Browsing by Author "Li, Bo"

Now showing 1 - 6 of 6
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    Dissection of transcriptome dysregulation and immune characterization in women with germline BRCA1 mutation at single-cell resolution
    (Springer, 2022-09-09) Yu, Xuexin; Lin, Wanrun; Spirtos, Alexandra; Wang, Yan; Chen, Hao; Ye, Jianfeng; Parker, Jessica; Liu, Ci Ci; Wang, Yiying; Quinn, Gabriella; Zhou, Feng; Chambers, Setsuko K.; Lewis, Cheryl; Lea, Jayanthi; Li, Bo; Zheng, Wenxin; Obstetrics and Gynecology, School of Medicine
    Background: High-grade serous carcinoma (HGSC) is the most frequent and lethal type of ovarian cancer. It has been proposed that tubal secretory cells are the origin of ovarian HGSC in women with familial BRCA1/2 mutations. However, the molecular changes underlying malignant transformation remain unknown. Method: We performed single-cell RNA and T cell receptor sequencing of tubal fimbriated ends from 3 BRCA1 germline mutation carriers (BRCA1 carriers) and 3 normal controls with no high-risk history (non-BRCA1 carriers). Results: Exploring the transcriptomes of 19,008 cells, predominantly from BRCA1+ samples, we identified 5 major cell populations in the fallopian tubal mucosae. The secretory cells of BRCA1+ samples had differentially expressed genes involved in tumor growth and regulation, chemokine signaling, and antigen presentation compared to the wild-type BRCA1 controls. There are several novel findings in this study. First, a subset of the fallopian tubal secretory cells from one BRCA1 carrier exhibited an epithelial-to-mesenchymal transition (EMT) phenotype, which was also present in the mucosal fibroblasts. Second, we identified a previously unreported phenotypic split of the EMT secretory cells with distinct evolutionary endpoints. Third, we observed increased clonal expansion among the CD8+ T cell population from BRCA1+ carriers. Among those clonally expanded CD8+ T cells, PD-1 was significantly increased in tubal mucosae of BRCA1+ patients compared with that of normal controls, indicating that T cell exhaustion may occur before the development of any premalignant or malignant lesions. Conclusion: These results indicate that EMT and immune evasion in normal-looking tubal mucosae may represent early events leading to the development of HGSC in women with BRCA1 germline mutation. Our findings provide a probable molecular mechanism explaining why some, but not all, women with BRCA1 germline mutation present with early development and rapid dissemination of HGSC.
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    Pedestrian Detection based on Clustered Poselet Models and Hierarchical And-Or Grammar
    (IEEE, 2015-04) Li, Bo; Chen, Yaobin; Wang, Fei-Yue; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    In this paper, a novel part-based pedestrian detection algorithm is proposed for complex traffic surveillance environments. To capture posture and articulation variations of pedestrians, we define a hierarchical grammar model with the and-or graphical structure to represent the decomposition of pedestrians. Thus, pedestrian detection is converted to a parsing problem. Next, we propose clustered poselet models, which use the affinity propagation clustering algorithm to automatically select representative pedestrian part patterns in keypoint space. Trained clustered poselets are utilized as the terminal part models in the grammar model. Finally, after all clustered poselet activations in the input image are detected, one bottom-up inference is performed to effectively search maximum a posteriori (MAP) solutions in the grammar model. Thus, consistent poselet activations are combined into pedestrian hypotheses, and their bounding boxes are predicted. Both appearance scores and geometry constraints among pedestrian parts are considered in inference. A series of experiments is conducted on images, both from the public TUD-Pedestrian data set and collected in real traffic crossing scenarios. The experimental results demonstrate that our algorithm outperforms other successful approaches with high reliability and robustness in complex environments.
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    Pipeline for characterizing alternative mechanisms (PCAM) based on bi-clustering to study colorectal cancer heterogeneity
    (Elsevier, 2023-03-17) Cao, Sha; Chang, Wennan; Wan, Changlin; Lu, Xiaoyu; Dang, Pengtao; Zhou, Xinyu; Zhu, Haiqi; Chen, Jian; Li, Bo; Zang, Yong; Wang, Yijie; Zhang, Chi; Biostatistics and Health Data Science, School of Medicine
    The cells of colorectal cancer (CRC) in their microenvironment experience constant stress, leading to dysregulated activity in the tumor niche. As a result, cancer cells acquire alternative pathways in response to the changing microenvironment, posing significant challenges for the design of effective cancer treatment strategies. While computational studies on high-throughput omics data have advanced our understanding of CRC subtypes, characterizing the heterogeneity of this disease remains remarkably complex. Here, we present a novel computational Pipeline for Characterizing Alternative Mechanisms (PCAM) based on biclustering to gain a more detailed understanding of cancer heterogeneity. Our application of PCAM to large-scale CRC transcriptomics datasets suggests that PCAM can generate a wealth of information leading to new biological understanding and predictive markers of alternative mechanisms. Our key findings include: 1) A comprehensive collection of alternative pathways in CRC, associated with biological and clinical factors. 2) Full annotation of detected alternative mechanisms, including their enrichment in known pathways and associations with various clinical outcomes. 3) A mechanistic relationship between known clinical subtypes and outcomes on a consensus map, visualized by the presence of alternative mechanisms. 4) Several potential novel alternative drug resistance mechanisms for Oxaliplatin, 5-Fluorouracil, and FOLFOX, some of which were validated on independent datasets. We believe that gaining a deeper understanding of alternative mechanisms is a critical step towards characterizing the heterogeneity of CRC. The hypotheses generated by PCAM, along with the comprehensive collection of biologically and clinically associated alternative pathways in CRC, could provide valuable insights into the underlying mechanisms driving cancer progression and drug resistance, which could aid in the development of more effective cancer therapies and guide experimental design towards more targeted and personalized treatment strategies. The computational pipeline of PCAM is available in GitHub (https://github.com/changwn/BC-CRC).
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    SpaIM: Single-cell Spatial Transcriptomics Imputation via Style Transfer
    (bioRxiv, 2025-01-27) Li, Bo; Tang, Ziyang; Budhkar, Aishwarya; Liu, Xiang; Zhang, Tonglin; Yang, Baijian; Su, Jing; Song, Qianqian; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Spatial transcriptomics (ST) technologies have revolutionized our understanding of cellular ecosystems. However, these technologies face challenges such as sparse gene signals and limited gene detection capacities, which hinder their ability to fully capture comprehensive spatial gene expression profiles. To address these limitations, we propose leveraging single-cell RNA sequencing (scRNA-seq), which provides comprehensive gene expression data but lacks spatial context, to enrich ST profiles. Herein, we introduce SpaIM, an innovative style transfer learning model that utilizes scRNA-seq information to predict unmeasured gene expressions in ST data, thereby improving gene coverage and expressions. SpaIM segregates scRNA-seq and ST data into data-agnostic contents and data-specific styles, with the contents capture the commonalities between the two data types, while the styles highlight their unique differences. By integrating the strengths of scRNA-seq and ST, SpaIM overcomes data sparsity and limited gene coverage issues, making significant advancements over 12 existing methods. This improvement is demonstrated across 53 diverse ST datasets, spanning sequencing- and imaging-based spatial technologies in various tissue types. Additionally, SpaIM enhances downstream analyses, including the detection of ligand-receptor interactions, spatial domain characterization, and identification of differentially expressed genes. Released as open-source software, SpaIM increases accessibility for spatial transcriptomics analysis. In summary, SpaIM represents a pioneering approach to enrich spatial transcriptomics using scRNA-seq data, enabling precise gene expression imputation and advancing the field of spatial transcriptomics research.
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    Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence
    (IEEE, 2021) Chang, Wennan; Dang, Pengdao; Wan, Changlin; Lu, Xiaoyu; Fang, Yue; Zhao, Tong; Zang, Yong; Li, Bo; Zhang, Chi; Cao, Sha; Biostatistics, School of Public Health
    In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial domain, assuming that the relationships may exhibit complex spatially dynamic patterns that cannot be captured by constant regression coefficients. Our method integrates the robust finite mixture Gaussian regression model with spatial constraints, to simultaneously handle the spatial non-stationarity, local homogeneity, and outlier contaminations. Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships. As such, the proposed model not only accounts for non-stationarity in the spatial trend, but also clusters observations into a few distinct and homogenous groups. This provides an advantage on interpretation with a few stationary sub-processes identified that capture the predominant relationships between response and predictor variables. Moreover, the proposed method incorporates robust procedures to handle contaminations from both regression outliers and spatial outliers. By doing so, we robustly segment the spatial domain into distinct local regions with similar regression coefficients, and sporadic locations that are purely outliers. Rigorous statistical hypothesis testing procedure has been designed to test the significance of such segmentation. Experimental results on many synthetic and real-world datasets demonstrate the robustness, accuracy, and effectiveness of our proposed method, compared with other robust finite mixture regression, spatial regression and spatial segmentation methods.
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    Stem cell therapy in necrotizing enterocolitis: Current state and future directions
    (Elsevier, 2018-02) Drucker, Natalie A.; McCulloh, Christopher J.; Li, Bo; Pierro, Agostino; Besner, Gail E.; Markel, Troy A.; Surgery, School of Medicine
    Stem cell therapy is a promising treatment modality for necrotizing enterocolitis. Among the many promising stem cells identified to date, it is likely that mesenchymal stem cells will be the most useful and practical cell-based therapies for this condition. Using acellular components such as exosomes or other paracrine mediators are promising as well. Multiple mechanisms are likely at play in the positive effects provided by these cells, and further research is underway to further elucidate these effects.