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Guo, C. C.

Publications and source records attributed to Guo, C. C..

10 recordsLinked to original sources
bioRxiv2025

A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics

Spatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for understanding complex tissue architectures, such as those found in cancers. Seurat, by far the most popular tool for analyzing ST data, uses the Wilcoxon rank-sum test by default for differential expression analysis. However, as a nonparametric method that disregards spatial correlations, the Wilcoxon test can lead to inflated false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we propose a Generalized Estimating Equations (GEE) framework as a robust solution for differential gene expression analysis in ST. We conducted a comprehensive comparison of the GEE-based tests with existing methods, including the Wilcoxon rank-sum test and z-test. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE test with robust standard error, referred to as the Independent GEE, demonstrated superior Type I error control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed poor calibration of the p-values and potential false positive findings from the Wilcoxon rank-sum test. Our comparative study based on simulations and real data applications suggests that the Independent GEE test is well-suited for ST data, offering more accurate identification of biologically relevant gene expression changes and complementing the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub. Author SummarySpatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for studying complex tissue architectures and disease etiology. Seurat, a widely used software tool for analyzing ST data, relies on the Wilcoxon rank-sum test for differential expression analysis. However, this test ignores spatial correlations, leading to inaccurate control of false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we have proposed a Generalized Estimating Equation (GEE) framework as a robust solution for differential gene expression analysis in ST. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE-based test demonstrated superior false positive rate control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed potential false positive findings from the Wilcoxon rank-sum test. We recommend the GEE method to be a useful complement to the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub.

bioinformatics↗
bioRxiv2024

TUSCAN: Tumor segmentation and classification analysis in spatial transcriptomics

The identification of tumor cells is pivotal to understanding tumor heterogeneity and the tumor microenvironment. Recent advances in spatially resolved transcriptomics (SRT) have revolutionized the way that transcriptomic profiles are characterized and have enabled the simultaneous quantification of transcript locations in intact tissue samples. SRT is a promising alternative method of studying gene expression patterns in spatial domains. Nevertheless, the precise detection of tumor regions within intact tissue remains a great challenge. The common way of identifying tumor cells is via tumor-specific marker gene expression signatures, which is highly dependent on marker accuracy. Another effective approach is through aneuploid copy number events, as most types of cancer exhibit copy number abnormalities. Here, we introduce a novel computational method, called TUSCAN (TUmor Segmentation and Classification ANalysis in spatial transcriptomics), which constructs a spatial copy number variation profile to improve the accuracy of tumor region identification. TUSCAN combines the gene information from SRT data and the hematoxylin-and-eosin-staining image to annotate tumor sections and other benign tissues. We benchmark the performance of TUSCAN and several existing methods through the application to multiple datasets from different SRT platforms. We demonstrate that TUSCAN can effectively delineate tumor regions, with improved accuracy compared to other approaches. Additionally, the output of TUSCAN provides interpretable clonal evolution inferences that may lead to novel insights into disease development and potential druggable targets.

bioinformatics↗
bioRxiv2022

StandardRat: A multi-center consensus protocol to enhance functional connectivity specificity in the rat brain

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows comparison with invasive or terminal procedures. To date, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. We introduce StandardRat, a consensus rat functional MRI acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired in rats from 46 centers. We developed a reproducible pipeline for the analysis of rat data acquired with diverse protocols and determined experimental and processing parameters associated with a more robust functional connectivity detection. We show that the standardized protocol enhances biologically plausible functional connectivity patterns, relative to pre-existing acquisitions. The protocol and processing pipeline described here are openly shared with the neuroimaging community to promote interoperability and cooperation towards tackling the most important challenges in neuroscience.

neuroscience↗
bioRxiv2021

The Origin of Bladder Cancer from Mucosal Field Effects

We used whole-organ mapping to study loco-geographic molecular changes in evolution of human bladder cancer from mucosal field effects. The integrative multi-platform analyses based on genome-wide RNA sequencing, methylation, copy number variations, and whole exome sequencing identified over 100 dysregulated canonical pathways involving immunity, tissue differentiation and transformation as initiators of bladder carcinogenesis. Widespread dysregulation of interleukin signaling was the dominant change signifying the important role of inflammation and immunity in the incipient phases of urothelial carcinogenesis. The analyses of mutational patterns identified three types of mutations based on their geographic distribution and variant allele frequencies. The most common were low frequency subclonal mutations restricted to individual mucosal samples which were the progeny of their respective uroprogenitor cells. The two additional types of mutations were associated with clonal expansion and involved large areas of bladder mucosa. The first group referred to as mutations, showed a low mutational frequency across the mucosa. The second group referred to as {beta} mutations increased in their frequencies with disease progression and a large proportion of them represented mutated transcriptional regulators controlling proliferation. Time modeling revealed that bladder carcinogenesis is spanning 10-15 years and can be divided into dormant and progressive phases. The progressive phase lasted 1-2 years and was primarily driven by {beta} mutations with high proliferative advantage. This is the first detailed molecular characterization of mucosal field effects initiating bladder carcinogenesis on the whole-organ scale. It provides novel insights into incipient phases of bladder carcinogenesis and biomarkers for early detection of bladder cancer as well as targets for preventive therapies.

genomics↗
bioRxiv2021

3D models of Alzheimer's disease patient microglia recapitulate disease phenotype and show differential drug responses compared to 2D

Alzheimers disease (AD) is an incurable neurodegenerative disorder with a rapidly increasing prevalence worldwide. Current approaches targeting hallmark pathological features of AD have had no consistent clinical benefit. Neuroinflammation is a major contributor to neurodegeneration and hence, microglia, the brains resident immune cells, are an attractive target for potentially more effective therapeutic strategies. However, there is no current in vitro model system that faithfully recapitulates patient-specific microglial characteristics. To address this shortcoming, we developed novel 3D models of monocyte-derived microglia-like cells (MDMi) from AD patients. MDMi in 3D exhibited mature microglial features, including a highly branched morphology and enhanced bonafide microglial marker expression compared to 2D. Moreover, AD MDMi in 3D co-cultures with neuro-glial cells showed altered cell-to-cell interactions, growth factor and cytokine secretion profiles and responses to amyloid-{beta}. Drug screening assays in 3D AD MDMi revealed different cytokine responses compared to 2D. Our study demonstrates disease- and drug-specific responses in 3D MDMi models that are not apparent in 2D and presents a new 3D platform for more effective and personalised drug testing.

neuroscience↗
bioRxiv2020

ALS monocyte-derived microglia reveal cytoplasmic TDP-43 accumulation, DNA damage, and cell-specific impairment of phagocytosis associated with disease progression.

AimsAmyotrophic lateral sclerosis (ALS) is a multifactorial neurodegenerative disease characterised by the loss of upper and lower motor neurons. Neuroinflammation mediated by microglial activation is evident in post-mortem brain tissues, and in brain imaging of patients with ALS. However, the exact role of microglia in ALS remains to be elucidated partly due to the lack of an accurate microglial model system that is able to recapitulate the clinical pathology of ALS. Moreover, direct sampling of microglia from patients with ALS is not feasible, further limiting the study of microglial function in ALS. To address this shortcoming, we describe an approach that generates monocyte-derived microglia (MDMi) that are capable of expressing molecular markers, and functional characteristics similar to resident human brain microglia. Importantly, MDMi can be routinely and reproducibly generated from ALS patient blood, and reveal patient heterogeneity associated with age, sex and disease subgroup. MethodsMDMi were successfully established from all 30 ALS patients, including 15 patients with slow disease progression, 6 with intermediate progression, and 9 with rapid progression, together with 20 non-affected heathy controls (HC). ResultsOur ALS MDMi model recapitulated canonical pathological features of ALS including non-phosphorylated and phosphorylated-TDP-43-positive pathological inclusions. We further observed significantly impaired phagocytosis, altered cytokine expression and microglial morphology, as well as elevated DNA damage in ALS compared to HC MDMi. Abnormal phagocytosis was observed in all ALS cases, and was correlated to the progression of disease. Moreover, in-depth analysis of individual microglia revealed cell-specific variation in phagocytic function that was significantly altered, and exacerbated in rapid disease progression. ConclusionsOur approach enabled us to generate ALS patient microglia from peripheral blood samples using a rapid, robust, cost-effective, and reproducible protocol. We have shown that ALS monocyte-derived microglia have significantly altered functional behaviour compared to age-matched HCs, with a major deficit in phagocytic activity. This is also the first demonstration of abnormal TDP-43 localisation in microglia grown from ALS patients. Overall, this approach is highly applicable to monitor disease progression and can be applied as a functional readout in clinical trials for anti-neuroinflammatory agents. Additionally, this model system can be used as a basis for personalised therapeutic treatment for ALS, as well as other neurodegenerative diseases.

neuroscience↗
bioRxiv2019

High-Spatial-Resolution Multi-Omics Atlas Sequencing of Mouse Embryos via Deterministic Barcoding in Tissue

We present DBiT-seq -Deterministic Barcoding in Tissue for spatial omics sequencing - for co-mapping of mRNAs and proteins in a formaldehyde-fixed tissue slide via NGS sequencing. Parallel microfluidic channels were used to deliver DNA barcodes to the surface of a tissue slide and crossflow of two sets of barcodes A1-50 and B1-50 followed by ligation in situ yielded a 2D mosaic of tissue pixels, each containing a unique full barcode AB. Application to mouse embryos revealed major tissue types in early organogenesis as well as fine features like microvasculature in a brain and pigmented epithelium in an eye field. Gene expression profiles in 10m pixels conformed into the clusters of single-cell transcriptomes, allowing for rapid identification of cell types and spatial distributions. DBiT-seq can be adopted by researchers with no experience in microfluidics and may find applications in a range of fields including developmental biology, cancer biology, neuroscience, and clinical pathology. In BriefMicrofluidic deterministic barcoding of mRNAs and proteins in tissue slides followed by high-throughput sequencing enables the construction of a high-spatial-resolution multi-omics atlas at the genome scale. Application to mouse embryos (E10-12) identified major tissue types in early organogenesis and revealed fine tissue features such as retinal pigmented epithelium and endothelial microvasculature at the cellular level. HighlightsO_LIDeterministic barcoding in tissue enables NGS-based spatial multi-omics mapping. C_LIO_LIDBiT-seq identified spatial patterning of major tissue types in mouse embryos. C_LIO_LIDBiT-seq revealed fine features such as retinal pigmented epithelium and microvascular endothelium at the cellular level. C_LIO_LIDirect integration with scRNA-seq data allows for rapid cell type identification. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/788992v2_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@41d937org.highwire.dtl.DTLVardef@11bdc46org.highwire.dtl.DTLVardef@1aa143org.highwire.dtl.DTLVardef@51755c_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗
bioRxiv2018

Dysregulation of EMT Drives the Progression to Clinically Aggressive Sarcomatoid Bladder Cancer

The sarcomatoid variant of urothelial bladder cancer (SARC) displays a high propensity for distant metastasis and is associated with short survival. We report a comprehensive genomic analysis of 28 cases of SARCs and 84 cases of conventional urothelial carcinomas (UCs), with the TCGA cohort of 408 muscle-invasive bladder cancers serving as the reference. SARCs showed a distinct mutational landscape with enrichment of TP53, RB1, and PIK3CA mutations. They were related to the basal molecular subtype of conventional UCs and could be divided into epithelial/basal and more clinically aggressive mesenchymal subsets based on TP63 and its target genes expression levels. Other analyses revealed that SARCs are driven by downregulation of homotypic adherence genes and dysregulation of cell cycle and EMT networks, and nearly half exhibited a heavily infiltrated immune phenotype. Our observations have important implications for prognostication and the development of more effective therapies for this highly lethal variant of bladder cancer.

genomics↗
bioRxiv2016

Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms

Functional connectivity analysis has become a powerful tool for probing the human brain function and its breakdown in neuropsychiatry disorders. So far, most studies adopted resting state paradigm to examine functional connectivity networks in the brain, thanks to its low demand and high tolerance that are essential for clinical studies. However, the test-retest reliability of resting state connectivity measures is moderate, potentially due to its low behavioral constraint. On the other hand, naturalistic neuroimaging paradigms, an emerging approach for cognitive neuroscience with high ecological validity, could potentially improve the reliability of functional connectivity measures. To test this hypothesis, we characterized the test-retest reliability of functional connectivity measures during a natural viewing condition, and benchmarked it against resting state connectivity measures acquired within the same functional magnetic resonance imaging (fMRI) session. We found that the reliability of connectivity and graph theoretical measures of brain networks is significantly improved during natural viewing conditions over resting state conditions, with an average increase of almost 50% across various connectivity measures. Not only sensory networks for audio-visual processing become more reliable, higher order brain networks, such as default mode and attention networks, also appear to show higher reliability during natural viewing. Our results support the use of natural viewing paradigms in estimating functional connectivity of brain networks, and have important implications for clinical application of fMRI.

neuroscience↗