A5102717333- Single-cell and spatial transcriptomics
- Gene expression and cancer classification
- Genetics, Aging, and Longevity in Model Organisms
- Amyotrophic Lateral Sclerosis Research
- Advanced Condensed Matter Physics
- AI in cancer detection
- Neurogenetic and Muscular Disorders Research
- Advanced NMR Techniques and Applications
- Gene Regulatory Network Analysis
- Cancer Cells and Metastasis
- Scientific Computing and Data Management
- Neuroinflammation and Neurodegeneration Mechanisms
- Bioinformatics and Genomic Networks
- Molecular Biology Techniques and Applications
- Spectroscopy Techniques in Biomedical and Chemical Research
- Telomeres, Telomerase, and Senescence
- Machine Learning in Materials Science
- Advanced MRI Techniques and Applications
- Cancer-related molecular mechanisms research
- Bat Biology and Ecology Studies
- RNA Research and Splicing
- Neurogenesis and neuroplasticity mechanisms
- thermodynamics and calorimetric analyses
New York Genome Center
2023-2025
Flatiron Institute
2020-2021
Flatiron Health (United States)
2020-2021
Half of all amyotrophic lateral sclerosis (ALS) patients demonstrate a spectrum cognitive and behavioral changes over the course disease, but mechanisms underlying this heterogeneity remain unclear. We assemble high-resolution cellular map prefrontal cortex regions ALS brain by integrating spatial single-nucleus transcriptomic profiles cognitively stratified patient cohort that includes non-neuropathological controls. find programs characteristic ALS, including frequent gliotic response....
Summary Identifying senescent cells poses challenges due to their rarity, heterogeneity, and lack of a definitive marker. We performed Visium spatial transcriptomics (ST) single nucleus RNA sequencing (snRNA-seq) on non-pathological human tissue build transcriptomic atlas aging senescence in the dorsolateral prefrontal cortex (dlPFC). identified markers characteristic dlPFC cortical layers cell types. also observed an increase astrocyte abundance decrease somatostatin expressing inhibitory...
Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as situ characterization materials. These seek to decompose a dataset into small number components and weights that can compactly represent the underlying signal while effectively reconstructing observations with minimal error. However, canonical NMF have no requirement reconstruction uses or are...
ABSTRACT Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of underlying cues remain unclear. Here, we built a cellular spatial atlas across three anatomical regions 11 age groups, encompassing ∼1,500 mouse gut tissues profiled transcriptomics ∼400,000 single nucleus RNA-seq profiles. We developed new computational framework, cSplotch, which learns hierarchical Bayesian model spatially resolved expression associated...
Registration of histology images from multiple sources is a pressing problem in large-scale studies spatial -omics data. Researchers often perform 'common coordinate registration', akin to segmentation, which samples are partitioned based on tissue type allow for quantitative comparison similar regions across samples. Accuracy such registration requires both high image resolution and global awareness, mark difficult balancing act contemporary deep learning architectures.
Development of therapeutic approaches that target specific microglia responses in amyotrophic lateral sclerosis (ALS) is crucial due to the involvement ALS progression. Our study identifies predominant subset human primary motor cortex and spinal cord as an undifferentiated phenotype with dysregulated respiratory electron transport. Moreover, we find interferon response enriched donors aggressive disease progression, while a previously described potentially protective depleted ALS....
Abstract Registration of histology images from multiple sources is a pressing problem in large-scale studies spatial -omics data. Researchers often perform “common coordinate registration,” akin to segmentation, which samples are partitioned based on tissue type allow for quantitative comparison similar regions across samples. Accuracy such registration requires both high image resolution and global awareness, mark difficult balancing act contemporary deep learning architectures. We present...
Non-negative Matrix Factorization (NMF) methods offer an appealing unsupervised learning method for real-time analysis of streaming spectral data in time-sensitive collection, such as $\textit{in situ}$ characterization materials. However, canonical NMF are optimized to reconstruct a full dataset closely possible, with no underlying requirement that the reconstruction produces components or weights representative true physical processes. In this work, we demonstrate how constraining...