Sandro Santagata

The paper investigates somatic copy number variants (CNVs) in human neurons across development, finding that perinatal elimination of neurons with aberrant genomes reduces CNV prevalence and targets dosage-sensitive genes.

The paper introduces v-CyCIF, a method for 3D spatial profiling of human FFPE tissue, enabling detailed analysis of tumor, stroma, and immune structures.

This study reconstructs transcriptional trajectories of Fallopian-tube epithelium to show STICs occupy diverse positions along a progression continuum and exhibit variable PARPi resistance signatures.

This study shows that immunogenic tumor mass dormancy, not cell-level quiescence, persists in residual melanoma lesions after immunotherapy, with active tumor cells and immune interactions.

This study uses 3D reconstruction and optimal transport modeling to analyze TLS development in TB-infected lungs, revealing distinct maturation trajectories and spatial dynamics.

This paper introduces a human-in-the-loop framework using AI foundation models and sparse autoencoders to interpret H&E histology images and integrate them with molecular spatial profiling.

This study identifies epigenetic dysregulation of metabolic programs in liposarcoma, linking IGF1 loss and PPARG2 deficiency to dedifferentiation and proposing IGF1R-targeted therapy.

This paper shows that bleb-induced septin signaling supports survival of metastatic melanoma cells in vivo, and disrupting this pathway reduces metastasis.

The paper maps oxidative stress response in TNBC using single-cell and spatial data, identifying GCLC+ mesenchymal tumor cells linked to better patient outcomes.

This paper introduces 3D CyCIF, a method for high-plex 3D profiling of cell states and immune niches in human tumors, revealing detailed spatial interactions and cytokine niches previously unattainable in thin sections.

This paper introduces a method that combines autoencoders with segmentation to improve single-cell analysis of multiplexed tissue images by capturing morphology and reducing signal spillover.

The paper maps spatial patterns of tumor cell dedifferentiation and plasticity in melanoma by analyzing RNA and protein expression across 300 histological domains.

The paper describes how the Human Tumor Atlas Network shares data through standards, infrastructure, and community engagement, making datasets accessible via multiple platforms.

This study identifies suppressive myeloid cell populations in human TB granulomas using spatial transcriptomics and single-cell immunophenotyping, revealing IDO1+ dendritic cells as key regulators of immune suppression.

This study identifies ZNFX1 as a master regulator of epigenetically-induced pathogen mimicry and inflammasome signaling in ovarian cancer, linking its expression to disease progression and survival.

This study uses spatial profiling to show immune suppression and microenvironment changes in fallopian tube precursors of high-grade serous ovarian cancer.

This paper introduces CyLinter, a quality control tool that removes imaging artefacts from high-plex spatial profiling data, improving single-cell analysis of archival tumor specimens.

The paper identifies a subset of mismatch repair proficient colorectal cancers with high immune infiltration that may benefit from immune checkpoint inhibitors, using spatial profiling and genomics.

This paper introduces a MALDI MSI method to spatially profile metals in tissues, applying it to study embryogenesis, genetic disorders, and chemotherapy delivery.

Cell Spotter (CSPOT) uses supervised machine learning to automate cell spotting and quantification in highly multiplexed tissue images, improving speed, accuracy, and reproducibility.

The paper shows nuclear rupture is common in cancer, especially glioblastoma, and links it to lamin proteins and immune signaling.

The paper proposes deep learning methods to reconstruct 3D CyCIF images and select optimal ROIs from histology and MTI data for tumor representation.

The paper introduces two methods to improve tissue segmentation in highly multiplexed images using real augmentations and nuclear envelope imaging, resulting in more accurate models.

ASHLAR is a Python tool that stitches and registers highly multiplexed whole slide images to generate accurate mosaics, outperforming existing software.

This study quantifies cell proliferation in tumors using multiplexed imaging and identifies spatial and temporal patterns of cell cycle regulation.

MCMICRO processes multiplexed tissue images into single-cell data, demonstrating its versatility across different tissues and imaging platforms.