A MATLAB-based program for three-dimensional quantitative analysis of micronuclei reveals that neuroinflammation induces micronuclei formation in the brain

Abstract

The micronucleus is known to be a biomarker for genomic instability, which is a hallmark of tumors and aging. Normally, micronuclei are produced by segregation errors and mechanical stresses arising from dividing or migrating cells, leading to activation of the innate immune response pathway. Although micronuclei often emerge in damaged tissues, the quantitative procedure for analyzing micronuclei accurately has been problematic. Here, we introduce a novel MATLAB-based program for quantifying micronuclei (CAMDi: calculating automatic micronuclei distinction) in vitro and in vivo. CAMDi is adaptable to various experimental imaging techniques and is useful for obtaining reproducible data. CAMDi enables us to measure the accurate size of micronuclei from the three-dimensional images. Using CAMDi, we revealed a novel link between the emergence of micronuclei and neuroinflammation. We found that inflammatory stimulation does not increase the number of micronuclei in primary neurons. On the other hand, the administration of lipopolysaccharide into mice slightly increases micronuclei formation in neurons of the hippocampus region. These findings demonstrate that neuronal micronuclei formations are induced by an inflammatory response in a non-cell-autonomous manner. We provide a novel tool, CAMDi, to quantify micronuclei and demonstrate that neuronal micronuclei are produced not only by the cell-autonomous process but also by the intercellular communication associated with neuroinflammation in vivo.

Propagation of neuronal micronuclei regulates microglial states

“…Neurons in the cerebral cortex are generated from NPCs in the ventricular and subventricular zone during the embryonic period and migrate to the brain surface. Previously, we observed that MNs exist in the brain of 2-month-old mice 39 . To examine whether MNs also appear in the developing brain, we analyzed the prenatal brain on embryonic day (E) 18.5.…”

“…FIJI ImageJ software (NIH) and ZEN (Carl Zeiss) are used for converting each Z-stack image (interval; 1 μm per image, a total thickness of images staining with MAP2; 10 to 15 μm, a total thickness of images staining with Iba1; 30 to 50 μm) to a single TIFF file. The data were imported into the MATLAB-based program CAMDi for quantifying MNs 39 . Briefly, the rate of overlap area of the nucleus is defined as 0.5.…”

Telomere length and micronuclei trajectories in APP/PS1 mouse model of Alzheimer's disease: Correlating with cognitive impairment and brain amyloidosis in a sexually dimorphic manner

“…Although bone marrow is widely used, because it can be easily obtained, whether it is a proxy for many other tissue types for MN assessment is poorly understood. A recently developed tool, called MATLAB‐based program for quantifying MN, would provide opportunity to quantitatively and accurately analyze MN in brain tissues of AD mice models (Yano et al., 2021 ). Interestingly, a recent study developed a fluorescence probe that is able to specifically target MN (Ding et al., 2023 ).…”

micronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability

“…The complexity of the task is further exacerbated by the unavailability of a standard protocol leading to inter-observer variability while counting. Additionally, density of nuclei in each image and resulting crowding makes it challenging to confidently separate and accurately assign MN events to nuclei for both humans and automated methods 26 . Thus, improved methods for automated CIN quantification are necessary to increase speed, accuracy and robustness of CIN-related research.…”

“…Here, we present micronuclAI, a novel deep-learning-based pipeline for assessment of CIN through automated quantification of MN from nuclei-stained images. micronuclAI distinguishes itself over previous methods [26][27][28][29][30][31][32] as it 1) can quantify for both MN and NBUDs, 2) requires only nuclear staining, 3) is able to work with 10x to 100x image objectives, 4) can work with any segmentation mask, and most importantly 5) is extensively evaluated in multiple cell lines and thus, ready for use by the community (Fig. 1 and Table 1).…”