Machine learning-based morphological quantification of replicative senescence in human fibroblasts.

Although aging has been investigated extensively at the organismal and cellular level, the morphological changes that individual cells undergo along their replicative lifespan have not been precisely quantified. Here, we present the results of a readily accessible machine learning-based pipeline that uses standard fluorescence microscope and open access software to quantify the minute morphological changes that human fibroblasts undergo during their replicative lifespan in culture. Applying this pipeline in a widely used fibroblast cell line (IMR-90), we find that advanced replicative age robustly increases (+28-79%) cell surface area, perimeter, number and total length of pseudopodia, and nuclear surface area, while decreasing cell circularity, with phenotypic changes largely occurring as replicative senescence is reached. These senescence-related morphological changes are recapitulated, albeit to a variable extent, in primary dermal fibroblasts derived from human donors of different ancestry, age, and sex groups. By performing integrative analysis of single-cell morphology, our pipeline further classifies senescent-like cells and quantifies how their numbers increase with replicative senescence in IMR-90 cells and in dermal fibroblasts across all tested donors. These findings provide quantitative insights into replicative senescence, while demonstrating applicability of a readily accessible computational pipeline for high-throughput cell phenotyping in aging research.

BET protein inhibition promotes non-myeloid cell mediated neuroprotection after rodent spinal cord contusion.

Spinal cord injuries (SCI) often lead to multiple neurological deficits as a result from the initial trauma and also the secondary damage that follows. Despite abundant preclinical data proposing anti-inflammatory therapies to minimize secondary injury and improve functional recovery, the field still lacks an effective neuroprotective treatment. Epigenetic proteins, such as bromodomain and extraterminal domain (BET) proteins, are emerging as new targets to regulate inflammation. More importantly, pharmacological inhibition of BET proteins suppresses pro-inflammatory gene transcription after SCI. In this study, we tested the therapeutic potential of inhibiting BET proteins after SCI with clinically relevant compounds, and investigated the role of the BET protein BRD4 in macrophages during progression of SCI pathology. Systemic inhibition of BET proteins with I-BET762 significantly reduced lesion size 8 weeks after a contusion injury in rats. However, we observed no histological or locomotor improvements after SCI when we deleted Brd4 in macrophages through the use of myeloid-specific Brd4 knockout mice or after macrophage-targeted pharmacological BET inhibition. Taken together, our data indicate that systemic I-BET762 treatment is neuroprotective, and the histopathological improvement observed is likely to be a result of effects on non-macrophage targets. Expanding our understanding on the role of BET proteins after SCI is necessary to identify novel therapeutic targets that can effectively promote repair after SCI.

Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord.

The wound healing process that occurs after spinal cord injury is critical for maintaining tissue homeostasis and limiting tissue damage, but eventually results in a scar-like environment that is not conducive to regeneration and repair. A better understanding of this dichotomy is critical to developing effective therapeutics that target the appropriate pathobiology, but a major challenge has been the large cellular heterogeneity that results in immensely complex cellular interactions. In this study, we used single-cell RNA sequencing to assess virtually all cell types that comprise the mouse spinal cord injury site. In addition to discovering novel subpopulations, we used expression values of receptor-ligand pairs to identify signaling pathways that are predicted to regulate specific cellular interactions during angiogenesis, gliosis, and fibrosis. Our dataset is a valuable resource that provides novel mechanistic insight into the pathobiology of not only spinal cord injury but also other traumatic disorders of the CNS.