Computational Analysis of Cell Type-Specific Transcriptional Dynamics in Major Depressive Disorder

Elaine Zhang

doi:10.62051/mx5z0958

Authors

Elaine Zhang

DOI:

https://doi.org/10.62051/mx5z0958

Keywords:

Major depressive disorder (MDD); Dorsolateral prefrontal cortex (dlPFC); Transcriptomic dynamics.

Abstract

Depression, also known as major depressive disorder (MDD), is a complex mental health condition that affects millions of people worldwide. Since disease treatments often depend on levels of understanding, this project strives to learn more about the transcriptional components of MDD to delineate the transcriptomic dynamics in MDD. The sample includes the single-cell transcriptomic profiles in the dorsolateral prefrontal cortex (dlPFC) of 34 postmortem patients. This data was acquired from publicly available datasets and run through R-Studio code. Cell clusters were classified into cell types using cell-type-specific signature markers in the original resource datasets. After analyzing immediate early genes (IEGs), MDD-associated genes, and differentially expressed genes, the trend showed broadly lowered expression levels and frequency in MDD patients than in healthy control with some exceptions. After studying 10 different IEGs, the trend showed a lowered gene expression frequency in MDD patients than in control. For MDD-associated genes, a similar trend showed lower frequency and expression levels in the MDD sample, apart from GAD1 and RELN, which had a higher expression level in the MDD sample. Then, by analyzing the top differentially expressed genes in each cell type, most cells showed a lowered frequency in MDD patients besides macrophage/microglial, which had higher expression frequency in IGHG1 and RP11-315A16.1 for the control sample. With each analytical finding, the underlying elements of MDD may be better understood, leading to more precise wet lab experiments and the eventual treatment of MDD.

Downloads

Download data is not yet available.

References

[1] Lee, B., Wang, Y., Carlson, S. A., Greenlund, K. J., Lu, H., Liu, Y., Croft, J. B., Eke, P. I., Town, M., & Thomas, C. W. (2023). National, State-Level, and County-Level Prevalence Estimates of Adults Aged ≥18 Years Self-Reporting a Lifetime Diagnosis of Depression — United States, 2020. MMWR. Morbidity and Mortality Weekly Report, 72 (24), 644–650. https://doi.org/10.15585/mmwr.mm7224a1.

[2] American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787.

[3] Mojtabai, R. (2013). Clinician-Identified Depression in Community Settings: Concordance with Structured-Interview Diagnoses. Psychotherapy and Psychosomatics, 82 (3), 161–169. https://doi.org/10.1159/000345968.

[4] Goyal, N., Siddiqui, S., Chatterjee, U., Kumar, D., & Siddiqui, A. (2008). Neuropsychology of prefrontal cortex. Indian Journal of Psychiatry, 50 (3), 202–208. https://doi.org/10.4103/0019-5545.43634.

[5] Northoff, G., Sibille, E. (2014). Why are cortical GABA neurons relevant to internal focus in depression? A cross-level model linking cellular, biochemical and neural network findings. Molecular Psychiatry 19, 966–977. https://doi.org/10.1038/mp.2014.68.

[6] Christodoulou, C., Onisiforou, A., Zanos, P., & Papanicolaou, E. Z. (2023). Unraveling the transcriptomic signatures of Parkinson’s disease and major depression using single-cell and bulk data. Frontiers in Aging Neuroscience, 15 (1663-4365). https://doi.org/10.3389/fnagi.2023.1273855.

[7] Li, H. J., Su, X., Zhang, L. W., Zhang, C. Y., Wang, L., Li, W. Q., Yang, Y. F., Lv, L. X., Li, M., & Xiao, X. (2020). Transcriptomic analyses of humans and mice provide insights into depression. Zoological Research, 41 (6), 632–643. https://doi.org/10.24272/j.issn.2095-8137.2020.174.

[8] Pérez-Cadahía, B., Drobic, B., & Davie, J. R. (2011). Activation and function of immediate-early genes in the nervous system. Biochemistry and Cell Biology, 89 (1), 61–73. https://doi.org/10.1139/O10-138.

[9] Covington, H. E., Lobo, M. K., Maze, I., Vialou, V., Hyman, J. M., Zaman, S., LaPlant, Q., Mouzon, E., Ghose, S., Tamminga, C. A., Neve, R. L., Deisseroth, K., & Nestler, E. J. (2010). Antidepressant Effect of Optogenetic Stimulation of the Medial Prefrontal Cortex. Journal of Neuroscience, 30 (48), 16082–16090. https://doi.org/10.1523/jneurosci.1731-10.2010.

[10] Zeng, D., He, S., Ma, C., Wen, Y., Song, W., Xu, Q., Zhao, N., Wang, Q., Yu, Y., Shen, Y., Huang, J., & Li, H. (2020). Network-based approach to identify molecular signatures in the brains of depressed suicides. Psychiatry Research, 294, 113513. https://doi.org/10.1016/j.psychres.2020.113513.

[11] Nagy, C., Maitra, M., Tanti, A., Suderman, M., Théroux, J.-F., Davoli, M. A., Perlman, K., Yerko, V., Wang, Y. C., Tripathy, S. J., Pavlidis, P., Mechawar, N., Ragoussis, J., & Turecki, G. (2020). Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nature Neuroscience, 23, 771–781. https://doi.org/10.1038/s41593-020-0621-y.

[12] Luscher, B., Shen, Q., & Sahir, N. (2010). The GABAergic deficit hypothesis of major depressive disorder. Molecular Psychiatry, 16 (4), 383–406. https://doi.org/10.1038/mp.2010.120.

[13] Hettema, J. M., An, S. S., Neale, M. C., Bukszar, J., van den Oord, E. J. C. G., Kendler, K. S., & Chen, X. (2006). Association between glutamic acid decarboxylase genes and anxiety disorders, major depression, and neuroticism. Molecular Psychiatry, 11 (8), 752–762. https://doi.org/10.1038/sj.mp.4001845.

[14] Bučić, M., Pregelj, P., Zupanc, T., & Videtič Paska, A. (2016). Completed suicide, depression, and RELN polymorphisms. Psychiatric Genetics, 26 (5), 218–222. https://doi.org/10.1097/ypg.0000000000000142.

[15] Dey, A., & Hankey Giblin, P. (2018). Insights into Macrophage Heterogeneity and Cytokine-Induced Neuroinflammation in Major Depressive Disorder. Pharmaceuticals, 11 (3), 64. https://doi.org/10.3390/ph11030064.

[16] Wang, H., He, Y., Sun, Z., Ren, S., Liu, M., Wang, G., & Yang, J. (2022). Microglia in depression: an overview of microglia in the pathogenesis and treatment of depression. Journal of Neuroinflammation, 19 (1), 132. https://doi.org/10.1186/s12974-022-02492-0.

[17] Wei, D. C., & Morrison, E. H. (2023, May 1). Histology, Astrocytes. National Library of Medicine; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK545142/.

[18] Medina-Rodriguez, E. M., & Beurel, E. (2022). Blood brain barrier and inflammation in depression. Neurobiology of Disease, 175, 105926. https://doi.org/10.1016/j.nbd.2022.105926.