Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

This study aimed to expand our understanding of myelin basic protein (MBP), a key component of central nervous system myelin, by developing a protocol to track and quantifying individual MBP particles during oligodendrocyte (OL) differentiation. MBP particle directionality, confinement, and diffusion were tracked by rapid TIRF and HILO imaging of Dendra2 tagged MBP in three stages of mouse oligodendroglia: OL precursors, early myelinating OLs, and mature myelinating OLs. The directionality and confinement of MBP particles increased at each stage consistent with progressive transport toward, and recruitment into, emerging myelin structures. Unexpectedly, diffusion data presented a more complex pattern with subpopulations of the most diffusive particles disappearing at the transition between the precursor and early myelinating stage, before reemerging in the membrane sheets of mature OLs. This diversity of particle behaviors, which would be undetectable by conventional ensemble-averaged methods, are consistent with a multifunctional view of MBP involving roles in myelin expansion and compaction.

Due to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description of their spatial properties. One such network is the extracellular matrix (ECM), a biological scaffold for which changes in its spatial organization significantly impact tissue functions in health and disease. Quantifying variations in the fibrillar architecture of major ECM proteins should considerably advance our understanding of the link between tissue structure and function. Inspired by the analysis of functional magnetic resonance imaging (fMRI) images, we propose a novel statistical analysis approach embedded into a machine learning paradigm, to measure and detect local variations of meaningful ECM parameters. We show that parametric maps representing fiber length and pore directionality can be analyzed within the proposed framework to differentiate among various tissue states. The parametric maps are derived from graph-based representations that reflect the network architecture of fibronectin (FN) fibers in a normal, or disease-mimicking in vitro setting. Such tools can potentially lead to a better characterization of dynamic matrix networks within fibrotic tumor microenvironments and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention.