The paper outlines a holistic conceptual framework for defining cell states and their transitions in the modern era of big data. Moving beyond a strictly molecular view, the authors propose integrating four practical categories of observables: the molecular census, cellular organization, cell function, and the cellular environment. They visualize these interactions through a spring-connected tetrahedron model, where the "push and pull" of various factors determines the stability of a cellular state. This framework accommodates bi-directional feedback, acknowledging that physical changes in cell shape or environment can drive gene expression just as often as the reverse. Ultimately, the source seeks to "Keplerize" cell biology by finding general, predictive patterns within complex datasets. This approach aims to provide a rigorous, data-driven method for understanding how cells maintain identity or transform during development and disease.

References:

• Rafelski S M, Theriot J A. Establishing a conceptual framework for holistic cell states and state transitions[J]. Cell, 2024, 187(11): 2633-2651.