Gioele La Manno

The paper maps the lipidomic architecture of the adult mouse brain at micrometric scale, revealing spatial lipid organization and its functional relevance.

This paper uses ex utero culture to study mouse embryogenesis from gastrulation to fetal development, revealing metabolically independent programs and environmental influences on metabolic transitions.

The paper develops a limb organoid model to study how signaling centers direct cell fate and spatial organization during limb development.

The paper maps lipid distribution across vertebrate development using mass spectrometry imaging and reveals spatially organized metabolic circuits.

Spotiflow improves spot detection in microscopy images using deep learning, achieving subpixel accuracy and efficiency across diverse datasets.

LUNA reconstructs tissue architecture from dissociated single-cell RNA-seq data using spatial priors learned from spatial transcriptomics, validating on mouse brain datasets and disease models.

This paper introduces a statistical RNA velocity model that improves dynamical consistency and inference of cell cycle speed from scRNA-seq data.

The paper identifies two distinct radial glia subtypes in the developing midbrain and shows their different roles in dopaminergic neuron development using human stem cells.

The paper identifies regional glial heterogeneity in human white matter using single nucleus RNA sequencing, revealing tissue-specific glial populations and age/sex-related gene expression differences.

The paper develops a machine learning framework to estimate cell subtype-specific drug sensitivity from bulk data, applying it to leukemia datasets to identify potential therapeutic targets.

Pyro-Velocity improves RNA velocity inference by modeling raw sequencing counts with synchronized cell time across genes to better estimate cell fate and trajectory dynamics.

This study evaluates differential expression methods in single-cell transcriptomics, finding that many produce false discoveries due to ignoring biological variation.

This study uses single-cell and spatial transcriptomics to reveal clonal relationships and cell types in the mouse brain, showing fate-restricted progenitor cells and microglia origins.

The paper creates a single-cell transcriptome atlas of mouse brain development, identifying over 1000 cell states and linking tumor cells to developmental states.

The paper introduces an imaging-free spatial transcriptomics technique called STRP-seq, validated on murine and lizard brains, revealing regional molecular anatomy.

This study maps the single-cell transcriptomic dynamics of hESC-RPE differentiation, identifies an intermediate progenitor, and shows in vivo maturation after transplantation.

The paper introduces a machine-learning method, Augur, to identify cell types most responsive to biological perturbations in single-cell data and validates it on multiple datasets.

This study uses single-cell mRNA profiling to reveal the transcriptional logic of cell fate specification and axon guidance in early retinal neurons, identifying new molecular markers and regulatory sequences.

Unspliced versus spliced mRNA in standard scRNA-seq gives RNA velocity, a per-cell vector predicting future state over hours, validated in neural crest and used to trace mouse hippocampal lineages.

The paper maps cell types in the mouse nervous system using single-cell RNA sequencing and identifies distinct astrocyte and oligodendrocyte subtypes with specific molecular and spatial characteristics.

The paper develops a cyclic smFISH method to map cell-specific markers in the somatosensory cortex, revealing its spatial organization using markers from single-cell transcriptomics.

The paper introduces STRT-seq-2i, a high-throughput single-cell RNA-seq platform, and demonstrates its application on fresh mouse and frozen human cortical samples.

The paper identifies three radial glia types contributing to midbrain dopaminergic neurogenesis through distinct developmental signals and transcription factor networks.