Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor

Fischer, Stephan; Crow, Megan; Harris, Benjamin; Gillis, Jesse

doi:10.1038/s41596-021-00575-5

Cited by 30 publications

(42 citation statements)

References 31 publications

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“…For each species, cell type annotations defined on the basis of unsupervised clustering of snRNA-seq datasets at different levels of granularity were obtained from the BICCN. To assess the replicability of cell classes and subclasses across species, we used MetaNeighbor ( 31, 32 ), which identifies cell types with highly similar transcriptional signatures within and across species. Cells in each species were categorized into three classes (non-neurons, excitatory and inhibitory neurons) and 24 subclasses, and were near-perfectly replicable across species, confirming that cell types have distinct transcriptomic profiles that distinguish them at broad levels of cell classification ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…1F ). We also functionally characterized our consensus clusters by identifying HGNC- and SynGO-curated gene groups that contributed the most to replicability ( 32 ). Genes related to cell adhesion and neuronal signaling were most informative of cell type identity, and showed similar classification performance when trained and tested in the same or different species ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…All analyses were performed in R version 4. MetaNeighbor v1.12 ( 31, 32 ) was used to provide a measure of neuronal and non-neuronal subclass and cluster replicability within and across species. We used OrthoDB v10.1 ( 34 ) to shortlist 14,131 orthologs across five primates, and subset snRNA-seq datasets from each species to this list of common orthologs before further analysis.…”

Section: Methodsmentioning

confidence: 99%

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Conserved coexpression at single cell resolution across primate brains

Suresh

Crow²,

Jorstad

et al. 2022

Preprint

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Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic differences remain poorly understood, in part due to the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell-type specific gene coexpression networks for comparative analysis. While ortholog expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3383/14,131), which are also associated with multiple brain disorders. To validate these observations, we perform a meta-analysis of coexpression networks across 19 animals, and find that a subset of these genes have deeply conserved coexpression across all non-human animals, and strongly divergent coexpression relationships in humans (139/3383, <1% of primate orthologs). Genes with human-specific cellular expression and coexpression networks (like NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Conserved coexpression at single cell resolution across primate brains

Suresh

Crow²,

Jorstad

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Cell type identities for each cluster in the integrated data set were assigned using a combination of R package MetaNeighbor (Crow et al, 2018) and differential gene expression analysis (Cain et al, 2020). Reference data set previously trained on the Brain Initiative Cell Census Network (BICCN) mouse primary motor cortex data sets was utilized in MetaNeighbor as previously described (Fischer et al, 2021) to determine best cell type match for each cluster. The AUROC (area under the receiver operator characteristic curve) was calculated for each cluster, with an average of 0.86 ± 0.10 for highest AUROC (Table S2).…”

Section: Methodsmentioning

confidence: 99%

Conserved cell-type specific signature of resilience to Alzheimer’s disease nominates role for excitatory intratelencephalic cortical neurons

Telpoukhovskaia

Hadad

Gurdon

et al. 2022

Preprint

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Alzheimer’s disease (AD), the leading cause of dementia, affects millions of people worldwide. With no disease-modifying medication currently available, the human toll and economic costs are rising rapidly. Under current standards, a patient is diagnosed with AD when both cognitive decline and pathology (amyloid plaques and neurofibrillary tangles) are present. Remarkably, some individuals who have AD pathology remain cognitively normal. Uncovering factors that lead to “cognitive resilience” to AD is a promising path to create new targets for therapies. However, technical challenges discovering novel human resilience factors limit testing, validation, and nomination of novel drugs for AD. In this study, we use single-nuclear transcriptional profiles of postmortem cortex from human individuals with high AD pathology who were either cognitively normal (resilient) or cognitively impaired (susceptible) at time of death, as well as mouse strains that parallel these differences in cognition with high amyloid. Our cross-species discovery approach highlights a novel role for excitatory layer 4/5 cortical neurons in promoting cognitive resilience to AD, and nominates several resilience genes that include ATP1A1, GABRB1, PTK2, and ROCK2. Nominated resilience genes were tested for replication in orthogonal data sets and confirmed to be correlated with cognitive resilience. Additionally, we identified several potential mechanisms of resilience, including regulation of membrane potential, axonal and dendritic growth, and general increase of protein cycle, potentially of membrane proteins. Because our discovery of resilience-associated genes in layer 4/5 cortical neurons originates from an integrated human and mouse transcriptomic space from susceptible and resilient individuals, we are positioned to test causality and perform mechanistic, validation, and pre-clinical studies in our human-relevant AD-BXD mouse panel.

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“…To systematically assess the transcriptional similarity between cell types within species, we ran MetaNeighbor analysis 35 for each pair of cell atlases using python package pyMN 36 . Area Under the Receiver Operating Characteristic Curve (AUROC) scores were used to quantify the similarity of cell-type pairs.…”

Section: Metaneighbor Analysismentioning

confidence: 99%

Inferring predictive genetic models and regulatory elements by deep learning of cross-species single-cell gene expression landscapes

Li¹,

Wang

Zhang³

et al. 2022

Preprint

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Despite extensive efforts to sequence different genomes, genetic models to interpret gene regulation and cell fate decisions are lacking for most species. Here, we performed whole-body single-cell transcriptomic analysis of zebrafish, Drosophila, and earthworm. We then mapped cell landscapes covering eight representative metazoan species to study gene regulation through evolution. With uniformly constructed cross-species datasets, we developed a deep learning-based strategy, Nvwa, to predict gene expression landscapes and decipher cis-regulatory elements (CREs) at the single-cell level. We systematically compared cell type-specific transcription factors (TFs) and CREs to reveal conserved genetic regulation among vertebrates and invertebrates. Our work provides a valuable resource and a novel strategy for studying regulatory language in diverse biological systems.

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Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor

Cited by 30 publications

References 31 publications

Conserved coexpression at single cell resolution across primate brains

Conserved coexpression at single cell resolution across primate brains

Conserved cell-type specific signature of resilience to Alzheimer’s disease nominates role for excitatory intratelencephalic cortical neurons

Inferring predictive genetic models and regulatory elements by deep learning of cross-species single-cell gene expression landscapes

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