Biology subjects

Wong, S. T.

Publications and source records attributed to Wong, S. T..

13 recordsLinked to original sources

bioRxiv2026

Interpretable computational SHG microscopy enables quantitative tissue phenotyping of cardiac remodeling in nonischemic heart failure

Heart failure is characterized by extracellular matrix remodeling and collagen deposition that impair myocardial function. Second harmonic generation (SHG) microscopy provides label-free visualization of fibrillar collagen and myosin but lacks standardized, interpretable, and quantitative frameworks with clinical-level performance. We present a feature extraction pipeline integrating 87 biologically interpretable SHG-derived metrics for automated classification of nonischemic heart failure in murine specimen. The framework distinguished normal, heart failure, and natural recovery states with area-under-the-curve values of 0.84-0.99, outperforming established open-source SHG feature extraction tools. These findings support interpretable, SHG-based quantification of cardiac remodeling and establish a methodology for future multimodal biophotonics applications.

bioengineering↗

bioRxiv2025

Site-Specific Immune and Stromal Architecture Drive Resistance to Trastuzumab Deruxtecan in HER2+ Metastatic Breast Cancer

Trastuzumab deruxtecan (T-DXd) is an antibody-drug conjugate (ADC) that has demonstrated remarkable efficacy in HER2+ and HER2-low metastatic breast cancer (mBC). Yet, 40-50% of patients fail to respond, and mechanisms underlying resistance, particularly those involving the immune microenvironment, remain poorly understood. To address this gap, we conducted spatial proteomic profiling of metastatic lesions from 25 HER2+ mBC patients treated with T-DXd, using the Bruker GeoMX Digital Spatial Profiler (DSP) platform. Regions of interest (ROIs) were segmented by CD45 expression to distinguish immune-rich versus immune-poor compartments, and spatial marker expression was compared between responders (R) and non-responders (NR). Spearman correlation analysis was used to assess spatial coordination among HER2, fibronectin, and immune markers. Non-responders exhibited greater HER2 heterogeneity, lower CD45+ immune infiltration, and consistent upregulation of fibronectin and granzyme B. In both bone and brain metastases, NR tissues showed strong correlations between fibronectin and T cell markers, indicating spatial immune exclusion. In contrast, NR lymph node metastases displayed negative correlations between fibronectin and immune markers and uncoordinated B-T cell clustering, suggesting an alternative resistance mechanism rooted in immune disorganization rather than exclusion. Collectively, these results implicate HER2 heterogeneity, fibrotic immune exclusion, and immune suppression as key drivers of T-DXd resistance across metastatic sites. By integrating spatial proteomics with clinical outcomes, this study provides novel insights into the spatial immune landscape of ADC resistance and identifies actionable biomarkers for patient stratification and combination therapy design, laying a critical foundation for developing biomarker-guided therapies and personalized treatment strategies to overcome ADC resistance in metastatic breast cancer. Translational SignificanceT-DXd represents a major advance in antibody-drug conjugate (ADC) therapies to address HER2 heterogeneity in tumors through bystander killing. However, treatment options following resistance to T-DXd remain limited, leaving a critical unmet need. As new ADCs continue to emerge, resistance will likely remain a persistent challenge across tumor types. This work extends beyond T-DXd resistance--it provides a framework to address resistance mechanisms common to other ADCs. By investigating immune-mediated mechanisms of ADC resistance, this research identifies therapeutic targets that can be leveraged to develop new treatment strategies. By integrating spatial biology with clinical data, this study offers unprecedented insights into immune-tumor interactions within the resistant tumor microenvironment. This approach deepens mechanistic understanding and enables personalized treatment strategies guided by spatially resolved biomarkers. This novel combination of spatial mechanistic research with clinical data represents advancement toward overcoming ADC resistance and improving outcomes for patients with few therapeutic options.

cancer biology↗

bioRxiv2024

Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR

Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov models attributes, reconstructs cellular lineage pathways, shedding light on the cells evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.

bioinformatics↗

bioRxiv2023

NOS2 and COX2 Provide Key Spatial Targets that Determine Outcome in ER- Breast Cancer

Estrogen receptor-negative (ER-) breast cancer is an aggressive breast cancer subtype with limited therapeutic options. Upregulated expression of both inducible nitric oxide synthase (NOS2) and cyclo-oxygenase (COX2) in breast tumors predicts poor clinical outcomes. Signaling molecules released by these enzymes activate oncogenic pathways, driving cancer stemness, metastasis, and immune suppression. The influence of tumor NOS2/COX2 expression on the landscape of immune markers using multiplex fluorescence imaging of 21 ER-breast tumors were stratified for survival. A powerful relationship between tumor NOS2/COX2 expression and distinct CD8+ T cell phenotypes was observed at 5 years post-diagnosis. These results were confirmed in a validation cohort using gene expression data showing that ratios of NOS2 to CD8 and COX2 to CD8 are strongly associated with poor outcomes in high NOS2/COX2-expressing tumors. Importantly, multiplex imaging identified distinct CD8+ T cell phenotypes relative to tumor NOS2/COX2 expression in Deceased vs Alive patient tumors at 5-year survival. CD8+NOS2-COX2-phenotypes defined fully inflamed tumors with significantly elevated CD8+ T cell infiltration in Alive tumors expressing low NOS2/COX2. In contrast, two distinct phenotypes including inflamed CD8+NOS2+COX2+ regions with stroma-restricted CD8+ T cells and CD8-NOS2-COX2+ immune desert regions with abated CD8+ T cell penetration, were significantly elevated in Deceased tumors with high NOS2/COX2 expression. These results were supported by applying an unsupervised nonlinear dimensionality-reduction technique, UMAP, correlating specific spatial CD8/NOS2/COX2 expression patterns with patient survival. Moreover, spatial analysis of the CD44v6 and EpCAM cancer stem cell (CSC) markers within the CD8/NOS2/COX2 expression landscape revealed positive correlations between EpCAM and inflamed stroma-restricted CD8+NOS2+COX2+ phenotypes at the tumor/stroma interface in deceased patients. Also, positive correlations between CD44v6 and COX2 were identified in immune desert regions in deceased patients. Furthermore, migrating tumor cells were shown to occur only in the CD8-NOS2+COX2+ regions, identifying a metastatic hot spot. Taken together, this study shows the strength of spatial localization analyses of the CD8/NOS2/COX2 landscape, how it shapes the tumor immune microenvironment and the selection of aggressive tumor phenotypes in distinct regions that lead to poor clinical outcomes. This technique could be beneficial for describing tumor niches with increased aggressiveness that may respond to clinically available NOS2/COX2 inhibitors or immune-modulatory agents.

cancer biology↗

bioRxiv2023

Spatial analysis of NOS2 and COX2 interaction with T-effector cells reveals immunosuppressive landscapes associated with poor outcome in ER- breast cancer patients

Multiple immunosuppressive mechanisms exist in the tumor microenvironment that drive poor outcomes and decrease treatment efficacy. The co-expression of NOS2 and COX2 is a strong predictor of poor prognosis in ER- breast cancer and other malignancies. Together, they generate pro-oncogenic signals that drive metastasis, drug resistance, cancer stemness, and immune suppression. Using an ER- breast cancer patient cohort, we found that the spatial expression patterns of NOS2 and COX2 with CD3+CD8+PD1- T effector (Teff) cells formed a tumor immune landscape that correlated with poor outcome. NOS2 was primarily associated with the tumor-immune interface, whereas COX2 was associated with immune desert regions of the tumor lacking Teff cells. A higher ratio of NOS2 or COX2 to Teff was highly correlated with poor outcomes. Spatial analysis revealed that regional clustering of NOS2 and COX2 was associated with stromal-restricted Teff, while only COX2 was predominant in immune deserts. Examination of other immunosuppressive elements, such as PDL1/PD1, Treg, B7H4, and IDO1, revealed that PDL1/PD1, Treg, and IDO1 were primarily associated with restricted Teff, whereas B7H4 and COX2 were found in tumor immune deserts. Regardless of the survival outcome, other leukocytes, such as CD4 T cells and macrophages, were primarily in stromal lymphoid aggregates. Finally, in a 4T1 model, COX2 inhibition led to a massive cell infiltration, thus validating the hypothesis that COX2 is an essential component of the Teff exclusion process and, thus, tumor evasion. Our study indicates that NOS2/COX2 expression plays a central role in tumor immunosuppression. Our findings indicate that new strategies combining clinically available NOS2/COX2 inhibitors with various forms of immune therapy may open a new avenue for the treatment of aggressive ER- breast cancers.

cancer biology↗

bioRxiv2023

MUSTANG: MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance

AO_SCPLOWBSTRACTC_SCPLOWSpatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. We here present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on two real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in cellular characterization of tissue samples under the study. MUSTANG is publicly available at at https://github.com/namini94/MUSTANG

bioinformatics↗

bioRxiv2023

Interferon-gamma is Quintessential for NOS and COX Expression in ER- Breast Tumors that Lead to Poor Outcome

A strong correlation between NOS2 and COX2 tumor expression and poor clinical outcomes in ER-breast cancer has been established. However, mechanisms of tumor induction of these enzymes are unclear. Analysis of The Cancer Genome Atlas (TCGA) revealed correlations between NOS2 and COX2 expression and Th1 cytokines. Herein, single cell RNAseq analysis of TNBC cells shows potent NOS2 and COX2 induction by IFN{gamma} combined with IL1{beta} or TNF. Given that IFN{gamma} is secreted by cytolytic lymphocytes, which improve clinical outcomes, this role of IFN{gamma}presents a dichotomy. To explore this conundrum, tumor NOS2, COX2, and CD8+ T cells were spatially analyzed in aggressive ER-, TNBC, and HER2+ breast tumors. High expression and clustering of NOS2-expressing tumor cells occurred at the tumor/stroma interface in the presence of stroma-restricted CD8+ T cells. High expression and clustering of COX2-expressing tumor cells extended into immune desert regions in the tumor core where CD8+ T cell penetration was limited or absent. Moreover, high NOS2-expressing tumor cells were proximal to areas with increased satellitosis suggestive of cell clusters with a higher metastatic potential. Further in vitro experiments revealed that IFN{gamma}+IL1{beta}/TNF increased elongation and migration of treated tumor cells. This spatial analysis of the tumor microenvironment provides important insight of distinct neighborhoods where stroma-restricted CD8+ T cells exist proximal to NOS2-expressing tumor niches that could have increased metastatic potential.

cancer biology↗

bioRxiv2022

Long-term ovarian cancer survivors: spatial transcriptomics depict ligand-receptor crosstalk heterogeneity at the tumor-stroma interface

Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The identification of predictive markers associated with tumors developed from these long-term survivors (LTS) is crucial to identifying therapeutic targets for the disease, and thus improving patient survival rates. Reports to date have not fully established the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer and its association with clinical outcomes. We used a spatial transcriptomics platform to generate spatially resolved transcript profiles in treatment naive advanced HGSC from LTS and short-term survivors (STS), and determined whether cancer-associated fibroblasts (CAFs) heterogeneity is associated with survival in patients with advanced HGSC. We integrated spatial transcriptomics with single-cell RNA sequencing data to distinguish tumor and stroma regions, and developed a method to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. In addition, we used multiplex immunohistochemistry techniques to validate our findings. We found that a specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlate with long-term survival in advanced HGSC patients. We also demonstrated that significant APOE-LRP5 crosstalk occurred at the stroma-tumor interface in tumor tissues from STS compared to LTS, suggesting that such crosstalk plays a crucial role in modulating the malignant phenotype of HGSC, and could serve as a predictive biomarker of patient survival.

cancer biology↗

bioRxiv2022

NOS2 and COX2 Blockade Limits TNBC Disease Progression and Alters CD8+ T Cell Spatial Orientation and Density

Anti-tumor immune polarization is a key predictor of clinical outcomes to cancer therapy. An emerging concept influencing clinical outcome involves the spatial location of CD8+ T cells, within the tumor. Our earlier work demonstrated immunosuppressive effects of NOS2/ COX2 tumor expression. Here, we show that NOS2/COX2 levels influence the polarization and spatial location of lymphoid cells including CD8+ T cells. Importantly, elevated tumor NOS2/COX2 correlated with exclusion of CD8+ T cells from the tumor epithelium. In contrast, tumors expressing low NOS2/COX2 had increased CD8+ T cell penetration into the tumor epithelium. Consistent with a causative relationship between these observations, pharmacological inhibition of COX2 with indomethacin dramatically reduced tumor growth of the 4T1 model of TNBC in both WT and Nos2-/- mice. This regimen led to complete tumor regression in [~]20% of tumor-bearing Nos2-/- mice, and these animals were resistant to tumor rechallenge. Th1 cytokines were elevated in the blood of treated mice and intratumoral CD4+ and CD8+ T cells were higher in mice that received indomethacin when compared to control untreated mice. Multiplex immunofluorescence imaging confirmed our phenotyping results and demonstrated that targeted Nos2/Cox2 blockade improved CD8+ T cell penetration into the 4T1 tumor core. These findings are consistent with our observations in low NOS2/COX2 expressing breast tumors` proving that COX2 activity is responsible for limiting the spatial distribution of effector T cells in TNBC. Together these results suggest that clinically available NSAIDs may provide a cost-effective, novel immunotherapeutic approach for treatment of aggressive tumors including triple negative breast cancer.

cancer biology↗

bioRxiv2021

imageseg: an R package for deep learning-based image segmentation

O_LIConvolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications. CNNs can perform very well in various tasks, especially for visual tasks and image data. Image segmentation (the classification of all pixels in images) is one such task and can for example be used to assess forest vertical and horizontal structure. While such methods have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. C_LIO_LIHere, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with two models for forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 3288 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1468 understory vegetation images. C_LIO_LIOverall classification accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively), indicating robustness to variation in input images and good generalization strength across forest types and biomes. C_LIO_LIThe package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with multiple classes and based on color or grayscale images, e.g. in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond. C_LI

ecology↗

bioRxiv2020

Non-invasive surveys of mammalian viruses using environmental DNA

O_LIEnvironmental DNA (eDNA) and invertebrate-derived DNA (iDNA) have been used to survey biodiversity non-invasively to mitigate difficulties of obtaining wildlife samples, particularly in remote areas or for rare species. Recently, eDNA/iDNA have been applied to monitor known wildlife pathogens, however, most wildlife pathogens are unknown and often evolutionarily divergent. C_LIO_LITo detect and identify known and novel mammalian viruses from eDNA/iDNA sources, we used a curated set of RNA oligonucleotides as viral baits in a hybridization capture system coupled with high throughput sequencing. C_LIO_LIWe detected multiple known and novel mammalian RNA and DNA viruses from multiple viral families from both waterhole eDNA and leech derived iDNA. Congruence was found between detected hosts and viruses identified in leeches and waterholes. C_LIO_LIOur results demonstrate that eDNA/iDNA samples represent an effective non-invasive resource for studying wildlife viral diversity and for detecting novel potentially zoonotic viruses prior to their emergence. C_LI

molecular biology↗

bioRxiv2018

Habitat-Net: Segmentation of habitat images using deep learning

Understanding environmental factors that influence forest health, as well as the occurrence and abundance of wildlife, is a central topic in forestry and ecology. However, the manual processing of field habitat data is time-consuming and months are often needed to progress from data collection to data interpretation. Computer-assisted tools, such as deep-learning applications can significantly shortening the time to process the data while maintaining a high level of accuracy. Here, we propose Habitat-Net: a novel method based on Convolutional Neural Networks (CNN) to segment habitat images of tropical rainforests. Habitat-Net takes color images as input and after multiple layers of convolution and deconvolution, produces a binary segmentation of the input image. We worked on two different types of habitat datasets that are widely used in ecological studies to characterize the forest conditions: canopy closure and understory vegetation. We trained the model with 800 canopy images and 700 understory images separately and then used 149 canopy and 172 understory images to test the performance of Habitat-Net. We compared the performance of Habitat-Net with a simple threshold based method, a manual processing by a second researcher and a CNN approach called U-Net upon which Habitat-Net is based. Habitat-Net, U-Net and simple thresholding reduced total processing time to milliseconds per image, compared to 45 seconds per image for manual processing. However, the higher mean Dice coefficient of Habitat-Net (0.94 for canopy and 0.95 for understory) indicates that accuracy of Habitat-Net is higher than that of both the simple thresholding (0.64, 0.83) and U-Net (0.89, 0.94). Habitat-Net will be of great relevance for ecologists and foresters, who need to monitor changes in their forest structures. The automated workflow not only reduces the time, it also standardizes the analytical pipeline and, thus, reduces the degree of uncertainty that would be introduced by manual processing of images by different people (either over time or between study sites). Furthermore, it provides the opportunity to collect and process more images from the field, which might increase the accuracy of the method. Although datasets from other habitats might need an annotated dataset to first train the model, the overall time required to process habitat photos will be reduced, particularly for large projects.

ecology↗

bioRxiv2018

Shifting up a gear with iDNA: from mammal detection events to standardized surveys

O_LIInvertebrate-derived DNA (iDNA), in combination with high throughput sequencing, has been proposed as a cost-efficient and powerful tool to survey vertebrate species. Previous studies, however, have only provided evidence that vertebrates can be detected using iDNA, but have not taken the next step of placing these detection events within a statistical framework that allows for robust biodiversity assessments.\nC_LIO_LIHere, we compare concurrent iDNA and camera-trap surveys. Leeches were repeatedly collected in close vicinity to 64 camera-trap stations in Sabah, Malaysian Borneo. We analyze iDNA-derived mammalian detection events in a modern occupancy model that accounts for imperfect detection and compare the results with those from occupancy models parameterized with camera-trap-derived detection events. We also combine leech-iDNA and camera-trap data in a single occupancy model.\nC_LIO_LIWe found consistent estimates of occupancy probabilities produced by our camera-trap and leech datasets. This indicates that the metabarcoding of leech-iDNA method provides reasonable estimates of occupancy and can be a suitable method for studying and monitoring mammal species in tropical rainforests. However, we also show that a more extensive collection of leeches would be needed to assess mammal biodiversity with a similar robustness as with camera traps. As certain taxa were only detected in leeches, we see great potential in complementing camera-trap studies with the iDNA approach, as long as the collection of leeches follows a similar robust and standardized sampling scheme.\nC_LIO_LISynthesis and applications. The approach we describe here is not restricted to the processing of leech samples, but can be used for the analysis of other iDNA and environmental DNA (eDNA) data. Our study is the first step to shift the application of e/iDNA studies from opportunistic ad-hoc collections to systematic surveys required for long-term wildlife populations and biodiversity monitoring programs.\nC_LI

ecology↗