Assaf Zaritsky
@assafzaritsky.bsky.social
97 followers 170 following 23 posts
Computational cell biologist https://www.assafzaritsky.com/
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assafzaritsky.bsky.social
Jennifer Lippincott-Schwartz, Emma Lundberg, Alex Mogilner, Maddy Parsons, @loicaroyer.bsky.social, Guillaume Salbreux, Anđela Šarić, Timm Schroeder, Hervé Turlier, @virginieuhlmann.bsky.social, Vincenzo Vitelli

Please save the date and spread the word!

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assafzaritsky.bsky.social
Excited to announce the first @cshlnews.bsky.social meeting on Cell Modeling in Space and Time, June 2026 💫

meetings.cshl.edu/meetings.asp...

Bringing together experiments, computation and theory to explore dynamic, multi-scale cellular organization and function!

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Reposted by Assaf Zaritsky
ritastrack.bsky.social
Nature Biomedical Engineering is hiring again! This time we're hoping to add an editor with machine learning expertise to the team (although we are open to those with other relevant expertise!) Deadline is Oct 20. Please RT! springernature.wd3.myworkdayjobs.com/SpringerNatu...
Reposted by Assaf Zaritsky
hhmijanelia.bsky.social
📢We're #hiring Group Leaders!

Apply to lead a lab at Janelia & advance biology using theory, computational modeling & machine learning.

🔹5-year renewable appointment
🔹Pioneer new tools & approaches
🔹Collaborate across disciplines

Apply by Nov. 4👉 https://janelia.link/groupleader
Reposted by Assaf Zaritsky
nicolettapetridou.bsky.social
We are searching for an enthusiastic scientist to join our team and establish annual killifish as a model system for early developmental biology and biophysics! @embl.org @embldbunit.bsky.social

Apply here:
embl.wd103.myworkdayjobs.com/EMBL/job/Hei...

Please re-post 🙏
assafzaritsky.bsky.social
A creative and technically challenging idea led by @zamir_amos, with key contributions from Yuval Tamir and @yaelam75. This would not have been possible without the great collaboration with @leeat_keren! And thanks to @WellcomeLeap ΔTissue for funding!

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assafzaritsky.bsky.social
Our results suggest that the local organization of a few cells in discriminative motifs are emergent properties that may define an intermediate spatial scale driving tissue function

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assafzaritsky.bsky.social
CISM’s unsupervised motif enumeration followed by supervised context-dependent selection distils the discriminative motifs from the huge and noisy landscape of all putative sub-networks of intercellular interactions and provides discrimination along with interpretability

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assafzaritsky.bsky.social
To demonstrate the general applicability of CISM for identifying local cellular structures linked to disease states, we applied it to investigate the tumor microenvironment in a human cohort of Breast Cancer (TNBC) patients, comparing short-term and long-term survivors.

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assafzaritsky.bsky.social
The same motifs can be differentially analyzed according to different context-dependent selection of the discriminative motifs. We demonstrate this by investigating the immune microenvironment of NP versus PN (metastatic lymph nodes that did not develop distant metastases)

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assafzaritsky.bsky.social
Spatial interpretation of motifs localization patterns reveals an association between (local) motifs to (global) tissue compartments highlighting the potential contribution of CISM to multi-scale analysis and interpretation

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assafzaritsky.bsky.social
The spatial arrangement of cell types within the discriminative motifs contributed to disease state classification, indicating that the specific intra-motif cell-cell interactions are sensitive markers for disease state beyond their cell type composition

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assafzaritsky.bsky.social
Exploring the landscape of the discriminative motifs-induced cell distribution and motifs-induced pairwise cell-cell interactions revealed differential composition of cell type and cell-cell interactions

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assafzaritsky.bsky.social
Classifying NN vs. NP disease states using discriminative four-cell motifs outperformed other methods, suggesting that these motifs can act as multicellular signatures of disease

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assafzaritsky.bsky.social
We applied CISM to investigate future metastases by analyzing the immune microenvironment in tumor-free lymph nodes of melanoma patients using multiplexed imaging.

Cohort and data by our collaborators @yaelam75 and @leeat_keren are described here doi.org/10.1101/2024...

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assafzaritsky.bsky.social
These discriminative motifs’ representations can be interpreted based on their prevalence, contribution to classification, identity of the cells that comprise them and their pairwise interactions and their spatial location in respect to more global tissue compartments

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assafzaritsky.bsky.social
CISM context-dependent discriminative motifs can be used to formulate a machine learning-based prediction of the tissue’s disease state where each patient is represented by the (sparse) vector of its discriminative motifs' frequencies

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assafzaritsky.bsky.social
CISM reduces the number of potential high-order interactions with unsupervised selection of ‘motifs’, enriched local multicellular structures, and then associates these motifs with the tissue disease state according to their presence in patients at different clinical states

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assafzaritsky.bsky.social
The clinical manifestation of diseased tissue arises from intricate intercellular interactions that extend beyond pairwise cell-cell interactions. Deciphering these processes is challenging due to the combinatorial complexity of multicellular organization.

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Source: https://doi.org/10.15252/msb.202110726