Studying CNS cell responses is essential for understanding disease, injury, and developing effective therapies. While immunofluorescence and transgenic reporter models allow for specific labeling, automated quantification remains difficult due to tissue heterogeneity. Consequently, most analyses are conducted manually, introducing user bias and limiting reproducibility. We developed a MATLAB-based semi-automated workflow for quantifying immunofluorescence-stained CNS cells, focusing on nuclear signal detection. The pipeline uses DAPI masking and the imfindcircles function to detect round nuclei, requiring minimal user input. The pipeline enabled robust quantification of CNS-resident cells. Automated analyses of brain and spinal cord tissue sections closely resembled manual quantification, with minimal error. In a mouse model of contusion spinal cord injury, it revealed a rostro-caudal decline in myelinating oligodendrocytes from the lesion epicenter, confirming the method's accuracy and sensitivity in detecting injury-induced cellular changes. Unlike many commonly used quantification-based software, this novel pipeline does not perform full image segmentation. Instead, it uses nuclear morphology to detect round shapes. Moreover, the pipeline has been specifically designed and optimized for the quantification of CNS cells, whose heterogeneity and cytoarchitecture pose specific challenges to existing methods that are more generalized. This study presents an alternative to classical segmentation models by offering a reproducible quantification of CNS-resident cells using nuclear morphology. Its simplicity, minimal input requirements, reduced time for semi-automated quantification, and specificity for CNS tissues make it a valuable tool for studying cellular responses in both healthy and pathological contexts.

High-grade gliomas are aggressive primary brain tumors that affect the surrounding brain tissue, which can lead to neurological deficits, epilepsy, cognitive deficits, and tumor progression. Research has focused on glutamatergic alterations, but the understanding of the underlying network mechanisms is still scarce. To investigate how gliomas might affect neural network integrity and capacity for spiking activity, we induced network bursting events in neocortical slices from F98 glioma-bearing rats. Network activity was recorded using a 64 × 64 high-density multielectrode array to assess the network's capacity for extended periods of high activity. We describe glioma cell implantation, brain slice preparation, and detailed data analysis of electrophysiological recordings, focusing on computational methods for characterization of the network-wide bursting events. Spike sorting was performed using Kilosort4 and employed to identify network bursts. Burst origins were estimated from multi-unit activity, and network burst propagation speed was assessed. Network-level functional connectivity was analyzed using metrics derived from graphs based on the spike time tiling coefficient. Our results indicate reduced network integrity in glioma-bearing slices. The combination of a high-density multielectrode array with Kilosort4 is advantageous over conventional recording techniques in terms of detecting network bursting and plotting center-of-activity trajectories. This protocol provides a robust platform for dissecting network activity and establishes a basis for future mechanistic studies. We also discuss the limitations and experimental challenges of our preclinical approach to investigating glioma-associated effects on surrounding neuronal networks.

The rat sciatic nerve crush injury model is commonly employed to evaluate the efficacy of treatments aimed at promoting nerve regeneration. This review examines various crush techniques and the types of nerve injuries they are designed to produce. A literature review was conducted using PubMed, Embase, Web of Science, and SCOPUS for studies published between 2010 and 2024. Studies were included if they involved surgical crush injuries on the rat sciatic nerve, performed at least one sensory functional evaluation, and compared outcomes with naive or sham controls. Crush techniques and the resulting nerve injuries were analyzed based on postoperative sensory evaluations. Of the 48 studies reviewed, 21 met the inclusion criteria. The crush techniques used included clamp (11 studies), forceps (5), clip (4), and others (1), all of which resulted in sensory impairments. Ten studies reported transient impairments with recovery to control levels, while 11 reported no recovery during the observation period (permanent). The transient group had significantly longer observation periods compared to the permanent group (57.7 vs. 17.4 days, p = 0.0009). ROC analysis determined 25 days as the optimal cutoff to distinguish transient from permanent injuries, with 90.0 % sensitivity and 81.8 % specificity. Regardless of the technique used, the induced nerve injury aligns with axonotmesis, characterized by spontaneous recovery over time. To accurately evaluate functional recovery, a minimum postoperative observation period of 4 weeks is recommended. This model is best suited for assessing rapid-acting agents, as spontaneous recovery may obscure the effects of slower-acting treatments.

Normative maps of brain tracts are key tools for assessing the extent of tract injury and plasticity after brain lesions such as stroke. The conventional procedure for tract mapping reduces diffusion tractography streamlines to a volumetric image before warping them to a standard coordinate space. Unfortunately, this volumetric reduction discards information about tract connectivity across voxels, complicating estimation of tract injury from the resulting map. This issue could be addressed by using a streamline-based tract map, but prior efforts to do so using group-based tractography have not accounted for individual variability, and thus are at risk of underestimating tract extent. Direct streamline normalization (DSN) directly warps individual tractography streamlines to a standard coordinate space, which can be pooled across individuals to account for individual variability. Here, DSN was used to create normative tract maps for the first time. Novel probabilistic maps of the corticospinal tract (CST) and cortico-reticulospinal tract (CRST) were generated in streamline format using DSN. Compared with group-based tractography, tract maps generated with DSN had 3.1-5.2 times greater volume, 1.0-1.5 times greater mean diameter and 7.8-15.3 times greater cortical origin area. DSN produces tract maps with more accurate representation of axonal geometry than the conventional procedure and greater incorporation of individual variability than group-based tractography, likely resulting in larger maps. Future studies can use the CST and CRST maps generated here to optimize tract injury and plasticity measurements, which may provide an improved understanding of motor impairment and recovery.