Advanced Organoid Studies

Nature (5 Aug 26) published three coordinated papers that together generate and validate the largest clinically annotated collection of patient-derived 3D cancer models assembled to date, and demonstrate that these models reveal therapeutic gene dependencies undetectable in traditional 2D cell lines. 🔅 The Human Cancer Models Initiative (HCMI) generated 665 models from 637 patients across 25 cancer types, from a consent cohort of 2,780 donors, including 153 rare cancer models and 71 models from non-European donors. Across 421 matched tumour-model pairs, DNA concordance was 97.8% and epigenetic concordance 95%. Single-nucleus RNA sequencing identified three mechanisms of model-tumour divergence: stromal purification, clonal selection, and culture-medium-driven epigenomic plasticity, the last reversible by switching growth conditions. Extrachromosomal DNA was model-specific in 43.9% of cases, yet MYCN, KRAS and EGFR amplifications were preserved in a subset. GBM models from patients with prolonged temozolomide exposure retained the SBS11 treatment resistance mutational signature. All models are available via ATCC, the NCI GDC portal and the HCMI Explorer Suite. 🔅 The Wellcome Sanger Institute derived 256 clinically annotated organoids from five cancer types and completed genome-wide CRISPR-Cas9 screens across 162 organoids (AUROC = 0.97), identifying 97 core fitness genes unique to 3D organoid cultures, enriched in isoprenoid and steroid biosynthesis. In colorectal organoids, KRAS dependency varied by allele: G12X variants retained co-dependency on upstream EGFR signalling while Q61H organoids were fully unresponsive to EGFR inhibition, consistent with signalling-independent oncogenic activation. In paired pre- and post-treatment oesophageal organoids, chemotherapy-driven clonal evolution reduced KRAS and DNMT1 dependency and increased PSMB5 dependency, confirmed pharmacologically with proteasome inhibitors bortezomib and ixazomib. 🔅 The Broad Institute added 314 NextGen models across 10 cancer types to DepMap via 147 CRISPR screens. NextGen models matched annotated cancer lineage in 69% of cases versus 35% for traditional cell lines; CNS spheroids matched at 93% versus 11%. A PDAC-classical/mucinous transcriptional program, preserved in GI organoids but silenced in 2D lines, correlated with WNT pathway dependency (WLS, MESD, FZD5, LGR4 and TCF7L2). In glial GBM spheroids, CDKN2A loss predicted CDK6 dependency and CDK4/6 inhibitor sensitivity. Direct comparison of 3D versus 2D conditions showed that serum-containing media masked SCD dependency in KRAS-amplified oesophagus-stomach organoids, demonstrating that growth medium independently shapes gene essentiality. 📑 HCMI compendium: https://lnkd.in/gjgFTp-3 📑 Sanger organoid biobank: https://lnkd.in/gFXhqeii 📑 Broad DepMap NextGen: https://lnkd.in/ggbssEH4 #CancerResearch #PrecisionOncology #CRISPR

Following our recent breakthrough in developing mouse mini-intestines for ex vivo tumor development (https://lnkd.in/eAc6YzAr) and building on our ability to generate in vitro models of healthy human colon (https://lnkd.in/ep7Xni-3), we asked ourselves: can this technology be applied to cells from colorectal cancer patients? We're thrilled to announce that our latest publication provides the answer: https://rdcu.be/dMuAr We've created long-lived human 'mini-colons' that stably integrate patient cancer cells and their native tumor microenvironment. This innovative format is optimized for real-time, high-resolution evaluation of cellular dynamics, offering exciting experimental possibilities. Our research highlights include: 1) Multi-faceted evaluation of drug efficacy, toxicity, and resistance in anti-cancer therapies. 2) Discovery of a cancer-associated fibroblast (CAF)-triggered mechanism driving colorectal cancer invasion. 3) Identification of immunomodulatory interactions among different components of the tumor microenvironment. This work has been led by Luis Francisco Lorenzo Martín, with invaluable support from Nicolas Broguiere, Jakob Langer, Lucie Tillard, Mike Nikolaev, George Coukos, and Krisztian Homicsko. Thank you all!! #Organoid #Tumoroid #Bioengineering #CancerResearch #TeamScience

Brains in a dish are telling us something no supplement influencer will: Alzheimer’s doesn’t start when you forget your keys. It starts decades earlier. Long before the symptoms. And the clues are buried in the biology. At UTSA, researchers are now growing Alzheimer’s in the lab. Literally. Using brain organoids (lab-grown neural cultures), they’re tracking the molecular fingerprints of the disease years before it shows up on a scan. And with gene editing (CRISPR), they’re identifying subtle signs: - fewer neurons - degraded connections - patterns that predict who’s at risk. This isn’t theory. It’s happening in petri dishes right now. It confirms what I wrote in last Sunday’s newsletter: https://lnkd.in/g5PwQHxh Your intelligence won’t save you from dementia. But rhythm might. Because the root issue is neurodegeneration. And that’s a structural problem, not a motivational one. The real edge in longevity science is shifting: From optimizing energy… to preserving tissue. From hacks and stacks… to inputs that prevent decay. From late-stage symptom hunting… to early cellular diagnostics. But here’s the harsh truth: You can’t wait for diagnostics to save you. If you’re 35+, your brain health decisions are compounding right now. Sleep debt? Inflammatory diet? Chronic stress? They don’t just make you tired. They erode your hippocampus. So while the future looks promising, here’s what the smart people are already doing: - Treating recovery as mandatory - Targeting inflammation like a performance metric - Choosing rhythm over stimulation Not sexy. But neither is forgetting your children’s names. #UpwardARC

Scientists have developed a powerful new system to study how autism spectrum disorder (ASD) emerges during early brain development—combining lab-grown brain organoids with high-throughput CRISPR screening to investigate genetic risk at the cellular level. Key Findings: 📍CHOOSE: A scalable gene-screening platform in brain organoids: The researchers introduced CHOOSE, a method that allows the testing of dozens of ASD-linked genes simultaneously in brain organoids. This approach enables the observation of how individual genes influence the development of specific brain cell types, thereby bringing experimental models closer to real human neurodevelopment. 📍Cell-type vulnerabilities and early neurodevelopmental disruption: The study identified certain early brain cells—particularly those involved in neurogenesis and neural circuit formation—as especially sensitive to autism-associated gene disruptions. One gene in focus, ARID1B, which regulates chromatin structure, was shown to impair the differentiation of neural progenitors into oligodendrocyte precursors, even in organoids derived from ASD patient cells. 📍Implications for early diagnosis and intervention: These findings suggest that the origins of ASD may lie in very early stages of brain formation, well before behavioral symptoms appear. By linking specific genes to developmental processes, this work helps build a clearer map of autism’s biological roots. By Sun et al., Nature, 2023 🔗 https://lnkd.in/gRwaUdkG Implication: This study represents a major advancement in modeling neurodevelopmental disorders, offering a scalable framework to explore how genetic risk translates into cellular dysfunction in ASD. #Neuroscience #BrainResearch #AutismAwareness #CRISPR #BrainOrganoids #Science #ASD

AI can now create virtual tumor models in minutes For decades, turning published papers into realistic computational models has been nearly impossible—particularly for complex biological processes such as tumor evolution. In my previous post exploring what AGI for biomedical research might look like, I highlighted the critical role of modeling complex systems (https://lnkd.in/gkSJKAcE). I had wondered if recent AI advances could dramatically change our ability to build these sophisticated models. Realistic computational models are essential, as they enable rapid hypothesis testing and deeper exploration of complex biological mechanisms. In two recent studies from our group and colleagues (paper 1: https://lnkd.in/gUj4_d57, paper 2: https://lnkd.in/ekh_HkH4), we characterized early lung cancer evolution at single-cell resolution, uncovering immune cell dynamics and tumor microenvironment interactions. Curious if these findings could rapidly become detailed virtual models, I gave Sonnet 3.7 a simple request: "Based on the content of the paper, create a comprehensive hybrid, multi-scale agent-based model in Python (using Mesa or similar) to recapitulate our results." Remarkably, Sonnet 3.7 immediately generated ~600 lines of robust Python code, requiring only modest refinement with Sonnet-assisted Cursor AI. The resulting hybrid agent-based model, built using Mesa (a Python framework for modeling complex adaptive systems), includes tumor cells, immune cells (cytotoxic T cells, regulatory T cells, polarized macrophages), endothelial cells, and environmental signaling molecules (VEGFA, TREM2, CXCL13). Agents follow biologically informed rules directly derived from experimental observations. Remarkably and despite many parameter assumptions, the virtual tumor faithfully reproduced key experimental observations: 🔸 Stepwise progression from preinvasive to invasive adenocarcinoma 🔸 Immune shifts: fewer cytotoxic cells, more suppressive populations 🔸 Realistic spatial signaling patterns (angiogenesis, immune polarization) As statistician George Box famously said, "All models are wrong, but some are useful." While no model is perfect, this AI-enabled approach rapidly bridges scientific papers to highly useful virtual experiments. The ability to create virtual tumor models in minutes could profoundly accelerate discovery—enabling entirely new ways of exploring and answering some of cancer’s most complex and pressing questions.

Excited to share our latest publication in Biomaterials! We developed a 3D, vascularized liver tumor model that more closely replicates the complex tumor microenvironment—helping researchers better understand how chemotherapy and immunotherapies (like CAR-T cells) perform in solid tumors. By integrating hypoxia, extracellular matrix, and perfusable vessels in one system, we can more accurately predict therapeutic responses and move closer to personalized treatments. Take a look at how this microphysiological model bridges the gap between standard lab tests and patient outcomes, and why it could serve as a powerful tool to accelerate drug discovery while reducing animal testing. Read the full article here: https://lnkd.in/gQvicmEh Huge thanks to my incredible co-authors and collaborators who made this research possible! Jyothsna Vasudevan, Ph.D., Ragavi Vijayakumar, Jose Antonio Reales Calderon, Maxine Lam, Jin Rong Ow, Joey Aw, Zhi Ming Damien Tan, Anthony Tanoto TAN, Antonio Bertoletti, Giulia Adriani #cancerresearch, #drugdiscovery, #organonchip #ImmunoOncology, #Microfluidics, #Bioengineering, #3DCellCulture #NTULKC

I spent my final year at Nottingham Trent University researching neuroblastoma. It's the most common and deadliest solid tumour in infants. And here's what most people don't know: The way we study cancer is fundamentally changing. → Traditional 2D cell cultures don't replicate reality For decades, cancer research used flat petri dishes. Cells growing in a single layer. But tumours don't grow that way in the body. They're 3D structures. With complex architecture. With different cell layers receiving different oxygen and nutrients. 2D cultures miss ALL of that. → 3D bioprinting is revolutionising cancer research My dissertation evaluated a novel bioink for 3D-printed neuroblastoma models. The goal: Create tumour models that actually mimic what happens in a patient's body. Why does this matter? Because drugs that work in 2D often fail in 3D. The tumour microenvironment changes everything. Better preclinical models → better drug testing → faster treatment development. → The results were fascinating Cell viability in 3D-embedded environments was significantly higher than in 2D cultures. The cells developed more biomimetic morphology. They behaved more like actual tumour cells. This isn't just academic. This is the future of personalised cancer medicine. Here's the bigger picture: Imagine bioprinting patient-derived tumour models. Testing multiple drugs on THAT specific patient's cancer cells. Before ever treating the actual patient. That's precision medicine. And Clinical Scientists are making it happen. Why I'm sharing this: Because healthcare innovation isn't just about treating patients today. It's about building the tools that will treat patients tomorrow. That's what drew me to Clinical Engineering. The intersection of cutting-edge research and real clinical application. For anyone interested in biomedical engineering or cancer research: This field is moving FAST. 3D bioprinting. Organoids. Tumour-on-a-chip models. The next decade will transform how we study and treat cancer. And there's room for passionate people who want to contribute. Are you working on anything in this space? P.S. I promiiiise I only undid my hairtie for the picture. PPE always 🫡👩🔬 #CancerResearch #3DBioprinting #Neuroblastoma #BiomedicalEngineering #MedicalInnovation #TissueEngineering #PrecisionMedicine #HealthcareResearch

I woke up to this news that: Scientists Just Solved Organoids' Biggest Problem! I’m happy to share highlights from a new Science paper by Dr. Oscar Abilez, Dr. Huaxiao 'Adam' Yang, Dr. Joseph C. Wu, and colleagues, a leap forward for organoid technology and regenerative medicine! What Did They Do? Stanford researchers have created the first heart and liver organoids with integrated, functional blood vessels. This solves a critical bottleneck: until now, organoids could only grow a few millimeters before their centers died from lack of oxygen and nutrients. With built-in vasculature, these mini-organs can grow larger, mature further, and better mimic real human tissues. How Did They Do It? *The team meticulously optimized a “recipe” of growth factors and signaling molecules, guiding pluripotent stem cells to differentiate into not just heart or liver cells, but also endothelial and smooth muscle cells that self-organize into branching blood vessels. *Their protocol mirrors early embryonic development, allowing the organoids to achieve a cellular complexity similar to a 6.5-week-old human embryonic heart, including beating function! Why Is This Important? *Better Disease Models: Vascularized organoids allow researchers to study early human development and test how drugs impact organ growth and blood vessel formation. *Personalized Medicine: These models can be tailored from patient-derived stem cells, paving the way for individualized drug testing and disease modeling. *Regenerative Therapies: In the future, vascularized cardiac organoids could be implanted to repair damaged heart tissue, offering a more complete cellular environment than current cell therapies Clinical Context As Dr Joseph C. Wu notes, ongoing clinical studies are already injecting lab-grown cardiomyocytes into patients with heart dysfunction. But real heart tissue is much more complex, containing blood vessels, pericytes, fibroblasts, and more. Vascularized organoids could one day provide all these cell types in a single, implantable tissue patch, dramatically improving integration and function. What’s Next? The team aims to: *Grow organoids longer to assess their maturation and size limits *Further refine the recipes to include immune and blood cells *Adapt this vascularization approach to other organs, moving closer to true “mini-organs” for research and therapy A huge CONGRATULATIONS to the entire Stanford team! References: https://lnkd.in/gmYc-cX9 https://lnkd.in/gbntyWgN https://lnkd.in/g-YT5wdU

Excited to share our new paper: 'Phenoscaping Reveals Multimodal γδ T-cell Cytotoxicity as a Strategy to Overcome Cancer Cell–Mediated Immunomodulation' 👉 https://lnkd.in/e4jB_xde This work, led by Callum Nattress in collaboration with Jonathan Fisher’s lab, explores how γδ T cells kill cancer cells — and how this killing varies across both γδ T cell donors and cancer patients. 🔬 The challenge γδ T cells can eliminate cancer cells through two modes: — Antibody-independent cytotoxicity (AIC) — Antibody-dependent cellular cytotoxicity (ADCC) But how consistent is this killing across different γδ T-cell donors and different tumours? 🧠 The approach We performed a systematic single-cell phenoscaping study across >1,000 3D organoid cultures to map how γδ T-cell killing modalities (AIC vs ADCC) function across inter-donor heterogeneity (IDH) and inter-tumour heterogeneity (ITH). ⚙️ Key findings 1️⃣ Sustained survival in 3D: Engineering γδ T cells to express stabilised IL-15Rα–IL-15 (stIL15) enabled them to survive without serum or cytokine support — more closely mimicking nutrient-deprived 3D tumour conditions. 2️⃣ Multimodal killing: stIL15-engineered γδ T cells rapidly killed microsatellite unstable (MSI) colorectal cancer (CRC) organoids via AIC. Killing was further enhanced when we induced ADCC using a novel anti–B7-H3 antibody, revealing multimodal cytotoxicity. 3️⃣ Overcoming tumour immunomodulation: When γδ T cells relied only on AIC, their signalling was shaped by the target tumour (ITH dominated over IDH). However, when both AIC + ADCC were engaged, γδ T-cell signalling was restored — allowing effective killing across all patient-derived organoids. 4️⃣ Mechanistic validation: Only a full IgG (not an Fc-null IgG) triggered this multimodal killing, confirming that the effect was ADCC-driven rather than due to B7-H3 checkpoint signalling. 5️⃣ Targeting chemoresistant cells: Colorectal cancer organoids contain both chemosensitive proliferative colonic stem cells (proCSCs) and chemoresistant revival colonic stem cells (revCSCs). Encouragingly, stIL15-engineered γδ T cells effectively killed both — including the chemoresistant revCSCs. 💡 The takeaway Multimodal γδ T-cell cytotoxicity can buffer immune signalling from patient-specific tumour modulation — enabling T cells from diverse donors to kill chemorefractory cancer cells. 🙌 A huge team effort This project was an experimental tour de force from Callum Nattress, with vital contributions from Rhianna O'Sullivan, Daniel Fowler, Colin Hutton, Petra Vlckova, Vivian Li, Kerry Chester, john Anderson, Marta Barisa , and Jonathan Fisher. Huge thanks to Cancer Research UK City of London Centre for funding.