Gioele La Manno

Lipids are fundamental components of the brain, crucial for synaptic transmission and signal propagation. Altered brain lipid composition is associated with common and rare neuropathologies, yet the spatial organization of the mammalian brain lipidome remains insufficiently characterized compared with other modalities ^1–6 . Here we mapped the membrane-lipid architecture of the adult mouse brain at micrometric scale, across sexes and during pregnancy. This lipid brain atlas reveals that lipids describe a fine-grained biochemical structure that aligns with functional anatomy. Membrane-lipid spatial heterogeneity clusters into territories, which we termed ‘lipizones’. Lipizones partially mirror cell-type territories, but also capture distal axon terminals. Through lipizones, we (1) reveal the organizing principles of the grey matter lipidome, related to connectivity and cytoarchitecture; (2) discover a new axis of oligodendrocyte heterogeneity in the white matter; and (3) find biochemical zonation in the choroid plexus and in the ventricular walls. We show that this lipidomic architecture can adapt to changing physiological needs. In the brain of pregnant female mice, the white matter is metabolically activated and the cortex undergoes a lipizone-specific remodelling that is particularly pronounced in layer 4. These results are a foundational resource ( https://lbae-v2.epfl.ch/ ) poised to reshape the understanding of lipids in brain development, physiology and pathology.

Understanding the development of midbrain dopaminergic (mesDA) neurons is essential for advancing cell replacement therapies for Parkinson’s disease. In the developing ventral midbrain (VM), radial glia (Rgl) cells are the progenitors of mesDA neurons. However, distinct Rgl subtypes have recently been identified, and their individual roles are unclear. Here we analyze transcriptomic data from mouse and human VM Rgl to define their contributions to mesDA neuron development. We identify Rgl1 as the progenitor of the mesDA lineage, and reveal a Rgl1 transcriptional network coordinated by BMAL1 , which we validate as a new regulator of mesDA neurogenesis. Moreover, we uncover Rgl3 as a key signaling subtype and show that factors expressed by Rgl3 promote the survival and yield of human stem cell-derived mesDA neurons. Our findings delineate distinct roles of Rgl subtypes, elucidate lineage relationships in the developing VM and uncover new factors that improve the derivation of clinically relevant human mesDA neurons.

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.

Lipids are fundamental components of the brain, crucial for synaptic transmission and signal propagation. Altered brain lipid composition is associated with common and rare neuropathologies, yet, the spatial organization of the mammalian brain lipidome remains insufficiently characterized compared to other modalities. Here, we mapped the membrane lipid architecture of the adult mouse brain at micrometric scale, across sexes, and during pregnancy. This Lipid Brain Atlas reveals that lipids define a fine-grained biochemical structure that aligns with functional anatomy. Membrane lipid spatial heterogeneity clusters into territories, which we termed lipizones. Lipizones partially mirror cell type territories, but also capture distal axon terminals. Through lipizones, (i) we reveal the organizing principles of the gray matter lipidome, related to connectivity and cytoarchitecture; (ii) we discover a new axis of oligodendrocyte heterogeneity in the white matter; (iii) and we find biochemical zonation in the choroid plexus and in the ventricular walls. We show that this lipidomic architecture can adapt to changing physiological needs. In the brain of pregnant females, the white matter is metabolically activated and the outer cortex is reorganized. These results are a foundational resource (https://lbae-v2.epfl.ch/), poised to reshape our understanding of lipids in brain development, physiology, and pathology.

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.

The spatial arrangement of cells is fundamental to their function, but single-cell RNA sequencing dissociates cells from their native spatial context. We present LUNA, a generative AI model that reconstructs tissue architecture from dissociated gene expression profiles by learning spatial priors from spatially resolved transcriptomics datasets. We validate LUNA by reconstructing the MERFISH mouse brain atlas comprising over 1.2 million cells, inferring the spatial locations of dissociated cells in a mouse central nervous system single-cell RNA-sequencing atlas, and enriching the Slide-tags data by imputing the locations of nuclei lost during profiling. LUNA generalizes to previously unseen cell types in the MERFISH mouse brain atlas and to pathological samples, identifying regions of disease-associated alterations in a mouse model of Parkinson’s disease. Our results establish generative tissue reconstruction as an approach for overcoming current technological limitations in spatial biology and advancing our understanding of tissue organization and function.

Single-cell and single-nucleus genomic approaches can provide unbiased and multimodal insights. Here, we discuss what constitutes a molecular cell atlas and how to leverage single-cell omics data to generate hypotheses and gain insights into cell transitions in development and disease of the nervous system. We share points of reflection on what to consider during study design and implementation as well as limitations and pitfalls.

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.