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17 Publications visible to you, out of a total of 17
Spotting a unicorn: spatial transcriptome analysis of the eyelid reveals gene regulatory networks enriched in Moll glands.

Abstract (Expand)

Moll glands, found in the margin of the eyelid next to the base of the eyelashes, are likely to play an important role in maintaining the tear film and therefore in securing adequate visual function. … However, information about their secretion and its regulation is extremely scarce. Here, we subjected spatial transcriptome data of the human eyelid to bioinformatics workflows incorporating machine learning to shed light on the Moll-specific transcriptional program. We identified Moll-specific genes such as HPD, CYP4Z1, PIP, GLYATL2, or SCGB2A2, which delineate a transcriptional core, i.e. not shared with other eyelid elements. Gene ontology enrichment analyses further depicted the biological functions of the Moll gland transcriptional programs, which include tyrosine metabolism and biosynthesis, extracellular exosome, small molecule metabolism, and erythrose 4-phosphate/phosphoenolpyruvate-family amino acid metabolism. Expression of GLYATL2 and HPD, identified as a specific and sensitive transcripts in the Moll gland transcriptome, was confirmed by immunofluorescence in the eyelid of four different patients, thus supporting the validity of our approach. Collectively, these results indicate that Moll-associated gene sets exhibit distinct but complementary functional programs, reflecting the gland's specialized metabolic capacity and secretory function within the eyelid tissue microenvironment. Our study provides the first in-depth analysis of the human Moll gland transcriptional landscape and identifies novel targets for regulating Moll gland homeostasis in health and disease.

Authors: T. Konecny, H. Binder, U. Hampel, F. Hansmann, H. Pfannkuche, M. R. Schneider

Date Published: 1st Mar 2026

Publication Type: Journal

Spatial transcriptome analysis of the human eyelid depicts meibomian gland cell differentiation: A pilot study.

Abstract (Expand)

Meibomian glands (MGs) are an integral component of the ocular defense system, as their secretion product, meibum, is essential for protecting the eye surface. To characterize the transcriptional program … underlying meibum production, we employed spatial transcriptomics (ST) analysis of the human eyelid from a sample from a 60-year-old male. We resolved 18 distinct eyelid clusters, representing structures such as the conjunctiva, epidermis, hair-associated sebaceous glands, and MGs. Focusing on the MG, we distinguished basal (MEI-B cluster) and differentiating (MEI-DIFF cluster) meibocytes, as well as a third, duct-related cluster (MEI-DUCT). Self-organizing maps (SOM) portrayal of ST images and pseudotime analysis confirmed progress from MEI-B to MEI-DIFF and further to MEI-DUCT, as the latter turned out to include terminally differentiated meibocytes. Accordingly, gene set enrichment analysis associated early/intermediate meibocyte maturation with energy and lipid metabolism, and later stages with barrier functions. We also identified significant differences between the MG and sebaceous gland transcriptomes. The MG-specific signature included transcripts such as AQP9, MMP3, and PITX1, and selective expression of PITX1 in the MG compared to the sebaceous gland was confirmed by immunohistochemistry on the same sample and samples from three other elderly adults. We deliver the first spatial portrait of the human MG transcriptional landscape. Besides enhancing our understanding of MG physiology, our study identifies novel targets for regulating MG homeostasis in health and disease.

Authors: H. Binder, U. Hampel, H. Loeffler-Wirth, F. Hansmann, H. Pfannkuche, M. Schmidt, M. R. Schneider

Date Published: 19th Sep 2025

Publication Type: Journal

Topology-aware pathway analysis of spatial transcriptomics

Abstract (Expand)

Spatial transcriptomics (ST) has transformed genomics by mapping gene expression onto intact tissue architecture, uncovering intricate cellular interactions that bulk and single-cell RNA sequencing … often overlook. Traditional ST workflows typically involve clustering spots, performing differential expression analyses, and annotating results via gene-set methods such as overrepresentation analysis (ORA) or gene set enrichment analysis (GSEA). More recent spatially-aware techniques extend these approaches by incorporating tissue organization into gene-set scoring. However, because they operate primarily at the level of individual genes, they may overlook the connectivity and topology of biological pathways, limiting their capacity to trace the propagation of signaling events within tissue regions. In this study, we address that gap by translating gene expression into pathway-level activity using the Pathway Signal Flow (PSF) algorithm. PSF integrates expression data with curated interaction networks to compute numeric activity scores for each branch of a biological pathway, producing a functionally annotated feature space that captures downstream signaling effects as branch-specific activity values. We applied PSF to two public 10x Genomics Visium datasets (human melanoma and mouse brain) and compared clustering based on PSF-derived pathway activities from 40 curated Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene expression with standard Seurat Louvain clustering and spatially aware methods (Vesalius, spatialGE). We observed good correspondence between PSF-based and expression-based clustering when spatially aware clustering methods were used. This suggests that branch-level pathway activities can themselves drive clustering and pinpoint spatially deregulated processes. To assess cluster-specific functional annotation, we compared PSF results to conventional ORA (based on marker genes) and GSDensity (based on cluster-specific gene sets). PSF identified a broader set of significant pathways with substantial overlap with both ORA and GSDensity, providing increased sensitivity due to its branch-level resolution. We further demonstrated that PSF-derived activity values can be used to detect spatially deregulated pathway branches, yielding results comparable to those obtained with spatially aware gene set analysis approaches such as GSDensity and spatialGE. The availability of pathway topology and branch-specific information also enabled the identification of potential intercellular communication via ligand-receptor interactions between deregulated pathways in adjacent tumor regions. To support interactive exploration of results, we developed the PSF Spatial Browser, an R Shiny application for visualizing pathway activities, gene expression patterns, and deregulated pathway networks.

Authors: Siras Hakobyan, Maria Schmidt, H. Binder, A. Arakelyan

Date Published: 14th Aug 2025

Publication Type: Journal

The metabolic underpinnings of sebaceous lipogenesis.

Abstract (Expand)

Sebaceous glands synthesize and secrete sebum, a melange of lipids and other cellular products that safeguards the mammalian integument. Differentiating sebocytes delaminate from the basal membrane and … dislodge towards the gland's middle, where they eventually undergo a poorly understood death mode in which the whole cell becomes a secretion product (holocrine secretion). Supported by recent transcriptomics data, this review examines the idea that peripheral sebocytes have a remarkable ability to draw nutrients from the blood and become committed to unrestrainedly invest all available resources into synthetic processes for accomplishing sebum synthesis, thereby exploiting core metabolic fluxes as glycogen turnover, glutamine-directed anaplerosis, the pentose phosphate pathway and de novo lipogenesis. Finally, we propose that metabolic-driven processes are an important mechanistic component of holocrine secretion. A deeper understanding of these metabolic adaptations could indicate novel strategies for modulating sebum synthesis, a key pathogenic factor in acne and other skin diseases.

Authors: M. Schmidt, H. Binder, M. R. Schneider

Date Published: 27th Apr 2025

Publication Type: Journal

Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder.

Abstract (Expand)

The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little … is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.

Authors: A. Arakelyan, S. Avagyan, A. Kurnosov, T. Mkrtchyan, G. Mkrtchyan, R. Zakharyan, K. R. Mayilyan, H. Binder

Date Published: 17th Feb 2024

Publication Type: Journal

PSF toolkit: an R package for pathway curation and topology-aware analysis.

Abstract (Expand)

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based … pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

Authors: S. Hakobyan, A. Stepanyan, L. Nersisyan, H. Binder, A. Arakelyan

Date Published: 8th Sep 2023

Publication Type: Journal

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

Abstract (Expand)

organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative … analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and time-demanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

Authors: Maria Nikoghosyan, Henry Loeffler-Wirth, Suren Davidavyan, Hans Binder, Arsen Arakelyan

Date Published: 27th Dec 2021

Publication Type: Journal

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