It was wonderful to have Bijun with us @mpi-mp-potsdam.bsky.social & @incavirus.bsky.social to finally "use our microscope to its full potential" as our core-facility put it! 🔬

Congrats to all, esp. @bijuntang.bsky.social and @xanderjones.bsky.social ! Super happy to be a part of this great story!

Huge thanks to everyone involved! This has been a fun project—useful for our own experiments and, hopefully, valuable for others as well!

Samplify can also provide detailed parameters of your seed population—size, color, shape, and more. For further information, check out our preprint.

Naturally, this reduces user bias—here, mostly my own. So we built a full framework for training models and annotating images, making it easy for anyone to create their own seed classifier.

This approach to quantifying the triploid block worked remarkably well, achieving near-perfect classification—closely matching human annotations of seed abortion:

Luckily, I already had thousands of manually scored seed images— over 13,000 seeds in total. This gave us a solid base to train a random forest classifier, which our bioinformatician used to achieve reliable seed classification.

In a great collaboration with our institute’s bioinformatics group, we developed "Samplify"—a tool that blends classic image segmentation with the transformer-based Segment Anything Model (SAM) to accurately count seeds, even when they touch or cluster, aligning almost perfectly with manual counting

We always dreamed of automating seed phenotyping to save time and reduce user bias. But Arabidopsis seeds are tiny and often cluster, making analysis tricky—traditional imaging methods usually just excluded those clusters.

Quantification of these aborted seeds was traditionally done manually, i.e., by taking a picture and counting how many normal, abnormal, and totally aborted seeds were present in a given image. The problem was always the huge variation between different people doing the counting.

Our lab has a keen interest in hybridization barriers, such as those that occur in Arabidopsis when parents of different ploidy are crossed — a phenomenon often referred to as the "triploid block." Normal seeds appear light brown, plump, and healthy, whereas seeds from interploidy crosses do not.

www.biorxiv.org/content/10.1...
Here it comes, first preprint from my postdoc time at @mpi-mp-potsdam.bsky.social in the lab of @claudiakohler11.bsky.social

Samplify: A versatile tool for image-based segmentation and annotation of seed abortion phenotypes https://www.biorxiv.org/content/10.1101/2025.09.18.677122v1

Excited to share the first preprint from my lab @mpi-mp-potsdam.bsky.social in Potsdam! Huge thanks to @o-sulkowski.bsky.social and the whole team for their hard work and dedication.

📄 www.biorxiv.org/content/10.1...

Awesome @gmivienna.bsky.social alumni reunion @mpi-mp-potsdam.bsky.social Thx @thevirusmc.bsky.social @incavirus.bsky.social @gesahoffmann.bsky.social & @hebente.bsky.social for a really fun & exciting visit!!!

Our group has an open PhD position in this call! Interested in viruses and stem cell immunity? Apply 🍀 don’t hesitate to drop @incavirus.bsky.social or me a message if you have any questions 😊

It is happening
MPI-DS Göttingen announced that they suspend their activity on X

the tool of the elite bioinformatician

🎉 Congrats to @duartedf.bsky.social on receiving an ERC grant for his project NoSexSeed! 🌱 His goal? Enabling crops to reproduce pollen-free 🌍 #ERCCoG

More on this: www.mpimp-golm.mpg.de/2778892/