Biology subjects

Yeong, J.

Publications and source records attributed to Yeong, J..

11 recordsLinked to original sources

bioRxiv2024

Spatially Resolved Tumor Ecosystems and Cell States in Gastric Adenocarcinoma Progression and Evolution

Gastric cancer (GC) is a major cause of global cancer mortality with high heterogeneity levels. To explore geospatial interactions in tumor ecosystems, we integrated 1,563 spatial transcriptomic regions-of-interest (ROIs) with 152,423 single-cell expression profiles across 130 GC samples from 70 patients. We observed pervasive expression-based intratumor heterogeneity, recapitulating tumor progression through spatially localized and functionally ordered subgroups with specific immune microenvironments and immune checkpoint profiles. Evolutionary phylogenetic analysis revealed two different evolutionary trajectories (branched evolution and diaspora evolution) associated with distinct molecular subtypes, clinical prognoses, stromal neighborhoods including VWF+ ACKR1+ endothelial cells, and genetic drivers such as SOX9. Spatial analysis of tumor-stromal interfaces across multiple GCs highlighted new ecosystem states not attributable to mere tumor/stroma admixture, landmarked by increased GREM1 expression. Our results provide insights into how the cellular ecosystems of individual GCs are sculpted by tumor intrinsic and extrinsic selective pressures, culminating in individualized patient-specific cancer cartographies.

cancer biology↗

bioRxiv2023

Comparing virtual staining artificial intelligence model performance on same-tissue versus serial-tissue sections based on CD3+ T-cell ground truth

Immunophenotyping via multi-marker assays significantly contributes to patient selection, therapeutic monitoring, biomarker discovery, and personalized treatments. Despite its potential, the multiplex immunofluorescence (mIF) technique faces adoption challenges due to technical and financial constraints. Alternatively, hematoxylin and eosin (H&E)-based prediction models of cell phenotypes can provide crucial insights into tumor-immune cell interactions and advance immunotherapy. Current methods mostly rely on manually annotated cell label ground truths, with limitations including high variability and substantial labor costs. To mitigate these issues, researchers are increasingly turning to digitized cell-level data for accurate in-situ cell type prediction. Typically, immunohistochemical (IHC) staining is applied to a tissue section serial to one stained with H&E. However, this method may introduce distortions and tissue section shifts, challenging the assumption of consistent cellular locations. Conversely, mIF overcomes these limitations by allowing for mIF and H&E staining on the same tissue section. Importantly, the multiplexing capability of mIF allows for a thorough analysis of the tumor microenvironment by quantifying multiple cell markers within the same tissue section. In this study, we introduce a Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining model, using CD3+ T-cells in lung cancer as a proof-of-concept. Using an independent CD3 IHC-stained lung cohort, we demonstrate that the model trained with cell label ground-truth from the same tissue section as H&E staining performed significantly better in both CD3+ and CD3- T-cell prediction. Moreover, the model also displayed prognostic significance on a public lung cohort, demonstrating its potential clinical utility. Notably, our proposed P2P-GAN virtual staining model facilitates image-to-image translation, enabling further spatial analysis of the predicted immune cells, deepening our understanding of tumor-immune interactions, and propelling advancements in personalized immunotherapy. This concept holds potential for the prediction of other cell phenotypes, including CD4+, CD8+, and CD20+ cells.

immunology↗

bioRxiv2023

Regulation of Non-Canonical Proteins Encoded by Small Open Reading Frames via the Nonsense-Mediated Decay Pathway.

Immunotherapy interventions relies heavily on neoantigen availability. The human genome encodes non-canonical/mutant proteins that potentially contain neoantigenic peptides. Nevertheless, their typically low expression, potentially moderated by the Nonsense-Mediated Decay (NMD) pathway, restricts their therapeutic utility. In this study, we explored the NMD pathway influence on non-canonical/mutant protein expression, specifically focusing on UPF1 knockdown. We implemented proteogenomic approaches to ascertain if the encoding transcripts and their respective proteins were upregulated post-knockdown. Complementary to this, we conducted a comprehensive pan-cancer survey of UPF1 expression and an in vivo evaluation of UPF1 expression in Triple-Negative Breast Cancer (TNBC) tissue. Our empirical results delineated that UPF1 knockdown precipitates an increase in the transcription of non-canonical/mutant proteins, especially those originating from retained-introns, pseudogenes, long non-coding RNAs, and unannotated biotypes. Furthermore, the analysis revealed that UPF1 expression was conspicuously high across a range of neoplastic tissues, with protein levels notably amplified in patient derived TNBC tumours in comparison to adjacent tissues. Our study elucidates UPF1 functional role in attenuating transcriptional noise through the degradation of transcripts encoding non-canonical/mutant proteins. Interestingly, we observed an upregulation of the NMD pathway in cancer, potentially functioning as a "neoantigen masking" mechanism that subdues non-canonical/mutant protein expression. Suppressing this mechanism may unveil a new cadre of neoantigens accessible to the antigen presentation pathway. Our novel findings proffer a solid base for devising therapeutic strategies targeting UPF1 or the NMD pathway, given the pronounced presence of UPF1 in malignant cells, thus potentially augmenting immunotherapeutic responses in cancer.

cell biology↗

bioRxiv2023

ProFAST: a fast and scalable factor analysis for spatially aware dimension reduction of multi-section spatial transcriptomics data

Biological techniques for spatially resolved transcriptomics (SRT) have advanced rapidly in both throughput and spatial resolution for a single spatial location. This progress necessitates the development of efficient and scalable spatial dimension reduction methods that can handle large-scale SRT data from multiple sections. Here, we developed FAST as a fast and efficient generalized probabilistic factor analysis for spatially aware dimension reduction, which simultaneously accounts for the count nature of SRT data and extracts a low-dimensional representation of SRT data across multiple sections, while preserving biological effects with consideration of spatial smoothness among nearby locations. Compared with existing methods, FAST uniquely models the count data across multiple sections while using a local spatial dependence with scalable computational complexity. Using both simulated and real datasets, we demonstrated the improved correlation between FAST estimated embeddings and annotated cell/domain types. Furthermore, FAST exhibits remarkable speed, with only FAST being applicable to analyze a mouse embryo Stereo-seq dataset with >2.3 million locations in only 2 hours. More importantly, FAST identified the differential activities of immune-related transcription factors between tumor and non-tumor clusters and also predicted a carcinogenesis factor CCNH as the upstream regulator of differentially expressed genes in a breast cancer Xenium dataset.

genomics↗

bioRxiv2022

Probabilistic embedding and clustering with alignment for spatial transcriptomics data integration with PRECAST

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.

genomics↗

bioRxiv2022

SpatialDEG: Identification of differentially expressed genes by leveraging spatial information in spatially resolved transcriptomic studies

MotivationSpatially resolved transcriptomics (SRT) technologies have been developed to simultaneously profile gene expression while retaining physical information. To explore differentially expressed genes using SRT in the context of various conditions, statistical methods are needed to perform spatial differential expression analysis. ResultsWe propose that a new probabilistic framework, spatialDEG, can perform differential expression analysis by leveraging spatial information on gene expression with spatial information. SpatialDEG utilizes the average information algorithm and can be scalable to tens of thousands of genes. Comprehensive simulations demonstrated that spatialDEG can identify genes differentially expressed in tissues across different conditions with a controlled type-I error rate. We further applied spatialDEG to analyze datasets for human dorsolateral prefrontal cortex and mouse whole liver. AvailabilityThe R package spatialDEG can be downloaded from https://github.com/Shufeyangyi2015310117/spatialDEG.

genetics↗

bioRxiv2021

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large sample sizes. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

genomics↗

bioRxiv2020

Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Growing evidence supports the importance of understanding tumor-immune spatial relationship in the tumor microenvironment in order to achieve precision cancer therapy. However, existing methods, based on oversimplistic cell-to-cell proximity, are largely confounded by immune cell density and are ineffective in capturing tumor-immune spatial patterns. Here we developed a novel computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), to offer an effective solution for spatially informed tumor subtyping. Our method could measure the extent of immune cell partitioning between tumor epithelial and stromal areas as well as the degree of immune cell clustering. Using a U.S. nation-wide colorectal cancer database, we showed that TIPC could determine tumor subtypes with unique tumor-immune spatial patterns that were significantly associated with patient survival and key tumor molecular features. We also demonstrated that TIPC was robust to parameter settings and readily applicable to different immune cell types. The capability of TIPC in delineating clinically relevant patient subtypes that encapsulate tumor-immune spatial relationship, immune density, and tumor morphology is expected to shed light on underlying immune mechanisms. Hence, TIPC can be a useful bioinformatics tool for effective characterization of the spatial composition of the tumor-immune microenvironment to inform precision immunotherapy.

cancer biology↗

bioRxiv2019

Data-driven inference of crosstalk in the tumor microenvironment

Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is key to tumorigenesis yet challenging to decipher from tumor transcriptomes. Here, we report an unbiased, data-driven approach to deconvolute bulk tumor transcriptomes and predict crosstalk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. Our approach recovers known transcriptional hallmarks of cancer and stromal cells and is concordant with single-cell and immunohistochemistry data, underlining its robustness. Pan-cancer analysis reveals previously unrecognized features of cancer-stromal crosstalk. We find that autocrine cancer cell cross-talk varied between tissues but often converged on known cancer signaling pathways. In contrast, many stromal cross-talk interactions were highly conserved across tumor types. Interestingly, the immune checkpoint ligand PD-L1 was overexpressed in stromal rather than cancer cells across all tumor types. Moreover, we predicted and experimentally validated aberrant ligand and receptor expression in cancer cells of basal and luminal breast cancer, respectively. Collectively, our findings validate a data-driven method for tumor transcriptome deconvolution and establishes a new resource for hypothesis generation and downstream functional interrogation of the TME in tumorigenesis and disease progression.

cancer biology↗

bioRxiv2019

CD38 is a good predictor of anti-PD-1 immunotherapy responsiveness in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-associated mortality in the world. However, with the associated low five-year survival and high recurrence rates, alternative treatment modalities specifically immunotherapy have been researched. A correlation between CD38+ tumour-infiltrating leukocyte (TIL) density and improved prognosis was found in a recent study. However, studies relating to CD38 expression in immune infiltrates within tumours are limited. In the present study, we confirmed the expression of CD38 on macrophages in HCC and determined the relationship between CD38+ leukocytes and lymphocytes and patient response to immunotherapy. Using immunohistochemistry, we analysed tissue samples obtained from 20 patients from Singapore with HCC prior to immunotherapy. Tumour infiltrating leukocytes expression within tumour were correlated to the responsiveness of patients to immunotherapy.\n\nExpression of CD38 was found within the tumour cells and surrounding immune infiltrates including lymphocytes and macrophages. We then ask whether CD38 expression by the distinct cell populations may acquire theranostic relevance. Patients with higher level of CD38+ immune infiltrate subsets had significantly better response to anti-PD-1 immunotherapy, and this is also true for CD38+ lymphocytes within the tumour microenvironment. In particular, a cut-off of 13.0% positive out of total leukocytes and 12.4% positive out of total lymphocytes is found to be of strong predictive value of responsiveness to immunotherapy treatment, thus a strong theranostic impact is seen by using CD38 as a biomarker for anti-PD-1 therapy.\n\nThe establishment of an association between CD38 expression and the response to anti-PD-1 immunotherapy in HCC, could be applied to a larger cohort outside Singapore. These may eventually change the routine testing in clinical practice to identify HCC patients suitable for immunotherapy.

cancer biology↗

bioRxiv2019

Comparison Between UMAP and t-SNE for Multiplex-Immunofluorescence Derived Single-Cell Data from Tissue Sections

Using human hepatocellular carcinoma (HCC) tissue samples stained with seven immune markers including one nuclear counterstain, we compared and evaluated the use of a new dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP), as an alternative to t-Distributed Stochastic Neighbor Embedding (t-SNE) in analysing multiplex-immunofluorescence (mIF) derived single-cell data. We adopted an unsupervised clustering algorithm called FlowSOM to identify eight major cell types present in human HCC tissues. UMAP and t-SNE were ran independently on the dataset to qualitatively compare the distribution of clustered cell types in both reduced dimensions. Our comparison shows that UMAP is superior in runtime. Both techniques provide similar arrangements of cell clusters, with the key difference being UMAPs extensive characteristic branching. Most interestingly, UMAPs branching was able to highlight biological lineages, especially in identifying potential hybrid tumour cells (HTC). Survival analysis shows patients with higher proportion of HTC have a worse prognosis (p-value = 0.019). We conclude that both techniques are similar in their visualisation capabilities, but UMAP has a clear advantage over t-SNE in runtime, making it highly plausible to employ UMAP as an alternative to t-SNE in mIF data analysis.

bioinformatics↗