Biology subjects

Christensen, B.

Publications and source records attributed to Christensen, B..

30 records · Page 2Linked to original sources

bioRxiv2021

Improving the Virtual Trichrome Assessment through Bridge Category Models

Non-alcoholic steatohepatitis (NASH) is a liver disease characterized by excessive lipid accumulation and disease progression is typically assessed through inspection of a Trichrome stain for Fibrosis staging. As the public health burden of NASH worsens due to evolving lifestyle habits, pathology laboratory resources will become increasingly strained due to rising demand for specialized stains. Virtual staining processes, computational methods which can synthesize the application of chemical staining reagents, can potentially provide resource savings by obviating the need to acquire specialized stains. Virtual staining technologies are assessed by comparing virtual and real tissue stains for their realism and ability to stage. However, these assessment methods are rife with statistical mistreatment of observed phenomena that are difficult to account for. Bridge category ratings represent a phenomenon where a pathologist may assign two adjacent stages simultaneously, which may bias and/or reduce the power of research findings. Such stage assignments were frequently reported in a large-scale assessment of Virtual Trichrome technologies yet were unaccounted for since no statistical adjustment procedures existed. In this work, we provide an updated assessment of Virtual Trichrome technologies using Bridge Category Models, which account for these bridge ratings. We report that two of four pathologists tended to assign lower Fibrosis stages to virtually stained tissue while the other two pathologists assigned similar stages. These research findings differ when bridge ratings are not accounted for. While promising, these results indicate further room for algorithmic finetuning of Virtual Trichrome technologies.

pathology↗

bioRxiv2021

Estimating the Inter- and Intra-Rater Reliability for NASH Fibrosis Staging in the Presence of Bridge Ordinal Ratings with Hierarchical Bridge Category Models

The public health burden of non-alcoholic steatohepatitis (NASH), a liver condition characterized by excessive lipid accumulation and subsequent tissue inflammation and fibrosis, has burgeoned with the spread of western lifestyle habits. Progression of fibrosis into cirrhosis is assessed using histological staging scales (e.g., NASH Clinical Research Network (NASH CRN)). These scales are used to monitor disease progression as well as to evaluate the effectiveness of therapies. However, clinical drug trials for NASH are typically underpowered due to lower than expected inter-/intra-rater reliability, which impacts measurements at screening, baseline, and endpoint. Bridge ratings represent a phenomenon where pathologists assign two adjacent stages simultaneously during assessment and may further complicate these analyses when ad hoc procedures are applied. Statistical techniques, dubbed Bridge Category Models, have been developed to account for bridge ratings, but not for the scenario where multiple pathologists assess biopsies across time points. Here, we develop hierarchical Bayesian extensions for these statistical methods to account for repeat observations and use these methods to assess the impact of bridge ratings on the inter-/intra-rater reliability of the NASH CRN staging scale. We also report on how pathologists may differ in their assignment of bridge ratings to highlight different staging practices. Our findings suggest that Bridge Category Models can capture additional fibrosis staging heterogeneity with greater precision, which translates to potentially higher reliability estimates in contrast to the information lost through ad hoc approaches.

pathology↗

bioRxiv2021

Bridge Category Models: Development of Bayesian Modelling Procedures to Account for Bridge Ordinal Ratings for Disease Staging

Disease grading and staging is accomplished through the assignment of an ordinal rating. Bridge ratings occur when a rater assigns two adjacent categories. Most statistical methodology necessitates the use of a single ordinal category. Consequently, bridge ratings often go unreported in clinical research studies. We propose three methodologies (Expanded, Mixture, and Collapsed) Bridge Category Models, to account for bridge ratings. We perform simulations to examine the impact of our approaches on detecting treatment effects, and comment on a real-world scenario of staging liver biopsies. Results indicate that if bridge ratings are not accounted for, disease staging models may exhibit significant bias and precision loss. All models worked well when they corresponded to the data generating mechanism.

pathology↗

bioRxiv2020

Estimating latent positions of actors using Neural Networks in R with GCN4R

Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While numerous methods have been developed for the R statistical software to analyze such data, deep learning methods have not been implemented in this language. Here, we introduce GCN4R, an R library for fitting graph neural networks on independent networks to aggregate actor covariate information to yield meaningful embeddings for a variety of network-based tasks (e.g. community detection, peer effects models, social influence). We provide an extensive overview of insights and methods utilized by the deep learning community on learning on social and biological networks, followed by a tutorial that demonstrates some of the capabilities of the GCN4R framework to make these methods more accessible to the R research community.

bioinformatics↗

bioRxiv2020

Investigation of Capsule-Inspired Neural Network Approaches for DNA Methylation

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules - such as gene promoter context, CpG island relationship, or user-defined groupings - and relate them to diagnostic and prognostic outcomes. We demonstrate these models utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep learning analyses interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.

bioinformatics↗

bioRxiv2020

Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks

Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to digital pathology typically extract information from sub-images (patches) and treat the sub-images as independent entities, ignoring contributing information from vital large-scale architectural relationships. Modeling approaches that can capture higher-order dependencies between neighborhoods of tissue patches have demonstrated the potential to improve predictive accuracy while capturing the most essential slide-level information for prognosis, diagnosis and integration with other omics modalities. Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through message passing, and Topological Data Analysis, which distills contextual information into its essential components. We introduce a modeling framework, WSI-GTFE that integrates these two approaches in order to identify and quantify key pathogenic information pathways. To demonstrate a simple use case, we utilize these topological methods to develop a tumor invasion score to stage colon cancer.

pathology↗

bioRxiv2020

A Large-Scale Internal Validation Study of Unsupervised Virtual Trichrome Staining Technologies on Non-alcoholic Steatohepatitis Liver Biopsies

Non-alcoholic steatohepatitis (NASH) is a fatty liver disease characterized by accumulation of fat in hepatocytes with concurrent inflammation and is associated with morbidity, cirrhosis and liver failure. After extraction of a liver core biopsy, tissue sections are stained with hematoxylin and eosin (H&E) to grade NASH activity, and stained with trichrome to stage fibrosis. Methods to computationally transform one stain into another on digital whole slide images (WSI) can lessen the need for additional physical staining besides H&E, reducing personnel, equipment, and time costs. Generative adversarial networks (GAN) have shown promise for virtual staining of tissue. We conducted a large-scale validation study of the viability of GANs for H&E to trichrome conversion on WSI (n=574). Pathologists were largely unable to distinguish real images from virtual/synthetic images given a set of twelve Turing Tests. We report high correlation between staging of real and virtual stains ({rho} = 0.86; 95% CI: 0.84-0.88). Stages assigned to both virtual and real stains correlated similarly with a number of clinical biomarkers and progression to End Stage Liver Disease (Hazard Ratio HR = 2.06, CI 95% 1.36-3.12, P < 0.001 for real stains; HR = 2.02, CI 95% 1.40-2.92, p < 0.001 for virtual stains). Our results demonstrate that virtual trichrome technologies may offer a software solution that can be employed in the clinical setting as a diagnostic decision aid.

pathology↗

bioRxiv2020

Limiting Self-Renewal of the Basal Compartment Induces Differentiation and Alters Evolution of Mammary Tumors

Differentiation therapy is an approach that utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift towards terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have proven challenging. In this study we show that activation of PKA drives aberrant mammary differentiation by diminishing the self-renewing potential of the basal compartment. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. PKA activation represents a promising approach as an adjuvant to chemotherapy for certain breast cancers, reviving the paradigm of differentiation therapy for solid tumors.

cancer biology↗

bioRxiv2020

Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center

Evaluation of a tissue biopsy is often required for the diagnosis and prognostic staging of a disease. Recent efforts have sought to accurately quantitate the distribution of tissue features and morphology in digitized images of histological tissue sections, Whole Slide Images (WSI). Generative modeling techniques present a unique opportunity to produce training data that can both augment these models and translate histologic data across different intra-and-inter-institutional processing procedures, provide cost-effective ways to perform computational chemical stains (synthetic stains) on tissue, and facilitate the creation of diagnostic aid algorithms. A critical evaluation and understanding of these technologies is vital for their incorporation into a clinical workflow. We illustrate several potential use cases of these techniques for the calculation of nuclear to cytoplasm ratio, synthetic SOX10 immunohistochemistry (IHC, sIHC) staining to delineate cell lineage, and the conversion of hematoxylin and eosin (H&E) stain to trichome stain for the staging of liver fibrosis.

pathology↗

bioRxiv2019

Comparing dopamine release, uptake, and D2 autoreceptor function across the ventromedial to dorsolateral striatum in adolescent and adult rats

Adolescents have increased vulnerability for the development of a range of psychiatric disorders, including substance abuse disorders (SUD) and mood disorders. Adolescents also have increased rates of sensation seeking and risk taking. The mesolimbic dopamine system plays a role in all these behaviors and disorders and reorganization of the dopamine system during adolescence may be important in mediating these developmental changes in behavior and vulnerability. Here, we used ex vivo fast scan cyclic voltammetry to examine developmental differences in dopamine release and its local circuitry regulation across the striatum. We found that adolescents have significantly decreased dopamine release in the nucleus accumbens core across a range of stimulation frequencies that model tonic and phasic firing of mesolimbic dopamine neurons. We show this is not mediated by differences in rate of dopamine uptake, but may be driven by hypersensitive dopamine autoreceptors, indicated by increased inhibition in dopamine release following agonism of D2/D3 receptors, in the adolescent nucleus accumbens core. Additionally, we observed increases in dopamine uptake in the dorsomedial striatum. No other significant differences between release, uptake, or D2 autoreceptor function was observed between adolescent and adult rats in all brain areas tested (nucleus accumbens shell, nucleus accumbens core, dorsomedial striatum, and dorsolateral striatum). These developmental differences in dopamine release may be important in mediating some of the unique behavioral repertoire seen in adolescents, such as increases in sensation seeking, and its associated vulnerabilities.

neuroscience↗

bioRxiv2019

PyMethylProcess - highly parallelized preprocessing for DNA methylation array data

SummaryThe ability to perform high-throughput preprocessing of methylation array data is essential in large scale methylation studies. While R is a convenient language for methylation analyses, performing highly parallelized preprocessing using Python can accelerate data preparation for downstream methylation analyses, including large scale production-ready machine learning pipelines. Here, we present a methylation data preprocessing pipeline called PyMethylProcess that is highly reproducible, scalable, and that can be quickly set-up and deployed through Docker and PIP.\n\nAvailability and ImplementationProject Name: PyMethylProcess\n\nProject Home Page: https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess. Available on PyPI as pymethylprocess.\n\nAvailable on DockerHub via joshualevy44/pymethylprocess.\n\nHelp Documentation: https://christensen-lab-dartmouth.github.io/PyMethylProcess/\n\nOperating Systems: Linux, MacOS, Windows (Docker)\n\nProgramming Language: Python, R\n\nOther Requirements: Python 3.6, R 3.5.1, Docker (optional) License: MIT\n\nContactjoshua.j.levy.gr@dartmouth.edu

epidemiology↗

bioRxiv2017

Intermolecular Disulfide-Bond Formation In Human FICD Modulates The Activity Of The Hyperactive E234G Mutant

Endoplasmic reticulum (ER) stress that leads to the accumulation of misfolded proteins in the ER initiates the unfolded protein response (UPR). This homeostatic response activates signaling pathways that seek to reinstate a proper ER protein folding balance or induce apoptosis if ER stress persists. Recently, we and others identified human FICD (Filamentation induced by cyclic AMP domain-containing protein), an enzyme with adenylyltransferase (aka AMPylation) activity, as a new UPR target. Here, we demonstrate that FICD is functionally linked to the UPR, as evidenced by the finding that the adenylyltransferase activity of the protein induces ER stress, while FICD silencing increases sensitivity to ER stress. We identify BiP, an abundant ER chaperone and key regulator of the UPR, as the main substrate of FICD AMPylation in ER-derived microsomes, further emphasizing close functional connection of FICD to the UPR and in line with recent reports that AMPylation inactivates BiP. Notably, BiP overexpression increased the levels of BiP AMPylation as well as FICD auto-AMPylation, suggesting a homeostatic response that balances the pool of active BiP to modulate its functions in protein folding as well as UPR signaling. Finally, we show that overexpressed FICD forms a disulfide-bonded homo-dimer through Cys51 and Cys75 and demonstrate that mutation of these two cysteines in the context of a hyperactive FICD mutant leads to increased BiP AMPylation. This latter finding opens up the possibility that FICD activity is redox regulated and closely connected with ER redox homeostasis.

molecular biology↗