Finally, we investigated the Polarity Cell Inversion process in the fish’s neuromast. This is a highly dynamic cell rearrangement process, in which ForSys predicts mechanical asymmetries among cell types. You can read more about this process at
doi.org/10.1242/dev....

(8/10)

Then, we applied our tool to the migrating primordium in Zebrafish’s lateral line and were able to pinpoint the location of the rossetogenesis before neuromast deposition by looking at the stresses in the basal plane.

(7/10)

Afterwards, we used ForSys’s static modality to predict stress in Xenopus embryos and found that ForSys could predict the distributions of tensions with good accuracy.

(6/10)

ForSys is an open source tool that is usable in static (dynamic) conditions to infer the intracellular stresses and intracellular pressures, given segmented (time-lapse) microscopies.

(4/10)

ForSys is an open-source software, written in Python and open to the community to contribute. We have already included new capabilities through this interaction, such as CellPose support and a GUI in Fiji!!.

(2/10)

Our ForSys method to infer the mechanical state of a tissue in space and time is published now in @cp-iscience.bsky.social !
www.cell.com/iscience/ful...

(1/10)

We propose that this velocity results from a proximalisation force, derived from a proximalisation potential. This gives us a quantitative, testable framework to study a central mechanism of regenerative patterning.
(11/n)

By fitting our model to the data, we inferred—for the first time—the proximalisation velocity (vp): how fast cells shift identity from distal to proximal!
(10/n)

To interpret the data, we developed a reaction–diffusion–advection (RDA) model. This allowed us to describe proximalisation as a velocity field resulting from changes in positional cues due to Tig1/Prod1.
(9/n)

To go beyond qualitative, we built a tool: Meandros, our new Python-based software for analysing spatio-temporal cell distributions in regenerating limbs.
It’s open-source—feel free to use it!
🔗 doi.org/10.5281/zeno...
(8/n)

We began by electroporating distal blastema cells in axolotl limbs with Tig1 or Prod1. What we saw: a clear qualitative shift of the cells toward proximal locations.
(7/n)

Cells in a regenerating axolotl limb somehow know their place—but how?
In our new paper, we trace the dynamics of positional memory, release open-source code to quantify it, and introduce a theoretical framework for proximalisation.
🧵👇
🔗 www.nature.com/articles/s41...
(1/n)

Congratulations for your talks at the Symposium, Mia^2, Irene, Aitana and Zanah!! It was a pleasure to hosting you in our Lab!!

When tissue motion is significant, dynamic inference opens up new possibilities for understanding the mechanical state of tissues.
(7/8)

In the minireview, we discussed the three dynamic algorithms currently in the literature: VFM, the first dynamic algorithm; DLITE, a pseudo-dynamic method; and ForSys, a bonafide dynamic pipeline which we have recently made available.

(6/8)

Many developmental processes are highly dynamic and, instead of the standard static approach, require a dynamic method of stress inference. Here, we summarize how stress inference works and give an overview of the challenges in dynamic stress inference.
(4/8)