Biology subjects

Dyjack, N.

Publications and source records attributed to Dyjack, N..

4 recordsLinked to original sources

bioRxiv2023

Atopic dermatitis complicated by recurrent eczema herpeticum is characterized by multiple, concurrent epidermal inflammatory endotypes

BACKGROUNDA subgroup of atopic dermatitis (AD) patients suffer from recurrent, disseminated herpes simplex virus (HSV) skin infections, termed eczema herpeticum (EH), which can be life-threatening and contribute to AD morbidity. The pathobiology underlying ADEH is unknown. OBJECTIVETo determine transcriptional mechanisms of skin and immune system pathobiology that underlie ADEH disease. METHODSWe performed whole transcriptome RNA-sequencing of non-lesional skin samples (epidermis, dermis) of AD patients with (ADEH+, n=15) and without (ADEH-, n=13) recurrent EH history, and healthy controls (HC, n=15). We also performed RNA-sequencing on plasmacytoid dendritic cells (pDCs) collected from these participants and infected in vitro with HSV-1. Differential expression, gene set enrichment, and endotyping analyses were performed. RESULTSADEH+ disease was characterized by dysregulation in skin gene expression, which was limited in dermis (differentially expressed genes [DEGs]=14) and widespread in epidermis (DEGs=129). ADEH+-upregulated epidermal DEGs were enriched in type 2 cytokine (T2) (IL4R, CCL22, CRLF2, IL7R), interferon (CXCL10, ICAM1, IFI44, and IRF7), and IL-36{gamma} (IL36G) inflammatory pathway genes. At a person-level, all ADEH+ participants exhibited T2 and interferon endotypes and 87% were IL36G-high. In contrast, these endotypes were more variably expressed among ADEH- participants. ADEH+ patient skin also exhibited dysregulation in epidermal differentiation complex (EDC) genes within the LCE, S100, and SPRR families, which are involved in skin barrier function, inflammation, and antimicrobial activities. pDC transcriptional responses to HSV-1 infection were not altered by ADEH status. CONCLUSIONSADEH+ pathobiology is characterized by a unique, multi-faceted epidermal inflammation that accompanies dysregulation in the expression of EDC genes. Key MessagesO_LIAD patients with a history of recurrent EH exhibit molecular skin pathobiology that is similar in form, but more severe in degree, than in AD patients without this complication. C_LIO_LINon-lesional skin of ADEH+ patients concurrently exhibits excessive type 2 cytokine, interferon, and IL-36{gamma}-driven epidermal inflammation. C_LIO_LIExpression of these inflammatory skin endotypes among ADEH+ patients is associated with dysregulation in expression of epidermal differentiation complex genes involved in barrier function, inflammation, and antimicrobial activity. C_LI Capsule SummaryAD patients with a history of recurrent disseminated HSV-1 skin infections form a unique molecular skin endotype group that concurrently exhibits type 2 cytokine, interferon, and IL-36{gamma}-driven skin inflammation, accompanied by dysregulation in expression of epidermal differentiation complex genes involved in barrier function, inflammation, and antimicrobial activity.

molecular biology↗

bioRxiv2022

A scalable and unbiased discordance metric with H+

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or consistency of the cluster, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the scale-agnostic G+ discordance metric, however this internal metric is slow to calculate for large data. Furthermore, we show that G+ varies as a function of the proportion of observations in the predicted cluster labels (group balance), which is an undesirable property. To address this problem, we propose a modification of G+, referred to as H+, and demonstrate that H+ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate H+, which are available in the fasthplus R package.

bioinformatics↗

bioRxiv2021

minicore: Fast scRNA-seq clustering with various distance measures

Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient k-means++ center finding and k-means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction. Minicores novel vectorized weighted reservoir sampling algorithm allows it to find initial k-means++ centers for a 4-million cell dataset in 1.5 minutes using 20 threads. Minicore can cluster using Euclidean distance, but also supports a wider class of measures like Jensen-Shannon Divergence, Kullback-Leibler Divergence, and the Bhattacharyya distance, which can be directly applied to count data and probability distributions. Further, minicore produces lower-cost centerings more efficiently than scikit-learn for scRNA-seq datasets with millions of cells. With careful handling of priors, minicore implements these distance measures with only minor (<2-fold) speed differences among all distances. We show that a minicore pipeline consisting of k-means++, localsearch++ and minibatch k-means can cluster a 4-million cell dataset in minutes, using less than 10GiB of RAM. This memory-efficiency enables atlas-scale clustering on laptops and other commodity hardware. Finally, we report findings on which distance measures give clusterings that are most consistent with known cell type labels. AvailabilityThe open source library is at https://github.com/dnbaker/minicore. Code used for experiments is at https://github.com/dnbaker/minicore-experiments.

bioinformatics↗

bioRxiv2019

Dissecting the cellular specificity of smoking effects and reconstructing lineages in the human airway epithelium

Cigarette smoke first interacts with the lung through the cellularly diverse airway epithelium and goes on to drive development of most chronic lung diseases. Here, through single cell RNA-sequencing analysis of the tracheal epithelium from smokers and nonsmokers, we generated a comprehensive atlas of epithelial cell types and states, connected these into lineages, and defined cell-specific responses to smoking. Our analysis inferred multi-state lineages that develop into surface mucus secretory and ciliated cells and contrasted these to the unique lineage and specialization of submucosal gland (SMG) cells. Our analysis also suggests a lineage relationship between tuft, pulmonary neuroendocrine, and the newly discovered CFTR-rich ionocyte cells. Our smoking analysis found that all cell types, including protected stem and SMG populations, are affected by smoking, through both pan-epithelial smoking response networks and hundreds of cell type-specific response genes, redefining the penetrance and cellular specificity of smoking effects on the human airway epithelium.

cell biology↗