Biology subjects

Naranjo, A.

Publications and source records attributed to Naranjo, A..

8 recordsLinked to original sources

bioRxiv2026

Digital pathological classification of peripheral neuroblastic tumors across multi-continent cohorts

Peripheral neuroblastic tumors (pNTs) are embryonal tumors of neural crest origin with highly variable clinical behaviors, ranging from spontaneous regression to aggressive and fatal disease. The International Neuroblastoma Pathology Classification (INPC) defines four diagnostic categories and stratifies tumors into Favorable Histology (FH) or Unfavorable Histology (UH) group based on age-linked assessment of tumor differentiation/maturation and the Mitosis-Karyorrhexis Index (MKI). Prognostic distinction according to INPC offers significant prediction of clinical outcomes, with patients harboring FH tumors demonstrating longer overall and event-free survival than those with UH tumor. Here, we assembled the largest multi-continental whole-slide histology imaging dataset of pNTs to date, comprising 2,030 tumors from the United States, Canada, Australia, New Zealand, Germany and Japan. Using this dataset, we developed NB-PathAssist, a computational pathology framework for automated INPC-based classification from digitized whole-slide images. NB-PathAssist integrates foundation-model-based feature extraction with an archetype-guided SummaryMixing architecture that compresses whole-slide morphology into representative histologic tokens for tumor-level classification. To automate MKI assessment, we manually annotated 14,049 nuclei, classifying them as mitotic-karyorrhectic (MK) tumor cells, non-MK tumor cells, or non-tumor cells. The resulting model was applied to phenotype cells in whole-slide images across 1,649 tissue sections. Across geographically and institutionally diverse cohorts, NB-PathAssist reproducibly assessed key diagnostic features and generated prognostic classifications concordant with reference INPC annotations. The average balanced accuracy for classifying tumor categories, grade of neuroblastic differentiation, MKI and prognostic groups achieved 0.84, 0.86, 0.89 and 0.96, respectively. We packaged the NB-PathAssist algorithms into an end-to-end online platform for scalable and standardized AI-assisted assessment of pNTs.

pathology↗

bioRxiv2026

Single Cell Transcriptomics of Refractory Epilepsy patients in Colombia

Maintaining electrical signaling homeostasis in the human neocortex relies on cell-type specific gene expression programs. However, when these programs are disrupted, the resulting imbalances can contribute to the pathogenesis of neurological disorders like epilepsy. Genetic factors are particularly implicated in a specific subtype of epilepsy known as refractory epilepsy or drug-resistant epilepsy (RE/DRE). This study shows the main results of the analysis of single cell transcriptomics for five pediatric RE patients in Colombia. A total of six samples obtained through surgical resection were analyzed by single-nuclei RNA sequencing (snRNA-seq). The genome of one patient was sequenced using high fidelity long-read sequencing. Functional enrichment of differentially expressed genes (DEGs) revealed glia-driven dysregulation of synaptic signaling, impaired glial-neuronal communication, and altered expression of genes related to neurotransmitter transport and calcium signaling. Activation of taste receptors in neurons was associated with neuroinflammatory processes. Structural variants were detected in genes associated with alterations of expression in specific cell types. This new data resource increases the diversity of information needed to develop new strategies for diagnosis of refractory epilepsy.

neuroscience↗

bioRxiv2025

Frequency-dependent diffusion tensor distribution imaging in the evaluation of ischemic stroke

Non-invasive MRI is widely used to assess and monitor ischemic stroke, yet conventional approaches often lack sensitivity to subtle microstructural changes and struggle to evaluate tissue viability across lesion, penumbra, and distal regions. In this study, frequency dependent diffusion tensor distribution imaging ({omega}DTD) was combined with clustering of diffusion tensor distributions D({omega}) and multivariate regression modeling to characterize ischemic tissue alterations in a whole brain section. Ex vivo {omega}DTD and histology were performed in rats subjected to middle cerebral artery occlusion (MCAO) or sham surgery (P = 17) 24 hours after reperfusion. Lesions showed cell loss and an increased presence of smaller, likely glial, cells. A random forest (RF) model was used to explain and predict histological parameters from diffusion tensor imaging (DTI), manually bin resolved {omega}DTD features, and cluster resolved {omega}DTD parameters. Model performance was evaluated using leave one animal out cross validation (LOO CV). {omega}DTD features better represented cell number than DTI metrics ({omega}DTD R2 = 0.73 vs. DTI R2 = 0.49), with similar advantages for nuclear area and circularity ({omega}DTD R2 = 0.64 and 0.61 vs. DTI R2 = 0.40 and 0.35). The RF model further proved beneficial in capturing complex, nonlinear relationships between MRI parameters and tissue characteristics. Overall, these results indicate that {omega}DTD provides richer microstructural information than standard DTI, and that combining {omega}DTD with advanced machine learning methods enhances interpretation of ischemic tissue damage.

neuroscience↗

bioRxiv2024

New algorithms for unsupervised cell clusteringfrom scRNA-seq data

The identification of cell types is a basic step of the pipeline for Single-Cell RNA sequencing data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: the high dimensional nature of the data, the sparse nature of the gene expression matrix, and the presence of technical noise that can introduce false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds a k-MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps in which each step calculates and stores the edges of minimum spanning trees over different subgraphs obtained removing edges selected in previous iterations. The Louvain algorithm is executed on the k-MST graph for cell clustering. We also explored alternatives based on neural networks in which an autoencoder is used to learn the parameters of a Gaussian mixture model, aiming to improve the handling of clusters with different shapes and sizes. Benchmark experiments with simulated data and public datasets show that the algorithms proposed in this work have competitive accuracy, compared to previous solutions, but also that sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Moreover, we performed further experiments with scRNA-data taken from a patient with refractory epilepsy. The AE-GMM model achieved the best accuracy for this dataset, and the k-MST ranked first among methods that do not require previous information on the expected number of clusters.

bioinformatics↗

bioRxiv2022

Carbon footprint of South Dakota dairy production system and assessment of mitigation options

Livestock production contributes to greenhouse gas (GHG) emissions. However, there is a considerable variability in the carbon footprint associated with livestock production. Site specific estimates of GHG emissions are needed to accurately focus GHG emission reduction efforts. A holistic approach must be taken to assess the full environmental impact of livestock production using appropriate geographical scale. The objective of this study was to determine baseline GHG emissions from dairy production in South Dakota using a life cycle assessment (LCA) approach. A cradle-to-farm gate LCA was used to estimate the GHG emissions to produce 1 kg of energy-and-protein corrected milk (ECM) in South Dakota. The system boundary was divided into feed production, farm management, enteric methane emissions, and manure management as these activities are the main contributors to the overall GHG emissions. The production of 1 kg ECM in South Dakota dairies was estimated to emit 1.21 kg CO2 equivalents. The major contributors were enteric methane emissions (46.3%) and manure management (32.6%). Feed production and farm management made up 13.9 and 7.2 %, respectively. The estimate was similar to the national average but slightly higher than the California dairy system. The source of corn used in the dairies influences the footprint. For example, South Dakota corn had fewer GHG emissions than grain produced in Iowa. Therefore, locally and more sustainably sourced feed input will contribute to further reducing the environmental impacts. Improvements in efficiency of milk production through better genetics, nutrition animal welfare and feed production are expected to further reduce the carbon footprint of South Dakota dairies. Furthermore, use of feed additives and anaerobic digesters will reduce emissions from enteric and manure sources, respectively.

systems biology↗

bioRxiv2021

Polyclonal lymphoid expansion drives paraneoplastic autoimmunity in neuroblastoma

Neuroblastoma is a lethal childhood solid tumor of developing peripheral nerves. Two percent of children with neuroblastoma develop Opsoclonus Myoclonus Ataxia Syndrome (OMAS), a paraneoplastic disease characterized by cerebellar and brainstem-directed autoimmunity, but typically with outstanding cancer-related outcomes. We compared tumor transcriptomes and tumor infiltrating T- and B-cell repertoires from 38 OMAS subjects with neuroblastoma to 26 non- OMAS associated neuroblastomas. We found greater B- and T-cell infiltration in OMAS- associated tumors compared to controls, but unexpectedly showed that both were polyclonal expansions. Tertiary lymphoid structures (TLS) were enriched in OMAS-associated tumors. We identified significant enrichment of the MHC Class II allele HLA-DOB*01:01 in OMAS patients. OMAS severity scores were associated with the expression of several candidate autoimmune genes. We propose a model in which polyclonal autoreactive B lymphocytes act as antigen presenting cells and drive TLS formation, thereby crucially supporting both sustained polyclonal T-cell-mediated anti-tumor immunity and paraneoplastic OMAS neuropathology.

cancer biology↗

bioRxiv2018

Prohibitin is a prognostic marker of relapse and therapeutic target to block chemotherapy resistance in Wilms tumor

Wilms tumor (WT) is the most common childhood kidney cancer. To improve risk stratification and identify novel therapeutic targets for patients with WT, we used high-resolution mass spectrometry proteomics to identify urine tumor markers associated with WT relapse. We determined urine proteomes at diagnosis of 49 patients with WT, non-WT renal tumors, and age-matched controls, leading to the quantitation of 6,520 urine proteins. Supervised analysis revealed specific urine markers of renal rhabdoid tumors, kidney clear cell sarcomas, renal cell carcinomas, as well as those detected in cured and relapsed WT. In particular, urine prohibitin was significantly elevated at diagnosis in patients with relapsed as compared to cured WT. In a validation cohort of 139 patients, a specific urine prohibitin enzyme-linked immunosorbent assay demonstrated that prohibitin concentrations greater than 998 ng/mL at diagnosis were significantly associated with ultimate WT relapse. Immunohistochemical analysis revealed that prohibitin was highly expressed in primary WT specimens and associated with disease stage. Using functional genetic experiments, we found that prohibitin was required for the growth and survival of WT cells. Overexpression of prohibitin was sufficient to block intrinsic mitochondrial apoptosis and to cause resistance to diverse chemotherapy drugs, at least in part by dysregulating factors that control apoptotic cytochrome c release from mitochondrial cristae. Thus, urine prohibitin may improve therapy stratification, non-invasive monitoring of treatment response and early disease detection. In addition, therapeutic targeting of chemotherapy resistance induced by prohibitin dysregulation may offer improved therapies for patients with Wilms and other relapsed or refractory tumors.

cancer biology↗

bioRxiv2018

MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma

Aggressive cancers often have activating mutations in growth-controlling oncogenes and inactivating mutations in tumor-suppressor genes. In neuroblastoma, amplification of the MYCN oncogene and inactivation of the ATRX tumor-suppressor gene correlate with high-risk disease and poor prognosis. Here we show that ATRX mutations and MYCN amplification are mutually exclusive across all ages and stages in neuroblastoma. Using human cell lines and mouse models, we found that elevated MYCN expression and ATRX mutations are incompatible. Elevated MYCN levels promote metabolic reprogramming, mitochondrial dysfunction, reactive-oxygen species generation, and DNA-replicative stress. The combination of replicative stress caused by defects in the ATRX-histone chaperone complex and that induced by MYCN-mediated metabolic reprogramming leads to synthetic lethality. Therefore, ATRX and MYCN represent an unusual example, where inactivation of a tumor-suppressor gene and activation of an oncogene are incompatible. This synthetic lethality may eventually be exploited to improve outcomes for patients with high-risk neuroblastoma.

cancer biology↗