Biology subjects

Ospina, O. E.

Publications and source records attributed to Ospina, O. E..

9 recordsLinked to original sources

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

bioRxiv2024

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

bioinformatics↗

bioRxiv2024

scSpatialSIM: a simulator of spatial single-cell molecular data

BackgroundSpatial molecular data is increasingly being generated in biological tissue studies to increase our understanding of cell infiltration and spatial architecture of tissues. Examples of technologies used to study the spatial contexture of tissues are single-cell protein expression assays and spatial transcriptomics. The increased use of spatial biology technologies has also resulted in an increase in the development of statistical methods to describe the spatial landscape in tissues. Due to the lack of consensus on "gold standard" statistical approaches for assessing the spatial contexture of tissues, we created an R package, scSpatialSIM, to assess different statistical and bioinformatic methods. scSpatialSIM allows users to simulate single-cell molecular data to mimic real tissues at scale, clustering of cell types, and co-clustering / co-localization of two or more cell types. scSpatialSIM also contains functions that give users the ability to simulate quantitative distributions for positive and negative cells (e.g., gene expression, fluorescence intensity). ResultsWe demonstrate that scSpatialSIM allows users to easily simulate various kernel densities of probability distributions used to create the marked point pattern - points distributed in space with either numeric or categorical features. Using scSpatialSIM, we used four univariate spatial simulation scenarios to compare three different measures for spatial clustering (Ripleys K(r), nearest neighbor G(r), and pair correlation g(r)). We found that Ripleys K(r) identifies the most radii with significant clustering in all four scenarios. Nearest neighbor G(r) only identified all samples as significantly clustered at one radius (r = 0.07) in one simulation scenario (high abundance large cluster size). Pair correlation g(r) was better able to detect significant clustering at low radii when abundance was low. ConclusionsVignettes developed for scSpatialSIM cover the creation of single-type and multi-type spatial single-cell molecular data, as well as how these simulated data can be used with other R packages, such as spatialTIME, to derive spatial statistics. Development of this package is crucial for furthering our understanding of the power of existing methods and the development of novel applications to assess the spatial contexture of tissues by providing an objective platform for simulating spatial single-cell molecular data.

cell biology↗

bioRxiv2024

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in the way the architecture of tissues is studied, by allowing for the exploration of cells in their spatial context. A common element in the analysis is the delineation of tissue domains or "niches" followed by the detection of differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate that is observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

bioinformatics↗

bioRxiv2023

Spatial transcriptomics analysis identifies a unique tumor-promoting function of the meningeal stroma in melanoma leptomeningeal disease

Leptomeningeal disease (LMD) remains a rapidly lethal complication for late-stage melanoma patients. The inaccessible nature of the disease site and lack of understanding of the biology of this unique metastatic site are major barriers to developing efficacious therapies for patients with melanoma LMD. Here, we characterize the tumor microenvironment of the leptomeningeal tissues and patient-matched extra-cranial metastatic sites using spatial transcriptomic analyses with in vitro and in vivo validation. We show the spatial landscape of melanoma LMD to be characterized by a lack of immune infiltration and instead exhibit a higher level of stromal involvement. We show that the tumor-stroma interactions at the leptomeninges activate pathways implicated in tumor-promoting signaling, mediated through upregulation of SERPINA3 at the tumor-stroma interface. Our functional experiments establish that the meningeal stroma is required for melanoma cells to survive in the CSF environment and that these interactions lead to a lack of MAPK inhibitor sensitivity in the tumor. We show that knocking down SERPINA3 or inhibiting the downstream IGR1R/PI3K/AKT axis results in re-sensitization of the tumor to MAPK-targeting therapy and tumor cell death in the leptomeningeal environment. Our data provides a spatial atlas of melanoma LMD, identifies the tumor-promoting role of meningeal stroma, and demonstrates a mechanism for overcoming microenvironment-mediated drug resistance unique to this metastatic site.

cancer biology↗

bioRxiv2023

Branched-chain keto acids promote an immune-suppressive and neurodegenerative microenvironment in leptomeningeal disease

Leptomeningeal disease (LMD) occurs when tumors seed into the leptomeningeal space and cerebrospinal fluid (CSF), leading to severe neurological deterioration and poor survival outcomes. We utilized comprehensive multi-omics analyses of CSF from patients with lymphoma LMD to demonstrate an immunosuppressive cellular microenvironment and identified dysregulations in proteins and lipids indicating neurodegenerative processes. Strikingly, we found a significant accumulation of toxic branched-chain keto acids (BCKA) in the CSF of patients with LMD. The BCKA accumulation was found to be a pan-cancer occurrence, evident in lymphoma, breast cancer, and melanoma LMD patients. Functionally, BCKA disrupted the viability and function of endogenous T lymphocytes, chimeric antigen receptor (CAR) T cells, neurons, and meningeal cells. Treatment of LMD mice with BCKA-reducing sodium phenylbutyrate significantly improved neurological function, survival outcomes, and efficacy of anti-CD19 CAR T cell therapy. This is the first report of BCKA accumulation in LMD and provides preclinical evidence that targeting these toxic metabolites improves outcomes.

cancer biology↗

bioRxiv2023

Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment

BackgroundImmunotherapy (IO) has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with IO naive and IO exposed primary ccRCC tumors to better understand IO resistance. Spatial molecular imaging (SMI) was obtained for tumor and adjacent stroma samples. Spatial gene set enrichment analysis (GSEA) and autocorrelation (coupling with high expression) of ligand-receptor transcript pairs were assessed. Multiplex immunofluorescence (mIF) validation was used for significant autocorrelative findings and the cancer genome atlas (TCGA) and the clinical proteomic tumor analysis consortium (CPTAC) databases were queried to assess bulk RNA expression and proteomic correlates. Results21 patient samples underwent SMI. Viable tumors following IO harbored more stromal CD8+ T cells and neutrophils than IO naive tumors. YES1 was significantly upregulated in IO exposed tumor cells. The epithelial-mesenchymal transition pathway was enriched on spatial GSEA and the associated transcript pair COL4A1-ITGAV had significantly higher autocorrelation in the stroma. Fibroblasts, tumor cells, and endothelium had the relative highest expression. More integrin V+ cells were seen in IO exposed stroma on mIF validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. In CPTAC, collagen IV protein was more abundant in advanced stages of disease. ConclusionsOn spatial transcriptomics, COL4A1 and ITGAV were more autocorrelated in IO-exposed stroma compared to IO-naive tumors, with high expression amongst fibroblasts, tumor cells, and endothelium. Integrin represents a potential therapeutic target in IO treated ccRCC.

cancer biology↗

bioRxiv2022

Establishment and Characterization of Novel Canine Organoids with Organ-Specific Physiological Similarity

Organoids are 3-dimensional (3D) stem cell-derived cell culture lines that offer a variety of technical advantages compared to traditional 2-dimensional (2D) cell cultures. Although murine models have proved useful in biomedical research, rodent models often fail to adequately mimic human physiology and disease progression, resulting in poor preclinical prediction of therapeutic drug efficacy and toxicity. With the advent of organoid technology, many of these challenges can be overcome. Previously, the use of canine organoids in drug testing and disease modeling was limited to organoids originating from the intestine, liver, kidney, lung, and urinary bladder. Here, we report the cultivation, maintenance, and molecular characterization of two novel adult-stem cell-derived canine organoid cell lines, including the endometrium and pancreas, in addition to previously reported bladder, lung, and liver organoids from two genetically related canines. Five tissues and organoid lines from each donor were characterized using bulk RNA-seq, allowing for a unique, multi-organ comparison between these two individuals and identification of specific cell types such as glandular epithelial cells in endometrial organoids.

genetics↗

bioRxiv2021

Quantification and visualization of the tumor microenvironment heterogeneity from spatial transcriptomic experiments

SummarySpatially-resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics (SThet) that can be compared against clinical information, spot-level cell deconvolution, and spatially-informed clustering (STclust), all using a new data object to store data and resulting analyses simultaneously. Availability and implementationThe R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. ContactFridley.Lab@Moffitt.org or Brooke.Fridley@Moffitt.org Supplementary informationAvailable at Bioinformatics online. O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC="FIGDIR/small/454023v2_figG1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@191cbe7org.highwire.dtl.DTLVardef@e1b6c7org.highwire.dtl.DTLVardef@a6f68forg.highwire.dtl.DTLVardef@1853601_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Overview of spatialGE features. A. The STList data object from spatialGE can be creared from several sources, including comma- or tab-separated files containing gene counts and spatial coordinates. The object can also be created directly from Visium outputs, Seurat objects, or GeoMx outputs. B. Users can optionally provide a metadata file, containing information associated with each sample (one row per sample, or per ROI if GeoMx data). C. Methods for quality control of data are provided by spatialGE, including visualizations of counts and genes per spot, as well as filtering of spots or genes within user-determined thresholds. D. A novel method (STclust) performs spatially informed clustering of spots and tissue domain identification. E. spatialGE provides different types of data visualization, including gene expression at each spot ("quilt plots"), as well as adaptation of spatial interpolation ("kriging") to spatial transcriptomics data (transcriptomic surface). F. spatialGE also leverages spatial statistics (Morans I, Gearys C, Getis-Ord Gi) to quantitatively describe heterogeneity within the tumor microenvironment and to explore associations between spatial heterogeneity and clinical oucomes. G. Gene expression deconvolution can also be applied to each spot to detect immune cell types (xCell) and classification of spots as tumor or stroma (ESTIMATE). C_FIG

bioinformatics↗