Imaging Flow Cytometry Quantifies Neural Genome Dynamics.

Somatic mosaicism is a common consequence of normal development. DNA repair is simply not perfect, and each cell's genome incurs continuous DNA damage as a consequence of transcription, replication, and other cell biological stressors. Brain somatic mosaicism is particularly noteworthy because the vast majority of an individual's neurons are with that individual for life and neural circuits give rise directly to behavioral phenotypes. Brain somatic mosaicism, now revealed and tractable due to advances in single cell 'omic approaches, has emerged as an intriguing and unexplored aspect of neuronal diversity. Furthermore, the study of DNA damage during early neurodevelopment, when the rate of mutagenesis is high, is the perfect starting point to understand the origins of brain mosaicism. Flow cytometry is a highly efficient technique to study cell cycle and intracellular proteins of interest, particularly those related to DNA damage, but it lacks the high resolution of microscopy to examine the localization of these proteins. In this study, we outline a novel single-cell approach to quantify DNA double-strand break (DNA DSB) dynamics during early human neurodevelopment by applying imaging flow cytometry (IFC) to human-induced pluripotent stem cell-derived neural progenitor cells (NPCs) undergoing neurogenesis. We establish an increase of DNA DSBs by quantifying γH2AX foci in mildly stressed NPCs using various single-cell approaches in addition to IFC including fluorescent microscopy, conventional flow cytometry, and measuring DNA DSBs with the comet assay. We demonstrate the dose-dependent sensitive detection of γH2AX foci through IFC and reveal the dynamics of DNA DSBs in proliferating and differentiating neural cells in early neurogenesis. © 2019 International Society for Advancement of Cytometry.

Multiple chronic conditions at a major urban health system: a retrospective cross-sectional analysis of frequencies, costs and comorbidity patterns.

OBJECTIVE: To (1) examine the burden of multiple chronic conditions (MCC) in an urban health system, and (2) propose a methodology to identify subpopulations of interest based on diagnosis groups and costs. DESIGN: Retrospective cross-sectional study. SETTING: Mount Sinai Health System, set in all five boroughs of New York City, USA. PARTICIPANTS: 192 085 adult (18+) plan members of capitated Medicaid contracts between the Healthfirst managed care organisation and the Mount Sinai Health System in the years 2012 to 2014. METHODS: We classified adults as having 0, 1, 2, 3, 4 or 5+ chronic conditions from a list of 69 chronic conditions. After summarising the demographics, geography and prevalence of MCC within this population, we then described groups of patients (segments) using a novel methodology: we combinatorially defined 18 768 potential segments of patients by a pair of chronic conditions, a sex and an age group, and then ranked segments by (1) frequency, (2) cost and (3) ratios of observed to expected frequencies of co-occurring chronic conditions. We then compiled pairs of conditions that occur more frequently together than otherwise expected. RESULTS: 61.5% of the study population suffers from two or more chronic conditions. The most frequent dyad was hypertension and hyperlipidaemia (19%) and the most frequent triad was diabetes, hypertension and hyperlipidaemia (10%). Women aged 50 to 65 with hypertension and hyperlipidaemia were the leading cost segment in the study population. Costs and prevalence of MCC increase with number of conditions and age. The disease dyads associated with the largest observed/expected ratios were pulmonary disease and myocardial infarction. Inter-borough range MCC prevalence was 16%. CONCLUSIONS: In this low-income, urban population, MCC is more prevalent (61%) than nationally (42%), motivating further research and intervention in this population. By identifying potential target populations in an interpretable manner, this segmenting methodology has utility for health services analysts.