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lorenzlamm.bsky.social
Lorenz Lamm
@lorenzlamm.bsky.social
240 followers 300 following 12 posts
PhD Student at Helmholtz AI | MemBrain analysis for Cryo-ET #teamTomo
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🦠🧠 MemBrain update! 🧠🦠
We’ve updated our preprint! It now covers the full MemBrain v2 pipeline for end-to-end membrane analysis in #CryoET: segmentation, particle picking, and spatial statistics.
🔗 Preprint: doi.org/10.1101/2024...
🔗 Code: github.com/CellArchLab/...
🧵(1/6) #TeamTomo
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Reposted by Lorenz Lamm
cellarchlab.com
Ben Engel @cellarchlab.com · 13d
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/🧵
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elife.bsky.social
eLife @elife.bsky.social · 18d
🌱 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
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Reposted by Lorenz Lamm
kyleharrington.com
Kyle Harrington @kyleharrington.com · Aug 26
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...
A realistic phantom dataset for benchmarking cryo-ET data annotation - Nature Methods
A standardized, realistic phantom dataset consisting of ground-truth annotations for six diverse molecular species is provided as a community resource for cryo-electron-tomography algorithm benchmarki...
doi.org
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michaelgrange.bsky.social
Michael Grange @michaelgrange.bsky.social · Jun 17
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
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Jun 16
Ooh that's awesome! Great to hear the new version improved your segmentations :)
Reposted by Lorenz Lamm
martenchaillet.bsky.social
Marten Chaillet @martenchaillet.bsky.social · Jun 9
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
cryoempapers.bsky.social
cryoEM papers @cryoempapers.bsky.social · Jun 8
pytom-match-pick: A tophat-transform constraint for automated classification in template matching pubmed.ncbi.nlm.nih.gov/40475324/ #cryoem
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Reposted by Lorenz Lamm
manondemulder.bsky.social
Manon Demulder @manondemulder.bsky.social · Jun 4
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
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florentwaltz.bsky.social
Florent Waltz @florentwaltz.bsky.social · May 6
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 !
3.8 angstrom resolution ribosome from 33 tomograms The TomoGuide website
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Reposted by Lorenz Lamm
phaips.vd.st
Philippe Van der Stappen @phaips.vd.st · May 6
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 🔬🧪
Welcome to TomoGuide
A step-by-step Cryo-ET guide
tomoguide.github.io
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Reposted by Lorenz Lamm
marius10p.bsky.social
Marius Pachitariu @marius10p.bsky.social · May 3
🚀🔬🦠 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
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aixbiobot.bsky.social
AI x Bio Discovery @aixbiobot.bsky.social · Apr 22
MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography [updated]
Cryo-ET membrane analysis via deep learning.
MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography Figure 1 Figure 2 Figure 3
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tingyingpeng.bsky.social
Tingying Peng @tingyingpeng.bsky.social · Apr 25
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!
lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🦠🧠 MemBrain update! 🧠🦠
We’ve updated our preprint! It now covers the full MemBrain v2 pipeline for end-to-end membrane analysis in #CryoET: segmentation, particle picking, and spatial statistics.
🔗 Preprint: doi.org/10.1101/2024...
🔗 Code: github.com/CellArchLab/...
🧵(1/6) #TeamTomo
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Reposted by Lorenz Lamm
florentwaltz.bsky.social
Florent Waltz @florentwaltz.bsky.social · Apr 25
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!
lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🦠🧠 MemBrain update! 🧠🦠
We’ve updated our preprint! It now covers the full MemBrain v2 pipeline for end-to-end membrane analysis in #CryoET: segmentation, particle picking, and spatial statistics.
🔗 Preprint: doi.org/10.1101/2024...
🔗 Code: github.com/CellArchLab/...
🧵(1/6) #TeamTomo
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
📣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
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🤝 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.
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🔑 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)
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
⚖️ 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)
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
⛏️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...
🧵(3/6)
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🎨 MemBrain-seg
This module allows out-of-the-box segmentation of membranes with just a single command line.
It’s based on a U-Net architecture, trained with a diverse dataset to enable generalization to many settings.

🔗 github.com/teamtomo/mem...
🧵(2/6)
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 25
🦠🧠 MemBrain update! 🧠🦠
We’ve updated our preprint! It now covers the full MemBrain v2 pipeline for end-to-end membrane analysis in #CryoET: segmentation, particle picking, and spatial statistics.
🔗 Preprint: doi.org/10.1101/2024...
🔗 Code: github.com/CellArchLab/...
🧵(1/6) #TeamTomo
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Reposted by Lorenz Lamm
cellarchlab.com
Ben Engel @cellarchlab.com · Apr 24
Yo #TeamTomo, check out our updated #MemBrain v2 preprint. And better yet, give it a whirl on your #CryoET membranes! Please send us your feedback! 🧪🧶🧬🔬
lifeonthewedge.bsky.social
Ricardo D. Righetto @lifeonthewedge.bsky.social · Apr 23
We have updated our #MemBrain v2 preprint with a lot more details about the MemBrain-pick and MemBrain-stats modules, as well as some application examples!

Stay tuned for the upcoming thread by lead author @lorenzlamm.bsky.social! 🧠🧵

#CryoET #TeamTomo
www.biorxiv.org/content/10.1...
Figure 4. MemBrain v2 end-to-end workflow detects periodic phycobilisome organization. A: Raw tomogram slice of EMD-31244. B: Out-of-the-box MemBrain-seg segmentation (light blue). C: A single membrane instance can be visualized in Surforama and manually annotated with GT phycobilisome positions (magenta). D: MemBrain-pick localizes particles (trained with data from C) on all membranes in the tomogram. E: MemBrain-stats computes Ripley’s O statistic using the positions from D with a bin size of 5nm. The distance between peaks (35 nm) was measured to estimate chain unit spacings.
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lifeonthewedge.bsky.social
Ricardo D. Righetto @lifeonthewedge.bsky.social · Apr 23
We have updated our #MemBrain v2 preprint with a lot more details about the MemBrain-pick and MemBrain-stats modules, as well as some application examples!

Stay tuned for the upcoming thread by lead author @lorenzlamm.bsky.social! 🧠🧵

#CryoET #TeamTomo
www.biorxiv.org/content/10.1...
Figure 4. MemBrain v2 end-to-end workflow detects periodic phycobilisome organization. A: Raw tomogram slice of EMD-31244. B: Out-of-the-box MemBrain-seg segmentation (light blue). C: A single membrane instance can be visualized in Surforama and manually annotated with GT phycobilisome positions (magenta). D: MemBrain-pick localizes particles (trained with data from C) on all membranes in the tomogram. E: MemBrain-stats computes Ripley’s O statistic using the positions from D with a bin size of 5nm. The distance between peaks (35 nm) was measured to estimate chain unit spacings.
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Reposted by Lorenz Lamm
dtegunov.bsky.social
Dimitry Tegunov @dtegunov.bsky.social · Apr 7
I've got bad news for #teamtomo: earlier today, our resident headcrab attacked @lorenzlamm.bsky.social, turning his Mem🧠 into 🧟‍♂️🧠
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lorenzlamm.bsky.social
Lorenz Lamm @lorenzlamm.bsky.social · Apr 8
Nothing to worry about. I could backpropagate the attack and MemBrain's neurons are now more robust than before 💪
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cellarchlab.com
Ben Engel @cellarchlab.com · Mar 20
We're on the cover of @science.org this week. Awesome work by @florentwaltz.bsky.social. #CryoET is reaching crazy resolutions inside cells, and this is just the beginning. Fantastic interpretation of the proton motive force by @verenaresch.bsky.social. Always a pleasure making #SciArt with you!🧪🧶🧬🌾
science.org
Science Magazine @science.org · Mar 20
In a new Science study, cryo–electron tomography captures the in-cell architecture of the mitochondrial respiratory chain, illuminating how the coordinated action of molecular machines drives life’s fundamental energy conversion.

Learn more in this week's issue: scim.ag/3FA3Ygq
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