Biology subjects

Pascual-Reguant, A.

Publications and source records attributed to Pascual-Reguant, A..

5 recordsLinked to original sources

bioRxiv2025

Decoding the immune response in leptomeningeal disease through single-cell sequencing of cerebrospinal fluid

AbstractAssessing anti-tumor immune responses and immune microenvironments in central nervous system (CNS) neoplasms, such as brain tumors and leptomeningeal disease (LMD), provides prognostic insights and predictive biomarkers. Liquid biopsy of the cerebrospinal fluid (CSF) represents a promising minimally-invasive approach, but its ability to reflect immune responses against tumors remains unclear. Here, we used single-cell sequencing of CSF cells and spatial transcriptomics of CNS lesions to compare and contrast LMD patients with CNS lymphoma (CNSL), glioblastoma (GB) and brain metastases (BrM), to neuroinflammatory CNS disorders. We identified disease-specific CSF environments, reflecting parenchymal tumor microenvironment features. CNSL showed robust T cell responses, while BrM and GB were dominated by both blood-derived and tissue-resident myeloid cells. Longitudinal CSF sampling unveiled mechanisms of disease progression and therapy resistance, highlighting the potential of CSF liquid biopsies for uncovering disease biology, discovering cellular biomarkers and developing personalized therapies for CNS neoplasms.

immunology↗

bioRxiv2024

Spatial Flux Balance Analysis reveals tissue-of-origin and spatially dependent metabolic rewiring in renal and colorectal cancer

1To fully understand how cancer metabolism differs between primary tumors and metastases, resolving cell metabolism with spatial precision is essential. Yet, spatial fluxomics lags behind advancements in spatial transcriptomics. To address this gap, we generated high-resolution spatial transcriptomics datasets from paired primary colorectal tumors and liver metastases, designed to capture metabolic adaptations across distinct tumor sites. Concurrently, we developed the Spatial Flux Balance Analysis (spFBA) computational framework to leverage them. Since broad metabolic differences between tumors and healthy tissues are established, we first validated spFBA on a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA detected cancer metabolic hallmarks, like enhanced glucose uptake and metabolic growth, but with unprecedented resolution, revealing lactate production with sustained oxygen consumption at the tumor interface and with reduced respiration in the core. Next, applying spFBA to our colorectal cancer dataset, we provided biological insights, confirming that metastases mimic the metabolic traits of their tissue of origin. Additionally, our approach uncovered the first in vivo evidence of lactate-consuming cancer cells, marking a significant advance in understanding cancer metabolism. spFBA stands out as a powerful approach to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.

systems biology↗

bioRxiv2024

Spatio-temporal T cell tracking for personalized TCR-T designs in childhood cancer

Immune checkpoint inhibition (ICI) has revolutionized oncology, offering extended survival and long-term remission in previously incurable cancers. While highly effective in tumors with high mutational burden, lowly mutated cancers, including pediatric malignancies, present low response rate and limited predictive biomarkers. Here, we present a framework for the identification and validation of tumor-reactive T cells as a biomarker to quantify ICI efficacy and as candidates for a personalized TCR-T cell therapy. Therefore, we profiled a pediatric malignant rhabdoid tumor patient with complete remission after ICI therapy using deep single-cell T cell receptor (TCR) repertoire sequencing of the tumor microenvironment (TME) and the peripheral blood. Specifically, we tracked T cell dynamics longitudinally from the tumor to cells in circulating over a time course of 12 months, revealing a systemic response and durable clonal expansion of tumor-resident and ICI-induced TCR clonotypes. We functionally validated tumor reactivity of TCRs identified from the TME and the blood by co-culturing patient-derived tumor cells with TCR-engineered autologous T cells. Here, we observed unexpectedly high frequencies of tumor-reactive TCR clonotypes in the TME and confirmed T cell dynamics in the blood post-ICI to predict tumor-reactivity. These findings strongly support spatio-temporal tracking of T cell activity in response to ICI to inform therapy efficacy and to serve as a source of tumor-reactive TCRs for personalized TCR-T designs.

genomics↗

bioRxiv2024

STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging

We introduce Single-Cell Transcriptomics Analysis and Multimodal Profiling (STAMP), a scalable profiling approach of individual cells. Leveraging transcriptomics and proteomics imaging platforms, STAMP eliminates sequencing costs, to enable single-cell genomics from hundreds to millions of cells at an unprecedented low cost. Stamping cells in suspension onto imaging slides, STAMP supports single-modal (RNA or protein) and multimodal (RNA and protein) profiling and flexible, ultra-high-throughput formats. STAMP allows the analysis of a single or multiple samples within the same experiment, enhancing experimental flexibility, throughput and scale. We tested STAMP with diverse sample types, including peripheral blood mononuclear cells (PBMCs), dissociated cancer cells and differentiated embryonic stem cell cultures, as well as whole cells and nuclei. Combining RNA and protein profiling, we applied immuno-phenotyping of millions of blood cells simultaneously. We also used STAMP to identify ultra-rare cell populations, simulating clinical applications to identify circulating tumor cells (CTCs). Performing in vitro differentiation studies, we further showed its potential for large-scale perturbation studies. Together, STAMP establishes a new standard for cost-effective, scalable single-cell analysis. Without the need for sequencing, STAMP makes high-resolution profiling more affordable and accessible. Designed to meet the needs of research labs, diagnostic cores and pharmaceutical companies, STAMP holds the promise to transform our capacity to map human biology, diagnose diseases and drug discovery.

genomics↗

bioRxiv2023

VoltRon: A Spatial Omics Analysis Platform for Multi-Resolution and Multi-omics Integration using Image Registration

The growing number of spatial omic technologies have created a demand for computational tools capable of managing, storing, and analyzing spatial datasets with multiple modalities and spatial resolutions. Meanwhile, computer vision is becoming an integral part of processing spatial data readouts where image registration and spatial data alignment of tissue sections are essential prior to data integration. Hence, there is a need for computational platforms that analyze data across spatial datasets with diverse resolutions as well as those that manipulate and process images of microanatomical tissue structures. To this end, we have developed VoltRon, a novel R package for spatial omics analysis with a unique data structure that accommodates data readouts with many levels of spatial resolutions (i.e., multi-resolution) including regions of interest (ROIs), spots, single cells, and even subcellular entities such as molecules. To connect and integrate these spatially diverse omic profiles, VoltRon accounts for spatial organization of tissue blocks (samples), layers (sections) and assays given a multi-resolution collection of spatial data readouts. An easy-to-use computer vision toolbox, OpenCV, is fully embedded in VoltRon that allows users to both automatically and manually register spatial coordinates across adjacent layers for data transfer without the need for external software tools. VoltRon is implemented in the R programming language and is freely available at https://github.com/BIMSBbioinfo/VoltRon.

bioinformatics↗