Single-cell and Single-nuclei RNA Sequencing As Powerful Tools to Decipher Cellular Heterogeneity and Dysregulation in Neurodegenerative Diseases

Overview

Journal Front Cell Dev Biol

Specialty Cell Biology

Date 2022 Nov 10

PMID 36353512

Authors

Raquel Cuevas-Diaz Duran

Juan Carlos Gonzalez-Orozco

Ivan Velasco

Jia Qian Wu

Affiliations

Soon will be listed here.

Abstract

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

Citing Articles

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research.

Molla Desta G, Birhanu A Acta Biochim Pol. 2025; 72:13922.

PMID: 39980637 PMC: 11835515. DOI: 10.3389/abp.2025.13922.

Genetic Analysis of Retinal Cell Types in Neuropsychiatric Disorders.

Boudriot E, Stephan M, Rabe F, Smigielski L, Schmitt A, Falkai P JAMA Psychiatry. 2025; 82(3):285-295.

PMID: 39775833 PMC: 11883512. DOI: 10.1001/jamapsychiatry.2024.4230.

Unraveling Alzheimer's disease: insights from single-cell sequencing and spatial transcriptomic.

He Y, Lu W, Zhou X, Mu J, Shen W Front Neurol. 2025; 15:1515981.

PMID: 39741706 PMC: 11685091. DOI: 10.3389/fneur.2024.1515981.

A most important gift: the critical role of postmortem brain tissue in autism science.

Natowicz M, Bauman M, Edelson S Front Neurol. 2024; 15:1486227.

PMID: 39726759 PMC: 11670190. DOI: 10.3389/fneur.2024.1486227.

The interplay between epitranscriptomic RNA modifications and neurodegenerative disorders: Mechanistic insights and potential therapeutic strategies.

Hashmi M, Fatima H, Ahmad S, Rehman A, Safdar F Ibrain. 2024; 10(4):395-426.

PMID: 39691424 PMC: 11649393. DOI: 10.1002/ibra.12183.

References

Street K, Risso D, Fletcher R, Das D, Ngai J, Yosef N . Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018; 19(1):477. PMC: 6007078. DOI: 10.1186/s12864-018-4772-0. View

Campbell K, Yau C . A descriptive marker gene approach to single-cell pseudotime inference. Bioinformatics. 2018; 35(1):28-35. PMC: 6298060. DOI: 10.1093/bioinformatics/bty498. View

Kotliar D, Veres A, Nagy M, Tabrizi S, Hodis E, Melton D . Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. Elife. 2019; 8. PMC: 6639075. DOI: 10.7554/eLife.43803. View

Cuevas-Diaz Duran R, Wei H, Wu J . Single-cell RNA-sequencing of the brain. Clin Transl Med. 2017; 6(1):20. PMC: 5465230. DOI: 10.1186/s40169-017-0150-9. View

Kharchenko P . The triumphs and limitations of computational methods for scRNA-seq. Nat Methods. 2021; 18(7):723-732. DOI: 10.1038/s41592-021-01171-x. View

Heemels M . Neurodegenerative diseases. Nature. 2016; 539(7628):179. DOI: 10.1038/539179a. View

Grun D, van Oudenaarden A . Design and Analysis of Single-Cell Sequencing Experiments. Cell. 2015; 163(4):799-810. DOI: 10.1016/j.cell.2015.10.039. View

Duren Z, Chen X, Zamanighomi M, Zeng W, Satpathy A, Chang H . Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations. Proc Natl Acad Sci U S A. 2018; 115(30):7723-7728. PMC: 6065048. DOI: 10.1073/pnas.1805681115. View

Regev A, Teichmann S, Lander E, Amit I, Benoist C, Birney E . The Human Cell Atlas. Elife. 2017; 6. PMC: 5762154. DOI: 10.7554/eLife.27041. View

10.

Leng K, Li E, Eser R, Piergies A, Sit R, Tan M . Molecular characterization of selectively vulnerable neurons in Alzheimer's disease. Nat Neurosci. 2021; 24(2):276-287. PMC: 7854528. DOI: 10.1038/s41593-020-00764-7. View

11.

Zhu Y, Wang L, Yin Y, Yang E . Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci Rep. 2017; 7(1):5435. PMC: 5511187. DOI: 10.1038/s41598-017-05882-0. View

12.

Jiang L, Chen H, Pinello L, Yuan G . GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol. 2016; 17(1):144. PMC: 4930624. DOI: 10.1186/s13059-016-1010-4. View

13.

Kilpinen H, Goncalves A, Leha A, Afzal V, Alasoo K, Ashford S . Common genetic variation drives molecular heterogeneity in human iPSCs. Nature. 2017; 546(7658):370-375. PMC: 5524171. DOI: 10.1038/nature22403. View

14.

La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V . RNA velocity of single cells. Nature. 2018; 560(7719):494-498. PMC: 6130801. DOI: 10.1038/s41586-018-0414-6. View

15.

Stahl P, Salmen F, Vickovic S, Lundmark A, Fernandez Navarro J, Magnusson J . Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353(6294):78-82. DOI: 10.1126/science.aaf2403. View

16.

Griffiths J, Scialdone A, Marioni J . Using single-cell genomics to understand developmental processes and cell fate decisions. Mol Syst Biol. 2018; 14(4):e8046. PMC: 5900446. DOI: 10.15252/msb.20178046. View

17.

Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland T . A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. Cell. 2017; 169(7):1276-1290.e17. DOI: 10.1016/j.cell.2017.05.018. View

18.

Chan T, Stumpf M, Babtie A . Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures. Cell Syst. 2017; 5(3):251-267.e3. PMC: 5624513. DOI: 10.1016/j.cels.2017.08.014. View

19.

Baran-Gale J, Chandra T, Kirschner K . Experimental design for single-cell RNA sequencing. Brief Funct Genomics. 2017; 17(4):233-239. PMC: 6063265. DOI: 10.1093/bfgp/elx035. View

20.

Bendall S, Davis K, Amir E, Tadmor M, Simonds E, Chen T . Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014; 157(3):714-25. PMC: 4045247. DOI: 10.1016/j.cell.2014.04.005. View