Thanks Maria!

Somehow missed acknowledging Alon Shpigler, the first author 😱

Congratulations to Naor Kolet who led this project and thanks to co-authors Shahar Golan for his contribution and to @erinweisbart.bsky.social l for her critical insights regarding interpretability 🎉

3/3

Anomaly-based representations have several benefits:
1. Reducing batch effects
2. Improving reproducibility and mechanism of action classification
3. Complementing classical representations
4. Revealing alterations in biologically interpretable dependencies

2/3

Join us!

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!

3/3

Co-organized with Meghan Driscoll, @quantumjot.bsky.social and Susanne Rafelski

Speakers include: @kevin-dean.bsky.social, @ellenberglab.bsky.social, Margaret Gardel, @florianjug.bsky.social, @krishnaswamylab.bsky.social, @levayerr.bsky.social, @priscaliberali.bsky.social, ...

Sorry, I meant @leeat-keren.bsky.social and Yael Amitay....

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!

16/n

CISM source code is publicly available github.com/zaritskylab/...

Also, check CISM’s backbone FANMOD+, that enables efficient motifs extraction in “multi-colored” networks github.com/zaritskylab/...

Try it out!

15/n

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

14/n

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

13/n

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.

12/n

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)

11/n

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

10/n

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

9/n

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

8/n

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

7/n