Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling

Spinosa, Phillip C.; Kinnunen, Patrick C.; Humphries, Brock A.; Luker, Gary D.; Linderman, Jennifer J.

doi:10.1007/s12195-020-00640-1

Cited by 10 publications

(9 citation statements)

References 60 publications

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“…Apart from the highlighted cell-to-cell variations in cell movement, signaling, and morphology fluctuations, initial and average values also show a range of deviations among cells in our single-cell analysis, as reported in past works (5,7,(23)(24)(25). We ask to what extent fluctuation is an independent descriptor of heterogeneity on top of cell-to-cell variability in initial and average values (Fig.…”

Section: A-ii)supporting

confidence: 58%

“…However, our group discovered marked heterogeneity in activation of intracellular signaling and chemotaxis among seemingly identical CXCR4-positive cancer cells treated with CXCL12 (4,5). Such heterogeneity is not unique to CXCL12-CXCR4 as prior studies report similar responses to other signaling molecules, such as EGF and EGFR (6)(7)(8)(9). These responses are not simply due to stochasticity but contain governing rules behind (10,11).…”

Section: Introductionmentioning

confidence: 79%

“…Our group previously demonstrated that activations of ERK and Akt differ among cells in response to CXCR4 signaling and other signaling molecules (5, 7). The signaling heterogeneity is regulated by pre-existing cell states, determined by Ras, mTORC1, and PI3K activities.…”

Section: Discussionmentioning

confidence: 99%

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Cell-to-cell variability of dynamic CXCL12-CXCR4 signaling and morphological processes in chemotaxis

Srivastava

Kinnunen

et al. 2022

Preprint

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Chemotaxis drives critical processes in cancer metastasis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze chemotaxis at single-cell resolution. Here we focus on chemotaxis driven by the CXCL12-CXCR4 pathway, a signaling network that promotes metastasis in more than 20 different human cancers. CXCL12-CXCR4 activates ERK and Akt, kinases known to promote chemotaxis, but how cells couple signaling to chemotaxis remain poorly defined. To address this challenge, we implemented single-cell analysis of MDA-MB-231 breast cancer cells migrating in a chemotaxis device towards chemokine CXCL12. We integrated live, single-cell imaging with advanced computational analysis methods to discover processes defining subsets of cells that move efficiently toward a CXCL12 gradient. We identified dynamic oscillations in ERK and Akt signaling and associated morphological transitions as key determinants of successful chemotaxis. Cells with effective chemotaxis toward CXCL12 exhibit faster and more persistent movement than non-migrating cells, but both cell populations show similar random motion. Migrating cells exhibit higher amplitude fluctuations in ERK and Akt signaling and greater frequencies of generating lateral cell membrane protrusions. Interestingly, computational analysis reveals less correlated network coupling of signaling and morphological changes in migrating cells. These data reveal processing events that enable cells to convert a signaling input to chemotaxis and highlight how cells in a uniform environment produce heterogeneous responses.

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Section: A-ii)supporting

confidence: 58%

Section: Introductionmentioning

confidence: 79%

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Cell-to-cell variability of dynamic CXCL12-CXCR4 signaling and morphological processes in chemotaxis

Srivastava

Kinnunen

et al. 2022

Preprint

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“…The prevailing dogma is that heterogeneity among cancer cells arises randomly, generating "greedy individuals" that compete for growth factors and optimal environments. However, recent data suggest that cancer cells function cooperatively as a tissue-like entity, and work by our group and others demonstrate that single-cell differences in signaling and function among cancer cells can arise predictably with consistent variations across a population as a whole (Spencer et al, 2009;Overton et al, 2014;Spinosa et al, 2019;Spinosa et al, 2020;Zhan et al, 2020;Kinnunen et al, 2022). These observations imply that tumor progression benefits from or even requires interactions among distinct subgroups of cells (Marusyk et al, 2014).…”

Section: Introductionmentioning

confidence: 86%

Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity

Kinnunen,

Ho,

Srivastava

et al. 2024

Front. Syst. Biol.

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Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.

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“…We engineered the MDA-MB-231 cells used in this work to stably express 3 fluorescent proteins: kinase translocation reporters (KTRs) for kinases Akt (mAquamarine) and ERK (mCitrine), and a stable histone-2B (H2B) nuclear marker (mCherry), as described previously 15,16 . MDA-MB-231 cells also expressed a puromycin selection marker.…”

Section: Stable Cell Line Generationmentioning

confidence: 99%

Partial differential equation-based inference of migration and proliferation mechanisms in cancer cell populations

Kinnunen¹,

Srivastava²,

Wang³

et al. 2023

Preprint

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Targeting signaling pathways that drive cancer cell migration or proliferation is a common therapeutic approach. A popular experimental technique, the scratch assay, measures the migration and proliferation-driven cell monolayer formation. Scratch assay analyses do not differentiate between migration and proliferation effects and do not attempt to measure dynamic effects. To improve upon these methods, we combine high-throughput scratch assays, continuous video microscopy, and variational system identification (VSI) to infer partial differential equation (PDE) models of cell migration and proliferation. We capture the evolution of cell density fields over time using live cell microscopy and automated image processing. We employ VSI techniques to identify cell density dynamics modeled with first-order kinetics of advectiondiffusion-reaction systems. We present a comparison of our methods to results obtained using traditional inference approaches on previously analyzed 1-dimensional scratch assay data. We demonstrate the application of this pipeline on high throughput 2-dimensional scratch assays and find that decreasing serum levels can decrease random cell migration by approximately 20%. Our integrated experimental and computational pipeline can be adapted for automatically quantifying the effect of biological perturbations on cell migration and proliferation in various cell lines.

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Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling

Cited by 10 publications

References 60 publications

Cell-to-cell variability of dynamic CXCL12-CXCR4 signaling and morphological processes in chemotaxis

Cell-to-cell variability of dynamic CXCL12-CXCR4 signaling and morphological processes in chemotaxis

Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity

Partial differential equation-based inference of migration and proliferation mechanisms in cancer cell populations

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