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🫁 A new type of “breathing” lung #organoid enables quantitative measurement of lung compliance - a mechanical indicator of how easily the #lung expands - and may provide a new way to study diseases such as pulmonary #fibrosis. http://dlvr.it/TRt8Ly

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What animal tests missed, human immune organoids captured.

Parallel Bio’s human lymph node #organoid platform reproduced the same dangerous immune response experienced by TGN 1412 trial participants.

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Organoid models for infectious diseases Editors welcome the submission of primary research Articles focusing on recent advances of stem cell-based organoid models for studying infectious diseases, ...

#CallForPapers! 📣 @commsbio.nature.com and @natcomms.nature.com are seeking primary research articles from authors who use stem cell-based #organoid models for studying #InfectiousDisease.

Submit by December 21!

www.nature.com/collections/...

#assembloid #stemcell #IDsky #Virosky #Microsky

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Too fast, too short: mini-hearts, genome editing, and the new biology of sudden cardiac death This editorial refers to ‘SLC4A3-related short QT syndrome assessed in human induced pluripotent stem cell-derived cardiomyocytes: mechanisms of ventricula

Thanks to the #EuropeanHeartJournal for inviting me to write an #editorial on the important topic and article on #SuddenCardiacDeath 🫀

academic.oup.com/eurheartj/ad...

@escardio.bsky.social #arrhythmia #fibrillation #organoid #crispr #stemcell #SQTS

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Photo of Hans Clevers

Photo of Hans Clevers

Our new Special Issue has launched with a fascinating interview with @hansclevers.bsky.social where he discusses the future of #organoid and #StemCell research

"My personal opinion is that the strength of the organoids is their simplicity."

doi.org/10.1242/dmm....
@hubrechtinstitute.bsky.social

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Awakari App

Lifelike 3D-Printed ‘Training Brains’ React Like Real Organs With ANY BRAINS AT ALL in our stroke medical 'professionals' we could do research that tells us exactly what interventions...

#3d #brains #any #brains #at #all #brain #organoid #cascade #of #death

Origin | Interest | Match

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Recapitulating apicobasal tissue polarity in extracellular matrix-incorporated airway organoids The airway epithelium is a dynamic barrier that interfaces with the external environment and internal matrix along its apicobasal axis. To recapitulat…

Gong et al. of @cmu.edu, @biologyuvm.bsky.social, & @uvmlarnermed.bsky.social developed a human airway #organoid that enables the study of epithelial-ECM crosstalk during airway homeostasis, pathogenesis, and injury responses.

www.sciencedirect.com/science/arti...

#mechanobiology 🫁 🧪

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If we want to understand and treat human diseases, we need to have better and human-specific models. Many excellent talks and posters today at #BonnOrganiodSymposium2026 highlight new directions in #organoid, ex vivo tissue models and #organ-on-chip development and application:

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Frontiers | Innovations in Regenerative Medicine: Harnessing the Power of Cells Regenerative medicine has emerged as one of the most dynamic fields of modern biomedical research, and stem cells continue to stand at the center of this pro...

#CallForPapers on #stemcells & #regenerativemedicine at @frontiersin.bsky.social

Submit by June 20!

Accepted topics include #organoid & #organonchip disease modeling, #biofabrication & #3Dbioprinting to guide tissue formation, translational challenges, & more.

www.frontiersin.org/research-top...

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#BaCell3D #3DCellBiology #Organoids #InVitroModels #StemCells #3R #Bioengineering #NAMs #Networking #Collaboration #DiseaseModeling #NonAnimalModels #InVitro #Organoid #OoC #OrganOnChip #NewApproachMethodologies #MPS #MicrophysiologicalSystems #Assembloids #Tumoroids #LifeSciences#BiomedicalResearch

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#BaCell3D #3DCellBiology #Organoids #InVitroModels #StemCells #3R #Bioengineering #NAMs #Networking #Collaboration #DiseaseModeling #NonAnimalModels #InVitro #Organoid #OoC #OrganOnChip #NewApproachMethodologies #MPS#MicrophysiologicalSystems #Assembloids #Tumoroids #LifeSciences #BiomedicalResearch

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Super excited to announce The Matrix: Adipose Remodelled 🧬Join us in Nottingham 📍| 7–8 December 2026 🗓️
Register your interest👇
www.biochemistry.org/events-and-t...

#NTU #SHiMR_NTU #NTUResearch #NTUMetabolic #Adipose #AdiposeTissue #conference #organoid #metabolism #OMICS #ExtracellularMatrix

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Human assembloids recapitulate periportal liver tissue in vitro - Nature Hepatocyte organoids derived directly from human tissue enable long-term hepatocyte expansion and can be combined with portal mesenchyme and cholangiocyte organoids to form a donor-specific periportal...

Much of combatting future human disease will require a keen focus on the liver. Work like this to develop better #organoid models will be key- e.g. what could be recapitulated/uncovered with the addition of cancer cells to these sorts of models?

www.nature.com/articles/s41...

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@afruk.bsky.social podcast

Released figures showing over 6.5 million animals were approved for use in UK labs
At the same time advances in #organoid and #organ-on-chip technologies highlight the accelerating shift towards human-relevant science

www.animalfreeresearchuk.org/our-podcast/...

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This week: A team at the @molgen.mpg.de used high-content screening to identify compounds that affect pancreatic #organoid differentiation or morphology.

📍: https://bit.ly/4qnMW6B
🎧: https://bit.ly/4thHuVG

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Timely poster board of #Atelerix at #Word2026, a true eyecatcher! Enjoying the #organoid community conference in Hinxton #OrganovirLabs

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The artist wearing a black top and purple cardigan. There is an Organoid and an Organ-On-A-Chip sticker on her top. White lanyard around her neck reads Hub Organoids.

The artist wearing a black top and purple cardigan. There is an Organoid and an Organ-On-A-Chip sticker on her top. White lanyard around her neck reads Hub Organoids.

Second day of handing out my #Organoid and #OrganOnAChip stickers at WORD+2025 World Organoid Research Day in Cambridge!

Part of me misses being in the lab but the other part thinks what I do now is way cooler 😎

#sciart #scicomm

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Intestinal #TregCells promote epithelial repair by combining #IFNγ and #IL10 signaling to drive #organoid growth, preserve #IntestinalStemCells, and enable #gut regeneration.
@tum.de @univparissaclay.bsky.social

#OpenAccess in #STTT: doi.org/10.1038/s413...

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How can 3D bioprinting achieve physiological cell density in engineered tissues? This article explores cutting-edge strategies that enable cells to communicate closely, offering new pathways for organoid and functional tissue fabrication.
doi.org/10.59717/j.x...
#lifesciences #Bioprinting #organoid

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Lab-grown mini-stomachs to boost understanding of rare diseases Researchers at University College London (UCL) and Great Ormond Street Hospital (GOSH) have developed the first-ever lab-grown mini-stomach that contains the key components of the full-sized human org...

A new type of 🧫 lab-grown #organoid that mimics the behaviour of a human #stomach could boost the understanding of gastric #RareDiseases, researchers from 🇬🇧 @ucl.ac.uk and @greatormondst.bsky.social say.

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Global morphological heterogeneity of retinal organoids across development. Top row: Image segmentation and analysis pipeline. A convolutional neural network (CNN) with the DeepLabV3 architecture was trained on 841 images and their manually annotated masks (left). The CNN was subsequently used to segment all images of the dataset that were finally subjected to a pipeline extracting a total of 165 morphological parameters (morphometrics, right), including, among others, shape descriptors and image moments.  Intra-experimental global morphological organoid heterogeneity. Time-series images from one representative experiment were analyzed using the image analysis pipeline and subjected to t-SNE dimensionality reduction on the first 20 principal components. Data points were colored by individual organoids (left graph) and time frames of organoid development within the imaging window (right graph). While organoids clustered closely at earlier time points (up to 24 h), they strongly diverged at later time points, suggesting increasing inter-individual changes of their morphological characteristics over time.

Global morphological heterogeneity of retinal organoids across development. Top row: Image segmentation and analysis pipeline. A convolutional neural network (CNN) with the DeepLabV3 architecture was trained on 841 images and their manually annotated masks (left). The CNN was subsequently used to segment all images of the dataset that were finally subjected to a pipeline extracting a total of 165 morphological parameters (morphometrics, right), including, among others, shape descriptors and image moments. Intra-experimental global morphological organoid heterogeneity. Time-series images from one representative experiment were analyzed using the image analysis pipeline and subjected to t-SNE dimensionality reduction on the first 20 principal components. Data points were colored by individual organoids (left graph) and time frames of organoid development within the imaging window (right graph). While organoids clustered closely at earlier time points (up to 24 h), they strongly diverged at later time points, suggesting increasing inter-individual changes of their morphological characteristics over time.

Organoids are key models for studying development & disease, but heterogeneity is a problem. @wittbrodtlab.bsky.social use #DeepLearning to predict differentiation paths & resulting tissues in #retinal organoids, with implications for other #organoid systems @plosborn.bsky.social 🧪 plos.io/45VTMt1

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Global morphological heterogeneity of retinal organoids across development. Top row: Image segmentation and analysis pipeline. A convolutional neural network (CNN) with the DeepLabV3 architecture was trained on 841 images and their manually annotated masks (left). The CNN was subsequently used to segment all images of the dataset that were finally subjected to a pipeline extracting a total of 165 morphological parameters (morphometrics, right), including, among others, shape descriptors and image moments.  Intra-experimental global morphological organoid heterogeneity. Time-series images from one representative experiment were analyzed using the image analysis pipeline and subjected to t-SNE dimensionality reduction on the first 20 principal components. Data points were colored by individual organoids (left graph) and time frames of organoid development within the imaging window (right graph). While organoids clustered closely at earlier time points (up to 24 h), they strongly diverged at later time points, suggesting increasing inter-individual changes of their morphological characteristics over time.

Global morphological heterogeneity of retinal organoids across development. Top row: Image segmentation and analysis pipeline. A convolutional neural network (CNN) with the DeepLabV3 architecture was trained on 841 images and their manually annotated masks (left). The CNN was subsequently used to segment all images of the dataset that were finally subjected to a pipeline extracting a total of 165 morphological parameters (morphometrics, right), including, among others, shape descriptors and image moments. Intra-experimental global morphological organoid heterogeneity. Time-series images from one representative experiment were analyzed using the image analysis pipeline and subjected to t-SNE dimensionality reduction on the first 20 principal components. Data points were colored by individual organoids (left graph) and time frames of organoid development within the imaging window (right graph). While organoids clustered closely at earlier time points (up to 24 h), they strongly diverged at later time points, suggesting increasing inter-individual changes of their morphological characteristics over time.

Organoids are key models for studying development & disease, but heterogeneity is a problem. @wittbrodtlab.bsky.social use #DeepLearning to predict differentiation paths & resulting tissues in #retinal organoids, with implications for other #organoid systems @plosborn.bsky.social 🧪 plos.io/45VTMt1

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Global morphological heterogeneity of retinal organoids across development. Top row: Image segmentation and analysis pipeline. A convolutional neural network (CNN) with the DeepLabV3 architecture was trained on 841 images and their manually annotated masks (left). The CNN was subsequently used to segment all images of the dataset that were finally subjected to a pipeline extracting a total of 165 morphological parameters (morphometrics, right), including, among others, shape descriptors and image moments.  Intra-experimental global morphological organoid heterogeneity. Time-series images from one representative experiment were analyzed using the image analysis pipeline and subjected to t-SNE dimensionality reduction on the first 20 principal components. Data points were colored by individual organoids (left graph) and time frames of organoid development within the imaging window (right graph). While organoids clustered closely at earlier time points (up to 24 h), they strongly diverged at later time points, suggesting increasing inter-individual changes of their morphological characteristics over time.

Global morphological heterogeneity of retinal organoids across development. Top row: Image segmentation and analysis pipeline. A convolutional neural network (CNN) with the DeepLabV3 architecture was trained on 841 images and their manually annotated masks (left). The CNN was subsequently used to segment all images of the dataset that were finally subjected to a pipeline extracting a total of 165 morphological parameters (morphometrics, right), including, among others, shape descriptors and image moments. Intra-experimental global morphological organoid heterogeneity. Time-series images from one representative experiment were analyzed using the image analysis pipeline and subjected to t-SNE dimensionality reduction on the first 20 principal components. Data points were colored by individual organoids (left graph) and time frames of organoid development within the imaging window (right graph). While organoids clustered closely at earlier time points (up to 24 h), they strongly diverged at later time points, suggesting increasing inter-individual changes of their morphological characteristics over time.

Organoids are key models for studying development & disease, but heterogeneity is a problem. @wittbrodtlab.bsky.social use #DeepLearning to predict differentiation paths & resulting tissues in #retinal organoids, with implications for other #organoid systems @plosborn.bsky.social 🧪 plos.io/45VTMt1

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Watch to learn our simple steps for reliable, automated #organoid counts.

Learn more:
go.denovix.com/organoids

#LifeScience #CancerResearch

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Novoron Bioscience Awarded $2.5 Million NIH/NIA SBIR Grant to Accelerate Alzheimer's Drug Discovery with Human Brain Organoids We are excited to announce that we have received a $2.5 million Small Business Innovation Research (SBIR) grant from the National Institute on Aging at the National Institutes of Health. The award wil...

Congrats to Novoron Bio on its $2.5M NIA SBIR grant! 🎉

Together with Brainstorm Cell & Defined Bio, Novoron will develop & commercialize its human brain #organoid platform to identify new drugs that can slow or stop the progression of #AlzheimersDisease.

novoron.com/news-events/...

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New #organoid research: Distinct SOX9 single-molecule dynamics characterize adult differentiation and fetal-like reprogrammed states in intestinal organoids https://ow.ly/b0H650Y1lRz

Cell Press | The Gairdner Foundation | SickKids Foundation | Universität Bayreuth

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Frontiers | Beyond the amyloid hypothesis: leveraging human-centered complex in vitro models to decode Alzheimer’s disease etiology Alzheimer’s disease (AD) is a complex neurodegenerative condition and the leading cause of dementia worldwide. Treatments that safely and effectively counter...

Read the full paper in Frontiers in Toxicology:
www.frontiersin.org/journals/tox...

#organonchip #organoid #invitro #NAMs 🧪

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New #organoid research! Differential synaptic signaling responses in human cortical organoids after photon & proton irradiation. Findings underscore distinct biological effects of spread-out Bragg peak protons & their potential impact on the developing brain. https://ow.ly/hGFW50XVBE5

ISSCR

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Image from the November 6, 2025, New York Times article titled: “What We Can Learn From Brain Organoids” by Carl Zimmer. The photo caption reads: “Organoids under a microscope in Dr. Arlotta’s lab. She and her colleagues are constructing organoids with light-sensitive neurons. This stimulus might let the organoids mature further. David Degner for The New York Times.”

Image from the November 6, 2025, New York Times article titled: “What We Can Learn From Brain Organoids” by Carl Zimmer. The photo caption reads: “Organoids under a microscope in Dr. Arlotta’s lab. She and her colleagues are constructing organoids with light-sensitive neurons. This stimulus might let the organoids mature further. David Degner for The New York Times.”

For @nytimes.com, @carlzimmer.com spoke with brain #organoid researchers about how rapidly the field has grown in recent years & their studies on genetic mutations, how compounds like sugar influence brain development, & traits of normal & disordered neurodevelopment.

#neuroskyence 🧪

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🎧 HC Senior Researcher Insoo Hyun joins @npr.org podcast to discuss the ethics of brain organoid research—lab-grown models that help study conditions like #autism and #schizophrenia.

🔗 Listen now: https://bit.ly/456xZOS

#bioethics #organoid #brainresearch

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