Biology subjects

Im, H.-J.

Publications and source records attributed to Im, H.-J..

6 recordsLinked to original sources

bioRxiv2024

Spatial Transcriptomics Reveals Spatially Diverse Cancer-Associated Fibroblast in Lung Squamous Cell Carcinoma Linked to Tumor Progression

While cancer-associated fibroblasts (CAFs) are crucial in influencing tumor growth and immune responses in lung cancer, we still lack a comprehensive understanding of their spatial organization associated with tumor progression and clinical outcomes. This gap highlights the need to elucidate how the intricate spatial arrangement of CAFs affects their interactions within the tumor microenvironment, ultimately shaping cancer progression and patient prognosis. Here, we unveil the spatial diversity of CAFs in lung squamous cell carcinoma (LUSC), a prevalent and aggressive lung cancer type, elucidating their impact on tumor progression and patient outcomes using spatial transcriptomics (ST). Image-based ST data from 33 LUSC patients demonstrated a significant association of spatial interactions of tumor epithelium and CAFs with tumor size and metabolic activity measured by [18F]fluorodeoxyglucose PET. Furthermore, the proximity of fibroblasts to tumor epithelial cells was linked to recurrence-free survival in LUSC patients. By characterizing CAFs based on their spatial relationship, we identified distinct molecular signatures related to spatially distinct fibroblast subpopulations. In addition, barcode-based ST data from 8 LUSC patients revealed spatially overlapping fibroblast regions characterized by upregulated glycolysis pathways. Our study underscores the importance of the complex spatial dynamics of the tumor microenvironment revealed by ST and its implications for patient outcomes in LUSC.

cancer biology↗

bioRxiv2023

Generation of Super-resolution Images from Barcode-based Spatial Transcriptomics Using Deep Image Prior

Spatial transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. However, current ST methods, particularly barcode-based methods, have limitations in reconstructing high-resolution images from barcodes sparsely distributed in slides. Here, we present SuperST, a novel algorithm that enables the reconstruction of dense matrices from low-resolution ST libraries. SuperST based on deep image prior reconstructs spatial gene expression patterns as image matrices. SuperST allows gene expression mapping to better reflect immunofluorescence (IF) images. Compared with previous methods, SuperST generated output images that more closely resembled IF images for given gene expression maps. Additionally, SuperST overcomes the limitations inherent in IF images, highlighting its potential applications in the realm of spatial biology. By providing a more detailed understanding of gene expression in situ, SuperST has the potential to contribute to comprehensively understanding biology from various tissues.

bioinformatics↗

bioRxiv2023

A Spatial Transcriptomics based Label-Free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis

Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. In this study, we present a label-free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. We administered hMSCs to mouse model of lung fibrosis and utilized ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between human and mouse. Furthermore, we could examine molecular changes associated with the spatial distribution of hMSCs. We suggest that our method has the potential to serve as an effective tool for various cell-based therapeutic agents.

bioinformatics↗

bioRxiv2022

3D Graph Contrastive Learning for MolecularProperty Prediction

MotivationSelf-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (1) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (2) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (3) Previous models that apply contrastive learning to molecules use the augmentation of permuting atoms and bonds. Therefore, molecules having different characteristics can be in the same positive samples. We propose a novel contrastive learning framework, small-scale 3D Graph Contrastive Learning (3DGCL) for molecular property prediction, to solve the above problems. Results3DGCL learns the molecular representation by reflecting the molecules structure through the pre-training process that does not change the semantics of the drug. Using only 1,128 samples for pre-train data and 0.5 million model parameters, we achieved state-of-the-art or comparable performance in six benchmark datasets. Extensive experiments demonstrate that 3D structural information based on chemical knowledge is essential to molecular representation learning for property prediction. AvailabilityData and codes are available in https://github.com/moonkisung/3DGCL. Contactsy.kwon@pusan.ac.kr

biochemistry↗

bioRxiv2022

Spatial Transcriptomics-based Identification of Molecular Markers for Nanomedicine Distribution in Tumor Tissue

The intratumoral accumulation of nanomedicine has been considered a passive process, referred to as the enhanced permeability and retention (EPR) effect. Recent studies have suggested that the tumor uptake of nanomedicines follows an energy-dependent pathway rather than being a passive process. Herein, to explore the factor candidates that are associated with nanomedicine tumor uptake, we developed a molecular marker identification platform by integrating microscopic fluorescence images of a nanomedicine distribution with spatial transcriptomics (ST) information. When this approach is applied to PEGylated liposomes, molecular markers related to hypoxia, glucose metabolism and apoptosis can be identified as being related to the intratumoral distribution of the nanomedicine. We expect that our method can be applied to explain the distribution of a wide range of nanomedicines and that the data obtained from this analysis can enhance the precise utilization of nanomedicines.

bioinformatics↗

bioRxiv2020

Discovery of potential imaging and therapeutic targets for severe inflammation in COVID-19 patients

The COVID-19 pandemic has caused more than 540,000 deaths globally. Hyperinflammation mediated by dysregulated monocyte/macrophage function is considered to be the key factor that triggers severe illness in COVID-19. However, no specific targeting molecule has been identified for detecting or treating hyperinflammation related to dysregulated macrophages in severe COVID-19. Herein, we suggest candidate targets for imaging and therapy in severe COVID-19 by analyzing single-cell RNA-sequencing data based on bronchoalveolar lavage fluid of COVID-19 patients. We found that expression of SLC2A3, which can be imaged by [18F]fluorodeoxyglucose, was higher in macrophages from severe COVID-19 patients. Furthermore, by integrating the surface target database and drug-target binding database with RNA-sequencing data of severe COVID-19, we identified CCR1 and FPR1 as surface and druggable targets for drug delivery as well as molecular imaging. Our results provide a resource for candidate targets in the development of specific imaging and therapy for COVID-19-related hyperinflammation.

bioinformatics↗