cytoNet: Spatiotemporal network analysis of cell communities

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Extended methods for spatial cell classification with DBSCAN-CellX

Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.

In Vivo Calcium Imaging of Neuronal Ensembles in Networks of Primary Sensory Neurons in Intact Dorsal Root Ganglia

Ca2+ imaging can be used as a proxy for cellular activity, including action potentials and various signaling mechanisms involving Ca2+ entry into the cytoplasm or the release of intracellular Ca2+ stores. Pirt-GCaMP3-based Ca2+ imaging of primary sensory neurons of the dorsal root ganglion (DRG) in mice offers the advantage of simultaneous measurement of a large number of cells. Up to 1,800 neurons can be monitored, allowing neuronal networks and somatosensory processes to be studied as an ensemble in their normal physiological context at a populational level in vivo. The large number of neurons monitored allows the detection of activity patterns that would be challenging to detect using other methods. Stimuli can be applied to the mouse hindpaw, allowing the direct effects of stimuli on the DRG neuron ensemble to be studied. The number of neurons producing Ca2+ transients as well as the amplitude of Ca2+ transients indicates sensitivity to specific sensory modalities. The diameter of neurons provides evidence of activated fiber types (non-noxious mechano vs. noxious pain fibers, Aβ, Aδ, and C fibers). Neurons expressing specific receptors can be genetically labeled with td-Tomato and specific Cre recombinases together with Pirt-GCaMP. Therefore, Pirt-GCaMP3 Ca2+ imaging of DRG provides a powerful tool and model for the analysis of specific sensory modalities and neuron subtypes acting as an ensemble at the populational level to study pain, itch, touch, and other somatosensory signals.

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.