Retina cell atlases of multiple species and an online platform for retina cell-type markers

Xu, Zaoxu; Wang, Changzheng; Chen, Min; Yuan, Yuting; Li, Linzhou; Huang, Zhen; Yuan, Yue; Yang, Huanming; Wang, Qi; Zhang, Xingliang

doi:10.1016/j.jgg.2021.10.008

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…s cell," and canonical markers of "microglial cell" (C1QC, C1QA, CSF1R, and CD74) (Xu et al 2022) are indeed highly expressed but rarely those of "amacrine cell" (Supplemental Fig. S16B), suggesting CAME could potentially correct prior annotations.…”

Section: A B Cmentioning

confidence: 99%

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

2022

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Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model CAME to learn aligned and interpretable cell and gene embeddings for cross-species cell type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species due to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Large-scale benchmarking study showed that CAME significantly outperformed four classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME could transfer the major cell types and interneuron subtypes of human brains to mouse ones and discover shared cell type-specific functions in homologous gene modules. CAME could well align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell type assignments even for nonmodel species, and uncover shared and divergent characteristics between two species from scRNA-seq data.

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Section: A B Cmentioning

confidence: 99%

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

2022

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“…However, there is still a lack of comprehensive understanding of the similarities and divergences for GABAergic neuron cell types, especially the GABAergic neuron cell heterogeneity across species.Unlike conventional sequencing methods, single-cell/nucleus RNA sequencing (scRNA-seq/snRNA-seq) technologies can accurately profile transcriptomic characterization of individual whole cell and nucleus, respectively. Especially, both scRNA-seq and snRNA-seq enable profiling the complete cellular makeup in the brain, classifying the cell type heterogeneity of neurons [6][7][8][9] . The GABAergic neuron cell types in the cerebral cortex for human 10,11 , macaque 12,13 , mouse 14,15 , and pig 16 were distinguished at the single-cell level.…”

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confidence: 99%

Integrative cross-species analysis of GABAergic neuron cell types and their functions in Alzheimer’s disease

Wang

Ding

Yuan

et al. 2022

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Understanding the phenotypic and functional diversity of cerebral cortical GABAergic neurons requires a comprehensive analysis of key transcriptional signatures and neuronal subtype identity. However, the diversity and conservation of GABAergic neurons across multiple mammals remain unclear. Here, we collected the single-nucleus RNA sequencing (snRNA-seq) datasets of cerebral cortex from human, macaque, mouse, and pig to identify the conserved neuronal cell types across species. After systematic analysis of the heterogeneity of GABAergic neurons, we defined four major conserved GABAergic neuron subclasses (Inc SST, Inc LAMP5, Inc PVALB, and Inc VIP) across species. We characterized the species-enriched subclasses of GABAergic neurons from four mammals, such as Inc Meis2 in mouse. Then, we depicted the genetic regulatory network (GRNs) of GABAergic neuron subclasses, which showed the conserved and species-specific GRNs for GABAergic neuron cell types. Finally, we investigated the GABAergic neuron subclass-specific expression modules of Alzheimer’s disease (AD)-related genes in GABAergic neuron cell types. Overall, our study reveals the conserved and divergent GABAergic neuron subclasses and GRNs across multiple species and unravels the gene expression modules of AD-risk genes in GABAergic neuron subclasses, facilitating the GABAergic neurons research and clinical treatment.

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Retina cell atlases of multiple species and an online platform for retina cell-type markers

Cited by 2 publications

References 18 publications

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

Integrative cross-species analysis of GABAergic neuron cell types and their functions in Alzheimer’s disease

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