Biology subjects

Yapp, C.

Publications and source records attributed to Yapp, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Modeling development of tertiary lymphoid structures in pulmonary tuberculosis by 3D profiling and trajectory analysis

Tertiary lymphoid structures (TLSs) are sites of immune organization in peripheral tissues that arise from chronic inflammation. They play important roles in infection control and cancer but the mechanisms controlling their formation remain only partly understood. Here, we combine high-plex imaging, serial-section 3D reconstruction, and optimal transport trajectory modeling to reconstruct TLSs in human lungs infected with Mycobacterium tuberculosis. We find that the extended and irregular shape of TLSs is poorly captured by 2D histopathology or spatial profiling, making assessment of developmental stage (early, primary, or secondary) error prone. In contrast, modeling TLS development in 3D using optimal transport reveals two trajectories that differ in the timing and coordination of follicular dendritic cell association, germinal center consolidation, and position within the tissue. These findings highlight the value of volumetric analysis in understanding immune organization and provide new insights into TLS biology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/725201v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@11b4daorg.highwire.dtl.DTLVardef@7871feorg.highwire.dtl.DTLVardef@1840c2dorg.highwire.dtl.DTLVardef@3b6028_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LI3D reconstruction of TB-infected lung reveals TLSs to be extended, heterogeneous structures that are frequently misclassified in 2D C_LIO_LITLS maturation follows continuous trajectories rather than discrete histological stages C_LIO_LIOptimal transport modeling identifies distinct maturation paths with divergent follicular dynamics C_LIO_LIGerminal center organization and Tfh-FDC interactions emerge along maturation trajectories C_LIO_LITLS maturation states are shaped by spatially localized microenvironmental niches. C_LI

immunology↗

bioRxiv2026

Volumetric Cyclic Immunofluorescence for 3D Spatial Profiling of Immune Structures in Human FFPE Tissue

Mapping how parenchymal, immune, and stromal cells interact with extended structures such as blood vessels and nerves is challenging using conventional thin-section tissue imaging. Volumetric methods such as light-sheet fluorescence microscopy (LSFM) are ideal in these cases and LSFM is widely used in animal models such as fish and mice. This is enabled by well-established clearing methods, genetically encoded reporters, and vascular dyes. However, LSFM is rarely utilized for human tissue, which is primarily available as formaldehyde-fixed paraffin embedded (FFPE) specimens. Here, we present an approach to volumetric cyclic immunofluorescence (v-CyCIF) that enables 40-plex or greater imaging of immune and other cell types in human specimens up to 1 mm thick. We demonstrate the use of v-CyCIF to study neuroimmune interactions and lymphoid structures in normal and cancer tissues. Following LSFM imaging, specimens can be re-embedded and sectioned to ~50 {micro}m thick for high-resolution confocal imaging of punctate structures and cell-cell junctions. v-CyCIF therefore provides a flexible framework for multi-scale 3D profiling of clinical specimens across imaging formats and resolutions.

cancer biology↗

bioRxiv2026

In vivo evidence for bleb-induced survival signaling in metastatic melanoma

Bleb signaling is a cellular process in which pressure-driven plasma membrane protrusions generate localized micron-scale membrane curvature that recruits cytosolic septin complexes, assembling signaling hubs that promote cell survival1. Our prior work showed that this morphology-encoded signaling pathway is necessary to sustain anchorage-independent survival of BRAF and NRAS mutant melanoma cells in vitro. However, whether bleb-induced signaling occurs in vivo and contributes to cancer progression is unclear. Here, we develop complementary in vivo and ex vivo assays spanning human patient samples and mouse xenograft models to mechanistically interrogate bleb signaling directly in physiologic contexts. We observe that bleb-associated septin hubs are exclusively formed in poorly adherent amoeboid tumor cells at the invasive margin, within malignant effusions, and at distant metastatic sites, while well-adhered cells in the tumor interior show no signs of septin hub formation. Accordingly, pharmacological pathway disruption specifically kills disseminated tumor cells within these low-adhesion microenvironments while having no appreciable effect on adhered cells in the tumor interior, resulting in reduced metastatic burden and delayed disease recurrence in vivo. This work confirms septin-mediated bleb signaling as a previously unrecognized vulnerability of disseminated cancer cells and demonstrates that this morphology-encoded pathway operates in vivo to support disease progression, preserving cancer cell viability under conditions in which cell survival signals from the environment are muted. These findings suggest novel opportunities to target survival signaling in micrometastatic disseminated cancer cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/697601v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1770384org.highwire.dtl.DTLVardef@87e567org.highwire.dtl.DTLVardef@1e5d6b2org.highwire.dtl.DTLVardef@724c5f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Regulatory T cells inhibit CD8+ TRM-like cells during the early stages of tumor immune escape

How the immune system surveys nascent tumors and how this surveillance is subverted remain poorly understood. Using high-plex cyclic immunofluorescence and 3D imaging, we identified regulatory T (Treg) cells that co-localize with tissue-resident memory (TRM)-like T cells in early-stage human melanoma. In an autochthonous Braf/PTEN melanoma model expressing a defined tumor antigen, the initial CD8+ T cell infiltrate adopts a CD103+CD101+ TRM-like fate, establishing active immunosurveillance within nascent lesions. TRM-like cells dominate early tumors, occupy a stable epidermal niche, express effector molecules, and initiate T cell recruitment. However, Treg cells adopt a parallel tissue-resident phenotype, co-localizing with TRM-like cells and restraining both cytotoxic and sentinel functions. Tumor-site-specific Treg depletion reactivated TRM-like cells, drove robust T cell recruitment, expanded tumor-specific responses, and limited tumor growth. These findings reveal how early immunosurveillance is established through tissue-resident programs and identify Treg co-option of this response as a critical mechanism of tumor immune evasion. One Sentence SummaryNascent melanoma imprints a tissue-resident program on the initial CD8+ T cell infiltrate, which is suppressed by regulatory T cells as a critical checkpoint in immune evasion.

immunology↗

bioRxiv2025

Exploring endothelial cell environments across organs in spatially resolved omics data

Endothelial cells are ubiquitously present in the human body and line the luminal surface of blood and lymphatic vessels. The oxygen-dependence of cells impacts their proximity to blood vessels, and consequently, to endothelial cells depending on their functional properties and priorities. This paper presents cell-to-nearest-endothelial-cell distance distributions for various cell types using 399 spatially resolved omics datasets from 14 studies comprising 12 tissue types with a total of 47,349,496 cells. Additionally, we developed an open-source web-based interactive tool, Cell Distance Explorer, that allows researchers to interactively visualize cell graphs and linkages in 2D and 3D datasets. Finally, we present a hierarchical neighborhood analysis focused on the endothelial cell neighborhoods in small and large intestine datasets. This paper provides an open-access resource (datasets, tools, and analyses) to characterize and compare cell distances and cell neighborhoods in spatially resolved omics data.

bioinformatics↗

bioRxiv2025

Baseline expression of c-Myc defines the tissue specificity of oncogenic K-Ras

KRAS is among the most frequently mutated oncogenes in cancer. Yet, mutations in KRAS are common only in tumors originating from a subset of tissues. It is critical to understand the molecular mechanisms underlying this oncogene tissue specificity. Utilizing genetically engineered mouse models carrying a conditional oncogenic allele of Kras, we expressed activated K-Ras in adult tissues to investigate its specificity. We discovered that the ability of K-RasG12D to influence the fitness of cells in a given tissue is not determined by its canonical signaling through MAPK. Instead, low baseline expression of c-Myc renders tissues non-permissive to oncogenic K-Ras, a context that can be reversed in the liver by ectopically expressing c-Myc. This functions independently of the proliferative index of the tissue or the induction of cell cycle arrest or apoptosis. Our findings reveal the importance of the basal state of the tissue-inherent signaling network for determining oncogene specificity.

cancer biology↗

bioRxiv2025

Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders

Spatial proteomics (highly multiplexed tissue imaging) provides unprecedented insight into the types, states, and spatial organization of cells within preserved tissue environments. To enable single-cell analysis, high-plex images are typically segmented using algorithms that assign marker signals to individual cells. However, conventional segmentation is often imprecise and susceptible to signal spillover between adjacent cells, interfering with accurate cell type identification. Segmentation-based methods also fail to capture the morphological detail that histopathologists rely on for disease diagnosis and staging. Here, we present a method that combines unsupervised, pixel-level machine learning using autoencoders with traditional segmentation to generate single-cell data that captures information on protein abundance, morphology, and local neighborhood in a manner analogous to human experts while overcoming signal spillover. We demonstrate the generality of this approach by applying it to CyCIF, Lunaphore COMET, and CODEX data, and show that it can learn histological features across multiple spatial scales.

pathology↗

bioRxiv2024

The KDM6 histone demethylase inhibitor GSK-J4 induces metal and stress responses in multiple myeloma cells

Thioneins are cysteine-rich apoproteins that regulate divalent metal homeostasis by virtue of their metal-chelation properties resulting in the ligand-bound metallothionein state. Previous studies show transient upregulation of the metallothionein (MT) gene cluster as part of a complex transcriptional response to a class of histone demethylase tool compounds targeting human Fe2+ dependent ketoglutarate oxygenases KDM6A (UTX) and KDM6B (JmjD3). The prototypic bioactive KDM6 inhibitor GSK-J4 induces apoptotic cell death in multiple myeloma cells and corresponding transcriptomic profiles are dominated by metal and metabolic stress response signatures, also observed in primary human myeloma cells. Here we investigate the hypothesis that metal-chelation by GSK-J4 provides the means for transport and intracellular release of Zn2+ leading to a metallothionein transcriptomic response signature. Live cell imaging of myeloma cells shows transient increases in intracellular free Zn2+ concentrations when exposed to GSK-J4, consistent with a model of inhibitor-mediated metal transport. Comparisons of GSK-J4 and ZnSO4 treatments in the presence or absence of metal chelators show that both treatment conditions induce different transcription factor repertoires with an overlapping MTF1 transcriptional regulation responsible for metallothionein and metal ion transport regulation. The data provide a possible explanation for the observed metal response upon GSK-J4 inhibition however the relationship with the pro-apoptotic mechanism in myeloma cells requires further investigation.

cell biology↗

bioRxiv2024

Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma

High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer. STATEMENT OF SIGNIFICANCEThis study maps the immune response in fallopian tube precursors of high-grade serous ovarian cancer, highlighting localized interferon signaling, CIN, and competing immune surveillance and suppression along the progression axis. It provides an explorable public spatial profiling atlas for investigating precancer mechanisms, biomarkers, and early detection and interception strategies.

cancer biology↗

bioRxiv2024

A general algorithm for consensus 3D cell segmentation from 2D segmented stacks

Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation, and computation. However, 3D cell segmentation, requiring dense annotation of 2D slices still poses significant challenges. Manual labeling of 3D cells to train broadly applicable segmentation models is prohibitive. Even in high- contrast images annotation is ambiguous and time-consuming. Here we develop a theory and toolbox, u- Segment3D, for 2D-to-3D segmentation, compatible with any 2D method generating pixel-based instance cell masks. u-Segment3D translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data, as demonstrated on 11 real-life datasets, >70,000 cells, spanning single cells, cell aggregates, and tissue. Moreover, u-Segment3D is competitive with native 3D segmentation, even exceeding when cells are crowded and have complex morphologies.

bioinformatics↗

bioRxiv2023

Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images

Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.

bioinformatics↗

bioRxiv2023

Multiplexed 3D Analysis of Cell Plasticity and Immune Niches in Melanoma

Diseases like cancer involve alterations in in cell proportions, states, and local interactions as well as complex changes in 3D tissue architecture. However, disease diagnosis and most multiplexed spatial profiling studies rely on inspecting thin (4-5 micron) tissue specimens. Here, we use confocal microscopy and cyclic immunofluorescence (3D CyCIF) to show that few if any cells are intact in these thin sections; this reduces the accuracy of cell phenotyping and interaction analysis. In contrast, high-plex 3D CyCIF imaging of intact cells in thick tissue sections enables accurate quantification of marker proteins and detailed analysis of intracellular structures and organelles. Precise imaging of cell membranes also makes it possible to detect juxtacrine signalling among interacting tumour and immune cells and reveals the formation of spatially-restricted cytokine niches. Thus, 3D CyCIF provides insights into cell states and morphologies in preserved human tissues at a level of detail previously limited to cultured cells.

cancer biology↗

bioRxiv2023

2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer

Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.

cancer biology↗

bioRxiv2023

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

bioinformatics↗

bioRxiv2022

Multi-modal digital pathology for colorectal cancer diagnosis by high-plex immunofluorescence imaging and traditional histology of the same tissue section

Precision medicine is critically dependent on better methods for diagnosing and staging disease and predicting drug response. Histopathology using Hematoxylin and Eosin (H&E) stained tissue - not genomics - remains the primary diagnostic method in cancer. Recently developed highly-multiplexed tissue imaging methods promise to enhance research studies and clinical practice with precise, spatially-resolved, single-cell data. Here we describe the "Orion" platform for collecting and analyzing H&E and high-plex immunofluorescence (IF) images from the same cells in a whole-slide format suitable for diagnosis. Using a retrospective cohort of 74 colorectal cancer resections, we show that IF and H&E images provide human experts and machine learning algorithms with complementary information that can be used to generate interpretable, multiplexed image-based models predictive of progression-free survival. Combining models of immune infiltration and tumor-intrinsic features achieves a hazard ratio of [~]0.05, demonstrating the ability of multi-modal Orion imaging to generate high-performance biomarkers.

cancer biology↗

bioRxiv2022

Single Cell Spatial Analysis Reveals the Topology of Immunomodulatory Purinergic Signaling in Glioblastoma

Glioblastoma develops an immunosuppressive microenvironment that fosters tumorigenesis and resistance to current therapeutic strategies. Here we use multiplexed tissue imaging and single-cell RNA-sequencing to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioblastoma. We show that glioblastoma exhibit strong expression of CD39 and CD73 ectoenzymes, correlating with increased adenosine levels. Microglia are the predominant source of CD39, while CD73 is principally expressed by tumor cells, particularly in tumors with amplification of EGFR and astrocyte-like differentiation. Spatially-resolved single-cell analyses demonstrate strong spatial correlation between tumor CD73 and microglial CD39, and that their spatial proximity is associated with poor clinical outcomes. Together, this data reveals that tumor CD73 expression correlates with tumor genotype, lineage differentiation, and functional states, and that core purine regulatory enzymes expressed by neoplastic and tumor-associated myeloid cells interact to promote a distinctive adenosine-rich signaling niche and immunosuppressive microenvironment potentially amenable to therapeutic targeting.

cancer biology↗

bioRxiv2021

Multiplexed and reproducible high content screening of live and fixed cells using the Dye Drop method

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g. CellTiter-Glo(R)). Here we describe a public domain "Dye Drop" method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

systems biology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗