p53 is a central regulator driving neurodegeneration caused by C9orf72 poly(PR).

The most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is a GGGGCC repeat expansion in the C9orf72 gene. We developed a platform to interrogate the chromatin accessibility landscape and transcriptional program within neurons during degeneration. We provide evidence that neurons expressing the dipeptide repeat protein poly(proline-arginine), translated from the C9orf72 repeat expansion, activate a highly specific transcriptional program, exemplified by a single transcription factor, p53. Ablating p53 in mice completely rescued neurons from degeneration and markedly increased survival in a C9orf72 mouse model. p53 reduction also rescued axonal degeneration caused by poly(glycine-arginine), increased survival of C9orf72 ALS/FTD-patient-induced pluripotent stem cell (iPSC)-derived motor neurons, and mitigated neurodegeneration in a C9orf72 fly model. We show that p53 activates a downstream transcriptional program, including Puma, which drives neurodegeneration. These data demonstrate a neurodegenerative mechanism dynamically regulated through transcription-factor-binding events and provide a framework to apply chromatin accessibility and transcription program profiles to neurodegeneration.

C9orf72 gene networks in the human brain correlate with cortical thickness in C9-FTD and implicate vulnerable cell types.

Introduction: A hexanucleotide repeat expansion (HRE) intronic to chromosome 9 open reading frame 72 (C9orf72) is recognized as the most common genetic cause of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and ALS-FTD. Identifying genes that show similar regional co-expression patterns to C9orf72 may help identify novel gene targets and biological mechanisms that mediate selective vulnerability to ALS and FTD pathogenesis. Methods: We leveraged mRNA expression data in healthy brain from the Allen Human Brain Atlas to evaluate C9orf72 co-expression patterns. To do this, we correlated average C9orf72 expression values in 51 regions across different anatomical divisions (cortex, subcortex, and cerebellum) with average gene expression values for 15,633 protein-coding genes, including 54 genes known to be associated with ALS, FTD, or ALS-FTD. We then performed imaging transcriptomic analyses to evaluate whether the identified C9orf72 co-expressed genes correlated with patterns of cortical thickness in symptomatic C9orf72 pathogenic HRE carriers (n = 19) compared to controls (n = 23). Lastly, we explored whether genes with significant C9orf72 imaging transcriptomic correlations (i.e., "C9orf72 imaging transcriptomic network") were enriched in specific cell populations in the brain and enriched for specific biological and molecular pathways. Results: A total of 2,120 genes showed an anatomical distribution of gene expression in the brain similar to C9orf72 and significantly correlated with patterns of cortical thickness in C9orf72 HRE carriers. This C9orf72 imaging transcriptomic network was differentially expressed in cell populations previously implicated in ALS and FTD, including layer 5b cells, cholinergic neurons in the spinal cord and brainstem and medium spiny neurons of the striatum, and was enriched for biological and molecular pathways associated with protein ubiquitination, autophagy, cellular response to DNA damage, endoplasmic reticulum to Golgi vesicle-mediated transport, among others. Conclusion: Considered together, we identified a network of C9orf72 associated genes that may influence selective regional and cell-type-specific vulnerabilities in ALS/FTD.

Moving Toward Patient-Tailored Treatment in ALS and FTD: The Potential of Genomic Assessment as a Tool for Biological Discovery and Trial Recruitment.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two devastating and intertwined neurodegenerative diseases. Historically, ALS and FTD were considered distinct disorders given differences in presenting clinical symptoms, disease duration, and predicted risk of developing each disease. However, research over recent years has highlighted the considerable clinical, pathological, and genetic overlap of ALS and FTD, and these two syndromes are now thought to represent different manifestations of the same neuropathological disease spectrum. In this review, we discuss the need to shift our focus from studying ALS and FTD in isolation to identifying the biological mechanisms that drive these diseases-both common and distinct-to improve treatment discovery and therapeutic development success. We also emphasize the importance of genomic data to facilitate a "precision medicine" approach for treating ALS and FTD.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

BACKGROUND: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. NEW METHOD: Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. RESULTS: Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. COMPARISON WITH EXISTING METHODS: We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. CONCLUSION: The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks.