Lateral communication along migrating monolayer

fronts as a model of cell-cell communication

at the single cell resolution

We observed long-range, lateral motility waves

propagating from cell-to-cell along the monolayer front (Zaritsky et al., 2015).


This mode of cell-cell communication was revealed  by tracking the monolayer front over time and constructing a protrusion-edge kymograph, a spatiotemporal

visualization and quantitative encoding of the complete evolution of the monolayer front over time.


These waves appeared in multiple cell lines and did not diminish in a comprehensive screen that we conducted for regulators of cytoskeleton and adhesion dynamics.


We are now using this system as model for quantitative mechanistic investigation of cell-cell communication at the single cell resolution.

Interpretable machine learning for classification of highly-metastatic melanoma 


Interpretable machine learning to uncover functional hallmarks of highly-metastatic melanoma 

We "reverse engineered" a neural network to identify the cellular information that distinguish between very aggressive and less aggressive metastatic cells. This was achieved by generating synthetic cell images in a controlled manner to amplify subtle cellular features that critically define the metastatic efficiency of a melanoma tumor.


Our methods, termed “quantitative live cell histology” was recently highlighted by The Scientist.


DeBias: generalized framework for analysis

of coupled cell biological variables.

We introduced DeBias, software for the analysis

of complex relations between coupled cellular variables

(Zaritsky et al., 2017).


DeBias decouples the global bias of external effectors from

direct interaction, proposing a second measurement for

co-localization and co-alignment analyses.


Global bias is often a neglected in interpretation of coupled variables, but encapsulates fundamental mechanistic insight into cellular behavior, as showcased in the paper in four different areas of cell biology.


The DeBias software package is freely accessible online.


RHOA GEFs regulate long-range intercellular communication during collective cell migration

Effective collective cell migration depends on delicate tuning of cell motility forces and cell–cell communication.


Coordination between these processes is regulated, among several pathways, by a family of proteins called the Rho family GTPases.


By design of quantitative measures that encode the dynamics of collectively migrating cells in space and time, we identified a surprising role of the Rho GTPases RhoA, RhoC, and their upstream activators in mediating

long-range transmission of guidance cues

(Zaritsky, Tseng et al., 2017).


Promoting computational cell biology by sharing and reusing cell image data

A key step toward involvement of computational scientists in cell biology is making image cell data publicly available for secondary analysis, and appreciation of such research by the cell biology community.

As the first massive datasets are becoming publicly available we are now at the dawn of this revolution.

We are using "old" live cell imaging data to learn new biology (e.g., Zaritsky et al., 2015, Zaritsky et al., 2017), read our recent perspective (Zaritsky 2018) or checkout the ASCB subgroup that we organized.  


Dry cell biology

The increasing complexity and volume of microscopy data presents an opportunity for deciphering fundamental biological processes by analyzing image data.


Discovery of complex dynamic patterns in such data require deep knowledge of the biological process, the experimental possibilities, the type of information that can be extracted and the computational tools to extract it.


The field is desperately in need of computational (or ‘dry’) cell biologists, interdisciplinary scientists who can drive biological inquiry by bridging cell biology and computer science.

We work at the boundaries of quantitative disciplines and cell biology, identify scientific problems, steer (but usually do not perform) experiments, import, adapt and apply quantitative tools, and interpret the data to conceive testable numerical predictions. Read our World View (Zaritsky 2016).

Interpretable machine learning for classification of highly-metastatic melanoma 

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 Assaf Zaritsky lab of computational cell dynamics, applying data science to microscopy cell images since 2018