Carlo Colantuoni

The paper constructs a cross-species single-cell transcriptomic atlas of hair cells across multiple vertebrates and identifies conserved and species-specific gene expression programs.

CellCover identifies marker gene panels for cell types by solving a set-covering problem, outperforming differential expression methods in capturing cell-specific signals and developmental progression.

Reprocessing nine human brain datasets (2,157,582 cells, 156 donors, GW6 to adulthood) under one marker-based taxonomy yields 35 replicable cell types with cell type-specific timing of adult-like maturation.

The paper compiles a large transcriptomic dataset of neocortical development and uses joint matrix decomposition to identify conserved transcriptome dynamics across mammals and developmental stages.

The paper identifies human-specific transcriptomic and regulatory features in the cerebellum, including ZP2's role in synapse development and regulation.

The paper identifies molecular programs coordinated with NPTX2 that distinguish cognitive resilience in AD pathology from non-pathological cognition and AD.

The temporal analysis of gene regulatory networks in human neural stem cells reveals multiple early critical phases associated with cortical disorders and neuropsychiatric traits.

The paper analyzes single-cell transcriptomics data of iPSC-derived neurons and astrocytes to assess their similarity to in vivo counterparts and their disease-related gene expression patterns.

The paper uses single-cell genomics to reconstruct developmental trajectories of 170 neuronal subtypes in the human hypothalamus across development.

The paper identifies transcriptional and epigenetic programs driving hair cell differentiation from supporting cells in cochlear organoids and predicts novel regulatory factors.

SJD identifies shared molecular variation across multi-omics datasets by decomposing matrices with hierarchical structure, applied to neurogenesis RNA-seq data.

The paper describes the Neuroscience Multi-Omic Archive, a repository for single-cell transcriptomic and epigenomic data from mammalian brains, providing tools for data access and analysis.

The paper examines how early-life inflammation affects cerebellar neuron maturation in children, finding transcriptomic changes in Purkinje and Golgi neurons linked to neurodevelopmental disorders.

The paper introduces 2s-LCA, a method for joint analysis of multiple biological data sets, and applies it to human brain development data to identify shared gene expression patterns.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

gEAR provides a community-driven portal for multi-omic data visualization, analysis, and sharing across species and data types.

The paper introduces RWAS, a method linking enhancer characteristics to genetic risk for schizophrenia, finding risk-associated enhancers are brain-active, conserved, AT-rich, and linked to neurodevelopmental transcription factors.

This study uses single-nucleus RNA-seq to analyze striatal cell types in a Huntington’s disease mouse model, revealing dysregulated gene expression and cell identity loss across multiple cell types.

The paper identifies transcriptomic differences in human pluripotent stem cells that correlate with retinoic acid response and neural fate bias.

The paper shows SOD2 is upregulated in SIV-infected macaque brains, primarily in astrocytes, and validates this finding in HIV datasets.

The paper models the progression of human neural stem cells in vitro, showing they transition through organizer states before committing to cortical excitatory or inhibitory neuronal fates.

projectR enables transfer learning for genomics data analysis using PCA, NMF, correlation, and clustering, with an example in spatial single-cell analysis.

The paper uses hiPSC transcriptomics to study neuronal differentiation and maturation across multiple conditions and develops a tool to estimate neuronal maturity.

The paper introduces scCoGAPS and projectR to decompose cell identity into shared and species-specific factors, enabling transfer learning across datasets and species.

The paper constructs a brain-specific transcriptional regulatory network model to identify core transcription factors involved in psychiatric and neurodegenerative diseases.

The paper identifies 237 genes associated with schizophrenia using RNA sequencing and eQTL analysis, showing enrichment for developmentally regulated synaptic genes.

The paper shows that placental gene expression mediates the interaction between obstetrical history and genetic risk for schizophrenia, with ELCs enhancing PRS effects.

The paper introduces PatternMarkers and GWCoGAPS to identify data-driven biomarkers from whole-genome data using NMF, demonstrating their application on GTex data.