Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/396143

Network analysis of lymphocyte nucleus staining image

Li, D.-D.·Yang, X.-L.·Xiong, Q.-Y.·Liang, Y.-D.·Liu, S.-Q.·Hu, H.-Y.·Zhou, X.-h.·Huang, H.
View original source ↗Export metadata ↓

Abstract

Background: A complex network has been studied and applied in various disciplines. As network analysis and image processing are based on matrices, this research analysed the changes in the chromatin image of lymphocyte nuclei in peripheral blood of humans using a network motif and static features (static parameters), so as to complete image classification with network method.\n\nMethods: Image processing technology was used to establish a chromatin image network of a cell nucleus; Network analysis tool Pajek was used to display the special motif of an isolated structural hole with different symmetric line values; afterwards, the frequency of occurrence of this structural hole in patients with nasopharyngeal carcinoma and AIDS, and healthy people was computed. Then by applying the network static features as variables, the chromatin images of stained lymphocytes from the three groups of people were classified and recognised by using an extreme learning machine (ELM).\n\nResults: The frequency of occurrence of the isolated structural hole with different symmetric line values was adopted to distinguish the structures of the chromatins of peripheral blood lymphocytes in patients with nasopharyngeal carcinoma and AIDS, and healthy people. Similarly, The static features of the chromatin image network of a cell nucleus were applied to classify and recognise the morphological and structural changes in chromatins for peripheral blood lymphocytes in the three groups of people.\n\nConclusion: The surface chemical and physical characteristics, as well as the polymerisation link status of biomacromolecules such as DNA, RNA, and protein in the lymphocyte nucleus change under certain pathological conditions. The change influences the combination of small molecular staining materials and any associated biomacromolecules. Therefore, various macroscopic and microscopic changes were found in the chromatin images of the cell nucleus. The microscopic changes include the variations of the extent of staining of chromatin in the nuclei, coarseness and direction of the texture therein, the size of stained conglomerations, etc. These changes contribute to the differences in chromatin image networks among the same type of cells across the three groups. Based on this, the model can be used to classify and reorganise certain diseases. The results prove that using complex network to analyse the chromatin structure of a cell nucleus is of significance.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

BibTeXRIS

Li, D.-D., Yang, X.-L., Xiong, Q.-Y., Liang, Y.-D., Liu, S.-Q., Hu, H.-Y., Zhou, X.-h., Huang, H.. 2018-08-20. Network analysis of lymphocyte nucleus staining image. https://doi.org/10.1101/396143

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗