Biology subjects

Rong, R.

Publications and source records attributed to Rong, R..

14 recordsLinked to original sources

bioRxiv2026

Integration of early-stage cryopreservation and cell cycle modulation into a flexible kidney organoid differentiation system

Kidney organoids derived from human pluripotent stem cells (hPSCs) represent a promising platform for modeling nephrogenesis and renal diseases. However, conventional differentiation protocols are continuous and time-sensitive, limiting their scalability and reproducibility. Here, we developed a method to pause and resume organoid formation through cryopreservation at an early differentiation stage using a chemically defined formulation that maintains high post-thaw viability and differentiation potential. We further found that synchronizing hPSCs in the G1 phase with PD-0332991 enhanced post-thaw organoid formation and transcriptional fidelity, while G2 phase enrichment with Ro-3306 promoted the development of SLC12A3-positive distal convoluted tubules. The post-thaw organoids exhibited well-organized nephron architecture and function comparable to uninterrupted cultured controls. This platform proved effective for modeling BK polyomavirus (BKV) infection, drug-induced nephrotoxicity, and renal fibrosis. Together, our cryopreservation and cell cycle synchronization strategy provides a flexible, practical framework to advance organoid-based research and translation.

developmental biology↗

bioRxiv2024

BayeSMART: Bayesian Clustering of Multi-sample Spatially Resolved Transcriptomics Data

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

bioinformatics↗

bioRxiv2023

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

Recent technological advances have highlighted the significant impact of the human microbiome and metabolites on physiological conditions. Integrating microbiome and metabolite data has shown promise in predictive capabilities. We developed a new supervised contrastive learning framework, MB-SupCon-cont, that (1) proposes a general contrastive learning framework for continuous outcomes and (2) improves prediction accuracy over models using single omics data. Simulation studies confirmed the improved performance of MB-SupCon-cont, and applied scenarios in type 2 diabetes and high-fat diet studies also showed improved prediction performance. Overall, MB-SupCon-cont is a versatile research tool for multi-omics prediction models.

bioinformatics↗

bioRxiv2023

ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data

MotivationSpatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing and visualizing large image datasets together with molecular information. ResultsWe introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) It visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) It enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) It displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) It bolsters data security by circumventing data transfers. AvailabilityScopeViewer is available at: https://datacommons.swmed.edu/scopeviewer ContactXiaowei.Zhan@UTSouthwestern.edu, Guanghua.Xiao@UTSouthwestern.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research; however, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedure and produces massive data that capture tumor histological details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nuclei segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nuclei segmentation and TME quantification within image patches; however, existing algorithms are computationally intensive and time-consuming for WSI analysis. In this study, we present Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nuclei segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing methods for WSI analysis in nuclei detection and classification accuracy, as well as computation time.

pathology↗

bioRxiv2022

Image-based quantification of histological features as a function of spatial location using the Tissue Positioning System

Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it would be important to measure cellular or genetic features as a function of position along a zonal axis. While it is possible to manually count, characterize, and locate features in relation to the zonal axis, it is very difficult to do this for more than a few hundred instances. We addressed this challenge by developing a deep-learning-based quantification method called the "Tissue Positioning System" (TPS), which can automatically analyze zonation in the liver lobule as a model system. By using algorithms that identified vessels, classified vessels, and segmented zones based on the relative position along the portal vein to central vein axis, TPS was able to spatially quantify gene expression in mice with zone specific reporters. TPS could discern expression differences between zonal reporter strains, ages, and disease states. TPS could also reveal the zonal distribution of cells previously thought to be randomly distributed. The design principles of TPS could be generalized to other tissues to explore the biology of zonation. The software is available at https://github.com/yunguan-wang/Tissue_positioning_system.

bioinformatics↗

bioRxiv2022

MB-SupCon: Microbiome-based predictive models via Supervised Contrastive Learning

Human microbiome consists of trillions of microorganisms. Microbiota can modulate the host physiology through molecule and metabolite interactions. Integrating microbiome and metabolomics data have the potential to predict different diseases more accurately. Yet, most datasets only measure microbiome data but without paired metabolome data. Here, we propose a novel integrative modeling framework, Microbiome-based Supervised Contrastive Learning Framework (MB-SupCon). MB-SupCon integrates microbiome and metabolome data to generate microbiome embeddings, which can be used to improve the prediction accuracy in datasets that only measure microbiome data. As a proof of concept, we applied MB-SupCon on 720 samples with paired 16S microbiome data and metabolomics data from patients with type 2 diabetes. MB-SupCon outperformed existing prediction methods and achieves high average prediction accuracies for insulin resistance status (84.62%), sex (78.98%), and race (80.04%). Moreover, the microbiome embeddings form separable clusters for different covariate groups in the lower-dimensional space, which enhances data visualization. We also applied MB-SupCon on a large inflammatory bowel disease study and observed similar advantages. Thus, MB-SupCon could be broadly applicable to improve microbiome prediction models in multi-omics disease studies.

bioinformatics↗

bioRxiv2020

HiddenVis: a Hidden State Visualization Toolkit to Visualize and Interpret Deep Learning Models for Time Series Data

Accelerometers provide continuous measurements of human movements. Utilizing their data exhibits great potential for monitoring or predicting human health outcomes. In this direction, researchers are active in developing machine-learning algorithms. However, compared to understanding deep learning models, visualizing and interpreting such models is equally important but has been less well-developed. To address these limitations, we constructed an online software tool to visualize the hidden states inferred from the deep learning models and to compare different cohorts based on outcome-associated temporal profiles. We demonstrated the utility of the model using the National Health and Nutrition Examination Survey (NHANES) dataset. Using this tool, we discovered that two time periods of daily activity contribute to significant differences in 5-year mortalities. To disseminate the software to the broader community for the analysis of accelerometer data, we provide the work as open-source code at https://github.com/jyan97/HiddenVis.

bioinformatics↗

bioRxiv2019

MB-GAN: Microbiome Simulation via Generative Adversarial Network

Simulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models. To address this challenge, we designed a novel simulation framework for microbiome data using a generative adversarial network (GAN), called MB-GAN, by utilizing methodology advancements from the deep learning community. MB-GAN can automatically learn from a given dataset and compute simulated datasets that are indistinguishable from it. When MB-GAN was applied to a case-control microbiome study of 396 samples, we demonstrated that the simulated data and the original data had similar first-order and second-order properties, including sparsity, diversities, and taxa-taxa correlations. These advantages are suitable for further microbiome methodology development where high fidelity microbiome data are needed.

bioinformatics↗

bioRxiv2019

Nightmares share strong genetic risk with sleep and psychiatric disorders

Nightmares are vivid, extended and extremely dysphoric dreams that awaken the dreamer. Twin studies indicate that nightmare frequency has a heritability between 36 and 51% providing evidence for genetic factors underlying predisposition to nightmares. Furthermore, while cross-sectional epidemiological findings suggest that heavy alcohol usage, traumatic experiences and psychiatric diseases associate with nightmares, the causal relationships between these conditions and nightmares have remained unknown. To examine the biological mechanisms behind nightmares, we performed a genome-wide association study in 28,596 individuals from Finland and the United States. We identified individual genetic variants that predispose to nightmares near MYOF (rs701873, p=2.18e-8) and PTPRJ (rs11039471,p=3.7e-8), a gene previously associated with sleep duration. We found a strong genetic correlation between the frequency of nightmares and traits related to personality and psychiatric disorders; neuroticism (rg=0.59, p=8e-7), post-traumatic stress disorder (PTSD) (rg=0.58, p=0.004) as well as major depressive disorder (rg=0.68, p=7e-4), and sleep traits; and daytime sleepiness (rg=0.62, p=1e-6) and insomnia (rg=0.50, p=1.87e-5). Analysis of directionality using mendelian randomization showed a significant effect from feelings of fed-up (p=0.001), nervous (p=0.004) and miserableness (p=0.0045) to nightmares with no evidence of pleiotropy and no evidence of nightmares predisposing to psychiatric or psychological problems. Our findings suggest that nightmares are caused by unique genetic risk factors, and here we identify the first individual genetic associations. In addition, a substantial effect on nightmares is conveyed through underlying psychological and sleep problems, with psychological problems being causal for nightmares.

genetics↗

bioRxiv2019

Computational staining of pathology images to study tumor microenvironment in lung cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally \"stain\" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.\n\nA Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.\n\nThis study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

bioinformatics↗

bioRxiv2018

Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations

Skin lesion is a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of skin lesion is extremely challenging manually visualization. Hence, reliable automatic classification of skin lesions is meaningful to improve pathologists accuracy and efficiency. In this paper, we proposed a two-stage method to combine deep learning features and clinical criteria representations to address skin lesion automated diagnosis task. Stage 1 - feature encoding: Modified deep convolutional neural networks (CNNs, in this paper, we used Dense201 and Res50) were fine-tuned to extract rich image global features. To avoid hair noisy, we developed a lesion segmentation U-Net to mask out the decisive regions and used the masked image as CNNs inputs. In addition, color features, texture features and morphological features were exacted based on clinical criteria; Stage 2 - features fusion: LightGBM was used to select the salient features and model parameters, predicting diagnosis confidence for each category. The proposed deep learning frameworks were evaluated on the ISIC 2018 dataset. Experimental results show the promising accuracies of our frameworks were achieved.

bioinformatics↗

bioRxiv2018

Skin Lesion Segmentation with Improved C-UNet Networks

--This paper proposes an innovative method for Part1, skin lesion segmentation of the ISIC 2018 Challenge. Our network C-UNet is based on UNet network, we combined several methods on this basic network which made some improvements on Jaccard Index ultimately, our method yield an average Jaccard Index of 0.77 on the On-line validation dataset.

bioengineering↗

bioRxiv2018

Lesion Attributes Segmentation for Melanoma Detection with Deep Learning

Melanoma is the most deadly form of skin cancer worldwide. Many efforts have been made for early detection of melanoma. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this paper, we describe our solutions for the task 2 of ISIC 2018 Challenges. We present two deep learning approaches to automatically detect lesion attributes of melanoma, one is a multi-task U-Net model and the other is a Mask R-CNN based model. Our multi-task U-Net model achieved a Jaccard index of 0.433 on official test data, which ranks the 5th place on the final leaderboard. The code for our solutions is publicly available.

bioinformatics↗