Mapping Transcriptomic Vector Fields of Single Cells
Summary
While single cell RNA sequencing, RNA velocity, and metabolic labeling provide hitherto unattainable insight into cellular states and transitions, the full potential of this technology depends on sophisticated kinetic models that can identify the regulating regulatory mechanisms. The authors offer the Dynamo analytical framework, which calculates absolute RNA velocity, reconstructs continuous vector fields, extracts underlying regulatory processes using differential geometry, and forecasts the best reprogramming paths and perturbation results. The PU.1-GATA1 circuit's early megakaryocyte emergence and asymmetrical regulation are caused by mechanisms that are shown by Dynamo, which overcomes the drawbacks of traditional splicing-based RNA velocity investigations. Dynamo forecasts hematopoietic transition drivers and in silico perturbations that cause cell-fate divergences as a result of gene perturbations using the least-action-path method.
Fig.1 Graphical abstract. (Qiu, 2022)
Research Criteria
The research criteria for this study required using a combination approach of single-cell RNA sequencing and vector field analysis to determine the patterns of gene expression within individual cells. In order to approximate the direction and speed of transcription in single cells, the researchers used RNA velocity, a method that measures the proportion of unspliced and spliced mRNA. The strength and direction of transcriptional activity in each cell are then visually represented as a field of arrows using vector field analysis. The researchers used this technology to test the effectiveness of their strategy on a variety of biological systems, including growing zebrafish embryos, mouse embryonic stem cells, and human cancer cells.
Sample Type
Human stem cells
Result—RNA Metabolic Labeling with Dynamo Overcomes Fundamental Limitations of Conventional Splicing-Based RNA Velocity
The article discusses the limitations of conventional splicing-based RNA velocity analysis and how RNA metabolic labeling with dynamo can overcome these limitations. The study analyzed a scRNA-seq dataset of human HSPCs undergoing multi-lineage differentiation and found that splicing RNA velocity analysis produced inaccurate and nonsensical velocity flow. On the other hand, dynamo's modeling framework using labeling data provided accurate results. Additionally, the article demonstrated how dynamo can accurately reveal cell cycle progression and commitment into rare 2C-like totipotent cells by deconvolving orthogonal cellular processes. The unbiased measurements of the nascent RNA and the assumption of a transcription rate that differs for each gene in each cell corrected velocity flow and produced positive velocities. Overall, the article emphasizes the advantages of using RNA metabolic labeling with dynamo for RNA velocity analysis, which can overcome intrinsic limitations in splicing RNA velocity estimation.
Fig.2 Metabolic labeling experiments improve and generalize RNA velocity estimation. (Qiu, 2022)
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Reference
- Qiu, X.J.; et al. Mapping transcriptomics vector fields of single cells. Cell. 2022, 185(4): 790-711.
