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.
Liquid biopsies that measure circulating cell-free RNA (cfRNA) offer an opportunity to study the development of pregnancy-related complications in a non-invasive manner and to bridge gaps in clinical care1–4. Here we used 404 blood samples from 199 pregnant mothers to identify and validate cfRNA transcriptomic changes that are associated with preeclampsia, a multi-organ syndrome that is the second largest cause of maternal death globally5. We find that changes in cfRNA gene expression between normotensive and preeclamptic mothers are marked and stable early in gestation, well before the onset of symptoms. These changes are enriched for genes specific to neuromuscular, endothelial and immune cell types and tissues that reflect key aspects of preeclampsia physiology6–9, suggest new hypotheses for disease progression and correlate with maternal organ health. This enabled the identification and independent validation of a panel of 18 genes that when measured between 5 and 16 weeks of gestation can form the basis of a liquid biopsy test that would identify mothers at risk of preeclampsia long before clinical symptoms manifest themselves. Tests based on these observations could help predict and manage who is at risk for preeclampsia—an important objective for obstetric care10,11.
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.
Indolethylamine-N-methyltransferase (INMT) is a Class 1 transmethylation enzyme known for its production of N,N-dimethyltryptamine (DMT), a hallucinogen with affinity for various serotonergic, adrenergic, histaminergic, dopaminergic, and sigma-1 receptors. DMT is produced via the action of INMT on the endogenous substrates tryptamine and S-adenosyl-l-methionine (SAM). The biological, biochemical, and selective small molecule regulation of INMT enzyme activity remain largely unknown. Kinetic mechanisms for inhibition of rabbit lung INMT (rabINMT) by the product, DMT, and by a new novel tryptamine derivative were determined. After Michaelis–Menten and Lineweaver–Burk analyses had been applied to study inhibition, DMT was found to be a mixed competitive and noncompetitive inhibitor when measured against tryptamine. The novel tryptamine derivative, N-[2-(1H-indol-3-yl)ethyl]-N′,N′-dimethylpropane-1,3-diamine (propyl dimethyl amino tryptamine or PDAT), was shown to inhibit rabINMT by a pure noncompetitive mechanism when measured against tryptamine with a Ki of 84 μM. No inhibition by PDAT was observed at 2 mM when it was tested against structurally similar Class 1 methyltransferases, such as human phenylethanolamine-N-methyltransferase (hPNMT) and human nicotinamide-N-methyltransferase (hNNMT), indicating selectivity for INMT. The demonstration of noncompetitive mechanisms for INMT inhibition implies the presence of an inhibitory allosteric site. In silico analyses using the computer modeling software Autodock and the rabINMT sequence threaded onto the human INMT (hINMT) structure (Protein Data Bank entry 2A14) identified an N-terminal helix–loop–helix non-active site binding region of the enzyme. The energies for binding of DMT and PDAT to this region of rabINMT, as determined by Autodock, were −6.34 and −7.58 kcal/mol, respectively. Assessment of the allosteric control of INMT may illuminate new biochemical pathway(s) underlying the biology of INMT.
Single cell RNA sequencing (scRNA-seq) enables detailed examination of a cell's underlying regulatory networks and the molecular factors contributing to its identity. We developed scRFE (single-cell identity definition using random forests and recursive feature elimination, pronounced 'surf') with the goal of easily generating interpretable gene lists that can accurately distinguish observations (single-cells) by their features(genes) given a class of interest. scRFE is an algorithm implemented as a Python package that combines the classical random forest method with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. The package is compatible with Scanpy, enabling a seamless integration into any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset. We applied scRFE to the Tabula Muris Senis and reproduced commonly known aging patterns and transcription factor reprogramming protocols, highlighting the biological value of scRFE's learned features.
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