Biology subjects

Marcao, M.

Publications and source records attributed to Marcao, M..

4 recordsLinked to original sources

bioRxiv2025

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular- resolution reference of healthy adult human tissues across multiple organ systems. Integrating spatial transcriptomics, proteomics, and histological features across over 15 million cells from more than 100 donors, SAHA maps conserved and organ-specific cellular niches in gastrointestinal and immune tissues. High-resolution profiling using CosMx SMI, 10x Xenium, RNAscope, GeoMx DSP, and single-nucleus RNA-seq reveals spatially organized cell states, rare adaptive immune populations, and tissue-specific cell-cell interactions and ligand-receptor pairs. Comparative analyses with colorectal cancer and inflammatory bowel disease demonstrate the power of SAHA to detect disease-associated spatial disruptions, including crypt dedifferentiation, perineural invasion, and therapy-resistant immune remodeling. All data are openly accessible through a FAIR-compliant interactive portal to support exploration, benchmarking, and machine learning model training. Through SAHA, we provide a foundational framework for spatial diagnostics and next-generation precision medicine grounded in a comprehensive human tissue atlas, enabling the development of context-aware models that simulate tissue behavior, decode complex pathologies, and accelerate therapeutic innovation at unprecedented scale.

systems biology↗

bioRxiv2024

Stemness prediction models reveal glioma aggressiveness and therapeutic targets in gliomas

Gliomas are complex and heterogeneous primary brain tumors with a high degree of therapeutic resistance, particularly glioblastomas, which carry a poor prognosis. Cellular plasticity and stem cell-like features, or "stemness," are increasingly recognized as key contributors to tumor progression and treatment resistance. In this study, we introduce two machine-learning-based prediction models designed to assess stemness in glioma samples using bulk gene expression data. One model captures fetal astrocyte characteristics (ASTsi), while the other identifies glioma stem cell traits (GSCsi). ASTsi was notably correlated with poor prognosis in IDHmut gliomas, whereas GSCsi was more indicative of stemness in IDHwt subtypes. Longitudinal data analysis showed that IDHwt and grade IV gliomas exhibit shifts in stemness indices upon recurrence, suggesting a phenotypic change linked to therapy resistance. Additionally, single-cell transcriptomic analysis confirmed that GSCsi can detect stem-like cell subsets in IDHwt gliomas. This approach enhances our understanding of glioma heterogeneity and reveals potential therapeutic targets.

bioinformatics↗

bioRxiv2023

Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas

Simple and complex carcinomas are the most common type of malignant Canine Mammary Tumors (CMTs), with simple carcinomas exhibiting aggressive behavior and poorer prognostic. Stemness is an ability associated with cancer initiation and progression, malignancy, and therapeutic resistance, but is still few elucidated in different canine mammary cancer subtypes. Here, we first validated, using CMT samples, a previously published canine one-class logistic regression machine learning algorithm (OCLR) to predict stemness (mRNAsi) in canine cancer cells. Then, we observed that simple carcinomas exhibit higher stemness than other histological subtypes and confirmed that stemness is higher and associated with basal-like tumors and with NMF2 tumor-specific metagene signature. Furthermore, using correlation analysis, we suggested two promise stemness-associated targets in CMTs, POLA2 and APEX1, especially in simple canine mammary tumors. Thus, our work elucidates stemness as a potential mechanism behind the aggressiveness and development of simple canine mammary tumors, describing novel pieces of evidence of a promising strategy to target canine mammary carcinomas, especially the simple subtype.

cancer biology↗

bioRxiv2023

A Machine Learning One-Class Logistic Regression Model to Predict Stemness in Single Cell Transcriptomics and Spatial Omics Datasets

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches are often biased and manually curated. Here we harness an existing stemness model for assessing oncogenic states to transform its application to single cell and spatial omic datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic or proteomic features from non-transformed stem cells to identify dedifferentiated cell states. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. Finally these stemness indices are applicable across a variety of spatial transcriptomic and proteomic technologies for the identification of oncogenic cell types in the tumor microenvironment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/539461v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@f2973forg.highwire.dtl.DTLVardef@a7aa9corg.highwire.dtl.DTLVardef@1b1ea27org.highwire.dtl.DTLVardef@183b4a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗