Single-molecule behavior and cell-growth regulation in human RTKs

  1. Mitsuhiro Abe
  2. Masataka Yanagawa
  3. Yotaro Koizumi
  4. Ryoji Kise
  5. Asuka Inoue
  6. Yasushi Sako

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Abstract

Receptor tyrosine kinases (RTKs) are a major family of cell surface receptor proteins responsible for various cellular functions in animal cells, including fate decisions, metabolism, polarization and migration. Lateral mobility, dimerization, clustering, and oligomerization are crucial behaviors in the activation process of RTKs on the cell surface. However, relationships between these molecular behaviors and molecular function remain to be elucidated, except for a few RTK members. Here, using an automated live-cell single-molecule imaging and analysis system, we studied the behavior of 52 of the 58 human RTK species on living cells over time during stimulation with ligands. We extracted 72 single-molecule parameters for each RTK species to examine their relationship to function, structure, and evolution. We noticed that RTKs' ability to inhibit or support cell growth, as observed in a large-scale loss-of-function experiment in the public domain, significantly relates to their behavior. Growth-inhibitory signaling was coupled with the immediate formation of immobile clusters, followed by the enlargement of immobile and slow-mobile domains. In contrast, growth-supportive signaling coupled with higher lateral diffusivity and delayed clustering of immobile molecules. The relationship between structure and function suggests that functional differences are related to partitioning into membrane rafts and changes in mobility associated with phosphatidylinositol turnover. In multiple linear regression models, molecular behavior explained half or more of the molecular function related to cell growth. This level of explainability is comparable to that of evolutionary grouping.

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    Referee #2

    Evidence, reproducibility and clarity

    In their manuscript entitled " Single-molecule behavior and cell-growth regulation in human RTKs" Abe et al. demonstrate automated single-particle tracking of 52 receptor tyrosine kinases (RTKs) in both resting state and upon stimulation with the respective ligands. The approach is based on transient transfection of cells with each RTK tagged with a Halo-tag, allowing for subsequent dye labeling and live cell video recording using TIRF microscopy. Subsequently, a seemingly commercial analysis software is used to then obtain particle trajectories from single molecule localizations and analyze their properties using a hidden Markov model. The authors have previously demonstrated pioneering work in the field of single-particle tracking with respect to automation (Yasui et al, 2018) and analysis (Yanagawa et al, 2021), and in this work they scale their approach up to characterize a broad set of RTKs. The resulting observations are a powerful demonstration of the benefits of SPT in general and significantly advance our understanding of the dynamics of RTKs as a class, beyond the most prominently studied candidate EGFR, as well as promising evolutionary insights.

    Comments and questions:

    1. The authors picked 52 out 58 human RTKs. Why not all?
    2. In contrast to the above mentioned previous publications, here a seemingly commercial software package was used (AAS by Zido). The methods part is very short on the specific parameters that were used to i) localize particles (e.g. net gradient threshold) or ii) connect localizations into trajectories (step size, allowed dark frames, min. trajectory length). Similarly a clearer explanation of the HMM calculus would significantly help to better follow the analysis approach and parameter choice. Perhaps this reviewer has missed it, but why did the authors e.g. choose 3 states for HMM?
    3. The replicate experiment in Fig. S2 is appreciated, but what condition was repeated here? Also experimental details are missing: was it two repeats of: i) seeding cells in a dish, transfection, labeling, imaging? An image from cells from those repeats would be important to show, also to which degree the density of particles F varies, i.e. to which degree this is an unprecise experimental parameter itself as compared to biologically meaningful. This is especially as Fig. S2 does not contain any density comparison at all, whereas in the main figures it is indeed an experimental observable used.
    4. The density raises another issue. Some of the movies show extremely dense signal. Here the authors should explain how they deal with particles whose trajectories cross. This could lead to artificial dynamics and a supplementary figure showing that their analysis is robust toward varying densities (again suggesting to include a simulation) could be helpful
    5. Fig. 2C is a bit hard to understand since here localizations are colored based on their state but not from which trajectory they come. Do e.g. individual trajectories show various dynamic behaviors or are the trajectories not long enough to observe this?
    6. The evolutionary aspects could use further and simpler explanations to make this passage easier to grasp

    Significance

    The manuscript by Abe et al. represents a significant advancement in the field of single-particle tracking (SPT) by scaling up recording 52 human receptor tyrosine kinases (RTKs), offering comprehensive insights into their dynamics beyond the traditionally studied EGFR. While the study demonstrates cutting edge single-particle tracking and provides promising evolutionary insights, it currently lacks certain methodological details that are essential for reproducibility, such as specific parameters used in particle localization and trajectory analysis. The exclusion of 6 out of 58 human RTKs without discussion also requires further explanation, but overall, the study fills a knowledge gap by providing a broad overview of RTK dynamics and their diffusion behavior. Overall, this work should have broad appeal to fields such as cell signaling as well as methods development int the area of single-particle tracking.

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    Referee #1

    Evidence, reproducibility and clarity

    Summary: "Single-molecule behavior and cell-growth regulation in human RTKs" studies 52 of the 58 human receptor tyrosine kinases (RTKs) on the surface of live HEK293A cells using a Total Internal Reflection Fluoresence Microscope (TIRFM) system previously described in Watanabe et al. Single molecule tracking is conducted automated commercial Auto Analysis System (AAS; Zido) software followed by analysis in Python via scikit-learn. The analysis includes use of a Variational Bayesian-Hidden Markov Model (VB-HMM) described previously (Hiroshima, 2018). The VB-HMM analysis was used to characterize movement of RTKs with the authors concluding that the movement could be explained by the receptors transitioning between three states: immobile, slow, and fast. Single molecular transport parameters are then extracted from the analysis and averaged results are reported. How these parameters change after stimulation are then evaluated and compared. To relate the diffusion "behaviors" with biological function, the authors retrieve cell growth data from loss-of-function data in the DepMap project (Arafeh, 2025). By correlating selected parameters with the functional data, the authors find that behaviors in the resting and response states partially "explained their function". The authors then relate specific parameters through correlation to growth-inhibitory or growth-supporting signaling, amino acid sequence characteristics ("structural"), and evolutionary parameters. Additionally, the authors build a regression model to evaluate how their behavior parameters may predict RTK functional characteristics.

    Major Comments:

    1. The primary findings from this article are the extraction of parameters of a 3-state hidden Markov model from the analyzed single molecule trajectories of 52 RTKs. It is difficult to evaluate these primary findings since the raw data, the analysis software, the intermediate results, the hidden Markov model, and the Python analysis scripts are not readily available to the public or this reviewer. Thus the evaluation of the underlying software, applicability of the software to the problem, or reproducibility of the results from the data are not possible to evaluate. I encourage the authors to provide as much data and software available as possible even if under restricted licenses. a. The availability of raw single molecule movies from this study are not generally available and thus it is difficult possible to evaluate the efficacy of the Zido AAS software or compare to the tracks generated to other single molecule tracking software. While it is appreciated that a mosaic of the movies is made available as Supplemental Movies 1A through 1D, these are not in a form analyzable by other tracking software. Ideally, all the raw experimental data would be made available, but it might suffice if a single raw movie and its analysis were made available for direct evaluation. b. The trajectory information of single particles from the Zido AAS are not available. While the raw movies may be voluminous in nature, the extracted trajectories would also be valuable. As multiple hidden Markov models are available to evaluate diffusive behaviors, it would be useful to have the trajectory information available for a comparison between distinct models to be conducted by reviewers. c. The Variational Bayesian-Hidden Markov Model (Hiroshima et al. 2018) that is used at the core of this paper is not readily available.

    2. While it is appreciated that the authors combine their extracted VB-HMM parameters of RTKs to other bioinformatic data sets, the relationship to functional and structural-sequence information is only correlative. There is no attempt in this article to validate the correlative findings.

    3. Many statistical comparisons are made in this article across cell lines, RTKs, and parameters, but it is not clear if multiple comparison corrections are applied to compensate for the false discovery rate. The authors should provide a detailed Statistical Analysis section in the methods section.

    Minor comments:

    1. The authors insufficiently cite the Broad Institute's Dependency Map project from which their functional analysis is derived. The following is quoted from https://depmap.org/portal/data_page/?tab=overview#how-to-cite . For DepMap Release data, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions: DepMap, Broad (2025). DepMap Public 25Q3. Dataset. depmap.org Please note, you may need to update the release quarter depending on which version of the data you are using. We ask that you also cite the DepMap program: Arafeh, R., Shibue, T., Dempster, J.M. et al. The present and future of the Cancer Dependency Map. Nat Rev Cancer 25, 59-73 (2025). https://doi.org/10.1038/s41568-024-00763-x

    Significance

    General assessment: The main significance of this paper comes from a broad and data-rich study of 52 of 58 human receptor tyrosine kinases. If this data, the intermediate analysis results such as the trajectories, or executable software used to conduct the analysis were made available, the value to the field would be high. However, the article lacks any statements regarding open availability of the data or the software. While correlating their "behavioral" parameters to other bioinformatic datasets creates context and suggests further studies, the lack of any validation of the correlative findings limits the significance of the results.

    Advance: While the authors share large final outputs of their parameter datasets, the unavailability of the raw data or intermediate results makes it difficult to compare their analysis and models to other readily available analysis pipelines or models. In particularly, it would be useful to the field if their analysis could be directly compared to the software packages in the following citations:

    1. Monnier, N., Barry, Z., Park, H. et al. Inferring transient particle transport dynamics in live cells. Nat Methods 12, 838-840 (2015). https://doi.org/10.1038/nmeth.3483
    2. Vega et al., Biophys. J. 2018. Multistep Track Segmentation and Motion Classification for Transient Mobility Analysis. https://pubmed.ncbi.nlm.nih.gov/29539390/.

    Audience: The potential audience is the field of receptor tyrosine kinases and more generally cell surface receptors. The lack of validation of their correlative results or comparison of their analysis pipeline to other analysis pipelines limits the value of the findings to the field. There is also a potential audience for other authors of computational trajectory analysis software if the raw data or intermediate analysis results were available. The effective audience is thus limited to biophysicists or quantitative biologists capable of replicating the VB-HMM model for validation of the correlative results in falsifiable experiments.

    Reviewer's Field of Expertise: I have a field of expertise in advanced microscopy, image analysis, single particle tracking, receptor tyrosine kinases, membrane biophysics, hidden Markov models, Bayesian analysis, and software engineering.

  4. Version published to 10.64898/2025.12.29.696957 on bioRxiv