Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

Li, Chen; Virgilio, Maria; Collins, Kathleen L.; Welch, Joshua D.

doi:10.1038/s41587-022-01476-y

Cited by 66 publications

(63 citation statements)

References 73 publications

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“…For the bone marrow dataset, we use pseudotime to order cells, and for the mouse brain dataset, we adopt MultiVelo to estimate the latent time. MultiVelo, an extension of the dynamical RNA velocity model, uses a combination of scRNA-seq and scATAC-seq data to account for epigenomic regulation of gene expression [17]. It infers gene regulation information using a probabilistic latent variable model for switch time and rate parameters in a system of three ordinary differential equations.…”

Section: Methodsmentioning

confidence: 99%

“…We also apply HALO to developing mouse brain dataset with cell type annotation generated by [17]. Cells were clustered using the Scanpy pipeline [37] and manually annotated using canonical marker genes (Fig.…”

Section: Halo Delineates Cell States In Embryonic Mouse Brainmentioning

confidence: 99%

“…For the PBMC dataset, we use the cell type annotation generated by the 10X Genomics R&D team as surrogates for the ground truth [3]. Similarly, we used cell type annotations generated by [2] and [4] for the mouse brain dataset and bone marrow dataset, respectively. For temporal information, we utilize diffusion pseudotime annotation from the bone marrow dataset, while we infer latent time through RNA velocity analysis [1,2] for the mouse brain and PBMC datasets.…”

Section: Fig S1mentioning

confidence: 99%

“…Specifically, chromatin remodeling of a gene region and its transcription may become decoupled in situations such as: (1) when chromatin opens before transcription begins (also called "priming"), (2) RNA degradation and nuclear export occur, or (3) chromatin closes before transcription ends. We refer to this phenomenon as time lag or "decoupling" (changing independently) [17]. For example, Tle4 exhibits a delay between the induction of chromatin accessibility and the corresponding gene expression (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The time lag between chromatin accessibility and transcription may occur under the following situations: (1) chromatin region is open, but transcription decreases (e.g., due to disassembly of the transcription machinery), (2) chromatin region is open, while transcription levels stay constant because the necessary machinery and regulatory proteins are still in the process of recruitment, and (3) chromatin region is closed while the transcription machinery is still active (Fig. 1) [17] . In addition, the mechanisms of chromatin remodeling are distinct from those of transcription machinery assembly/disassembly [6].…”

Section: Introductionmentioning

confidence: 99%

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HALO: Hierarchical Causal Modeling for Single Cell Multi-Omics Data

Mao

Jia

et al. 2022

Preprint

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As the available sequencing data modalities increase, so does the potential biological insight that they are able to provide. Most existing methods to integrate co-profiled single-cell multi-omics data focus only on learning representations that capture stationary and shared information among these modalities. Current methods do not account for time-dependent and modality-specific information delineating cell states and subtypes, nor do they consider dynamics resulting from causal relations among modalities. For example, open chromatin may cause active transcription; however, it is also possible that gene expression responses lag behind changes in chromatin accessibility. To account for this time lag, the epigenome and transcriptome relationship can be characterized as "coupled" (changing dependently) or "decoupled" (changing independently). We propose the framework HALO (Hierarchical cAusal representationLearning forOmics data), which adopts a causal approach to model these non-stationary causal relations using independent changing mechanisms in co-profiled single-cell ATAC- and RNA-seq data. Our model factorizes these two modalities into both coupled and decoupled latent representations, allowing us to identify the dynamic interplay between chromatin accessibility and transcription through temporal modulations. In blood lineage and developing mouse brain data, where the balance between proliferation and differentiation is tightly regulated, HALO distinguishes between coupled and decoupled genes and links them with disparate processes that constitute these two complementary states.

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Section: Methodsmentioning

confidence: 99%

Section: Halo Delineates Cell States In Embryonic Mouse Brainmentioning

confidence: 99%

Section: Fig S1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HALO: Hierarchical Causal Modeling for Single Cell Multi-Omics Data

Mao

Jia

et al. 2022

Preprint

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An Emerging Approach of Age‐Related Hearing Loss Research: Application of Integrated Multi‐Omics Analysis

Liu,

Zeng,

Zhang

2024

Advanced Biology

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As one of the most common otologic diseases in the elderly, age‐related hearing loss (ARHL) usually characterized by hearing loss and cognitive disorders, which have a significant impact on the elderly's physical and mental health and quality of life. However, as a typical disease of aging, it is unclear why aging causes widespread hearing impairment in the elderly. As molecular biological experiments have been conducted for research recently, ARHL is gradually established at various levels with the application and development of integrated multi‐omics analysis in the studies of ARHL. Here, the recent progress in the application of multi‐omics analysis in the molecular mechanisms of ARHL development and therapeutic regimens, including the combined analysis of different omics, such as transcriptome, proteome, and metabolome, to screen for risk sites, risk genes, and differences in lipid metabolism, etc., is outlined and the integrated histological data further promote the profound understanding of the disease process as well as physiological mechanisms of ARHL. The advantages and disadvantages of multi‐omics analysis in disease research are also discussed and the authors speculate on the future prospects and applications of this part‐to‐whole approach, which may provide more comprehensive guidance for ARHL and aging disease prevention and treatment.

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Mapping Cell Fate Transition in Space and Time

Gu,

Liu,

et al. 2024

Lecture Notes in Computer Science

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Multi-omic single-cell velocity models epigenome–transcriptome interactions and improves cell fate prediction

Cited by 66 publications

References 73 publications

HALO: Hierarchical Causal Modeling for Single Cell Multi-Omics Data

HALO: Hierarchical Causal Modeling for Single Cell Multi-Omics Data

An Emerging Approach of Age‐Related Hearing Loss Research: Application of Integrated Multi‐Omics Analysis

Mapping Cell Fate Transition in Space and Time

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