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Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test

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Abstract

Neuron morphology gives rise to distinct axons and dendrites and plays an essential role in neuronal functionality and circuit dynamics. In rat hippocampal neurons, morphological development occurs over roughly one week in vitro. This development has been qualitatively described as occurring in 5 stages. Still, there is a need to quantify cell growth to monitor cell culture health, understand cell responses to sensory cues, and compare experimental results and computational growth model predictions. To address this need, embryonic rat hippocampal neurons were observed in vitro over six days, and their processes were quantified using both standard morphometrics (degree, number of neurites, total length, and tortuosity) and new metrics (distance between change points, relative turning angle, and the number of change points) based on the Change-Point Test to track changes in path trajectories. Of the standard morphometrics, the total length of neurites per cell and the number of endpoints were significantly different between 0.5, 1.5, and 4 days in vitro, which are typically associated with Stages 2-4. Using the Change-Point Test, the number of change points and the average distance between change points per cell were also significantly different between those key time points. This work highlights key quantitative characteristics, both among common and novel morphometrics, that can describe neuron development in vitro and provides a foundation for analyzing directional changes in neurite growth for future studies.

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References

  • Bicknell, B. A., Pujic, Z., Dayan, P., & Goodhill, G. J. (2018). Control of neurite growth and guidance by an inhibitory cell-body signal. PLOS Computational Biology, 14, e1006218.

    Article  PubMed  PubMed Central  Google Scholar 

  • Boulan, B., Beghin, A., Ravanello, C., Deloulme, J.-C., Gory-Fauré, S., Andrieux, A., Brocard, J., & Denarier, E. (2020). AutoNeuriteJ: An ImageJ plugin for measurement and classification of neuritic extensions. PLOS ONE, 15, e0234529.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Byrne, R. W., Noser, R., Bates, L. A., & Jupp, P. E. (2009). How did they get here from there? Detecting changes of direction in terrestrial ranging. Animal Behaviour, 77, 619–631.

    Article  Google Scholar 

  • Conover, W. J. (1971). Practical Nonparametric Statistics. (1st ed.). John Wiley & Sons, Inc.

  • Cuntz, H., Borst, A., & Segev, I. (2007). Optimization principles of dendritic structure. Theoretical Biology and Medical Modelling, 4, 21.

    Article  PubMed  PubMed Central  Google Scholar 

  • Deinhardt, K., Kim, T., Spellman, D. S., Mains, R. E., Eipper, B. A., Neubert, T. A., Chao, M. V., & Hempstead, B. L. (2011). Neuronal growth cone retraction relies on proneurotrophin receptor signaling through Rac. Science Signaling, 4, ra82.

  • Dinno, A. (2017). dunn.test: Dunn’s Test of Multiple Comparisons Using Rank Sums. R package version 1.3.5.

  • Dotti, C. G., Sullivan, C. A., & Banker, G. A. (1988). The establishment of polarity by hippocampal neurons in culture. Journal of Neuroscience, 8, 1454–1468.

    Article  CAS  PubMed  Google Scholar 

  • Ferrante, M., Migliore, M., & Ascoli, G. A. (2013). Functional impact of dendritic branch-point morphology. Journal of Neuroscience, 33, 2156–2165.

    Article  CAS  PubMed  Google Scholar 

  • Ferreira Castro, A., Baltruschat, L., Stürner, T., Bahrami, A., Jedlicka, P., Tavosanis, G., & Cuntz, H. (2020). Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction. eLife, 9.

  • Gillette, T. A., & Grefenstette, J. J. (2009). On comparing neuronal morphologies with the constrained tree-edit-distance. Neuroinformatics, 7, 191–4.

  • Gross, J., & Ligges, U. (2015). nortest: Tests for Normality [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=nortest. (R package version 1.0-4).

  • Heumann, H., & Wittum, G. (2009). The tree-edit-distance, a measure for quantifying neuronal morphology. Neuroinformatics, 7, 179–90.

    Article  PubMed  Google Scholar 

  • Ho, S.-Y., Chao, C.-Y., Huang, H.-L., Chiu, T.-W., Charoenkwan, P., & Hwang, E. (2011). NeurphologyJ: An automatic neuronal morphology quantification method and its application in pharmacological discovery. BMC Bioinformatics, 12, 230.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jefferis, G. S., Potter, C. J., Chan, A. M., Marin, E. C., Rohlfing, T., Maurer, C. R., & Luo, L. (2007). Comprehensive maps of Drosophila higher olfactory centers: Spatially segregated fruit and pheromone representation. Cell, 128, 1187–1203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kaech, S., & Banker, G. (2006). Culturing hippocampal neurons. Nature Protocols, 1, 2406–2415.

    Article  CAS  PubMed  Google Scholar 

  • Kanari, L., Dłotko, P., Scolamiero, M., Levi, R., Shillcock, J., Hess, K., & Markram, H. (2018). A topological representation of branching neuronal morphologies. Neuroinformatics, 16, 3–13.

    Article  PubMed  Google Scholar 

  • Kang, S., Chen, X., Gong, S., Yu, P., Yau, S., Su, Z., Zhou, L., Yu, J., Pan, G., & Shi, L. (2017). Characteristic analyses of a neural differentiation model from iPSC-derived neuron according to morphology, physiology, and global gene expression pattern. Scientific Reports, 7, 12233.

    Article  PubMed  PubMed Central  Google Scholar 

  • Khalil, R., Farhat, A., & Dłotko, P. (2021). Developmental changes in pyramidal cell morphology in multiple visual cortical areas using cluster analysis. Frontiers in Computational Neuroscience, 15, 667696.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kim, K.-M., Son, K., & Palmore, G. T. R. (2015). Neuron image analyzer: Automated and accurate extraction of neuronal data from low quality images. Scientific Reports, 5, 17062.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kluyver, T., Ragan-Kelley, B., Pérez, F., & Granger, B. (2016). Jupyter Notebooks - a publishing format for reproducible computational workflows. In F. Loizides & B. Schmidt (Eds.), Positioning and Power in Academic Publishing: Players, Agents and Agendas (pp. 87–90). IOS Press.

    Google Scholar 

  • Krichmar, J. L., Nasuto, S. J., Scorcioni, R., Washington, S. D., & Ascoli, G. A. (2002). Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: A simulation study. Brain Research, 941, 11–28.

    Article  CAS  PubMed  Google Scholar 

  • Laturnus, S., Kobak, D., & Berens, P. (2020). A systematic evaluation of interneuron morphology representations for cell type discrimination. Neuroinformatics, 18, 591–609.

    Article  PubMed  PubMed Central  Google Scholar 

  • Li, A., Barati Farimani, A., & Zhang, Y. J. (2021). Deep learning of material transport in complex neurite networks. Scientific Reports, 11, 11280.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li, A., Chai, X., Yang, G., & Zhang, Y. J. (2019). An isogeometric analysis computational platform for material transport simulation in complex neurite networks. Molecular & Cellular Biomechanics, 16, 123–140.

    Article  Google Scholar 

  • Li, A., & Zhang, Y. J. (2022a). Modeling intracellular transport and traffic jam in 3D neurons using PDE-constrained optimization. Journal of Mechanics, 38, 44–59.

    Article  Google Scholar 

  • Li, A., & Zhang, Y. J. (2022b). Modeling material transport regulation and traffic jam in neurons using PDE-constrained optimization. Scientific Reports, 12, 3902.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liao, A. S., Webster-Wood, V. A., & Zhang, Y. J. (2021). Quantification of neuron morphological development using the change-point test. In 2021 Summer Biomechanics, Bioengineering and Biotransport Conference. Virtual.

  • Mainen, Z. F., & Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363–366.

    Article  CAS  PubMed  Google Scholar 

  • Meijering, E., Jacob, M., Sarria, J.-C., Steiner, P., Hirling, H., & Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry, 58A, 167–176.

    Article  Google Scholar 

  • Polavaram, S., Gillette, T. A., Parekh, R., & Ascoli, G. A. (2014). Statistical analysis and data mining of digital reconstructions of dendritic morphologies. Frontiers in Neuroanatomy, 8, 138.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pool, M., Thiemann, J., Bar-Or, A., & Fournier, A. E. (2008). NeuriteTracer: A novel ImageJ plugin for automated quantification of neurite outgrowth. Journal of Neuroscience Methods, 168, 134–139.

    Article  PubMed  Google Scholar 

  • Powell, S. K., Rivas, R. J., Rodriguez-Boulan, E., & Hatten, M. E. (1997). Development of polarity in cerebellar granule neurons. Journal of Neurobiology, 32, 223–236.

    Article  CAS  PubMed  Google Scholar 

  • Python Core Team. (2021). Python: A Dynamic, Open Source Programming Language. Python Software Foundation. Retrieved from https://www.python.org/

  • Qian, K., Pawar, A., Liao, A., Anitescu, C., Webster-Wood, V., Feinberg, A., Rabczuk, T., & Zhang, Y. J. (2022). Modeling neuron growth using isogeometric collocation based phase field method. Scientific Reports, 12, 8120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • R Core Team. (2021). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

  • RStudio Team. (2021). RStudio: Integrated Development Environment for R. Boston, MA: RStudio, PBC. Retrieved from http://www.rstudio.com/

  • Rueden, C. T., Schindelin, J., Hiner, M. C., DeZonia, B. E., Walter, A. E., Arena, E. T., & Eliceiri, K. W. (2017). Image J2: ImageJ for the next generation of scientific image data. BMC Bioinformatics, 18, 529.

    Article  PubMed  PubMed Central  Google Scholar 

  • Schaefer, A. T., Larkum, M. E., Sakmann, B., & Roth, A. (2003). Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. Journal of Neurophysiology, 89, 3143–3154.

    Article  PubMed  Google Scholar 

  • Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J. -Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: An open-source platform for biological-image analysis. Nature Methods, 9, 676–682.

    Article  CAS  PubMed  Google Scholar 

  • Sholl, D. A. (1953). Dendritic organization in the neurons of the visual and motor cortices of the cat. Journal of anatomy, 87, 387–406.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Su, C. -Z., Chou, K. -T., Huang, H. -P., Li, C. -J., Charng, C. -C., Lo, C. -C., & Wang, D. -W. (2021). Identification of neuronal polarity by node-based machine learning. Neuroinformatics, 19, 669–684.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tahirovic, S., & Bradke, F. (2009). Neuronal polarity. Cold Spring Harbor Perspectives in Biology, 1, a001644–a001644.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tamariz, E., & Varela-Echavarría, A. (2015). The discovery of the growth cone and its influence on the study of axon guidance. Frontiers in Neuroanatomy, 9, 51.

    Article  PubMed  PubMed Central  Google Scholar 

  • Thermo Fisher Scientific. (2018). B-27 Plus Neuronal Culture System. Life Technologies. Retrieved from https://www.thermofisher.com/document-connect/document-connect.html?url=https://assets.thermofisher.com/TFSAssets/LSG/manuals/MAN0017319_B27_PlusNeuronalCultureSystem_UG.pdf

  • Uylings, H. B. M., & van Pelt, J. (2002). Measures for quantifying dendritic arborizations. Network: Computation in Neural Systems, 13, 397–414.

  • van Elburg, R. A. J., & van Ooyen, A. (2010). Impact of dendritic size and dendritic topology on burst firing in pyramidal cells. PLoS Computational Biology, 6, e1000781.

    Article  PubMed  PubMed Central  Google Scholar 

  • Vetter, P., Roth, A., & Häusser, M. (2001). Propagation of action potentials in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85, 926–937.

    Article  CAS  PubMed  Google Scholar 

  • Waskom, M. (2021). Seaborn: Statistical data visualization. Journal of Open Source Software, 6, 3021.

    Article  Google Scholar 

  • Zomorrodi, R., Ferecskó, A. S., Kovács, K., Kröger, H., & Timofeev, I. (2010). Analysis of morphological features of thalamocortical neurons from the ventroposterolateral nucleus of the cat. The Journal of Comparative Neurology, 518, 3541–3556.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the anonymous reviewers for helpful comments on an earlier version of this manuscript.

Funding

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1745016, the Faculty Early Career Development Program under Grant No. ECCS-2044785 and the LEAP HI Program under Grant No. CMMI-1953323. The authors were also supported in part by a PITA (Pennsylvania Infrastructure Technology Alliance) grant and a PMFI (Pennsylvania Manufacturing Fellows Initiative) grant. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Conceptualization: VAW, YJZ; Data Curation: (Lead) ASL, (Supporting) WC; Formal Analysis: ASL; Funding Acquisition: VAW, YJZ; Investigation: ASL; Methodology: ASL, VAW; Software: ASL; Supervision: VAW, YJZ; Visualization: ASL; Writing - Original Draft: ASL; Writing - Review & Editing: ASL, VAW, YJZ, (Supporting) WC

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Correspondence to Victoria A. Webster-Wood.

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Appendix: Distributions and Analyses on All Morphometrics for All Observed Time Points

Appendix: Distributions and Analyses on All Morphometrics for All Observed Time Points

A summary of the Dunn tests along with each feature’s distributions are showcased in Fig. 8. The Dunn tests with a Bonferroni correction indicated significant differences between several time points, as outlined in the corresponding Tables below, for segment length (Fig. 8a, Table 10), number of change points (Fig. 8c, Table 12), total length (Fig. 8d, Table 13), number of neurites (Fig. 8e, Table 14), and degree (Fig. 8g, Table 16). No significant differences between time points were detected for turning angle (Fig. 8b, Table 11), and significant differences were only detected between DIV 1.5 and 3 for tortuosity (Fig. 8f, Table 15).

Fig. 8
figure 8

The distributions and results of the Dunn test with a Bonferroni correction used to assess each morphometric, a average segment length, b average relative turning angle, c number of change points; d total length, e number of neurites, f average tortuosity, g degree, for every time point pair are symbolically represented, as defined in h

Additionally, the sample sizes of the data set are reported in Table 1. The summary statistics and Anderson-Darling results for all of the morphometrics are detailed in Tables 2, 3, 4, 5, 6, 7 and 8. The \(\chi ^2\) and \(p\)-values from the Kruskal-Wallis tests for each feature are in Table 9. Lastly, the post-hoc Dunn tests with a Bonferroni correction \(p\)-values are in Tables 10, 11, 12, 13, 14, 15 and 16.

Table 1 Overall Data Set Size
Table 2 Summary Statistics and Anderson-Darling Results for Average Segment Length Per Cell (\(\mu\)m)
Table 3 Summary Statistics Anderson-Darling Results for Average Relative Turning Angle Per Cell (\(^\circ\))
Table 4 Summary Statistics Anderson-Darling Results for Number of Change Points Per Cell
Table 5 Summary Statistics Anderson-Darling Results for Total Length of All Neurites Per Cell (\(\mu\)m)
Table 6 Summary Statistics Anderson-Darling Results for Total Number of Neurites Per Cell
Table 7 Summary Statistics Anderson-Darling Results for Average Tortuosity Per Cell
Table 8 Summary Statistics Anderson-Darling Results for Degree Per Cell
Table 9 Kruskal-Wallis Test Results For All Features
Table 10 Dunn Test with Bonferroni Correction p-values for the Average Segment Length Per Cell (\(\mu\)m)
Table 11 Dunn Test with Bonferroni Correction p-values for the Average Relative Turning Angle Per Cell (\(^\circ\))
Table 12 Dunn Test with Bonferroni Correction p-values for the Number of Change Points Per Cell
Table 13 Dunn Test with Bonferroni Correction p-values for the Total Length of All Neurites Per Cell (\(\mu\)m)
Table 14 Dunn Test with Bonferroni Correction p-values for the Number of Neurites Per Cell
Table 15 Dunn Test with Bonferroni Correction p-values for the Average Tortuosity Per Cell
Table 16 Dunn Test with Bonferroni Correction p-values for the Degree Per Cell

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Liao, A.S., Cui, W., Zhang, Y.J. et al. Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test. Neuroinform 21, 163–176 (2023). https://doi.org/10.1007/s12021-022-09600-8

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