by chemical stains, gave a sense of the division of cellular labour needed to keep a body running. A sense, but only a superficial one. Tools now exist that are capable of looking inside the cells, breaking them open one at a time to release their complements of messenger rna (mrna), the molecule which carries genetic information from a cell’s nucleus to its protein factories. Molecules of mrna indicate which genes are active, thus revealing a cell’s inner nature. Cells that look alike under a microscope often turn out to be quite diverse. The cell-type count has thus risen above 5,000. The leader of this histological revolution is the Human Cell Atlas (hca) consortium, which was set up in 2016 and currently involves more than 3,600 collaborators in 190 laboratories in 102 countries. Other cell-atlas projects are limited to mapping particular organs or types of tissue. The hca aspires to catalogue the whole caboodle: identifying and locating all the cell types, healthy and diseased, in every human tissue over the course of a lifetime. Its remit extends even to “organoids”, science’s fumbling first attempts to grow living simulacra of organs." (Subscription may be required) #histology #medicalbreakthroughs Scientists are building a catalogue of every type of cell in our bodies https://lnkd.in/g8ft_aGw From The Economist

I am excited to share that I will be serving as a guest co-editor (with Jeffrey West and Ibrahim Chamseddine, PhD) for the upcoming special issue on "Mechanistic Learning in Biomedical Sciences" in the Mathematical Biosciences journal. https://lnkd.in/e-mgFcen 🔍 Focus of the Special Issue: The goal of this special issue is to highlight significant theoretical and practical advancements in Mechanistic Learning. This rapidly evolving field combines the predictive power of machine learning with the interpretability of mechanistic models, offering innovative solutions in biomedical sciences. We are actively seeking contributions in areas such as: - Machine learning predictions for mechanistic model inputs - Data-driven approaches for parameter estimation and uncertainty quantification - Integration of neural networks to reveal hidden dynamics within mechanistic models - Applications in cancer research, drug development, immunology, and more 📅 Submission Deadline: December 31, 2025 🔗 For manuscript submissions and more details, please visit the Mathematical Biosciences journal page on ScienceDirect. Let's advance the frontiers of biomedical research together! #BiomedicalSciences #MachineLearning #MechanisticLearning #MathematicalBiosciences #ResearchImpact #CallForPapers