Biology subjects

Baquer, G.

Publications and source records attributed to Baquer, G..

7 recordsLinked to original sources

bioRxiv2026

A generative reference grammar of healthy TCR repertoires reveals cancer-associated immune remodeling

T-cell receptor (TCR) repertoires record how adaptive immunity is organized and how cancer and therapy reshape it, but this signal is hard to read: treatment-associated change is entangled with the V(D)J recombination constraints that shape every repertoire. We present CRAFT (Cancer Repertoire Anomaly Finding Transformer), a conditional sequence-to-sequence transformer that learns a nucleotide-level generative grammar of productive TCR-beta CDR3 sequences from healthy donors, conditioned on germline V(D)J assignments. A dual-head decoder mirrors the independence of V-D and D-J recombination, and curriculum training produces embeddings that define a healthy-reference coordinate system in which cancer-associated change appears as structured, measurable deviation. In proof-of-concept applications to a neoadjuvant checkpoint-blockade cohort sampled longitudinally across blood, and to serial single-cell profiling of T-cell subsets during oncolytic immunotherapy, CRAFT geometric metrics capture response-associated remodeling, including shifts in repertoire organization over time. On antigen-labeled benchmarks, CRAFT organizes specificity classes coherently, recovering structure that reflects shared antigen recognition.

cancer biology↗

bioRxiv2026

Nanoparticle encapsulation enhances spatial distribution of Panobinostat to treat metastatic medulloblastoma via the intrathecal route

Medulloblastoma (MB) is an aggressive central nervous system (CNS) malignancy that primarily affects children and frequently exhibits metastasis to the leptomeninges of the brain and spinal cord. We developed a {beta}-Cyclodextrin-poly({beta}-Amino Ester) nanoparticle system to deliver the histone deactylase inhibitor (HDACi) Panobinostat to MB by the intrathecal route. Various imaging methods were utilized to study nanoparticle and payload fate following infusion into the cerebrospinal fluid (CSF) of mice via cisterna magna or lumbar access points. Nanoparticles dramatically improved penetration of hydrophobic small molecules into distal regions of the spinal cord. Panobinostat-loaded nanoparticles were effective at treating patient-derived MB, activating pharmacodynamic targets, slowing growth of the primary tumor, decreasing incidence of metastasis at the time of death, and ultimately prolonging survival. These studies provide insight into the mechanisms mediating transport of colloids and therapeutic molecules in the subarachnoid space and highlight new approaches for treating metastatic disease in the CNS.

bioengineering↗

bioRxiv2025

Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice

Conventional stable isotope tracing assays track one or several metabolites. However, cells use an array of nutrients to sustain nitrogen metabolic pathways. This incongruency hampers a system level understanding of cellular nitrogen metabolism. Therefore, we created a platform to simultaneously trace 30 nitrogen isotope-labeled metabolites. This platform revealed that while primitive cells engage both de novo and salvage pyrimidine synthesis pathways, differentiated cells nearly exclusively salvage uridine despite expressing de novo pathway enzymes. This link between cell state and pyrimidine synthesis routes persisted in physiological contexts, including primary murine and human tissues and tumor xenografts. Mechanistically, we found that Ser1900 phosphorylation of CAD, the first enzyme of the de novo pathway, was enriched in primitive cells and that mimicking this modification in differentiated cells abrogated their preference for pyrimidine salvage. Collectively, we establish a method for nitrogen metabolism profiling and define a mechanism of cell state-specific pyrimidine synthesis pathway choice.

cell biology↗

bioRxiv2025

A pathogenic subpopulation of human glioma associated macrophages linked to glioma progression

Malignant gliomas follow two distinct natural histories: de novo high grade tumors such as glioblastoma, or lower grade tumors with a propensity to transform into high grade disease. Despite differences in tumor genotype, both entities converge on a common histologically aggressive phenotype, and the basis for this progression is unknown. Glioma associated macrophages (GAM) have been implicated in this process, however GAMs are ontologically and transcriptionally diverse, rendering isolation of pathogenic subpopulations challenging. Since macrophage contextual gene programs are orchestrated by transcription factors acting on cis-acting promoters and enhancers in gene regulatory networks (GRN), we hypothesized that functional populations of GAMs can be resolved through GRN inference. Here we show via parallel single cell RNA and ATAC sequencing that a subpopulation of human GAMs can be defined by a GRN centered around the Activator Protein-1 transcription factor FOSL2 preferentially enriched in high grade tumors. Using this GRN we nominate ANXA1 and HMOX1 as surrogate cell surface markers for activation, thus permitting prospective isolation and functional validation in human GAMs. These cells, termed malignancy associated GAMs (mGAMs) are pro-invasive, pro-angiogenic, pro-proliferative, possess intact antigen presentation but skew T-cells towards a CD4+FOXP3+ phenotype under hypoxia. Ontologically, mGAMs share somatic mitochondrial mutations with peripheral blood monocytes, and their presence correlates with high grade disease irrespective of underlying tumor mutation status. Furthermore, spatio-temporally mGAMs occupy distinct metabolic niches; mGAMs directly induce proliferation and mesenchymal transition of low grade glioma cells and accelerate tumor growth in vivo upon co-culture. Finally mGAMs are preferentially enriched in patients with newly transformed regions in human gliomas, supporting the view that mGAMs play a pivotal role in glioma progression and may represent a plausible therapeutic target in human high-grade glioma.

cancer biology↗

bioRxiv2024

Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

The spatial-omic analysis of biomolecules such as nucleic acids, lipids, metabolites, and proteins is advancing the study of biological systems and processes in a physio-pathological context. Here, we describe an innovative matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) method to detect metals within biological tissues using instrumentation that is widely available in research and clinical laboratories. We characterize the spatial distribution of metals in diverse settings including mouse embryogenesis, genetic disorders leading to abnormal metal accumulation, and preclinical testing for improved platinum-based chemotherapy delivery through focused ultrasound across the blood-brain barrier. Spatial metal profiling will advance research studies and the clinical analysis of metal-related diseases, enabling more precise use of metal-based therapies and advances in diverse scientific fields beyond biomedicine. One-Sentence SummarySpatial metallomic profiling maps native metals or those coordinated to xenobiotics, antibodies, and biomolecules in tissues.

cancer biology↗

bioRxiv2023

Discovering Matrix Adducts for Enhanced Metabolite Profiling with Stable Isotope-Labeled MALDI-MSI

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a widely used technique for spatial metabolomics analysis, but the matrix introduces spectral interferences that impede data processing. In this study, we present an experimental and computational workflow utilizing isotopic labeling to discover and annotate matrix adducts in MALDI-MSI. Our approach enables the removal of matrix-related signals, improving metabolite annotation accuracy, extending metabolome coverage, and facilitating the interpretation of tissue morphology.

bioinformatics↗

bioRxiv2019

rMSIcleanup: An open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization

Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyte during acquisition cause spectral interferences in the low mass range that hinder downstream data processing in metabolomics applications. Thus, it is advisable to annotate and remove matrix-related peaks to reduce the number of redundant and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an open-source R package to annotate and remove matrix-related signals based on its chemical formula and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup was challenged with several images acquired using silver-assisted laser desorption ionization MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that annotation and removal of matrix-related signals improved spectral data post-processing. The results highlight the need for including matrix-related peak annotation tools such as rMSIcleanup in MSI workflows. Resources availabilityThe R package presented in this publication is freely available under the terms of the GNU General Public License v3.0 at https://github.com/gbaquer/rMSIcleanup. The datasets used in the experiments can be accessed upon request to the corresponding author.

bioinformatics↗