Construction of a cross-species cell landscape at single-cell level

Wang, Renying; Zhang, Peijing; Wang, Jingjing; Ma, Lifeng; Weigao, E; Suo, Shengbao; Jiang, Mengmeng; Li, Jiaqi; Chen, Haide; Sun, Huiyu; Fei, Lijiang; Zhou, Ziyu; Zhou, Yincong; Chen, Yao; Zhang, Weiqi; Wang, Xinru; Mei, Yuqing; Sun, Zhongyi; Yu, Chengxuan; Shao, Jingyuan; Fu, Yuting; Xiao, Yanyu; Ye, Fang; Fang, Xing; Wu, Hanyu; Guo, Qile; Fang, Xiunan; Li, Xia; Gao, Xianzhi; Wang, Dan; Xu, Peng-Fei; Zeng, Rui; Xu, Gang; Zhu, Lijun; Wang, Lie; Qu, Jing; Zhang, Dan; Ouyang, Hongwei; Huang, He; Chen, Ming; Shyh‐Chang, Ng; Liu, Guang‐Hui; Yuan, Guo‐Cheng; Guo, Guoji; Han, Xiaoping

doi:10.1093/nar/gkac633

Cited by 74 publications

(51 citation statements)

References 76 publications

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“…For instance, a Vegfd-linked CRE overlapping with an ENCODE enhancer displayed enrichment in dead-end destined cells (Day 3), while expression changes were not detectable until Day 12. Similar regulatory changes were observed for Aox3 46 , a liver-associated aldehyde oxidase, and Col28a1, an oligodendrocyte enriched collagen 47 , prior to changes in gene expression (Fig. 4e,…”

Section: Linking Early State To Fate Reveals Molecular Features Of Of...supporting

confidence: 74%

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

Jindal

Adil

Yamaguchi

et al. 2022

Preprint

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Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics enabling high-resolution analysis of cell states while preserving lineage relationships. However, reliance on transcriptional profiling limits their adaptation to an ever-expanding tool kit of multiomic single-cell assays. With CellTag-multi, we present a novel approach for profiling lineage barcodes with single-cell chromatin accessibility without relying on co-assay of transcriptional state, paving the way for truly multiomic lineage tracing. We validate CellTag-multi in mouse hematopoiesis, characterizing transcriptional and epigenomic lineage priming across progenitor cell populations. In direct reprogramming of fibroblasts to endoderm progenitors, we use CellTag-multi to comprehensively link early cell state with reprogramming outcomes, identifying core regulatory programs underlying on-target and off-target reprogramming. Further, we reveal the Transcription Factor (TF) Zfp281 as a novel regulator of reprogramming outcome, biasing cells towards an off-target mesenchymal fate via its regulation of TGF-β signaling. Together, these results establish CellTag-multi as a novel lineage tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

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Section: Linking Early State To Fate Reveals Molecular Features Of Of...supporting

confidence: 74%

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

Jindal

Adil

Yamaguchi

et al. 2022

Preprint

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“…Progressive changes in brain cell populations, which occur during the normal aging process, may contribute to functional decline of the entire organ and increased risks for neurodegenerative diseases such as Alzheimer’s disease (AD) (Mathys et al, 2019; Xia et al, 2018). While the recent advances in single-cell genomics have created unprecedented opportunities to explore the cell-type-specific dynamics across the entire mammalian brain in aging and AD models (Morabito et al, 2021; Tabula Muris Consortium, 2020; Wang et al, 2022; Ximerakis et al, 2019), most prior studies relied on a relatively shallow sampling of the brain cell populations, possibly resulting in poor sensitivity to investigate the dynamics of cell types during aging, particularly with respect to rare aging or AD-associated cell types. While providing proof of key concepts, these prior studies were also technically limited in several ways, including failing to recover isoform-level gene expression patterns for rare cell types, providing few insights into how the chromatin landscape regulates cell-type-specific alterations across aging stages, and often lacked integrative analyses with spatial visualization to explore the anatomic region-specific changes.…”

Section: Introductionmentioning

confidence: 99%

A global view of aging and Alzheimer’s pathogenesis-associated cell population dynamics and molecular signatures in the human and mouse brains

Sziraki

Lee

et al. 2022

Preprint

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Although several studies have applied single-cell approaches to explore gene expression changes in aged brains, they were limited by the relatively shallow sampling of brain cell populations, and thus may have failed to capture aspects of the molecular signatures and dynamics of rare cell types associated with aging and diseases. Here, we set out to investigate the age-dependent dynamics of transcription and chromatin accessibility across diverse brain cell types. With EasySci, an extensively improved single-cell combinatorial indexing strategy, we profiled ~1.5 million single-cell transcriptomes and ~400,000 single-cell chromatin accessibility profiles across mouse brains spanning different ages, genotypes, and both sexes. With a novel computational framework designed for characterizing cellular subtypes based on the expression of both genes and exons, we identified > 300 cell subtypes and deciphered the underlying molecular programs and spatial locations of rare cell types (e.g., pinealocytes, tanycytes) and subtypes. Leveraging these data, we generate a global readout of age-dependent cell population dynamics with high cellular subtype resolution, providing insights into cell types that expand (e.g., rare astrocytes and vascular leptomeningeal cells in the olfactory bulb, reactive microglia and oligodendrocytes) or are depleted (e.g., neuronal progenitors, neuroblasts, committed oligodendrocyte precursors) as age progresses. Furthermore, we explored cell-type-specific responses to genetic perturbations associated with Alzheimer's disease (AD) and identify rare cell types depleted (e.g., mt-Cytb+, mt-Rnr2+ choroid plexus epithelial cells) or enriched (e.g., Col25a1+, Ndrg1+ interbrain and midbrain neurons) in both AD models. Key findings are consistent between males and females, validated across the transcriptome, chromatin accessibility, and spatial analyses. Finally, we profiled a total of 118,240 single-nuclei transcriptomes from twenty-four human brain samples derived from control and AD patients, revealing highly cell-type-specific and region-specific gene expression changes associated with AD pathogenesis. Critical AD-associated gene signatures were validated in both human and mice. In summary, these data comprise a rich resource for exploring cell-type-specific dynamics and the underlying molecular mechanisms in both normal and pathological mammalian aging.

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“…S2). These clusters are representative of most major cell types found in these tissues (Jiang et al 2021; Wang et al 2022).…”

Section: Resultsmentioning

confidence: 99%

“…For each tissue, we used the ‘FindAllMarkers’ function in Seurat to identify differentially expressed genes for each cell cluster. We then annotated the major cell types within each cluster based on marker gene information from the Zebrafish Cell Landscape (Jiang et al 2021; Wang et al 2022) and other published work (Morrison et al 2021; Liu et al 2022; Qian et al 2022).…”

Section: Methodsmentioning

confidence: 99%

Sex-biased gene expression at single-cell resolution: Cause and consequence of sexual dimorphism

Darolti

Mank

2022

Preprint

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Gene expression differences between males and females are thought to be key for the evolution of sexual dimorphism, and sex-biased genes are often used to study the molecular footprint of sex-specific selection. However, gene expression is often measured from complex aggregations of diverse cell types, making it difficult to distinguish between sex differences in expression that are due to regulatory rewiring within similar cell types and those that are simply a consequence of developmental differences in cell type abundance. To determine the role of regulatory versus developmental differences underlying sex-biased gene expression, we use single-cell transcriptomic data from multiple somatic and reproductive tissues of male and female guppies, a species which exhibits extensive phenotypic sexual dimorphism. Our analysis of gene expression at single-cell resolution demonstrates that non-isometric scaling between the cell populations within each tissue and heterogeneity in cell type abundance between the sexes can influence inferred patterns of sex-biased gene expression by increasing both the false-positive and false-negative rates. Moreover, we show that at the bulk level, the subset of sex-biased genes that are the product of sex differences in cell type abundance can significantly confound patterns of coding-sequence evolution. Taken together, our results offer a unique insight into the evolution of sex-biased gene expression and highlight the power of single-cell RNA-sequencing in disentangling between genes that are a cause as opposed to a consequence of sexual dimorphism.

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Construction of a cross-species cell landscape at single-cell level

Cited by 74 publications

References 76 publications

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

A global view of aging and Alzheimer’s pathogenesis-associated cell population dynamics and molecular signatures in the human and mouse brains

Sex-biased gene expression at single-cell resolution: Cause and consequence of sexual dimorphism

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