Antonio Herrera

Why mammals cannot regenerate limbs like amphibians do presents a long-standing puzzle in biology. To uncover the underlying differences, we compared amputation responses of embryonic mouse ( Mus musculus ) and Xenopus laevis tadpole limbs. Lowering environmental oxygen or stabilizing the oxygen-sensitive hypoxia-inducible factor 1A (HIF1A) induced rapid wound healing in mouse limbs. This response was accompanied by altered cellular mechanics, metabolism, and a histone landscape that primed regenerative cell states. Conversely, Xenopus tadpole limbs retained these features even under high oxygen levels. Their reduced oxygen-sensing capacity was associated with decreased HIF1A-regulating gene expression. Our results thus identify species-specific oxygen-sensing capacity as a fundamental, targetable mechanism that can unlock latent regenerative programs in mammals.

Specialized signaling centers orchestrate robust development and regeneration. Limb morphogenesis, for instance, requires interactions between the mesoderm and the signaling center apical-ectodermal ridge (AER), whose properties and role in cell fate decisions have remained challenging to dissect. To tackle this, we developed mouse embryonic stem cell (mESC)–based heterogeneous cultures and a three-dimensional (3D) organoid model, termed budoids, comprising cells with AER, surface ectoderm, and mesoderm properties. mESCs were first induced into heterogeneous cultures that self-organized into domes in 2D. Aggregating these cultures formed mesodermal organoids with certain limb bud–like features in 3D, exhibiting chondrogenesis-based symmetry breaking and elongation. Using our organoids and quantitative in situ expression profiling, we uncovered that AER-like cells support nearby limb mesoderm and fibroblast identities while enhancing tissue polarization that permits distant cartilage formation. Together, our findings provide a powerful model to study epithelial signaling center-mesoderm interactions during morphogenesis and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.

Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here we established uMAIA, an analytical framework for the joint analysis of large MSI datasets, which enables the construction of multidimensional metabolomic atlases. Employing this framework, we mapped the four-dimensional (4D) distribution of over a hundred lipids at micrometric resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.

Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

Embryo development entails the formation of anatomical structures with distinct biochemical compositions. Compared with the wealth of knowledge on gene regulation, our understanding of metabolic programs operating during embryogenesis is limited. Mass spectrometry imaging (MSI) has the potential to map the distribution of metabolites across embryo development. Here, we established an analytical framework for the joint analysis of large MSI datasets that allows for the construction of multi-dimensional metabolomic atlases. Employing this framework, we mapped the 4D distribution of over a hundred lipids at quasi-single-cell resolution in Danio rerio embryos. We discovered metabolic trajectories that unfold in concert with morphogenesis and revealed spatially organized biochemical coordination overlooked by bulk measurements. Interestingly, lipid mapping revealed unexpected distributions of sphingolipid and triglyceride species, suggesting their involvement in pattern establishment and organ development. Our approach empowers a new generation of whole-organism metabolomic atlases and enables the discovery of spatially organized metabolic circuits.

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

Vinculin is an actin-binding protein (ABP) that strengthens the connection between the actin cytoskeleton and adhesion complexes. It binds to β-catenin/N-cadherin complexes in apical adherens junctions (AJs), which maintain cell-to-cell adhesions, and to talin/integrins in the focal adhesions (FAs) that attach cells to the basal membrane. Here, we demonstrate that β-catenin targets vinculin to the apical AJs and the centrosome in the embryonic neural tube (NT). Suppression of vinculin slows down the basal-to-apical part of interkinetic nuclear migration (BAINM), arrests neural stem cells (NSCs) in the G2 phase of the cell cycle, and ultimately dismantles the apical actin cytoskeleton. In the NSCs, mitosis initiates when an internalized centrosome gathers with the nucleus during BAINM. Notably, our results show that the first centrosome to be internalized is the daughter centrosome, where β-catenin and vinculin accumulate, and that vinculin suppression prevents centrosome internalization. Thus, we propose that vinculin links AJs, the centrosome, and the actin cytoskeleton where actomyosin contraction forces are required.

Adenylosuccinate lyase (ADSL) functions in de novo purine synthesis (DNPS) and the purine nucleotide cycle. ADSL deficiency (ADSLD) causes numerous neurodevelopmental pathologies, including microcephaly and autism spectrum disorder. ADSLD patients have normal serum purine nucleotide levels but exhibit accumulation of dephosphorylated ADSL substrates, S-Ado, and SAICAr, the latter being implicated in neurotoxic effects through unknown mechanisms. We examined the phenotypic effects of ADSL depletion in human cells and their relation to phenotypic outcomes. Using specific interventions to compensate for reduced purine levels or modulate SAICAr accumulation, we found that diminished AMP levels resulted in increased DNA damage signaling and cell cycle delays, while primary ciliogenesis was impaired specifically by loss of ADSL or administration of SAICAr. ADSL-deficient chicken and zebrafish embryos displayed impaired neurogenesis and microcephaly. Neuroprogenitor attrition in zebrafish embryos was rescued by pharmacological inhibition of DNPS, but not increased nucleotide concentration. Zebrafish also displayed phenotypes commonly linked to ciliopathies. Our results suggest that both reduced purine levels and impaired DNPS contribute to neurodevelopmental pathology in ADSLD and that defective ciliogenesis may influence the ADSLD phenotypic spectrum.

Adult neural plasticity is an important and intriguing phenomenon in the brain, and adult hippocampal neurogenesis is directly involved in modulating neural plasticity by mechanisms that are only partially understood. We have performed gain-of-function and loss-of-function experiments to study Smad2, a transcription factor selected from genes that are demethylated after exercise through the analysis of an array of physical activity-induced factors, and their corresponding gene expression, and an efficient inducer of plasticity. In these studies, changes in cell number and morphology were analyzed in the hippocampal dentate gyrus (cell proliferation and survival, including regional distribution, and structural maturation/differentiation, including arborization, dendritic spines, and neurotransmitter-specific vesicles) of sedentary male mice, after evaluation in a battery of behavioral tests. As a result, we reveal a role for Smad2 in the balance of proliferation versus maturation of differentiating immature cells (Smad2 silencing increases both the proliferation and survival of cycling cells in the dentate granule cell layer), and in the plasticity of both newborn and mature neurons in mice (by decreasing dendritic arborization and dendritic spine number). Moreover, Smad2 silencing specifically compromises spatial learning in mice (through impairments of spatial tasks acquisition both in long-term learning and working memory). These data suggest that Smad2 participates in adult neural plasticity by influencing the proliferation and maturation of dentate gyrus neurons. SIGNIFICANCE STATEMENT Smad2 is one of the main components of the transforming growth factor-β (TGF-β) pathway. The commitment of cell fate in the nervous system is tightly coordinated by SMAD2 signaling, as are further differentiation steps (e.g., dendrite and axon growth, myelination, and synapse formation). However, there are no studies that have directly evaluated the role of Smad2 gene in hippocampus of adult animals. Modulation of these parameters in the adult hippocampus can affect hippocampal-dependent behaviors, which may shed light on the mechanisms that regulate adult neurogenesis and behavior. We demonstrate here a role for Smad2 in the maturation of differentiating immature cells and in the plasticity of mature neurons. Moreover, Smad2 silencing specifically compromises the spatial learning abilities of adult male mice.