Biology subjects

Andorf, S.

Publications and source records attributed to Andorf, S..

8 recordsLinked to original sources

bioRxiv2026

A semi-automated tool for improving Assessments and Lab Tests data annotation in ImmPort

Public data sharing in immunological and clinical research is essential for transparency, reproducibility, and secondary analysis. However, many datasets in repositories such as ImmPort lack sufficient structured data to enable reuse. ImmPort's Assessments and Lab Tests templates -- which capture critical clinical information -- are often incomplete or omitted due to their complexity and the manual effort required. To address this challenge, an application with a graphical user interface requiring no programming was developed to facilitate population of these templates. The tool streamlines the submission process by reducing time and complexity for data contributors while ensuring template formatting. It integrates ImmPort files, study-specific data files, and a curated data dictionary to automate the mapping of study variables to the required fields within each template. The tool was evaluated on seven ImmPort studies with diverse designs. It successfully generated submission-ready templates that passed ImmPort's internal validation and will be incorporated into the repository. By improving the feasibility and completeness of templates, this approach enhances the quality, findability, and reusability of data within ImmPort.

immunology↗

bioRxiv2025

Characterization of Distinct Monocyte Subtypes and Immune Features Associated with HIV, Tuberculosis, and Coronary Artery Disease in a Ugandan Cohort Using Mass Cytometry

Coronary artery disease (CAD), tuberculosis (TB), and HIV represent major global health burdens. Individuals affected by one or more of these conditions often exhibit chronic inflammation and immune dysregulation, with monocytes playing a central role in these processes. Monocyte subsets are known to expand in individuals with HIV, TB, or CAD. However, the precise mechanisms by which these cells contribute to inflammation and immune responses in the context of these conditions remain poorly understood. In this study, we employed high-dimensional mass cytometry to characterize monocyte heterogeneity in 61 Ugandan adults with varying combinations of HIV, latent TB, and subclinical or overt CAD. Through an integrative approach combining manual gating, unsupervised clustering, and elastic net penalization, we identified distinct monocyte phenotypes associated with CAD and TB. Importantly, individuals with CAD, especially those with more extensive disease (Segment Involvement Score >2), showed reduced surface expression of the anti-inflammatory scavenger receptor CD163 on non-classical monocytes. Notably, unsupervised clustering further revealed two distinct non-classical monocyte subsets associated with disease states: A CD86dim CX3CR1dim CD45RA+ GPR56+ CXCR3+ subset significantly depleted in individuals with CAD, and a CD86+ CX3CR1++ CD45RA++ GPR56- CD38- CXCR3- subset enriched in individuals with TB. These findings underscore the complexity of the monocyte landscape in CAD progression, particularly within settings of HIV and TB co-endemicity. We hope this work motivates further research and offers insights for the development of new precision biomarkers and immune-targeted therapies to prevent or treat CAD, TB, and HIV in populations.

bioinformatics↗

bioRxiv2025

cytoFlagR: A comprehensive framework to objectively assess high-parameter cytometry data for batch effects

MotivationHigh-parameter cytometry is widely used in longitudinal studies, but technical variation across batches can confound biological signals. However, tools that objectively identify problematic batches and markers are limited. ResultsWe introduce cytoFlagR, a comprehensive tool to flag batch-related problems at the marker and cell cluster level based on robust statistical evaluations. Batch and marker variations are assessed based on median signal intensities of negative and positive cell populations and positive cell frequencies, along with Earth Movers Distance (EMD) of signal intensity distributions. Additionally, cytoFlagR identifies cell type specific batch problems via unsupervised clustering and is suitable for mass and spectral cytometry datasets where it objectively detects distinct types of batch issues. We demonstrated cytoFlagRs utility for assessing datasets that include or lack reference controls. Thus, cytoFlagR improves quality control by objective identification of technical variations that may impact downstream analysis. Availability and ImplementationcytoFlagR is freely available as R scripts with documentation and an example at https://github.com/AndorfLab/cytoFlagR. Contactandorfsa@ucmail.uc.edu

bioinformatics↗

bioRxiv2025

CytoPheno: Automated descriptive cell type naming in flow and mass cytometry

Advances in cytometry have led to increases in the number of cellular markers that are routinely measured. The resulting complexity of the data has prompted a shift from manual to automated analysis methods. Currently, numerous unsupervised methods are available to cluster cells based on marker expression values. However, phenotyping the resulting clusters is typically not part of the automated process. Manually identifying both marker definitions (e.g. CD4+, CCR7+, CD45RA+, CD19-) and descriptive cell type names (e.g. naive CD4+ T cells) based on marker expression values can be time-consuming, subjective, and error-prone. In this work we propose an algorithm that addresses these problems through the creation of an automated tool, CytoPheno, that assigns marker definitions and cell type names to unidentified clusters. First, post-clustered expression data undergoes per-marker calculations to assign markers as positive or negative. Next, marker names undergo a standardization process to match to Protein Ontology identifier terms. Finally, marker descriptions are matched to cell type names within the Cell Ontology. Each part of the tool was tested with benchmark data to demonstrate performance. Additionally, the tool is encompassed in a graphical user interface (R Shiny) to increase user accessibility and interpretability. Overall, CytoPheno can aid researchers in timely and unbiased phenotyping of post-clustered cytometry data.

bioinformatics↗

bioRxiv2024

Use of Antibody Structural Information in Disease Prediction Models Reveals Antigen Specific B Cell Receptor Sequences in Bulk Repertoire Data

Convergent antibodies are highly similar antibodies elicited in multiple individuals in response to the same antigen. Convergent antibodies provide insight into shared immunological responses and show great promise as diagnostic biomarkers. They have typically been identified using methods that consider the amino acid sequence of the third complementarity-determining region (CDR3) of immunoglobulin heavy chain (IgH). In this study, we extend the definition of convergent antibodies to use structural information about the three IgH CDR regions (CDR1-3). We benchmark the performance of both definitions of convergence by their ability to predict disease status from bulk IgH sequencing data for two different diseases (HIV infection and food sensitization). We show that using predicted structural information outperforms prior approaches for the prediction of food sensitization status and performs on par for HIV infection status. Additionally, the structurally convergent antibody groups driving HIV prediction are from known HIV binders. Thus, the use of structural information allows for the identification of antigen specific antibody groups from bulk IgH sequencing data.

immunology↗

bioRxiv2023

B cell repertoire in children with skin barrier dysfunction supports altered IgE maturation associated with allergic food sensitization

IgE B cells produce antibodies responsible for the inappropriate specificity seen in allergic disease. In this study, we sequence antibody gene repertoires from a large, well-characterized early life cohort at risk for developing food allergy. We found that food allergen sensitization was associated with lower IgE mutation frequencies that was abrogated in children living with a pet dog, suggesting potential molecular mechanisms underlying the hygiene hypothesis (i.e., protective effects of pet ownership and non-antiseptic environs reported for allergic disease). We also observed increased IgE diversity and increased isotype-switching to IgE, suggesting that B cell development is altered in those with food allergen sensitizations relative to those without. IgE diversity was even more extreme in subjects who were highly likely to be peanut allergic. Unlike food antigen sensitization, we detected no effect of aeroallergen sensitization on IgE mutation levels and diversity. Consistent patterns of antibody rearrangement were associated with food allergen sensitization. These results have important implications for the allergic response driven by mast cells and basophils and the underlying drivers of the atopic march.

immunology↗

bioRxiv2020

CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets

For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e. ungated cells, and that current (semi-)automated approaches ignore their modeling resulting in misclassified annotations. Therefore, we introduce CyAnno, a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of gated cell types and the ungated cells. By applying this framework on several CyTOF datasets, we demonstrated that including the ungated cells can lead to a significant increase in the prediction accuracy of the gated cell types. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches.

bioinformatics↗

bioRxiv2017

Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of 10 heterogeneous cytometry studies totaling 2926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between White and Asian adults. Software is released to the public through GitHub (github.com/hzc363/MetaCyto).

bioinformatics↗