Clarence Yapp

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

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

This study shows GSK-J4, a KDM6 inhibitor, induces metal and stress responses in multiple myeloma cells by increasing intracellular Zn2+ and activating metallothionein gene expression.

The paper introduces u-Segment3D, a tool that converts 2D cell segmentations into 3D consensus segmentations without training data, demonstrating effectiveness across diverse datasets.

The paper identifies regulatory T cells suppressing tissue-resident CD8+ T cell programs in early melanoma, revealing a mechanism of immune evasion.

The paper analyzes endothelial cell environments across organs using spatially resolved omics data and provides an interactive tool for visualizing cell distances and neighborhoods.

The paper shows that low baseline c-Myc expression restricts oncogenic K-Ras activity in tissues, and this can be reversed by increasing c-Myc in the liver.

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.

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

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 discusses challenges in digital image analysis of cancerous tissues and outlines solutions developed during a 2022 hackathon.

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.

The paper introduces a method called Dye Drop for high-throughput single-cell assays in 384-well plates, enabling detailed dose-response analysis of small molecules in breast cancer cell lines.

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.

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

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