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Related Experiment Video

Updated: May 18, 2026

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.

Yuval Tamir¹, Yuval Bussi², Claudia Owczarek³

¹Institute for Interdisciplinary Computational Science, Stein Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.

Cell Reports Methods

|May 16, 2026

Summary

This summary is machine-generated.

This study reveals a universal, two-way connection between cell shape and protein levels in human tissues. Machine learning helps uncover how cell shape influences protein function and disease states.

Keywords:

CP: systems biology cell shape graph neural network machine learning multiplexed imaging spatial proteomics

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Last Updated: May 18, 2026

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

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Published on: November 2, 2020

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

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Published on: October 29, 2019

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Biophysics
Cell Biology
Computational Biology

Background:

Cell shape is critical for tissue function, but its relationship with protein expression is poorly understood.
Existing methods struggle to link single-cell morphology to molecular profiles in complex tissues.

Purpose of the Study:

To investigate the link between cell shape and protein expression in heterogeneous human tissues.
To leverage multiplexed imaging and machine learning for this investigation.

Main Methods:

Utilized multiplexed imaging to capture detailed cellular and protein information.
Applied machine learning algorithms to analyze the relationship between cell shape features and protein expression.
Employed machine learning interpretability techniques to explore functional implications of shape features.

Main Results:

Established a universal, bi-directional link between cell shape and protein expression across diverse cell types and disease states.
Identified that this shape-protein link is amplified for cell state markers.
Discovered a subpopulation of large, p53-positive tumor cells in two cancer types.
Demonstrated that cell shape properties improve graph neural network-based disease state prediction.

Conclusions:

Cell shape and protein expression are intricately coupled, offering insights into cellular function and disease.
Shape features can guide hypotheses regarding protein function.
This approach enhances understanding of tissue organization and state prediction in physiological contexts.