Cameron Park, Texas, is a colonia (an isolated, unincorporated rural settlement without municipal improvements) on the Texas-Mexico border in the Lower Rio Grande Valley, in Cameron County near Brownsville, Texas. Cameron Park has a population of 5,961 residents, 99.3% of whom are Hispanic. The annual median income is 16,934 US dollars, about one-half of the state median. Fifty-eight percent of families generally and 68% of those with children younger than 5 years have incomes below poverty level. Cameron Park resides geographically in a region where agriculture has been, and continues to be, a dominant industry, a fact consistent with the intensive use of pesticides and increased potential for air, water, and ground contamination. The practice of good environmental health is extremely difficult under these conditions. In 1999 the Texas A&M University Center for Housing and Urban Development's Colonias Program and the Center for Environmental and Rural Health teamed up to create an environmental health education and outreach program called the Cameron Park Project (CPP). The CPP focused on how to reduce potential environmental exposures associated with human illness by providing residents with scientifically sound information on positive health practices and how to deal with environmental hazards. In this article we discuss the research methodology used in the CPP, a methodology specifically chosen to address four challenges presented by colonias to conducting valid and reliable research.
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http://dx.doi.org/10.1289/ehp.5771 | DOI Listing |
Genome Res
October 2024
Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA;
Characterizing cell-cell communication and tracking its variability over time are crucial for understanding the coordination of biological processes mediating normal development, disease progression, and responses to perturbations such as therapies. Existing tools fail to capture time-dependent intercellular interactions and primarily rely on databases compiled from limited contexts. We introduce DIISCO, a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points.
View Article and Find Full Text PDFNat Biotechnol
March 2024
Department of Biomedical Engineering, Columbia University, New York, NY, USA.
Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference.
View Article and Find Full Text PDFUnderstanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated specificity for autologous leukemia.
View Article and Find Full Text PDFbioRxiv
November 2023
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Characterizing cell-cell communication and tracking its variability over time is essential for understanding the coordination of biological processes mediating normal development, progression of disease, or responses to perturbations such as therapies. Existing tools lack the ability to capture time-dependent intercellular interactions, such as those influenced by therapy, and primarily rely on existing databases compiled from limited contexts. We present DIISCO, a Bayesian framework for characterizing the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points.
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