bioRxiv ScienceSearch

bioRxiv · 10.1101/155036

Proposal for minimum information guidelines to report and reproduce results of particle tracking and motion analysis

Abstract

The proposed Minimum Information About Particle Tracking Experiments (MIAPTE) reporting guidelines described here aim to deliver a set of rules representing the minimal information required to report and support interpretation and assessment of data arising from intracellular multiple particle tracking (MPT) experiments. Examples of such experiments are those tracking viral particles as they move from the site of entry to the site of replication within an infected cell, or those following vesicular dynamics during secretion, endocytosis, or exocytosis. By promoting development of community standards, we hope that MIAPTE will contribute to making MPT data FAIR (Findable Accessible Interoperable and Reusable). Ultimately, the goal of MIAPTE is to promote and maximize data access, discovery, preservation, re-use, and repurposing through efficient annotation, and ultimately to enable reproducibility of particle tracking experiments. This document introduces MIAPTE v0.2, which updates the version that was posted to Fairsharing.org in October 2016. MIAPTE v0.2 is presented with the specific intent of soliciting comments from the particle tracking community with the purpose of extending and improving the model. The MIAPTE guidelines are intended for different categories of users: 1) Scientists with the desire to make new results available in a way that can be interpreted unequivocally by both humans and machines. For this class of users, MIAPTE provides data descriptors to define data entry terms and the analysis workflow in a unified manner. 2) Scientists wishing to evaluate, replicate and re-analyze results published by others. For this class of users MIAPTE provides descriptors that define the analysis procedures in a manner that facilitates its reproduction. 3) Developers who want to take advantage of the schema of MIAPTE to produce MIAPTE compatible tools. MIAPTE consists of a list of controlled vocabulary (CV) terms that describe elements and properties for the minimal description of particle tracking experiments, with a focus on viral and vesicular traffic within cells. As part of this submission we provide entity relationship (ER) diagrams that show the relationship between terms. Finally, we also provide documents containing the MIAPTE-compliant XML schema describing the data model used by Open Microscopy Environment inteGrated Analysis (OMEGA), our novel particle tracking data analysis and management tool, which is reported in a separate manuscript. MIAPTE is structured in two sub-sections: 1) Section 1 contains elements, attributes and data structures describing the results of particle tracking, namely: particles, links, trajectories and trajectory segments. 2) Section 2 contains elements that provide details about the algorithmic procedure utilized to produce and analyze trajectories as well as the results of trajectory analysis. In addition MIAPTE includes those OME-XML elements that are required to capture the acquisition parameters and the structure of images to be subjected to particle tracking.

Explore related subjects

Keep this discovery

BibTeXRIS

Rigano, A., Strambio De Castillia, C.. 2017-07-13. Proposal for minimum information guidelines to report and reproduce results of particle tracking and motion analysis. https://doi.org/10.1101/155036

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

KEEP EXPLORING

Related preprints

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

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

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