Michael Angelo

CytoRipple identifies tissue-level polarization vector fields from spatial multi-omic data to reveal multi-cellular structures and their functional associations.

The paper introduces a method to predict longitudinal outcomes from single-timepoint data using a virtual library of granulomas, applied to tuberculosis in non-human primates.

This study identifies a novel intermediate cell state and a regulatory network in SCLC that drives lineage plasticity and immunogenicity.

This study shows that D-1MT treatment enhances CD8+ T cell infiltration and interactions with macrophages in TB granulomas, improving immune control of Mtb.

The paper identifies spatially distinct epithelial states in PDAC that correlate with immunosuppressive myeloid niches and poor prognosis.

The paper identifies ECM proteomic signatures associated with disease-free survival in DCIS and IBC, showing reduced collagen peptides correlate with poorer outcomes.

This study identifies APOE4/4-driven dysfunctional microglial states in Alzheimer's disease through multiomic analysis, revealing shifts toward terminal states with metabolic disruption and inflammatory signaling.

This paper maps mucin glycoforms in tumors using on-tissue digestion and mass spectrometry, revealing distinct glycosylation patterns in colorectal mucinous adenocarcinomas.

This study uses spatial multi-omics to compare EBV-positive and EBV-negative cHL, identifying distinct TME features and immunological signatures linked to EBV.

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

This study identifies hypoxia as a key driver of immune dysfunction in TB granulomas, correlating with bacterial persistence.

This study introduces MicroCart, a framework for spatial multi-omics analysis of host-microbiome interactions in colitis, revealing tissue-level immune responses and microbial shifts.

The paper uses spatial imaging and computational analysis to identify features in metastatic tumors that predict response to immune checkpoint inhibition in TNBC patients.

This study identifies molecular signatures of brain mimicry and adaptation in breast cancer brain metastases using spatial transcriptomic profiling and a new algorithm called Equivalent Expression Index.

QUICHE identifies low-prevalence cellular niches in spatial datasets, outperforming existing methods, and reveals structural anti-tumor immune responses in TNBC.

This study identifies IFNγ-expressing myeloid cells in lipoproteinosis regions of sJIA-daPAP patients, suggesting their role in immune pathology.

The paper introduces a platform-independent preprocessing framework for multiplex tissue imaging data to improve cross-platform consistency and reliability.

The paper introduces Pixie, a pipeline for robust pixel-level clustering in multiplexed imaging data to improve phenotyping and annotation across biological contexts.

This study uses spatial proteomics to analyze human brain tissues and identifies proteopathies and cell interactions in Alzheimer's disease.

The paper analyzes synaptic molecular changes in Alzheimer's disease and related conditions using mass cytometry and imaging, identifying disease-specific molecular differences.

The paper introduces Mesmer, a deep learning algorithm for cell segmentation in tissue images, achieving human-level performance and enabling automated feature extraction.

The paper constructs a spatiotemporal atlas of the maternal-fetal interface using multiomic data to show how gestational age influences immune and stromal cell interactions and spiral artery remodeling.

The paper introduces DeepLIIF, a deep learning framework that translates IHC images to multiplex IF images and quantifies single-cell IHC markers.