Excited to share our paper about copick, a dataset API and toolkit for collaborative annotation and analysis of #cryoET data! Whether you're picking particles or curating segmentations, copick reduces friction and brings #OME-Zarr to cryoET without breaking pipelines.
🧵👇
doi.org/10.1002/pro.70578
Long in the making, but happy to present the Chlamydomonas chlororibosome!
Cryo-ET🔬reveals a large new domain on the small subunit, built from multiple extensions in conserved ribosomal proteins.
bioRxiv 📖: shorturl.at/q44tG
This suggests greater chlororibosome diversity than expected!
1/n 🧵
How does molecular valency shape condensate assembly and function? We used the CO2-fixing organelle in algae—the pyrenoid—to find out… 🧵
Preprint here!:
www.biorxiv.org/content/10.6...
@cellarchlab.com @phaips.vd.st @biologyatyork.bsky.social
#Rubisco #PhaseSeparation #Condensates #Pyrenoid
This one was quite the journey! The paper describing the #ChlamyDataset is finally out and on the cover of Mol Cell!
This beautiful rendering made by co-author @jessheebner.bsky.social and Holly Peterson shows an instance of mitochondrial fission found in the dataset 😍
[Maybe long thread ahead]
The #ChlamyDataset is on the cover of @cp-molcell.bsky.social 🖼️🥰!
Read @lifeonthewedge.bsky.social's great thread🧵 for the inside scoop🍨 on all the #TeamTomo developments already made possible since these 1829 tomos hit EMPIAR 🧪 🧶🧬
For more, here's the old preprint thread:
bsky.app/profile/cell...
Online Now: Toward community-driven visual proteomics with large-scale cryo-electron tomography of Chlamydomonas reinhardtii Online now:
Attention maps and PCA visualisations comparing mode
In case you're missing my poster at #NeurIPS2025 about how I fine-tuned DINOv2 to ophthalmological images, here are some animations so you don't miss out!
🔗 Preprint: doi.org/10.48550/arX...
🔗 Code: github.com/peng-lab/rmlp
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Are you also often frustrated when DINOv2 puts very high attention on background patches, rather than cute fox heads?
@virtualhomo.bsky.social found an elegant way to regularize DINOv2 training using randomised linear algebra 🤯
Check out his thread or even his poster if you're at #NeurIPS2025.
I wrote a paper with @lorenzlamm.bsky.social, @marionjasnin.bsky.social, @tingyingpeng.bsky.social, F. Eckardt and B. Schworm and got accepted at #NeurIPS2025 and fun fact: 90% of viewers are enby vegans! 🤟
So if u fit there, u might wanna check it out! Maybe also if you don't. We're allies here <3
I'm pleased to announce 🍦 Icecream 🍨 v0.3!
New features include:
* Training on multiple tomograms (same training time, with linear increase in RAM) 🚀
* Logging and plotting of the loss function 📉
* The --scale option is now called --eq-weight for clarity 😉
We'd love to hear your feedback! 🙏🏽
You might have noticed lots of activity in the napari project recently! 🚀 We're grateful for a grant from CZI that's keeping us going, but grants don't last forever: we're trying to figure out sustainable long term funding. Read our blog post to find out how you can help:
napari.org/island-dispa...
✨Excited that the main project of my PhD is now available as a pre-print on #bioRxiv
Here, we used #CryoET to visualise mitochondrial proteostatic stress and together with SPA #CryoEM shed light into the functional cycle of the Hsp60:10 chaperone system. #TeamTomo
🔗 www.biorxiv.org/content/10.1...
Time for a thread!🧵 How different is the molecular organization of thylakoids in “higher” plants🌱? To find out, we teamed up with @profmattjohnson.bsky.social to dive into spinach chloroplasts with #CryoET ❄️🔬. Curious? ..Read on!
#TeamTomo #PlantScience 🧪 🧶🧬 🌾
elifesciences.org/articles/105...
1/🧵
🌱 Using ‘compelling’ methods, including #CryoET, researchers mapped spinach thylakoid membranes at single-molecule precision, revealing how photosynthetic complexes are organised and settling long-standing debates on chloroplast architecture.
buff.ly/j3TSIkn
Better ML for cryo-ET starts with better benchmarks.
We built a phantom cryo-ET dataset (~500 tomograms) + hosted a Kaggle challenge.
The result: community models beat expert tools.
Read more in the @natmethods.nature.com article that just came out:
🔗 doi.org/10.1038/s415...
Proud to share our latest paper. doi.org/10.1016/j.cr...
Through the dedication of @glynnca.bsky.social and @cryingem.bsky.social we report a thorough method to image molecular organisation within hippocampus tissue.
Structural biology in tissue is well and truly here!
@rosfrankinst.bsky.social
Ooh that's awesome! Great to hear the new version improved your segmentations :)
Final PhD paper now reviewed and published in JSB:X. I was happy with some great reviews that improved the paper! Thanks to Sander Roet for his help with the code base, and Remco Veltkamp and @fridof.bsky.social
We’re kicking off the DinoSphere Online Seminar Series! Join us for our first session with Karel Mockaer (Heidelberg) & Yong Heng Phua (OIST)
📅 1 July 9AM CEST
🔗 tinyurl.com/4mjaverj
Spread the word!
@protistwtmostest.bsky.social @ehehenberger.bsky.social @chandnibhickta.bsky.social&Norico Yamada
You want to start tomography? Solve structures inside cells? Reach Nyquist 😳 ? @phaips.vd.st and I have a website for you! tomoguide.github.io
You'll find a tutorial on how to reconstruct tomograms, pick particles and do subtomogram averaging, using different software!
Hope it will be useful !
Hey #TeamTomo,
Ever been in need of a tutorial about the fundamentals of cryo-electron tomography? From preprocessing raw frames to high-res subtomogram averaging?
That's why @florentwaltz.bsky.social and I made this website!
tomoguide.github.io
Follow the thread 1 /🧵
#CryoET #CryoEM 🔬🧪
🚀🔬🦠 Releasing 🤖Cellpose-SAM🤖, a cellular segmentation algorithm with superhuman generalization 🦸♀️. Try it now on 🤗 huggingface.co/spaces/mouse...
paper: www.biorxiv.org/content/10.1...
w/ @computingnature.bsky.social 1/n
MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography
Figure 1
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MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography [updated]
Cryo-ET membrane analysis via deep learning.
We’ve updated our powerful MemBrain-seg tool for CryoET membrane segmentation! Plus, we’re introducing two new tools: MemBrain-pick for particle picking and MemBrain-stats for statistical analysis. Feedback is warmly welcome!
Check out the latest version of MemBrain, spearheaded by computation superstar @lorenzlamm.bsky.social ! It can segment, pick particles and give you metrics on everything!
📣Huge thanks to Simon, Hanyi, @lifeonthewedge.bsky.social, @florentwaltz.bsky.social, @wojwie.bsky.social, @kevinyamauchi.bsky.social, @alisterburt.bsky.social, Ye, Antonio, Sebastian, Fabian, @ja-schnabel.bsky.social and especially @cellarchlab.com & @tingyingpeng.bsky.social for incredible support
🤝 Feedback
If you feel like trying one of our modules or even the full pipeline, please let us know how it goes. We are happy for any feedback and would love to improve MemBrain v2 even further to make it as helpful for the community as possible.
🧵(6/6)
🔑 Usability
We focused on making MemBrain v2 smooth to work with: MemBrain-seg works with a single command line, while MemBrain-pick enables data-efficient training. We facilitate the transition between modules with several Napari functionalities like the 3D lasso to crop areas of interest.
🧵(5/6)
⚖️ MemBrain-stats
This module analyzes the spatial organization of particles on membranes. It takes the outputs of MemBrain-seg and MemBrain-pick to compute metrics like particle concentrations and geodesic nearest neighbor distances.
🧵(4/6)
⛏️MemBrain-pick
If you’re interested in localizing membrane-associated particles, please give MemBrain-pick a try. It enables efficient training of a model to localize particles on membranes and works with the Surforama plugin for interactive annotation in Napari.
🔗 github.com/cellcanvas/s...
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