A team at Johns Hopkins University employs high-content imaging paired with machine learning to categorize three unique fibroblast senescence subtypes, revealing differential drug responses. This classification paves the way for tailored senolytic interventions that selectively clear pro-inflammatory cells in aging skin.

Key points

• Identification of three distinct senescent fibroblast subtypes via automated imaging
• Use of 87 morphological parameters and machine learning classification
• High prevalence of the C10 subtype in donors over 50 years old
• Differential drug responses across subtypes inform targeted senolytic design
• Data derived from fibroblasts in the Baltimore Longitudinal Study samples

Why it matters: This finding reveals heterogeneity within senescent cell populations, enabling precision targeting of disease-driving subtypes and reducing off-target effects. By moving beyond broad-spectrum senolytic approaches, it offers a transformative strategy for personalized anti-aging interventions and improved patient outcomes.