BMC Neuroscience

Electrical stunning is a humane euthanasia method for larval zebrafish, and a suitable euthanasia protocol has recently been established. Here, we assess the associated stress response and tissue integrity. When compared to MS-222 overdose and hypothermia, electrical stunning caused no significant Hsp70 increase. Brain tissue was preserved and muscle tissue affected only slightly. Our results support the suitability of electrical stunning for humane euthanasia and artifact-free post-mortem analyses.

The discrepancy between chronological age and predicted brain age, derived from neuroimaging data, serves as a biomarker for neurological health. However, the exact electroencephalography (EEG) mechanisms contributing to the variation of brain age are yet to be understood. This paper comprehensively investigated the relationship between brain age estimated from EEG-based functional connectivity and cognitive or mental manifestations of healthy adults. A mean absolute error (MAE) of 5.97 and R-squared (R2) of 0.85 were reported for training data, while an MAE of 10.73 and R2 of 0.56 were reported for the prediction data. Increased brain age was found to be negatively related to the scores of memory and attention tasks. Most young adults (19 of 26) suffering from mental symptoms such as addictions, depression, phobia, and anorexia nervosa demonstrated higher brain age than their chronological age. Alcohol consumption was also significantly correlated to the difference between brain and chronological ages. The functional connectivity of frontal and central motor regions was found to be most distinguishing between individuals with high and low brain ages. These brain regions were significantly correlated with brain age but not chronological age, suggesting a relatively stronger relationship with biological age. Ablation studies further confirmed the importance of the frontal brain and all bands EEG in the prediction of brain age. This work advances the understanding of EEG mechanisms contributing to brain age and could provide an interpretation for physiological or psychological conditions associated with brain age.

Whole plate detection of calcium flux signals using fluorescence is a powerful technology for monitoring oscillatory activity of excitable cells in a high throughput fashion that sees broad applications in neurotoxicity and cardiotoxicity research. However, analysis of calcium oscillation profiles can output more than 60 peak kinetic parameters, which makes manual data analysis challenging. We have created the CalFluxTools R package, an automated pipeline for parsing plate data for 384 well plates, generating quality control metrics and plots, and performing data analysis, including T-tests, Z-score analysis, and machine learning prediction of compound toxicity values. CalFluxTools requires a plate map and experimental manifest that includes parameter filtration specifications, as well as data in the ".statall" format. CalFluxTools runs in less than a minute on a Macbook Pro with a 2.6 GHz 6-Core Intel Core i7 processor.

Circadian disruption, notably sleep disturbances, serves as an early indicator of Alzheimers disease (AD), preceding cognitive symptoms like memory loss. The suprachiasmatic nucleus (SCN) governs biological rhythms and receives direct retinal input via melanopsin-expressing retinal ganglion cells (mRGCs) to synchronize with environmental light cycles. The anatomical and functional basis for circadian disruption in AD remains unclear. Here, we explored the multi-level relationships between gene expression, the SCN connectome, and regulations of sleep and circadian rhythms in the APP/PS1 mouse model. The sleep architecture of APP/PS1 mice displayed significantly reduced rapid eye movement sleep (REM), associated with a reduced daily core body temperature amplitude and locomotor hyperactivity. Lastly, APP/PS1 mice showed an impaired response to acute light pulse stimulation and present hyperactivity of mRGCs at a young age and hypoactivity of these cells at older ages. These physiological functions are known to be, at least in part, regulated by the SCN, the main target of mRGCs. We noted several modifications in SCN connectomics using serial blockface electron microscopy (SBEM), including a reduction of the dendro-dendritic chemical synapse (DDCS) network that receives a large part of the retinal input and is thought to be crucial for synchronicity between SCN neurons. In addition, we observed multiple signs of dystrophy, including modifications of the shape of dendrites and cell soma, accumulation of aggregated lysosomes, and swelling of axons. At the same time, we investigated the changes in gene expression using spatial transcriptomics. The SCN presents changes in the expression of genes associated with synapse formation, cell adhesion, and neurite growth. These results suggest that, despite the absence of amyloid plaques in the ventral hypothalamus, the SCN of APP/PS1 mice still undergo profound gene expression changes, impacting connectomics and physiological functions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/744599v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@ceedb0org.highwire.dtl.DTLVardef@156cfaaorg.highwire.dtl.DTLVardef@5bc262org.highwire.dtl.DTLVardef@36df4d_HPS_FORMAT_FIGEXP M_FIG C_FIG

We identified Sleep Bursts (SBs), as a novel phenomenon of brief (1-2 sec), often periodic bursts in cranial forces occurring during human sleep. Our goal was to characterize SBs in normal subjects, then compare SB in a cohort of subjects with neurodegenerative disease (NDD). We recorded 32 cognitively healthy subjects (23 -87 years) and 13 subjects with NDD (51 - 84 years). SBs occurred in all 45 subjects. SBs occurred at 0.57 SB/min (once per 105 seconds) in controls and 0.40 SB/min (once per 150 seconds) in NDD (p = 0.0043). SB occurred with equal rates across all sleep stages in both groups. When occurring periodically, SBs had modal intervals (3.75 bursts/min (0.0625 Hz) - 2.67 bursts/min (0.044 Hz)). EEG power increased in the delta range 1-2 seconds before and following the SB. EEG delta power during a SB was significantly lower in all NDD subjects across sleep stages compared to controls. The relatively low frequency of SB events and synchronization with EEG power has no parallel in human sleep; we hypothesize that SBs may represent a brain-generated pulsatile component of brain glymphatic drainage.

Resting-state electroencephalography (EEG) can capture the slowing of neural oscillations associated with Alzheimers disease (AD), but many machine-learning studies remain difficult to inspect, reproduce, or test. This study developed an interpretable, subject-level AD versus healthy-control classifier from the dataset OpenNeuro ds004504 and deployed it as a public research prototype. Preprocessed eyes-closed EEG recordings from 36 people with AD and 29 healthy controls were analysed. Welch power spectral density estimates were used to generate absolute and relative bandpower summaries, theta/alpha and delta/alpha ratios, a slow/fast ratio, and signal-level descriptors. Five-fold stratified cross-validation compared two Random Forest configurations, EEG Logistic Regression, an age-only baseline, and an EEG-plus-age model. EEG Logistic Regression gave the strongest mean fold-wise performance: accuracy 0.846, balanced accuracy 0.848, F1 score 0.849, and ROC-AUC 0.948. Age alone was near chance, while adding age did not improve the EEG-only model. The feature pattern was consistent with AD-related EEG slowing, including higher slow-wave-related ratios and lower relative alpha power. The trained pipeline was deployed as a Gradio application on Hugging Face Spaces. The result is a reproducible research and educational prototype, not a clinical diagnostic device, and requires external validation before any clinical interpretation.

Exposure to chemical cues released by predator or pathogen can evoke anxiety or fear responses in prey/host animals such as fight, flight or freeze both at behavioral and molecular levels. Freezing is a fundamental anxiety response when fighting or fleeing arent feasible. Despite the potential relevance of freezing as a stress-coping mechanism, its behavioral and molecular underpinnings are not understood yet. At molecular level danger cues are perceived by chemosensory receptors expressed in sensory neurons which may further regulate the animals behavioral responses(Ye et al., 2024){Citation}. 2-nonanone (2-NA) is one of the principal volatile organic compounds secreted by many pathogenic bacteria infecting Caenorhabditis elegans as well as humans and may signal danger to worms. Here, we show that olfactory exposure to threat-associated cue 2-NA induces a reversible fear-like freezing response characterized by immobility and halted feeding in C. elegans. With the application of in silico and behavioral approaches we showed that 2-NA is one of the ligands for an olfactory G-protein Coupled Receptor (GPCR) STR-211 and RNAi knockdown of the receptor leads to a defect in 2-NA induced avoidance behavior in worms. We next discovered that STR-211 is required for immediate behavioral changes in C. elegans during freezing response against 2-NA. The study proposes an environment relevant animal model to mimic human anxiety and fear-like behavior, along with the identification of one of the olfactory GPCRs mediating this behavior. The model may help in understanding the neuromolecular basis of freezing response in human anxiety, contributing towards treatment of mental health disorders.

Transgenic GAL4 lines are widely used in Drosophila to label and manipulate specific cell types, but the resulting reporter expression patterns do not always represent ongoing driver activity. We generated a collection of neuropeptide T2A-GAL4 knock-ins and characterized their expression by native-fluorescence imaging using UAS-CD8::GFP and a Brp-miRFP680 knock-in, eliminating the need for immunostaining. During characterization of these lines, we observed cases in which adult GFP patterns did not match the on-going GAL4 activity, as well as cases in which GAL4 produced adverse cellular effects. Using temperature-sensitive GAL80, we found that adult GFP expression in certain neurons driven by CCHa2-T2A-GAL4 and Dsk-T2A-GAL4 depended on GAL4 activity before eclosion and was not reproduced by adult-only induction. Several GAL4 lines also showed dominant developmental phenotypes. Heterozygous Burs-T2A-GAL4 animals, for example, displayed wing-expansion defects that were fully suppressed by GAL80. Severe wing defects were not accompanied by a reduction in the number of reporter-labeled neurons before eclosion, but the morphology of these neurons were affected. These observations illustrate how developmental driver history and GAL4-associated cellular effects can influence reporter patterns in the adult nervous system.

Behavioral annotation of synchronized video recordings is an essential step in developmental electroencephalography (EEG) research, supporting both the identification of behavior-related artifacts and the investigation of brain-behavior relationships. Existing annotation workflows, however, are often fragmented: proprietary EEG software provides limited flexibility for behavioral coding, whereas dedicated behavioral annotation platforms typically lack native integration with EEG data. We developed AVOCODO (Audio/VideO CODing Optimization), an open-source MATLAB-based software platform that integrates synchronized behavioral annotation directly into the EEG workflow. AVOCODO reads native EGI MFF recordings, synchronizes embedded video with EEG, visualizes the audio spectrogram to facilitate precise annotation of vocalizations, and writes user-defined behavioral events directly back into the original MFF recording as native EEG event markers while simultaneously exporting annotations as CSV files. The software supports fully customizable behavioral coding schemes, optional EEG visualization for quality control, and reloading of previously annotated recordings for review and inter-rater verification. Since its initial development in 2024, AVOCODO has been applied internally across five developmental EEG studies involving approximately 500 pediatric participants and more than 3,000 EEG recordings. By bridging behavioral annotation and EEG preprocessing within a unified open-source workflow, AVOCODO has the potential to improve the efficiency, reproducibility, and scalability of behavioral annotation in developmental EEG research.

Sleep disorders and wakefulness constitute a pronounced and pervasive health risk that contributes to numerous cerebral pathologies such as stroke, depression, and neurodegenerative disorders. The hippocampus (HC) plays vital roles in learning and memory and is especially susceptible to the pathological insults imposed by sleep deprivation (SD). While SD disrupts neuronal network signaling and triggers inflammatory processes, the impact of SD on metabolism within the HC presently remains poorly understood. In this study, we assessed the progressive and cumulative effects of partial SD in the live mouse HC CA1 region using two-photon microscopy. Using a custom-designed implantable cannula enabling direct HC visualization and administration of dyes and pharmacological agents directly to HC tissue, we longitudinally monitored spontaneous neuronal activity and partial pressure of oxygen (pO2) within HC CA1 tissue before and during a 7-day period of partial SD and after a 7- day period of recovery. Our results revealed a progressive decline in spontaneous neural activity, alongside sustained elevation in tissue pO2; during the 7-day partial SD period. Notably, after 7 days of recovery, neuronal activity did not return to baseline, and tissue oxygenation continued to increase. Our findings emphasize chronic SD's cumulative and enduring impairments to neuronal function and metabolism in the HC and reveal uncoupling between metabolic and functional recovery that outlasts the sleep restriction period.

Early embryos must mount stress defenses without compromising the developmental programs that drive morphogenesis, but whether genetic background alters this balance remains unclear. We compared cellularizing Drosophila melanogaster embryos from inbred and outcrossed backgrounds under control conditions and after acute or chronic heat-stress. Both backgrounds developed successfully and showed modest transcriptomic differences at 18{degrees}C. Heat stress at 32{degrees}C, however, sharply separated their phenotypic and transcriptional responses. Inbred embryos showed poor larval hatching after acute and chronic heat, and this deficit persisted through adult eclosion. Their transcriptomes exhibited a greater stress-response burden, with enrichment of proteostasis, DNA repair, mitochondrial maintenance, RNA processing, and metabolic remodeling programs. Outcrossed embryos retained substantially greater developmental survival and higher abundance of transcripts associated with development, adhesion, epithelial organization, signaling, transport, sensory responses, and morphogenesis. Background-dependent divergence was greatest during acute heat. Under chronic heat, background-dependent differences were fewer, with most overlapping those seen during acute heat and a smaller set detected only under chronic heat. Transcript-origin analysis further showed that acute outcrossed-biased genes were predominantly maternally derived and showed no enrichment for purely zygotic transcripts, whereas chronic outcrossed-biased genes contained a larger zygotic component. In summary, the less viable inbred background mounted the broader molecular stress signature, whereas the more viable outcrossed background retained greater abundance of development-associated transcripts. These findings identify genetic background as a major determinant of how embryonic heat responses are distributed between molecular stress defense and developmental gene expression.

This study presents a systematic investigation of the brain's own microwave emissions in the 0.85-5.0 GHz range, aiming to validate microwave encephalography (MWEG) as a novel tool for cognitive state assessment. Early experiments (2016-2019) using comparisons with background noise, a control phantom, and different antenna types confirmed the endogenous origin of the recorded signals. In a 2021 experiment (118 measurements on one subject, right and left occipital areas), we observed a significant functional hemispheric asymmetry: the number of "substantial spikes" was significantly higher in darkness for the right hemisphere (p<0.001) and higher in light for the left hemisphere (p<0.001). A 2025 group study (three subjects) confirmed the asymmetry (p<0.05 for right, p<0.01 for left). We describe the signal processing pipeline, including spike detection and statistical analysis. Two complementary mathematical models are presented: an energy-based model linking optical input to microwave emission via neural activity modes, and an information-based model interpreting spectral changes in terms of structural pattern overlap with memory engrams. An estimation of the physical dimensions of possible brain resonators (2-22 mm) is provided. The results are discussed in the context of hemispheric specialization and the information commutation theory. This work opens new perspectives for developing low cost, contactless brain monitoring systems.

Aging affects individuals at varying biological rates, prompting the development of the Brain Age Index (BAI) to quantify neurobiological health relative to chronological age and disease risk. While structural MRI has dominated brain age prediction, its high cost, immobility, and low temporal resolution restrict its clinical scalability and responsiveness to transient neurophysiological changes. Electroencephalography (EEG) offers a highly scalable, portable, and temporally precise alternative capable of capturing dynamic brain states. However, the transition of EEG-based models to clinical biomarkers is impeded by methodological limitations, including small or biased datasets, inconsistent preprocessing pipelines, and a distinct lack of interpretable machine learning approaches. To address these persistent challenges, this paper presents a comprehensive, open-source, end-to-end pipeline for large-scale EEG-based brain age modeling. Developed using the Temple University Hospital EEG Corpus (TUEG) the largest publicly available resting-state EEG dataset. The pipeline encompasses rigorous data engineering, reproducible preprocessing, and robust feature extraction. Following quality control and subject-level dataset partitioning to definitively prevent data leakage, exactly 41,181 recordings were successfully retained. Two independent feature sets were extracted: the Catch22 time-series characteristics and a comprehensive set of spectral, aperiodic, and non-linear dynamics from the CCS toolbox. The methodology evaluates seven regression models, optimized via Optuna for hyperparameter tuning, and integrates SHAP (SHapley Additive exPlanations) for transparent feature importance analysis. By making this infrastructure publicly available, this work lowers the barrier to entry for large-cohort studies, fostering reproducible development and clinical validation of dynamic brain age biomarkers.

Recent technological advances allow for the large-scale acquisition of genetic and morphological data: high-throughput sequencing has transformed the field of genomics while synchrotron X-ray microtomography enables rapid, noninvasive 3D imaging. However, integrating these approaches for the same specimens is challenging because X-rays can fragment DNA, and DNA extraction damages internal morphology, particularly relevant for small bodied organisms, such as insects. We systematically tested multiple extraction protocols and irradiation conditions across three model insect species. We irradiated more than 1,000 specimens under varying conditions and tested DNA quality through DNA barcoding and UCE sequencing. Our results demonstrate that high-quality DNA and high-resolution tomograms can be obtained from the same individuals, provided that the parameters are carefully optimized and rapid SR-CT scanning precedes DNA extraction. In this respect, our findings establish practical guidelines for combining genomics and phenomics, paving the way for comprehensive integrative digitization of biodiversity.

Alzheimer's disease (AD) pathology including amyloid beta (A{beta}) plaques and tau tangles accumulate with age in Down Syndrome (DS). Cholinergic abnormalities such as expression of nicotinic acetylcholine receptors (nAChRs) adversely contribute to cognitive decline and neurodegeneration in AD with similar features possible in DSAD. As a radiotracer for 4{beta}2* nAChRs, [18F]nifene was quantitatively evaluated in the frontal cortex (FCX) and temporal cortex (TCX) of DSAD, AD, and cognitively normal (CN) brain tissue using autoradiography. Anti-tau and anti-A{beta} immunostaining in adjacent sections confirmed the presence of tau tangles and A{beta} plaques. [18F]Nifene binding in brain sections demonstrated significantly more binding in gray matter (GM) than white matter (WM), with FCX exhibiting more binding than TCX (OptiQuant). Nicotine substantially displaced [18F]nifene binding in adjacent sections, resulting in average GM/nicotine ratios (DSAD=9.74, AD=10.5, CN=17; suggesting a 38% decrease in AD and a 43% decrease in DSAD) and WM/nicotine ratios (DSAD=2.73, AD=3.31, CN=5.62; suggesting a 41% decrease in AD and a 51% decrease in DSAD). [18F]Nifene was correlated with [125I]IBETA (A{beta}), showing a strong positive relationship between 4{beta}2* nAChRs and A{beta} in DSAD. [18F]Nifene GM/nicotine and WM/nicotine ratios were positively correlated with age in TCX and FCX. The findings of this study suggest that [18F]nifene has potential use in diagnostic investigations of the cholinergic system in DSAD using positron emission tomography. The study also points to a possible need for earlier therapeutic interventions to address the 4{beta}2* nAChRs deficit in DSAD.

Digital behaviour change interventions have emerged as a promising approach for improving health, well-being, and performance, yet relatively few studies have evaluated their effects using objective biological and neurophysiological measures. The present study examined the effectiveness of Autonomic, a neuroscience-informed digital coaching platform designed to improve student well-being through personalized behavioural interventions targeting sleep, stress, mood, energy, and focus. Thirty university students engaged with the platform for ten weeks and completed behavioural assessments, biomarker collection, and electroencephalographic (EEG) testing at baseline, five weeks, and ten weeks. Behavioural outcomes included self-reported ratings of focus, energy, sleep, mood, and stress. Biological measures included salivary cortisol and tear fluid dopamine concentrations. EEG assessments included resting-state recordings, frontal theta activity during a working memory task, and N200/P300 event-related potentials during a visual oddball task. Significant improvements were observed in self-reported focus, energy, and sleep quality across the intervention. Electrophysiological measures demonstrated reduced frontal theta power during working memory and shorter P300 latencies during attentional processing, consistent with more efficient cognitive processing following the intervention. Although cortisol, dopamine, and resting-state EEG measures did not reach statistical significance, all exhibited changes in the predicted direction. Collectively, these findings provide converging behavioural, biological, and neurophysiological evidence supporting the effectiveness of Autonomic. More broadly, the study demonstrates the value of combining objective biomarkers and EEG with traditional behavioural assessments when evaluating digital behaviour change interventions and highlights the potential of neuroscience-informed coaching platforms to improve health, well-being, and cognitive functioning in university students.

This study aims to improve the detection performance of age-related macular degeneration (AMD) in low-quality retinal images. BackgroundAMD is a leading cause of vision loss among older adults globally, and accurate detection is crucial for clinical management. However, low-quality optical coherence tomography (OCT) images significantly compromise diagnostic accuracy. ObjectiveTo enhance AMD detection in low-quality images using noise-augmented data augmentation and an improved YOLO deep learning model. MethodsPublic datasets from UCSD and Duke University were utilized; the training dataset comprised 24,980 OCT images (high-quality and noise-augmented low-quality), while the testing dataset included 1,000 images (584 AMD, 416 normal). The model is based on the YOLOv8n framework, integrated with Squeeze-and-Excitation blocks (SEblock) and Adaptive Sparse Self-Attention (ASSA), with an additional 160x160 detection layer for detecting small lesions. Evaluation metrics included accuracy, sensitivity, specificity, and F2-score. ResultsThe proposed model achieved an accuracy of 99.02%, sensitivity of 98.17%, specificity of 100%, and an F2-score of 98.50% on the Duke dataset. Detection rates were significantly improved compared to traditional methods, particularly in low-quality images, with a detection rate of 89.60%, markedly superior to original YOLOv8n (55.10%) and classical models like ResNet50. ConclusionThe enhanced model, employing noise-augmented training data and improved attention mechanisms, demonstrates excellent AMD detection capabilities in low-quality OCT images, showing broad potential for clinical applications.

Protein aggregates are a diverse array of intra- and extracellular structures containing misfolded proteins with limited macromolecular movement. Protein aggregation is induced by aging and cellular stress and is a hallmark of neurodegenerative diseases including Huntington's, Alzheimer's, and Parkinson's. Fluorescent tools to visualize global protein aggregation in whole organisms are, therefore, important for screening genes and compounds that may reduce or alter protein aggregation in connection with improved health outcomes. Fluorescent probes that report low fluidity structures in cells, such as molecular rotor dyes, are gaining popularity and overcome certain limitations of targeted markers such as tagged proteins or antibodies. However, permeability has been a key barrier preventing widespread use of such probes in whole organisms, including in C. elegans. To achieve visualization of non-specific protein aggregates in whole C. elegans nematodes without the need for strain engineering, we have developed a simple soaking protocol. Our protocol overcomes the permeability limitations of the commercially available molecular rotor dye, Proteostat, enabling widespread labeling of protein aggregates in live and post-fixed worms. Here, we present our novel approach and demonstrate quantification of protein aggregates in multiple tissues to detect differences in protein aggregation due to both genotype and treatment effects. We also outline anticipated future applications for this protocol, including its adaptation for other dyes, as well as limitations and special considerations for its use.

Environmental stress during early development can have lasting effects on reproduction and developmental plasticity in Caenorhabditis elegans. Here, we compared the consequences of two dauer-inducing stressors, high temperature and crowding, on fertility, dauer formation, and intergenerational gene expression. Entry into the dauer stage protected animals from stress-induced sterility, with high-temperatureinduced diapause (HID) providing strong preservation of reproductive capacity. Remarkably, the progeny of temperature-induced post-dauers (PD-temp) displayed a twofold increase in dauer formation upon re-exposure to heat, revealing a transient intergenerational enhancement of HID. This effect was stimulus-specific, as parental heat exposure suppressed pheromone-induced dauer formation in progeny, while parental pheromone exposure did not enhance HID. This increased dauer propensity was reset after a single stress-free generation. RNA-seq across three generations identified a transient F1-specific gene expression signature associated with enhanced dauer formation upon re-exposure to heat. Functional analyses showed that snpc-1.3, F49F1.7, and Y69A2AR.12 promote HID. In parallel, vit-3 expression was selectively reduced in F1 progeny of PD-temp animals, and vit-3 mutants exhibited increased dauer formation at 27{degrees}C, suggesting that vit-3 normally restrains HID. Consistent with previous work from our group implicating RNAi pathways in environmentally induced diapause and inherited stress responses, we find that endogenous RNAi pathways also modulate HID across generations. Multiple RNAi pathway components contributed to HID, while the nuclear RNAi factor nrde-2 was specifically required for the intergenerational increase in dauer formation. Tissue-specific rescue experiments further suggest that coordinated RNAi activity across tissues contributes differently to parental HID and progeny responses. Together, these findings identify HID as a distinct stress-induced developmental program that transiently modifies progeny responses to recurring thermal stress while preserving reproductive fitness. Our results further indicate that the physiological and intergenerational consequences of dauer entry depend on the environmental cue that induces diapause.

The field of microglial research has evolved throughout the years. Microglia, the immune cells of the central nervous system, are recognized as highly heterogeneous and dynamic cells, known for modifying their structure and function based on the local context. Investigating microglial density, spatial distribution, and morphological states is important for uncovering their distinct functional states in health and pathology. However, quantifying these features accurately is often a methodological challenge. Fully automated computational approaches often fail to capture subtle biological nuances and complex structural variations. Also, the high diversity of image sets makes it difficult to maintain consistent reliability. In contrast, entirely manual quantification is labor-intensive and prone to observer bias. To bridge this gap, we propose a semi-automated framework using the open-source software CellProfiler. Our workflow is divided into two distinct pipelines designed to combine automation of batch analysis with targeted user oversight, allowing for manual intervention when necessary to ensure maximum accuracy. Both pipelines are capable of recognizing microglial soma and tracing their processes. In the density workflow, it automatically calculates cell density and provides spatial distribution measurements, such as closest-neighbor distance and spacing index. For morphological profiling, it yields extensive structural data, including area, perimeter, convex area, form factor, and various shape descriptors. Furthermore, we demonstrate how researchers can optimize the pipeline settings to accommodate varied image datasets and experimental conditions. We hope this open-source framework can standardize microglial density, distribution, and morphological analysis and reduce systematic bias, providing researchers with a robust tool to better characterize their heterogeneity.