ST segment elevation myocardial infarction (STEMI) is associated with significant mortality leading to loss of productive life years, especially in younger patients. This study aims to compare the characteristics and outcomes of young versus older patients with STEMI undergoing primary percutaneous coronary intervention (PPCI) to help focus public health efforts in STEMI prevention. Data from the Coronary Care Unit database of the National University Hospital, Singapore from July 2015 to June 2019 were reviewed. Patients were divided into young (<50 years old) or older (≥50 years old) groups. Of the 1818 consecutive patients with STEMI who underwent PPCI, 465 (25.6%) were <50 years old. Young compared with older patients were more likely to be male, current smokers, of Indian ethnicity, have family history of ischaemic heart disease (IHD) and had lower 1 year mortality (3.4% vs 10.4%, p<0.0001). Although diabetes, hypertension or dyslipidaemia was less common among young patients, the prevalence of having any one of these risk factors was high in the range of 28% to 38%. Age was an independent predictor of mortality in the older but not younger patients with STEMI, and diabetes showed a trend towards mortality in both groups. Young patients with STEMI are more often smokers, of Indian ethnicity and had family history of IHD, although cardiometabolic risk factors are also prevalent. Mortality is lower, but not negligible, among the young patients with STEMI. Public health efforts are needed to reduce the prevalence of these risk factors among the constitutionally susceptible population.

Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death¹. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood². To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing-such as senescence³, genomic instability⁴ and changes in the immune system². This transcriptomic atlas-which we denote Tabula Muris Senis, or 'Mouse Ageing Cell Atlas'-provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.

Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.