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

Jindal, Kunal; Adil, Mohd Tayyab; Yamaguchi, Naoto; Wang, Helen C.; Yang, Xue; Kamimoto, Kenji; Rivera-Gonzalez, Guillermo C.; Morris, Samantha A.

doi:10.1101/2022.10.23.512790

Cited by 7 publications

(5 citation statements)

References 91 publications

(161 reference statements)

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“…Barcoding and tracking cells via scRNA-seq represents a powerful method to investigate how the early molecular state of a cell relates to its eventual fate ( Biddy et al., 2018 ; Jindal et al, 2022 ; Weinreb et al., 2020 ). Cells are labeled with combinations of heritable random barcodes, CellTags, delivered using lentivirus, enabling cells to be uniquely labeled and tracked over time; cells sharing identical barcodes are identified as clonal relatives; thus, early cell state can be directly linked to reprogramming outcome ( Biddy et al., 2018 ; Kong et al., 2020 ; Figure 2 A).…”

Section: Resultsmentioning

confidence: 99%

Gene regulatory network reconfiguration in direct lineage reprogramming

Kamimoto¹,

Adil²,

Jindal³

et al. 2023

Stem Cell Reports

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Section: Resultsmentioning

confidence: 99%

Gene regulatory network reconfiguration in direct lineage reprogramming

Kamimoto¹,

Adil²,

Jindal³

et al. 2023

Stem Cell Reports

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“…In summary, we provide a theoretical framework to quantify cell-fate transitions by leveraging both single-cell lineage and transcriptomic information. With the rapid development of single-cell lineage tracing technologies and emergence of lineage-traced multi-omic data 74 , we envision our method will facilitate the lineage analysis for complex cellular processes and the discovery of the cell-fate determinants in diverse organisms, tissues, and diseases.…”

Section: Discussionmentioning

confidence: 99%

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Wang,

Hou,

Wang

et al. 2023

Nat Biotechnol

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Single-cell RNA-sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell-fate transitions can be challenging, especially in disease conditions. Here, we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs), or genes with expression patterns that either increase or decrease, but don't cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and C. elegans ground-truth data, successfully recovering linear, bifurcated, and convergent differentiations. Applying PhyloVelo to seven lineagetraced scRNA-seq datasets, generated via CRISPR/Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories, while outperforming RNA velocity. Additionally, we discover that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis. MainOrganism development and disease progression both involve serial cell-fate transitions upon repeated cell divisions. Essentially, all cells in an organism are related by a phylogenetic tree where the root represents the zygote, the branches represent cell divisions, and the leaves represent the terminal cells at various phenotypic states (e.g. cell types) 1-4 . To understand how cell fate is determined, it is important to identify the order of

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“…Moslin could further be extended towards multi-modal scLT data 78,79 to link molecular layers across time. For example, this could reveal how epigenetic changes manifest in altered gene expression dynamics 80,81 .…”

Section: Discussionmentioning

confidence: 99%

Mapping lineage-traced cells across time points with moslin

Lange

Piran

Klein

et al. 2023

Preprint

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Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making beyond simpler pseudotime-based approaches. However, it is currently unclear how lineage and gene expression information across experimental time points can be combined in destructive experiments, which is particularly challenging for in-vivo systems. Here we present moslin, a Fused Gromov-Wasserstein-based model to couple matching cellular profiles across time points. In contrast to existing methods, moslin leverages both intra-individual lineage relations and inter-individual gene expression similarity. We demonstrate on simulated and real data that moslin outperforms state-of-the-art approaches that use either one or both data modalities, even when the lineage information is noisy. On C. elegans embryonic development, we show how moslin, combined with trajectory inference methods, predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration. We anticipate moslin to play a crucial role in deciphering complex state change trajectories from lineage-traced single-cell data.

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

Cited by 7 publications

References 91 publications

Gene regulatory network reconfiguration in direct lineage reprogramming

Gene regulatory network reconfiguration in direct lineage reprogramming

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Mapping lineage-traced cells across time points with moslin

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