Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).

Article  CAS  PubMed  Google Scholar 

Kim, S. S. et al. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nat. Commun. 15, 563 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sun, N. et al. Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease. Nat. Neurosci. 26, 970–982 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cain, A. et al. Multicellular communities are perturbed in the aging human brain and Alzheimer’s disease. Nat. Neurosci. 26, 1267–1280 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kim, C. N., Shin, D., Wang, A. & Nowakowski, T. J. Spatiotemporal molecular dynamics of the developing human thalamus. Science 382, eadf9941 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pineda, S. S. et al. Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD. Cell 187, 1971–1989 (2024).

Article  CAS  PubMed  Google Scholar 

Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

Article  CAS  PubMed  Google Scholar 

Green, G. S. et al. Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease. Nature https://doi.org/10.1038/s41586-024-07871-6 (2024).

Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Davis, A., Gao, R. & Navin, N. E. SCOPIT: sample size calculations for single-cell sequencing experiments. BMC Bioinformatics 20, 566 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Schmid, K. T. et al. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun. 12, 6625 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Su, K., Wu, Z. & Wu, H. Simulation, power evaluation and sample size recommendation for single-cell RNA-seq. Bioinformatics 36, 4860–4868 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Phipson, B. et al. Propeller: testing for differences in cell type proportions in single cell data. Bioinformatics 38, 4720–4726 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lin, Y. et al. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Mol. Syst. Biol. 16, e9389 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jeon, H. et al. Statistical power analysis for designing bulk, single-cell, and spatial transcriptomics experiments: review, tutorial, and perspectives. Biomolecules 13, 221 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ryaboshapkina, M. & Azzu, V. Sample size calculation for a NanoString GeoMx spatial transcriptomics experiment to study predictors of fibrosis progression in non-alcoholic fatty liver disease. Sci. Rep. 13, 8943 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Colonna, M. et al. Implementation and validation of single-cell genomics experiments in neuroscience. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01814-0 (2024).

Zhang, Y. et al. Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome. Comput. Struct. Biotechnol. J. 21, 176–184 (2023).

Article  CAS  PubMed  Google Scholar 

Im, Y. & Kim, Y. A comprehensive overview of RNA deconvolution methods and their application. Mol. Cells 46, 99–105 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Charytonowicz, D., Brody, R. & Sebra, R. Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve. Nat. Commun. 14, 1350 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen, Y. et al. Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis. Nat. Commun. 13, 6735 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Liao, J. et al. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution. Nat. Commun. 13, 6498 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Heimberg, G., Bhatnagar, R., El-Samad, H. & Thomson, M. Low dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing. Cell Syst. 2, 239–250 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 9, 75 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Boyce, R. W., Dorph-Petersen, K. -A., Lyck, L. & Gundersen, H. J. G. Design-based stereology: introduction to basic concepts and practical approaches for estimation of cell number. Toxicol. Pathol. 38, 1011–1025 (2010).

Article  PubMed  Google Scholar 

Adameyko, I. et al. Applying single-cell/nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system. Nat. Neurosci. https://doi.org/10.1038/s41593-024-01827-9 (2024).

Yu, L., Cao, Y., Yang, J. Y. H. & Yang, P. Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data. Genome Biol. 23, 49 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Swapna, L. S., Huang, M. & Li, Y. GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes. Genome Biol. 24, 190 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang, S., Yang, L., Yang, J., Lin, Z. & Ng, M. K. Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization. NAR Genom. Bioinform. 2, lqaa064 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).

Article  CAS  PubMed  PubMed Central 

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