Biology subjects

Moriel, N.

Publications and source records attributed to Moriel, N..

4 recordsLinked to original sources

bioRxiv2025

Development of the preterm infant gut and gastric residuals microbiome

Prematurity, defined as birth before 37 completed weeks of gestation, is the leading cause of mortality in children under five and affects approximately 11% of live births globally--around 15 million infants each year. Despite advances in neonatal care, preterm infants remain at an increased risk for a range of complications. One widely used clinical practice in neonatal intensive care units (NICUs) is the monitoring of gastric residuals (GRs) to assess feeding tolerance and guide enteral nutrition. While the clinical significance of GRs is debated, their microbial composition has not been extensively studied. In this study, we performed metagenomic sequencing of 199 stool and 69 GR samples from 39 preterm infants during hospitalization to characterize their gut and stomach microbiomes. To our knowledge, this is the first study to describe the microbial landscape of GR in preterm infants. We identified 11 distinct GR clusters, often dominated by Staphylococcus, Streptococcus, and Klebsiella, with microbial diversity correlating with GR aspiration frequency. Longitudinal analysis revealed temporal colonization patterns, with early dominance of Staphylococcus epidermidis and Bradyrhizobium, and later emergence of Escherichia coli, Staphylococcus hominis, and Streptococcus thermophilus. In stool samples, 8 microbial clusters were found, frequently enriched with Enterobacteriaceae. Early samples rich in S. epidermidis were associated with higher gestational age and lower microbial richness, while Bifidobacterium breve, a beneficial gut commensal, appeared later in hospitalization. Comparative analysis showed overlap between gut and gastric microbiota, though stomach samples were more dynamic and exhibited less intra-subject similarity. Strain-level resolution revealed both subject-specific (e.g., E. coli, K. pneumoniae) and widely shared (e.g., S. epidermidis) taxa. We also identified a pathogenic Klebsiella aerogenes strain associated with bacteremia with distinct genomic features a week ahead of its first clinical isolation. These findings provide novel insights into the dynamic and niche-specific microbial colonization of preterm infants.

microbiology↗

bioRxiv2022

Emergence of division of labor in tissues through cell interactions and spatial cues

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions vs. instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA-seq and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

systems biology↗

bioRxiv2022

TACCO: Unified annotation transfer and decomposition of cell identities for single-cell and spatial omics

Rapid advances in single-cell-, spatial-, and multi-omics, allow us to profile cellular ecosystems in tissues at unprecedented resolution, scale, and depth. However, both technical limitations, such as low spatial resolution and biological variations, such as continuous spectra of cell states, often render these data imperfect representations of cellular systems, best captured as continuous mixtures over cells or molecules. Based on this conceptual insight, we build a versatile framework, TACCO (Transfer of Annotations to Cells and their COmbinations) that extends an Optimal Transport-based core by different wrappers or boosters to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatio-molecular tissue structure at the cell and molecular level, and resolve differentiation trajectories. TACCO excels in speed, scalability, and adaptability, while successfully outperforming benchmarks across diverse synthetic and biological datasets. Along with highly optimized visualization and analysis functions, TACCO forms a comprehensive integrated framework for studies of high-dimensional, high-resolution biology.

bioinformatics↗

bioRxiv2022

Integrative single cell and spatial transcriptomics of colorectal cancer reveals multicellular functional units that support tumor progression

While advances in single cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single cell RNA-seq, spatial transcriptomics by Slide-seq, and in situ multiplex RNA analysis, to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled inducible genetic CRC mouse models that recapitulate key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Comparing to scRNA-seq and Slide-seq data from human CRC, we find that both cell composition and layout features were conserved between the species, with mouse neighborhoods correlating with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease. Our work offers a comprehensive framework that is applicable across various tissues, tumors, and disease conditions, with tools for the extrapolation of findings from experimental mouse models to human diseases.

cancer biology↗