ORCID Profile
0000-0002-4505-1034
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Publisher: Cold Spring Harbor Laboratory
Date: 26-11-2023
DOI: 10.1101/2023.07.18.549619
Abstract: Tumor microenvironments (TMEs) contain vast amounts of information on patient’s cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs among patients and cancer types, as well as determining the extent to which this information can predict variables such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of potential spatial cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells . Spatial data on the tumor microenvironment (TME) are becoming more prevalent. Existing methods to interrogate such data often have several deficiencies: (1) they rely on estimating the spatial relationships among cells by examining simple counts of cells within a single radius, (2) they do not come with ways to evaluate the statistical significance of any findings, or (3) they consider multiple in idual interactions resulting in overly optimistic estimates of interaction importances. Our approach, which leverages techniques in spatial statistics and uses a benchmark ensemble machine learning method addresses (1), since the K functions used encode the relative densities of cells over all radii up to a user-selected maximum radius, and (2) we have developed a custom approach based on permutation and cross-validation to evaluate the statistical significance of any findings of significant spatial interactions in the TME, (3) over potentially multiple interactions. Our approach is also freely available with an R implementation called funkycells . In the analysis of two real data sets, we have seen that the method performs well, and gives the expected results. We think this will be a robust tool to add to the toolbox for researchers looking to interrogate, what can be sometimes unwieldy, TME data.
Publisher: Public Library of Science (PLoS)
Date: 10-08-2021
DOI: 10.1371/JOURNAL.PONE.0255075
Abstract: Induced endothelial cells (iECs) generated from neonatal fibroblasts via transdifferentiation have been shown to have pro-angiogenic properties and are a potential therapy for peripheral arterial disease (PAD). It is unknown if iECs can be generated from fibroblasts collected from PAD patients and whether these cells are pro-angiogenic. In this study fibroblasts were collected from four PAD patients undergoing carotid endarterectomies. These cells, and neonatal fibroblasts, were transdifferentiated into iECs using modified mRNA. Endothelial phenotype and pro-angiogenic cytokine secretion were investigated. NOD-SCID mice underwent surgery to induce hindlimb ischaemia in a murine model of PAD. Mice received intramuscular injections with either control vehicle, or 1 × 10 6 neonatal-derived or 1 × 10 6 patient-derived iECs. Recovery in perfusion to the affected limb was measured using laser Doppler scanning. Perfusion recovery was enhanced in mice treated with neonatal-derived iECs and in two of the three patient-derived iEC lines investigated in vivo . Patient-derived iECs can be successfully generated from PAD patients and for specific patients display comparable pro-angiogenic properties to neonatal-derived iECs.
Publisher: The Royal Society
Date: 03-2021
Abstract: Swarming has been observed in various biological systems from collective animal movements to immune cells. In the cellular context, swarming is driven by the secretion of chemotactic factors. Despite the critical role of chemotactic swarming, few methods to robustly identify and quantify this phenomenon exist. Here, we present a novel method for the analysis of time series of positional data generated from realizations of agent-based processes. We convert the positional data for each in idual time point to a function measuring agent aggregation around a given area of interest, hence generating a functional time series. The functional time series, and a more easily visualized swarming metric of agent aggregation derived from these functions, provide useful information regarding the evolution of the underlying process over time. We extend our method to build upon the modelling of collective motility using drift–diffusion partial differential equations (PDEs). Using a functional linear model, we are able to use the functional time series to estimate the drift and diffusivity terms associated with the underlying PDE. By producing an accurate estimate for the drift coefficient, we can infer the strength and range of attraction or repulsion exerted on agents, as in chemotaxis. Our approach relies solely on using agent positional data. The spatial distribution of diffusing chemokines is not required, nor do in idual agents need to be tracked over time. We demonstrate our approach using random walk simulations of chemotaxis and experiments investigating cytotoxic T cells interacting with tumouroids.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 28-09-2022
Abstract: Deadwood is a large global carbon store with its store size partially determined by biotic decay. Microbial wood decay rates are known to respond to changing temperature and precipitation. Termites are also important decomposers in the tropics but are less well studied. An understanding of their climate sensitivities is needed to estimate climate change effects on wood carbon pools. Using data from 133 sites spanning six continents, we found that termite wood discovery and consumption were highly sensitive to temperature (with decay increasing .8 times per 10°C increase in temperature)—even more so than microbes. Termite decay effects were greatest in tropical seasonal forests, tropical savannas, and subtropical deserts. With tropicalization (i.e., warming shifts to tropical climates), termite wood decay will likely increase as termites access more of Earth’s surface.
No related grants have been discovered for Carolina Alvarez-Garzón.