Ryan Savill

This is one of the best learning experiences I’ve had so if you want to dive into deep learning go for it!

We’ve upgraded ShapeEmbed 🎉 ShapeEmbedLite decodes latent codes via an MLP to guarantee valid EDMs, making it lighter and ideal for small microscopy datasets or limited compute. Hear more at my BIC workshop talk or poster at #ICCV2025! Try it out at github.com/uhlmanngroup...

New preprint from the lab! We discovered a transient fluidization in the basal region of human forebrains by tracking microdroplets in cerebral organoids.This “basal fluidization”, absent in gorilla and mouse, may contribute to greater surface expansion in human forebrains
1/
doi.org/10.1101/2025...

ToSkA: Topological Skeleton Analysis for Network-Based Shape Representation and Evaluation of Objects from Cells to Death Stars Shape analysis and classification are popular methods for biologists, biophysicists and mathematicians investigating relationships between object function and form. Classic shape descriptors, such as ...

(14/14) Thanks to Allyson Q Ryan whose ToSkA method inspired SpinePy. Thanks to @jesseveenvliet.bsky.social for supervision, @campaslab.bsky.social & @carldmodes.bsky.social for input and guidance, the @mpi-cbg.de & @poldresden.bsky.social and the #EIC for funding @sumoconsortium.bsky.social

(13/14) Thank you to everyone involved, especially my fellow PhD students Alba Villaronga Luque and @mtrani.bsky.social for sharing their data and contributing to the development but also @yonitmms.bsky.social the rest of our lab for their input and help with annotation.

SpinePy enables automated 3D spatiotemporal quantification of multicellular in vitro systems Organoids and stem-cell-based embryo models such as gastruloids are powerful systems to quantitatively study morphogenesis and patterning. This requires 3D analysis in reference frames that emerge dyn...

(12/14) If you want to find out more about the synthetic data generation, see comparisons to 2D patterning approaches, or just want to dive into the details give the preprint a read and contact me or @jesseveenvliet.bsky.social if you’re interested in using the method!

(11/14)
Building on this we can identify features that correlate with the observed patterning classes. The 3D patterning maps thus provide framework to systematically investigate gastruloid patterning spaces!

(10/14) Alba Villaronga Luque generously shared her data investigating how initial cell number (N0) influences patterning. We extracted patterning maps of 54 gastruloids and were able to detect distinct patterning phenotypes using dimensionality reduction and clustering.

(9/14) With this normalized reference frame in hand we can use data with multiple developmental markers and quantify patterning maps using SLIC segmentation. Maps are a 2D representation of 3D patterning data, allowing comparison of patterning types in a unified reference frame.

(8/14) A core feature of SpinePy is the generation of a common reference frame, no matter the gastruloids size, shape, or orientation under the microscope. We define the AP and core-to-surface (CS) position and can use it to map the signals into a common space!

(7/14) Another neat feature is the quantification of scalar fields along the axis. We use density as an example but any signal of interest could be used to generate profiles along the AP axis. Again, the synthetic data facilitates verification of the pipeline!

(6/14) One core biological feature to quantify is gross morphology. Using optimized, non-intersecting planes, we measure radial profiles over the AP axis for fixed and live gastruloids giving insights into 3D morphodynamics. Big thanks to @mtrani.bsky.social who provided data and insight!

(5/14) This allowed me to create hundreds of gastruloids in silico, with defined spines and thickness profiles to benchmark SpinePy. We show that the spine detection performs well with low relative errors compared to the ground truth!

(4/14) Using manual annotations to benchmark the spine is a crucial but in 3D annotation can be challenging. To have a second method of verification I generated synthetic gastruloids with some Perlin noise to generate realistic structures (like this wobbly gastruloid)

(3/14) The first step is detecting the anteroposterior (AP) axis in gastruloids, based on gross morphology. We can either use a skeletonization approach using segmentation or a surface mesh approach using Non-linear PCA, which was important for timelapse data.

(2/14) Its modular architecture allows it to plop into pre-existing workflows. This means you can map any signal of interest into a common reference frame across gastruloids!

(1/14) I’m happy and proud to introduce: SpinePy – a framework to detect the "spine" of gastruloids and measure biological and physical signals in a local dynamic 3D coordinate system. www.biorxiv.org/content/10.1...

FINALLY just released the @napari.org clusters-plotter 0.9.0 🤗 pypi.org/project/napa... The latest version comes with a complete overhaul of the entire codebase to make #microscopy data introspection and browsing even more accessible, intuitive and versatile! #bioimaging

Let's have a look 👇🧪💻🔬

In preprints: exploring developmental robustness and timing with gastruloids How developmental processes are coordinated in time and space to ensure the robust formation of complex, functional patterns during embryogenesis remains an outstanding question. Investigating this in...

Check out our "In Preprints" @dev-journal.bsky.social w/ Yonit Maroudas-Sacks & Marc Trani Bustos @mpi-cbg.de @poldresden.bsky.social, putting recent #gastruloid work from @viktri08.bsky.social @thomasgregor.bsky.social @amartinezarias.bsky.social labs in broader perspective: doi.org/10.1242/dev....

🙌🏻🙌🏻🙌🏻

Signaling switches: Metabolism regulates gastruloid self-organization Metabolic regulation of embryonic development is increasingly recognized. Villaronga-Luque et al. and Stopornwongkul et al. show that metabolic activity influences gastruloid formation from mouse embr...

Thoughtful preview article by María Rodríguez Colman & @sonnenlab.bsky.social on recent papers from our lab and @jesseveenvliet.bsky.social lab! Grateful for their in-depth analysis of the papers and their big-picture perspective on the role of metabolism in development.
doi.org/10.1016/j.st...

This my first big project during my PhD and I couldn’t be happier about it being out! Please give it a read if you’re interested in embryo models, data fusion, machine learning, image analysis, metabolism, scSeq…. because a lot of great people made this project possible! 🚀

📢 Fresh off the press and featuring new exciting experiments! 🧪
We show how glycolytic activity instructs germ layer proportions through regulation of Nodal and Wnt signaling - happy to finally share this 😊
doi.org/10.1016/j.st...

B2B with @jesseveenvliet.bsky.social lab: doi.org/10.1016/j.st... 🤩

Festive times with @mpi-cbg.de predocs Ryan Savill @cryaaa.bsky.social & Alba Villaronga Luque! What were we celebrating? More tomorrow!

Supported by @sumoconsortium.bsky.social, part of @ec.europa.eu European Innovation Council Engineered Living Materials Portfolio #EUeic #EICPathfinder #ELMs

Some new additions for the upcoming @napari.org clusters-plotter version 👉0.9.0 around the corner! Powered by some work from @mazoc.bsky.social, it casually turns into a pretty neat tool for feature map creation 😀

github.com/BiAPoL/napar...