PT  - JOURNAL ARTICLE
AU  - Sumeet Pal Singh
AU  - Sharan Janjuha
AU  - Samata Chaudhuri
AU  - Susanne Reinhardt
AU  - Sevina Dietz
AU  - Anne Eugster
AU  - Halil Bilgin
AU  - Selçuk Korkmaz
AU  - John E. Reid
AU  - Gökmen Zararsiz
AU  - Nikolay Ninov
TI  - Machine learning based classification of cells into chronological stages using single-cell transcriptomics
AID  - 10.1101/303214
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 303214
4099  - http://biorxiv.org/content/early/2018/04/17/303214.short
4100  - http://biorxiv.org/content/early/2018/04/17/303214.full
AB  - Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their trans criptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the predictive power of GERAS to identify genome-wide molecular factors that correlate with aging. We show that one of these factors, junb, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling to detect pro-aging factors and candidate genes associated with aging.