Biology subjects

La Manno, G.

Publications and source records attributed to La Manno, G..

At least 19 recordsLinked to original sources

bioRxiv2025

The lipidomic architecture of the mouse brain

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 modalities1-8. 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.

systems biology↗

bioRxiv2024

Unified Mass Imaging Maps the Lipidome of Vertebrate Development

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.

systems biology↗

bioRxiv2024

Specialized signaling centers direct cell fate and spatial organization in a limb organoid model

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 cells (mESCs)-based heterogeneous cultures and a limb 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 resulted in formation of limb bud-like structures 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 aspects of limb morphogenesis, and reveal the ability of signaling center AER cells to concurrently modulate cell fate and spatial organization.

developmental biology↗

bioRxiv2024

Midbrain Dopaminergic Neuron Development is Regulated by Two Molecularly Distinct Subtypes of Radial Glia Cells

Understanding midbrain dopaminergic (mDA) neuron development is key to advancing cell replacement therapies for Parkinsons disease (PD). Recent single-cell RNA-sequencing (scRNA-seq) studies have identified different subtypes of transient radial glia (Rgl) cell types in the developing mouse and human ventral midbrain. However, their individual functions and their impact on mDA neuron development are unclear. Here we analyze the transcriptome of endogenous mouse and human ventral midbrain Rgl and assess the function of key midbrain floor plate Rgl factors in human stem cells during mDA neuron differentiation. We find that Rgl1 is defined by a neurogenic network centered on Arntl, and that this transcription factor regulates human mDA neurogenesis. Conversely, the transcriptome of Rgl3 is dominated by signaling and extracellular matrix molecules that control different aspects of human mDA neuron development. Thus, our results suggest a function of Rgl1 as a mDA progenitor, and of Rgl3 as a signaling mDA niche cell. Moreover, using human stem cells we demonstrate that new knowledge of cell-type specific intrinsic and extrinsic developmental factors can readily be applied to improve the generation of cells with therapeutic interest, such as human mDA neurons for PD.

neuroscience↗

bioRxiv2024

Spotiflow: accurate and efficient spot detection for imaging-based spatial transcriptomics with stereographic flow regression

Identifying spot-like structures in large and noisy microscopy images is a crucial step to produce high quality results in various life-science applications. Imaging-based spatial transcriptomics (iST) methods, in particular, critically depend on the precise detection of millions of transcripts in images with low signal-to-noise ratio. Despite advances in computer vision that have revolutionized many biological imaging tasks, currently adopted spot detection techniques are mostly still based on classical signal processing methods that often lack robustness to changing imaging conditions and thus require tedious manual tuning per dataset. In this work, we introduce Spotiflow, a deep learning method that achieves subpixel-accurate localizations by formulating the spot detection task as a multi-scale heatmap and stereographic flow regression problem. Spotiflow can be used for 2D images and 3D volumetric stacks and can be trained to generalize across different imaging conditions, tissue types and chemical preparations, while being substantially more time- and memory-efficient than existing methods. We show the efficacy of Spotiflow via extensive quantitative experiments on a variety of diverse datasets and demonstrate that the enhanced accuracy of SO_SCPLOWPOTIFLOWC_SCPLOW leads to meaningful improvements in the 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.

bioinformatics↗

bioRxiv2024

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

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.

systems biology↗

bioRxiv2023

Deconvolution of ex-vivo drug screening data and bulk tissue expression predicts the abundance and viability of cancer cell subpopulations

Ex-vivo drug sensitivity screening allows the prediction of cancer treatment effectiveness in a personalized fashion. However, it only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. To address this shortcoming, we developed a machinelearning framework to decompose drug sensitivity recorded at the bulk level into cell subtype-specific drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples with top-ranking accuracy across five cancer types compared to state-of-the-art bulk deconvolution methods. We emphasize its effectiveness in the realm of Acute Myeloid Leukemia, where it appears to offer the most precise estimation of leukemic stem cell fractions across three test datasets and three patient cohorts. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies and in-depth simulations. We then applied our deconvolution strategy to the beatAML cohort dataset, currently the most extensive database of ex-vivo drug screening data. We developed a drug sensitivity profile tailored to specific cell subtypes, focusing on four therapeutic compounds predicted to target leukemic stem cells: the previously known midostaurin and A-674563, as well as SNS-032 and foretinib, which have not been previously linked to leukemic stem cells. Our work provides an attractive new computational tool for drug development and precision medicine.

bioinformatics↗

bioRxiv2022

Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data

Single-cell RNA Velocity has dramatically advanced our ability to model cellular differentiation and cell fate decisions. However, current preprocessing choices and model assumptions often lead to errors in assigning developmental trajectories. Here, we develop, Pyro-Velocity, a Bayesian, generative, and multivariate RNA Velocity model to estimate the uncertainty of cell future states. This approach models raw sequencing counts with the synchronized cell time across all expressed genes to provide quantifiable and improved information on cell fate choices and developmental trajectory dynamics.

bioinformatics↗

bioRxiv2022

Marked regional glial heterogeneity in the human white matter of the central nervous system

The myelinated white matter tracts of the central nervous system (CNS) are essential for fast transmission of electrical impulses and are commonly affected in neurodegenerative diseases. However, these often uniquely human diseases differentially affect white matter regions, at various ages and between males and females, and we hypothesised that this is secondary to physiological variation in white matter glia with region, age and sex. Using single nucleus RNA sequencing of healthy human post-mortem samples, we find marked glial heterogeneity with tissue region (primary motor cortex, cerebellum, cervical spinal cord), with tissue-specific cell populations of oligodendrocyte precursor cells and astrocytes, and a spinal cord-enriched oligodendrocyte type that appears human-specific. Spinal cord microglia but not astrocytes show a more activated phenotype compared to brain. These regional effects, with additional differentially expressed genes with age and sex in all glial lineages, help explain pathological patterns of disease - essential knowledge for therapeutic strategies.

neuroscience↗

bioRxiv2021

Cell types and clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics

The mammalian brain contains a large number of specialized cells that develop from a thin sheet of neuroepithelial progenitor cells1,2. Recently, high throughput single-cell technologies have been used to define the molecular diversity of hundreds of cell types in the nervous system3,4. However, the lineage relationships between mature brain cells and progenitor cells are not well understood, because transcriptomic studies do not allow insights into clonal relationships and classical fate-mapping techniques are not scalable5,6. Here we show in vivo barcoding of early progenitor cells that enables simultaneous profiling of cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. We reconstructed thousands of clones to uncover the existence of fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. By coupling spatial transcriptomics with clonal barcoding, we disentangle migration patterns of clonally related cells in densely labelled tissue sections. Compared to classical fate mapping, our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.

neuroscience↗

bioRxiv2021

Confronting false discoveries in single-cell differential expression

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulation. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. Our results suggest an urgent need for a paradigm shift in the methods used to perform differential expression analysis in single-cell data.

bioinformatics↗

bioRxiv2021

Sphingolipid Control of Fibroblast Heterogeneity Revealed by Single-Cell Lipidomics

Human cells produce thousands of lipids that impact a wide range of biological processes in ways we are only starting to characterize. The cellular composition in lipids changes during differentiation events and also varies across individual cells of the same type. Yet, the precise differences in lipid composition that directly affect cell phenotypes remain unknown. Here we have measured the lipidomes and transcriptomes of individual human dermal fibroblasts by coupling high-resolution mass spectrometry imaging to single-cell transcriptomics. We found that the cell-to-cell variation of specific lipid metabolic pathways contributes to the establishment of cell states involved in wound repair and in skin cancer growth. Sphingolipid composition defined fibroblast subpopulations while sphingolipid metabolic rewiring drove cell state transitions. These data uncover a role for cell-to-cell lipid heterogeneity in the determination of cell states and reveal a new regulatory component to the homeostasis and self-organization of multicellular systems.

biochemistry↗

bioRxiv2021

Molecular profiling of retinal pigment epithelial cell differentiation for therapeutic use

Human embryonic stem cell-derived retinal pigment epithelial cells (hESC-RPE) are a promising cell source to treat age-related macular degeneration (AMD). Despite several ongoing clinical studies, detailed single cell mapping of the transient cellular and molecular dynamics from the pluripotent state to mature RPE has not been performed. Here we conduct single-cell transcriptomic analyses of 25,718 cells during differentiation as well as in embryonic and adult retina references, revealing differentiation progression through an un-expected initial cell diversification recapitulating early embryonic development before converging towards an RPE lineage. We also identified NCAM1 to track and capture an intermediate retinal progenitor with the potential to give rise to multiple neuroepithelial lineages. Finally, we profiled hESC-RPE cells after subretinal transplantation into the rabbit eye, uncovering robust in vivo maturation towards an adult state. Our detailed evaluation of hESC-RPE differentiation supports the development of safe and efficient pluripotent stem cell-based therapies for AMD.

developmental biology↗

bioRxiv2020

Spatial tissue profiling by imaging-free molecular tomography

Genomics techniques are currently being adapted to provide spatially resolved omics profiling. However, the adaptation of each new method typically requires the setup of specific detection strategies or specialized instrumentation. A generic approach to spatially resolve different types of high throughput data is missing. Here, we describe an imaging-free framework to localize high throughput readouts within a tissue by combining compressive sampling and image reconstruction. We implemented this framework to transform a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (STRP-seq) and validated this method with a transcriptome profiling of the murine brain. To verify the broad applicability of STRP-seq, we applied the technique on the brain of the Australian bearded dragon Pogona vitticeps. Our results reveal the molecular anatomy of the telencephalon of this lizard, providing evidence for a marked regionalization of the reptilian pallium and subpallium. Overall, the proposed framework constitutes a new approach that allows upgrading in a generic fashion conventional genomic assays to spatially resolved techniques.

genomics↗

bioRxiv2020

Molecular architecture of the developing mouse brain

The mammalian brain develops through a complex interplay of spatial cues generated by diffusible morphogens, cell-cell interactions, and intrinsic genetic programs that result in the generation of likely more than a thousand distinct cell types. Therefore, a complete understanding of mammalian brain development requires systematic mapping of cell states covering the entire relevant spatiotemporal range. Here we report a comprehensive single-cell transcriptome atlas of mouse brain development spanning from gastrulation to birth. We identified almost a thousand distinct cellular states, including the initial emergence of the neuroepithelium, a rich set of region-specific secondary organizers and a complete developmental program for the functional elements of the brain and its enclosing membranes. We used the atlas to directly test the hypothesis that human glioblastoma reflects a return to a developmental cell state. In agreement, most aneuploid tumor cells matched embryonic rather than adult types, while karyotypically normal cells predominantly matched adult immune cell types.Competing Interest StatementThe authors have declared no competing interest.View Full Text

developmental biology↗

bioRxiv2019

Cell type prioritization in single-cell data

We present a machine-learning method to prioritize the cell types most responsive to biological perturbations within high-dimensional single-cell data. We validate our method, Augur (https://github.com/neurorestore/Augur), on a compendium of single-cell RNA-seq, chromatin accessibility, and imaging transcriptomics datasets. We apply Augur to expose the neural circuits that enable walking after paralysis in response to spinal cord neurostimulation.

genomics↗

bioRxiv2018

Molecular architecture of the mouse nervous system

The mammalian nervous system executes complex behaviors controlled by specialised, precisely positioned and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse, and were grouped by developmental anatomical units, and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission and membrane conductance. We discovered several distinct, regionally restricted, astrocytes types, which obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity, followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system, and enables genetic manipulation of specific cell types.

neuroscience↗

bioRxiv2018

Spatial organization of the somatosensory cortex revealed by cyclic smFISH

The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.

neuroscience↗