ORCID Profile
0000-0002-2031-4096
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Publisher: American Society for Clinical Investigation
Date: 08-09-2022
Publisher: Springer Science and Business Media LLC
Date: 28-02-2022
DOI: 10.1038/S41587-021-01186-X
Abstract: As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16
Publisher: Springer Science and Business Media LLC
Date: 05-05-2023
DOI: 10.1038/S41467-023-37025-7
Abstract: Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer’s disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1 β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.
Publisher: Cold Spring Harbor Laboratory
Date: 28-01-2019
DOI: 10.1101/532846
Abstract: Current methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.
Publisher: Cold Spring Harbor Laboratory
Date: 29-03-2023
DOI: 10.1101/2023.03.28.534644
Abstract: While single-cell technologies have allowed scientists to characterize cell states that emerge during cancer progression through temporal s ling, connecting these s les over time and inferring gene-gene relationships that promote cancer plasticity remains a challenge. To address these challenges, we developed TrajectoryNet, a neural ordinary differential equation network that learns continuous dynamics via interpolation of population flows between s led timepoints. By running causality analysis on the output of TrajectoryNet, we compute rich and complex gene-gene networks that drive pathogenic trajectories forward. Applying this pipeline to scRNAseq data generated from in vitro models of breast cancer, we identify and validate a refined CD44 hi EPCAM + CAV1 + marker profile that improves the identification and isolation of cancer stem cells (CSCs) from bulk cell populations. Studying the cell plasticity trajectories emerging from this population, we identify comprehensive temporal regulatory networks that drive cell fate decisions between an epithelial-to-mesenchymal (EMT) trajectory, and a mesenchymal-to-epithelial (MET) trajectory. Through these studies, we identify and validate estrogen related receptor alpha as a critical mediator of CSC plasticity. We further apply TrajectoryNet to an in vivo xenograft model and demonstrate it’s ability to elucidate trajectories governing primary tumor metastasis to the lung, identifying a dominant EMT trajectory that includes elements of our newly-defined temporal EMT regulatory network. Demonstrated here in cancer, the TrajectoryNet pipeline is a transformative approach to uncovering temporal molecular programs operating in dynamic cell systems from static single-cell data.
No related grants have been discovered for Alexander Tong.