Biology subjects

Cassiano, M. H. A.

Publications and source records attributed to Cassiano, M. H. A..

3 recordsLinked to original sources

bioRxiv2026

Point-Process Modelling of Cell-Type Interaction Structure in CosMx Glioma Data for Hypothesis-Driven Tumor Inference from scRNA-seq

Glioblastoma (GBM) exhibits marked spatial heterogeneity that is lost after tissue dissociation for single-cell RNA sequencing. Here, we developed a spatial-statistical framework to characterize tumor-microenvironment organization and derive molecularly predictable spatial phenotypes from CosMx data. Eight GBM specimens comprising 2,427,362 cells, 15 cell populations, and four malignant states were analyzed, revealing substantial inter-patient differences in cellular composition and density. Fine finite-element meshes best preserved continuous spatial intensity and supported whole-tissue Log-Gaussian Cox Process modelling. Immune populations, including macrophages, microglia, monocytes, cDCs, neutrophils, and T cells, showed spatial attraction toward malignant states. These models generated cell-level measures of conditional intensity and tumor-proximity probability. Transcript abundances and metabolic pathway scores served as the sole predictive features to model these spatial phenotypes. Transcriptomic representations outperformed metabolic pathway scores. Leave-one-patient-out validation reduced predictive performance, and patient-wise harmonization failed to restore cross-patient transportability. Spatial outcome distributions varied between specimens, indicating that molecular-spatial relationships are strongly conditioned by patient-specific tumor architecture. Predicted interaction scores stratified transcriptional subclusters within cell types in external non-spatial single-cell datasets. This framework links spatial point-process phenotypes to molecular states and provides a basis for inferring spatial context from non-spatial single-cell data. Generalizing these models requires larger and more diverse spatial cohorts.

bioinformatics↗

bioRxiv2026

Cold acclimation reprograms hepatic lipid composition toward n-3 HUFAs to uncouple adipose-derived lipid flux from steatosis

While cold exposure drives lipid flux from adipose tissue to the liver, this enhanced inter-organ crosstalk does not result in sustained hepatic steatosis during prolonged acclimation, indicating that factors beyond lipid flux shape metabolic outcome. To interrogate this adaptation, we performed integrated lipidomic and metabolic profiling across tissues and circulating lipoproteins over the course of cold acclimation. We showed that cold acclimation induces systemic reprogramming of lipid quality in mice, characterized by enrichment of n-3 highly unsaturated fatty acids (HUFAs) as a consequence of upregulation of fatty acid desaturases (FADS1 and 2) in the liver and white adipose tissue, thus increasing hepatic n-3/n-6 ratio. Cold-induced increase in n-3 HUFAs cause the suppression of SCD1-mediated desaturation, thus yielding a depletion of monounsaturated fatty acids (MUFAs) in the liver, along with the suppression of lipogenic markers. Notably, this high-HUFA/low-MUFA lipid signature is present in both hepatic free fatty acid and triglyceride pools, indicating that lipid remodeling occurs upstream of triglyceride synthesis. Lipidomic analysis revealed that the remodeled triglycerides are incorporated into very-low-density and intermediate density lipoproteins (VLDL and IDL), thereby propagating hepatic lipid reprogramming to the circulation. Thus, by selectively increasing endogenous n-3 HUFA availability, cold adaptation suppresses hepatic DNL and MUFA-driven triglyceride assembly, buffering lipid accumulation despite sustained fatty acid influx and reshaping systemic lipid distribution with potential cardiometabolic impact.

biochemistry↗

bioRxiv2020

Benchmarking available bacterial promoter prediction tools: potentialities and limitations

BackgroundThe promoter region is a key element required for the production of RNA in bacteria. While new high-throughput technology allows massive mapping of promoter elements, we still mainly relay on bioinformatic tools to predict such elements in bacterial genomes. Additionally, despite many different prediction tools have become popular to identify bacterial promoters, there is no systematic comparison of such tools. ResultsHere, we performed a systematic comparison between several widely used promoter prediction tools (BPROM, bTSSfinder, BacPP, CNNProm, IBBP, Virtual Footprint, IPro70-FMWin, 70ProPred, iPromoter-2L and MULTiPly) using well-defined sequence data sets and standardized metrics to determine how well those tools performed related to each other. For this, we used datasets of experimentally validated promoters from Escherichia coli and a control dataset composed by randomly generated sequences with similar nucleotide distributions. We compared the performance of the tools using metrics such as specificity, sensibility, accuracy and Matthews Correlation Coefficient (MCC). We show that the widely used BPROM presented the worse performance among compared tools, while four tools (CNNProm, IPro70-FMWin, 70ProPreda and iPromoter-2L) offered high predictive power. From these, iPro70-FMWin exhibited the best results for most of the metrics used. ConclusionsTherefore, we exploit here some potentials and limitations of available tools and hope future works can be built upon our effort to systematically characterize such quite useful class of bioinformatics tools.

bioinformatics↗