Mapping transcriptomic vector fields of single cells.

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Multi-scale mechanisms driving root regeneration: From regeneration competence to tissue repatterning.

Plants possess an outstanding capacity to regenerate enabling them to repair damages caused by suboptimal environmental conditions, biotic attacks, or mechanical damages impacting the survival of these sessile organisms. Although the extent of regeneration varies greatly between localized cell damage and whole organ recovery, the process of regeneration can be subdivided into a similar sequence of interlinked regulatory processes. That is, competence to regenerate, cell fate reprogramming, and the repatterning of the tissue. Here, using root tip regeneration as a paradigm system to study plant regeneration, we provide a synthesis of the molecular responses that underlie both regeneration competence and the repatterning of the root stump. Regarding regeneration competence, we discuss the role of wound signaling, hormone responses and synthesis, and rapid changes in gene expression observed in the cells close to the cut. Then, we consider how this rapid response is followed by the tissue repatterning phase, where cells experience cell fate changes in a spatial and temporal order to recreate the lost stem cell niche and columella. Lastly, we argue that a multi-scale modeling approach is fundamental to uncovering the mechanisms underlying root regeneration, as it allows to integrate knowledge of cell-level gene expression, cell-to-cell transport of hormones and transcription factors, and tissue-level growth dynamics to reveal how the bi-directional feedbacks between these processes enable self-organized repatterning of the root apex.

The Cumulative Formation of R-loop Interacts with Histone Modifications to Shape Cell Reprogramming.

R-loop, a three-stranded RNA/DNA structure, plays important roles in modulating genome stability and gene expression, but the molecular mechanism of R-loops in cell reprogramming remains elusive. Here, we comprehensively profiled the genome-wide landscape of R-loops during cell reprogramming. The results showed that the R-loop formation on most different types of repetitive elements is stage-specific in cell reprogramming. We unveiled that the cumulative deposition of an R-loop subset is positively correlated with gene expression during reprogramming. More importantly, the dynamic turnover of this R-loop subset is accompanied by the activation of the pluripotent transcriptional regulatory network (TRN). Moreover, the large accumulation of the active histone marker H3K4me3 and the reduction in H3K27me3 were also observed in these R-loop regions. Finally, we characterized the dynamic network of R-loops that facilitates cell fate transitions in reprogramming. Together, our study provides a new clue for deciphering the interplay mechanism between R-loops and HMs to control cell reprogramming.

Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics.

Gene expression dynamics provide directional information for trajectory inference from single-cell RNA sequencing data. Traditional approaches compute RNA velocity using strict modeling assumptions about transcription and splicing of RNA. This can fail in scenarios where multiple lineages have distinct gene dynamics or where rates of transcription and splicing are time dependent. We present "LatentVelo," an approach to compute a low-dimensional representation of gene dynamics with deep learning. LatentVelo embeds cells into a latent space with a variational autoencoder and models differentiation dynamics on this "dynamics-based" latent space with neural ordinary differential equations. LatentVelo infers a latent regulatory state that controls the dynamics of an individual cell to model multiple lineages. LatentVelo can predict latent trajectories, describing the inferred developmental path for individual cells rather than just local RNA velocity vectors. The dynamics-based embedding batch corrects cell states and velocities, outperforming comparable autoencoder batch correction methods that do not consider gene expression dynamics.

The dynamics of three-dimensional chromatin organization and phase separation in cell fate transitions and diseases.

Cell fate transition is a fascinating process involving complex dynamics of three-dimensional (3D) chromatin organization and phase separation, which play an essential role in cell fate decision by regulating gene expression. Phase separation is increasingly being considered a driving force of chromatin folding. In this review, we have summarized the dynamic features of 3D chromatin and phase separation during physiological and pathological cell fate transitions and systematically analyzed recent evidence of phase separation facilitating the chromatin structure. In addition, we discuss current advances in understanding how phase separation contributes to physical and functional enhancer-promoter contacts. We highlight the functional roles of 3D chromatin organization and phase separation in cell fate transitions, and more explorations are required to study the regulatory relationship between 3D chromatin organization and phase separation. 3D chromatin organization (shown by Hi-C contact map) and phase separation are highly dynamic and play functional roles during early embryonic development, cell differentiation, somatic reprogramming, cell transdifferentiation and pathogenetic process. Phase separation can regulate 3D chromatin organization directly, but whether 3D chromatin organization regulates phase separation remains unclear.

Trajectory Algorithms to Infer Stem Cell Fate Decisions.

Single-cell trajectory analysis is an active research area in single-cell genomics aiming at developing sophisticated algorithms to reconstruct complex cell-state transition trajectories. Here, we present a step-by-step protocol to use CellRouter, a multifaceted single-cell analysis platform that integrates subpopulation identification, gene regulatory networks, and trajectory inference to precisely and flexibly reconstruct complex single-cell trajectories. Subpopulations are either user-defined or identified by a graph-clustering approach in which a k-nearest neighbor graph (kNN) is created from cell-to-cell distances in a low-dimensional embedding. Edges in this graph are weighted by network similarity metrics (e.g., Jaccard index) to robustly encode phenotypic relatedness, creating a representation of single-cell transcriptomes suitable for community detection algorithms to identify clusters of densely connected cells. This subpopulation structure represents a map of putative cell-state transitions. CellRouter implements a flow network algorithm to explore this map and reconstruct cell-state transitions in complex single-cell, multidimensional omics datasets. We describe a step-by-step application of CellRouter to hematopoietic stem and progenitor cell differentiation toward four major lineages-erythrocytes, megakaryocytes, monocytes, and granulocytes-to demonstrate key components of CellRouter for single-cell trajectory analysis.

Dynamic Variations in Genetic Integrity Accompany Changes in Cell Fate.

Pluripotent stem cells hold the potential to form the basis of novel approaches to treatment of disease in vivo as well as to facilitate the generation of models for human disease, providing powerful avenues to discovery of novel diagnostic biomarkers and/or innovative drug regimens in vitro. However, this will require extensive maintenance, expansion, and manipulation of these cells in culture, which raises a concern regarding the extent to which genetic integrity will be preserved throughout these manipulations. We used a mutation reporter (lacI) transgene approach to conduct direct comparisons of mutation frequencies in cell populations that shared a common origin and genetic identity, but were induced to undergo transitions in cell fate between pluripotent and differentiated states, or vice versa. We confirm that pluripotent cells normally maintain enhanced genetic integrity relative to that in differentiated cells, and we extend this finding to show that dynamic transformations in the relative stringency at which genetic integrity is maintained are associated with transitions between pluripotent and differentiated cellular states. These results provide insight into basic biological distinctions between pluripotent and differentiated cell types that impact genetic integrity in a manner that is directly relevant to the potential clinical use of these cell types.