Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets

Caglayan, Emre; Liu, Yuxiang; Konopka, Genevieve

doi:10.1101/2022.03.09.483658

Cited by 7 publications

(3 citation statements)

References 57 publications

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“…A second round of cell filtering was performed after clustering to remove cells that were enriched in both Snap25 and Plp1 . We suspected that these cells represent neuronal/glial contaminated mixtures, as previously reported in single cell data 66 . After second-round filtering, 355,307 cells remained.…”

Section: Methodssupporting

confidence: 56%

“…A second round of cell filtering was performed after clustering to remove cells that were enriched in both Snap25 and Plp1. We suspected that these cells represent neuronal/glial contaminated mixtures, as previously reported in single cell data 55 Spatially variable genes were identified using SpatialDE2 with default settings. Coordinates and full expression matrices of each brain were supplied, divided by cell type.…”

Section: Processing Of Raw Stereo-seq Data Quality Control and Cell T...mentioning

confidence: 99%

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Single cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging and disease

Kilfeather

Khoo

Wagner

et al. 2023

Preprint

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The brain is spatially organized and contains unique cell types, performing diverse functions, and exhibiting differential susceptibility to neurodegeneration. This is exemplified in Parkinson's disease (PD) with the preferential loss of dopamine (DA) neurons of the substantia nigra pars compacta. Using a PD transgenic model, we conducted a single-cell spatial transcriptomic and DA neuron translatomic analysis of young and old mouse brains. Through the high resolving capacity of single-cell spatial transcriptomics, we provide a deep characterization of the expression features of DA neurons within their spatial context, identifying markers of healthy and aging cells, spanning PD-relevant pathways. We integrate marker enrichment and GWAS data to prioritize putative causative genes for disease investigation; identifying CASR as a novel regulator of DA neuron calcium handling. These datasets (accessible at spatialbrain.org) represent an invaluable resource for the investigation into spatially governed gene expression in brain cells in health, aging and disease.

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Section: Methodssupporting

confidence: 56%

“…A second round of cell filtering was performed after clustering to remove cells that were enriched in both Snap25 and Plp1. We suspected that these cells represent neuronal/glial contaminated mixtures, as previously reported in single cell data 55 Spatially variable genes were identified using SpatialDE2 with default settings. Coordinates and full expression matrices of each brain were supplied, divided by cell type.…”

Section: Processing Of Raw Stereo-seq Data Quality Control and Cell T...mentioning

confidence: 99%

Single cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging and disease

Kilfeather

Khoo

Wagner

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…However, it is possible that this could be due to an unwanted technical error during processing or alignment. Nuclei extraction through homogenization can result in ambient RNA, which can result in RNA contamination and obscure downstream analysis 30 .…”

Section: Discussionmentioning

confidence: 99%

Cell-associated Transcriptional Alterations in the Retinal of Alzheimer’s Disease

Ngolab

Mark

Korouri

et al. 2022

Preprint

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Current approaches for studying pathologic changes in the retina associated with Alzheimer's Disease (AD) remain heterogeneous, limiting the use of retinal amyloid-beta as a viable biomarker for AD. Transcriptomic profiling of the retina has provided cell-specific insight into AD progression in the brain yet is lacking in the retina. In this study, we implemented a non-biased approach through next-generation sequencing to profile frozen archived retinal tissues from autopsy/pathologically confirmed AD and non-diagnosed cases (NonAD). A total of 37,211 nuclei were isolated from frozen retinal tissue punches originating from AD, and 31,326 were isolated from non-diagnosed cases. Gene expression patterns specific to the retinal region and major retinal cell types were represented in both tissue groups. AD-associated genes were differentially expressed in AD retinal glial cells, including microglia. A greater percentage of microglial nuclei from AD retinal nuclei expressed TYRO protein tyrosine kinase-binding protein (TYROBP) compared to nonAD retinal nuclei. However, compared to microglia from single retinal cell datasets from elderly non-diseased individuals, TYROBP expression is highly expressed in the single cell data set, indicating TYROBP transcripts reside within the cytoplasm. However, other AD-associated genes were differentially expressed in AD nuclei such as DOCK2, PICALM, and PLCG2 compared to non-diseased single-cell microglia, implicating a role of these genes in the AD retina. To summarize, we extracted a high number of nuclei from frozen retinal tissue that retain specific gene markers for cell classification and highlighted candidate AD-associated genes in retinal microglia that may be viable in future AD retinal studies.

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Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis

Lehrer

Gonzalez-Ferrer

Haussler

et al. 2023

Preprint

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Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Machine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably higher accuracy than state-of-the-art single cell classifiers. This accuracy is maintained in trans-sample label transfers of the adult human cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal subtypes, even in periods of fate refinement. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities in previously unclassified cells and to uncover genetic variations in the developmental trajectories of organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

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Ambient RNA analysis reveals misinterpreted and masked cell types in brain single-nuclei datasets

Cited by 7 publications

References 57 publications

Single cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging and disease

Single cell spatial transcriptomic and translatomic profiling of dopaminergic neurons in health, aging and disease

Cell-associated Transcriptional Alterations in the Retinal of Alzheimer’s Disease

Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis

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