Biology subjects

Angel, P.

Publications and source records attributed to Angel, P..

13 recordsLinked to original sources

bioRxiv2026

MSGPCA: Multi-Slice Graph PCA for replicate-aware Spatial Omics analysis

As spatial transcriptomics (ST) and spatial proteomics (SP) technologies mature, experimental designs are increasingly moving beyond single-slice analyses toward multi-slice studies involving one or more donors and experimental conditions. Although these designs enable the identification of reproducible spatial signals, they also introduce substantial biological heterogeneity, particularly when integrating non-serial slices or anatomically distinct regions. If not modeled carefully, such variation can blur slice-specific tissue structure, mask conserved molecular patterns, and limit the discovery of biologically relevant latent structure. Although dimension reduction is essential for representing high-dimensional molecular data in a lower-dimensional space, existing multi-slice methods typically enforce a globally shared representation that inadequately accommodates slice-level heterogeneity. To address this limitation, we propose Multi-Slice Graph Principal Component Analysis (MSGPCA), which decomposes molecular variation into shared spatial factors conserved across slices and slice-specific factors that capture local tissue microarchitecture. In downstream analyses, MSGPCA-derived representations recover spatial tissue structure, denoise molecular profiles, and reveal biologically interpretable metafeatures associated with shared and slice-specific biology. In a mass spectrometry imaging dataset comprising nonserial slices of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC), the shared factors captured broad biological differences across tissue regions, whereas the slice-specific factors revealed intratumoral spatial variation within the IBC microenvironment. In human dorsolateral prefrontal cortex ST data, MSGPCA recovered laminar cortical architecture across adjacent slices, closely aligning with expert pathologist annotations. Together, these findings demonstrate that MSGPCA resolves shared tissue architecture while preserving local microenvironmental variation in complex multi-slice spatial omics datasets.

bioinformatics↗

bioRxiv2026

RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data

High-throughput spatial omics technologies enable molecular profiling within intact tissue architecture, yet identifying concise, predictive, and biologically interpretable marker panels for cell types, tissue domains, and disease-associated tissue classes remains challenging. This limitation hinders the development of actionable panels for targeted validation and downstream translation. Existing pipelines rely largely on univariate differential-expression analyses, which ignore joint molecular structure and provide limited predictive insight. Multivariate machine-learning methods, including random forest,XGBoost, elastic net, and specialized single-cell panel-selection approaches, can capture predictive patterns but typically lack explicit spatial modeling and probabilistic feature selection, relying instead on model-specific importance scores or user-specified panel sizes. We develop rapid kernel machine regression (RKMR), a scalable framework for spatial-omics marker discovery that integrates nonlinear kernel modeling, spike-and-slab variable selection, and spatial dependence. RKMR uses automatic relevance determination (ARD) kernels and sparsity-inducing priors to capture nonlinear marker-outcome relationships and implicit feature interactions while producing approximate posterior inclusion probabilities (PIPs) that quantify model-based uncertainty in feature inclusion. To scale inference to large spatial datasets, RKMR combines low-rank kernel approximations with stochastic variational optimization. In simulations, RKMR consistently achieves higher AUPRC than competing methods across a range of molecular-signal and spatial-effect settings. Across spatial transcriptomics and scRNA-seq datasets, RKMR identifies parsimonious marker sets that recover reported cell-type signatures and reproducible tissue-layer markers. These results establish RKMR as a scalable and uncertainty-aware framework for translating high-dimensional spatial omics data into robust, experimentally actionable marker panels.

bioinformatics↗

bioRxiv2026

FusedFCR: A Fused Forward Continuation-Ratio model for marker selection along cell-fate trajectories

Time-course single-cell RNA sequencing (scRNA-seq) data collected across ordered stages provide population-level snapshots of differentiation, disease progression, and aging. Supervised pseudotime methods use observed stage labels to reconstruct continuous progression but generally do not identify marker genes associated with changes from one stage to the next. Unsupervised pseudotime-based marker selection methods infer latent trajectories directly from expression data and identify trajectory-associated genes, but do not explicitly link these associations to the observed stages. We propose FusedFCR, a regularized forward continuation-ratio model that represents cellular progression through a sequence of conditional transitions across ordered stages. FusedFCR combines a lasso penalty for gene selection with a fusion penalty that encourages similar effects across adjacent transitions while allowing transient and direction-changing associations. The resulting transition-specific coefficients support interpretable gene selection and a continuous pseudotime-like projection anchored to the observed developmental stages. In simulations, FusedFCR accurately recovers gene-effect trajectories and improved predictive performance relative to alternative methods. Applied to one mouse and three human datasets (mouse pancreatic beta-cells, human extravillous trophoblast, human induced pluripotent stem cell derived astrocytes, and human endometrial cells during the secretory phase), FusedFCR identifies biologically interpretable genes associated with distinct developmental transitions. Gene set enrichment analysis further reveals stage-specific pathway activity consistent with known developmental biology, while held-out stage-classification accuracy was competitive or superior across both datasets. Together, these results show that FusedFCR complements pseudotemporal ordering by identifying which molecular programs change and when those changes emerge along the developmental trajectory. An accompanying R package is available on GitHub.

bioinformatics↗

bioRxiv2026

Age-related Delays in Osteochondral Remodeling of Fracture Healing Illustrated by Mass Spectrometry Imaging

Age-related delays in fracture healing are prevalent and contribute to morbidity and mortality in elderly populations. Clinical and preclinical studies demonstrate that aging is associated with slower and less complete fracture repair characterized by delayed cartilage and bone formation, impaired matrix resorption, and an increased risk of delayed union or non-union. Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI MSI) enables spatially resolved, in situ molecular analysis of proteins directly from murine fracture tissues. We applied collagenase type III (MMP-13) mediated proteolytic digestion to formalin-fixed, paraffin-embedded (FFPE) tibia fracture callus sections harvested 10 days post-tibial fracture from young (3-month-old) and aged (18-month-old) mice to perform spatially resolved proteomic profiling. Spatial MS Imaging revealed pronounced age-dependent differences in extracellular matrix protein composition and remodeling within the fracture callus. We identified upregulation of canonical bone and matrix proteins, including Col1a1 and Col1a2 specifically in the young fracture callus demonstrating advancement into harden callus formation. Conversely, Col2a1 and other soft callus proteins were only seen in the aged callus tissues. Further, protein indicators of tissue state, such as fibronectin (upregulated) and calreticulin (downregulated) were selectively regulated aged tissues, demonstrating a failure for aged tissues to fully progress into harden calluses. Spatial proteomic patterns demonstrated a marked delay in progression from cartilaginous to osseous callus in aged mice, consistent with impaired matrix remodeling during fracture repair. Together, these findings establish spatial MS Imaging based proteomics as a powerful approach to elucidate age-related alterations in fracture healing and to identify molecular regulators of impaired skeletal regeneration. Lay SummaryUsing spatially-resolved proteomics via mass spectrometry imaging on fracture callus tissues from young and aged mice, we observed delayed healing in aged animals based on the composition of the extracellular matrices. Higher levels of bone specific collagens were detected in young animals, whereas cartilage specific collagens were detected in aged animals at higher levels. Further, detection of novel, non-canonical callus proteins revealed critical transitional steps that are delayed in aged-callus tissues, and these may also contribute to the delayed healing aged animals.

biochemistry↗

bioRxiv2025

Identification of sialic acid linkage profiles in primary liver cancers

Cholangiocarcinoma (CCA), also known as bile duct cancer, is the second most common form of primary liver cancer after hepatocellular carcinoma (HCC). Subtypes of CCA are diagnosed according to the anatomic location within the biliary tree, such as intrahepatic (iCCA) or extrahepatic (eCCA). Early diagnosis remains difficult due to the low sensitivity and specificity of current biomarkers. There has been significant interest in finding new biomarkers of CCA and much work has been performed to both discover and validate these new markers. In our previous work, we identified specific alterations in N-linked glycosylation that were associated with iCCA, which differed from non-malignant cholestasis disease and HCC. However, that analysis focused only on N-glycan structures devoid of sialic acid, as these N-glycan residues are not stable and were intentionally removed to minimize variability. Here, we explore the specificity of sialic acid residues in liver cancers, since sialic acid residues have been heavily linked to roles in tumor progression and metastasis by helping evade immunological surveillance. To circumvent the instability of sialic acid, we utilize a novel sialic acid stabilization method coupled with Matrix-Assisted Laser Desorption Ionization (MALDI) Imaging Mass Spectrometry (IMS) for detailed analysis of sialic acid linkages in iCCA tissue. We report a clear association between the linkages type of sialic acid residues with a HCC or CCA, including differentiation between CCA subtypes. Overall, we stabilize N-glycans with sialic acid residues to determine their linkages and elucidate their importance in different types of primary liver cancers and their subtypes by MALDI-IMS.

cancer biology↗

bioRxiv2025

GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics

Spatially structured, overdispersed count data with high-dimensional predictors are increasingly observed across studies from population-level epidemiology to cellular-level spatial omics. Feature selection is critical to identify influential predictors, such as key risk factors or biomarkers. Few Bayesian studies have assessed negative binomial regression (NBR) models with standard variable selection priors, like the mixture spike-and-slab (SS) or continuous horseshoe (HS), but mostly under aspatial settings. Features often form groups; for instance, in population surveys, caloric intake and physical activity may fall under "Diet & Exercise", while cigarette use and smoking laws belong to "Smoking". We propose a flexible NBR model that accommodates spatial autocorrelation and introduces a novel group-structured prior by hybridizing SS and HS shrinkage. The models performance with different priors is evaluated in terms of specificity, precision, and computational cost under challenging scenarios, including "large p, small n" cases. We further apply the model to CDC state-level cancer data, comprising demographic, screening, and behavioral covariates, to identify key drivers and population-level risk factors, and to a melanoma spatial omics dataset for predictive modeling expression of gene. An efficient R package is provided on GitHub.

bioinformatics↗

bioRxiv2024

Mass Spectrometry Imaging of the Subchondral Bone in Osteoarthritis Reveals Tissue Remodeling of Extracellular Matrix Proteins that Precede Cartilage Loss

Osteoarthritis (OA) is a degenerative condition of the skeletal extracellular matrix (ECM) marked by the loss of articular cartilage and changes to subchondral bone homeostasis. Treatments for OA beyond full joint replacement are lacking primarily due to gaps in molecular knowledge of the biological drivers of disease. Mass Spectrometry Imaging (MSI) enables molecular spatial mapping of the proteomic landscape of tissues. Histologic sections of human tibial plateaus from knees of human OA patients and cadaveric controls were treated with collagenase III to target ECM proteins prior to MS Imaging of bone and cartilage proteins using a timsTOF fleX mass spectrometer. Spatial MSI data of the knee were processed and automatically segmented identifying distinct areas of knee joint damage. ECM peptide markers were compared between i) the medial halves of OA patient joints and the medial side of non-OA (cadaveric) joints, and ii) between the same medial OA tissues and their corresponding, less OA impacted, lateral joint halves. Distinct peptide signatures distinguished OA medial tissues from the cadaveric medial and OA lateral tissues (AUROC >0.85). Overall, 31 peptide candidates from ECM proteins, including Collagen alpha-1(I), Collagen alpha-1(III), and surprisingly, Collagen alpha-1(VI) and Collagen alpha-3(VI), exhibited significantly elevated abundance in diseased tissues. Highly specific hydroxyproline-containing collagen peptides, mainly from collagen type I, dominated OA subchondral bone directly under regions of lost cartilage. The identification of specific protein markers for subchondral bone remodeling in OA advances our molecular understanding of disease progression in OA and provides potential new biomarkers for OA detection and disease grading.

cell biology↗

bioRxiv2023

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA

MotivationMultiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. ResultsWe present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. AvailabilityThe associated R package can be found here, https://github.com/sealx017/SpaceANOVA. Contactsealso@musc.edu Supplementary informationThe supplementary material is attached.

bioinformatics↗

bioRxiv2023

Finding and exploring reproducible cell phenotypes with the Stemformatics data portal

Stemformatics is an established online data portal which hosts hundreds of curated gene expression datasets. It has been serving the stem cell research community for over a decade, by hosting transcriptional profiles of pluripotent and adult stem cells and their progeny from multiple tissues and derivation methods. The portal provides easy-to-use online tools to explore gene expression patterns in published data. In recent years, Stemformatics has shifted its focus from curation to collation and integration of public data with shared phenotypes. It now hosts several integrated expression atlases based on human myeloid cells, which allow for easy cross-dataset comparisons and discovery of emerging cell subsets and activation properties. The inclusion of laboratory-derived cell types enables users to benchmark their own data, to assist with cell-type standardisation or improve cell-derivation methods. The sample annotations have been greatly improved to enable better data integration, and the website has also undergone a major upgrade to modernise its visualisation tools. An application programming interface server also provides the data directly for computational users. Stemformatics is an open-source project and readily available at stemformatics.org.

systems biology↗

bioRxiv2020

A novel neural stem cell-derived immunocompetent mouse model of glioblastoma for preclinical studies

Glioblastomas are the most lethal tumors affecting the central nervous system in adults. Simple and inexpensive syngeneic in vivo models that closely mirror human glioblastoma, including interactions between tumor and immune cells, are urgently needed for deciphering glioma biology and developing more effective treatments. Here, we generated glioblastoma cell lines by repeated in-vivo passaging of cells isolated from a neural stem cell-specific Pten/p53 double-knockout genetic mouse brain tumor model. Transcriptome and genome analyses of the cell lines revealed molecular heterogeneity comparable to that observed in human glioblastoma. Upon orthotopic transplantation into syngeneic hosts they formed high-grade gliomas that faithfully recapitulated the histopathological features, invasiveness and myeloid cell infiltration characteristic of human glioblastoma. These features make our cell lines unique and useful tools to study multiple aspects of glioblastoma pathomechanism and test novel treatments, especially immunotherapies in syngeneic preclinical models.

cancer biology↗

bioRxiv2020

Neuraminidase activity mediates IL-6 production through TLR4 and p38/ERK MAPK signaling in MRL/lpr mesangial cells

Mesangial cells (MCs), considered the immune cell of the kidney, secrete a number of cytokines including IL-6, which serves as an autocrine factor for MCs stimulating proliferation. IL-6 is associated with disease in patients and mouse strains with lupus nephritis, promoting tissue damage. Previously, we demonstrated the activity or levels of the enzyme neuraminidase (NEU) is increased in the kidneys of lupus mice and urine of human patients with nephritis and that NEU activity plays a role in mediating IL-6 secretion from lupus prone MRL/lpr primary mouse MCs. In this study, we further elucidate the mechanisms by which NEU activity mediates cytokine production by primary lupus prone MCs. MRL/lpr primary MCs were cultured with lupus serum to stimulate cytokine production in the absence or presence of NEU activity inhibitor. Our results show lupus serum increases NEU activity, and secretion of GM-CSF and MIP1, in addition to IL-6, is significantly reduced when NEU activity is inhibited. mRNA expression of Il-6 and Gm-csf was also increased in response to lupus serum, and reduced when NEU activity was inhibited. Using neutralizing antibodies to specific receptors, inhibitors of MAP kinase signaling pathways, and LPS stimulation we show TLR4 and p38/ERK MAPK play a role in NEU-mediated secretion of IL-6. Together, our results suggest NEU activity plays an important role in the response of lupus prone MCs to factor(s) in lupus serum that stimulates IL-6 expression and secretion through TLR4-p38/ERK MAPK signaling, likely through desialyation of one or more glycoproteins in this pathway.

cell biology↗

bioRxiv2019

iMAC: An interactive atlas to explore phenotypic differences between in vivo, ex vivo and in vitro-derived myeloid cells in the Stemformatics platform

The Stemformatics myeloid atlas is an integrated transcriptome atlas of human macrophages and dendritic cells that systematically compares freshly isolated tissue-resident, cultured, and stem-cell derived myeloid cell types. We identified two broad classes of tissue-resident macrophages with lung, gut and tumour-associated macrophages most similar to monocytes. Microglia, Kupffer cells and synovial macrophages shared similar profiles with each other, and with cultured macrophages. Pluripotent stem cell-derived macrophages were not reminiscent of fetal-derived cells. Instead, they were characterized by atypical expression of collagen and a highly efferocytotic phenotype. Likewise, Flt3L-derived cord blood dendritic cells were distinct from conventional dendritic cell subsets isolated from primary tissues and lacked expression of key pattern recognition receptors. Myeloid subsets were reproducible across different experimental series, showing the resource is a robust reference for new data. External users can annotate and benchmark their own samples, including annotation of myeloid single cell data at www.stemformatics.org/atlas/myeloid/.

systems biology↗

bioRxiv2019

Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

Glioblastoma multiforme (GBM) are devastating neoplasms with high invasive capacity. GBM has been difficult to study in vitro. Therapeutic progress is also limited by cellular heterogeneity within and between tumors. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived glioblastoma cell invasion. By tissue clearing and confocal microscopy, we show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Single-cell RNA-seq of GBM cells before and after co-culture with organoid cells reveals transcriptional changes implicated in the invasion process that are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Functional therapeutic targets are identified by an in silico receptor-ligand pairing screen detecting potential interactions between GBM and organoid cells. Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection at a time scale amenable to clinical practice.

cancer biology↗