Mariano I. Gabitto

Factorizing multiple monotone pseudotimes into one shared donor permutation plus cluster-specific warps (HiP-BIND) yields Alzheimer's donor orderings agreeing better with Braak, ADNC and CERAD staging than single-trajectory B-BIND.

The paper presents a benchmarking challenge to predict Alzheimer's disease neuropathology from single-nucleus transcriptomics using human and AI models, finding that top methods achieve near-perfect categorical staging predictions.

This study uses single-nucleus RNA-seq, ATAC-seq, and Multiome to analyze Alzheimer's disease across multiple brain regions, revealing shared and region-specific cellular vulnerabilities.

scVIP models single-cell transcriptomes to predict phenotypes and infer trajectories in development and disease.

MapMyCells maps unlabeled single-cell datasets to reference brain taxonomies, enabling cross-study integration and reproducible cell-type annotation.

This paper introduces a Bayesian framework to infer continuous disease states from discrete pathological protein measurements in Alzheimer's Disease, using pseudotime to model progression.

The paper maps AD-related pathological changes in the caudate nucleus at cellular resolution, identifying distinct features and cell type-specific responses.

This study identifies sex-specific gene expression associations with Alzheimer's disease neuropathology and cognitive decline using single-nucleus RNA sequencing from human brain tissue.

The paper compares spinal cord cell types across vertebrates, finding conserved developmental programs and divergent adult compositions, with mammalian-specific neuropeptide expression in the dorsal spinal cord.

The paper develops a toolkit of transgenic mouse lines and enhancer AAVs for selective targeting of cortical cell populations using transcriptomic and epigenomic data.

TabVI improves cell type annotation and integration using a probabilistic transformer model, producing interpretable feature attention masks and robust performance across dataset sizes.

This study creates an integrated multimodal cell atlas of Alzheimer's disease by analyzing single nuclei transcriptomes, epigenomes, and spatial coordinates across AD stages.

The paper shows that spinal neuron diversity increases exponentially during frog metamorphosis as motor behavior shifts from swimming to limb use, with molecular similarities to mammalian circuits.

The paper presents a single-nucleus transcriptomic atlas of spinal V1 interneurons across development, identifying En1's role in specifying a subset of V1Pou6f2 interneurons and its impact on locomotor output.

The paper identifies somatic driver mutations in microglia of Alzheimer's disease brains, showing increased clonal expansion and inflammatory signatures.

MultiVI integrates multi-modal single-cell data by creating a joint representation and imputing missing values while correcting batch effects.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.

This paper introduces a Bayesian nonparametric framework for identifying individual molecules in super-resolution microscopy time series, using ground-truth data to assess performance.

This study shows that CHD8 haploinsufficiency disrupts human cortical development by altering neuron generation timing, leading to macrocephaly-like organoid enlargement and cell-type specific molecular defects.

CRISPR-Decryptr analyzes noncoding CRISPR screens to infer regulatory elements by accounting for experimental design and validating on published data.

The paper introduces ChromA, a Bayesian method for analyzing ATAC-seq data to annotate chromatin accessibility, improving cell type identification and correcting biases in single-cell data.