NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health

Abstract

Cells respond to many stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a proinflammatory senescence-associated secretory phenotype. The heterogeneity of senescent cells (SnCs) and senescence-associated secretory phenotype are vast, yet ill characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and their detection a priority. The Cellular Senescence Network (SenNet), a National Institutes of Health Common Fund initiative, was established to address this need. The goal of SenNet is to map SnCs across the human lifespan to advance diagnostic and therapeutic approaches to improve human health. State-of-the-art methods will be applied to identify, define and map SnCs in 18 human tissues. A common coordinate framework will integrate data to create four-dimensional SnC atlases. Other key SenNet deliverables include innovative tools and technologies to detect SnCs, new SnC biomarkers and extensive public multi-omics datasets. This Perspective lays out the impetus, goals, approaches and products of SenNet.

Fig. 1 |. Geographic distribution of 2021 SenNet awards focused on mapping SnCs in human tissues.

TMCs, RFA-RM-21–008 U54; TDA sites, RFA-RM-21–009 UG3/UH3; CODCC, RFA-RM-21–010 U24. Bold font identifies institutes of contact principal investigators.

Fig. 2 |. Organs in which SnCs will be mapped by SenNet.

Human tissues in which SnCs will be identified and characterized by the SenNet Consortium to produce 4D atlases of senescence across the lifespan of humans.

Fig. 3 |. Overview of technologies that will be implemented and developed by SenNet Consortium scientists to detect, characterize and spatially map the location of SnCs.

CyTOF, cytometry by time-of-flight; scCITE-seq, cellular indexing of transcriptomes and epitopes by sequencing; sc/snRNA-seq, single-cell or single-nucleus RNA sequencing; snATAC-seq, single-nucleus assay for transposase-accessible chromatin using sequencing; MINA, multiplexed imaging of nucleome architectures; IMC, imaging mass cytometry; CODEX, co-detection by indexing immunofluorescence; DBiT-seq, deterministic barcoding in tissue for spatial-omics sequencing for co-mapping mRNAs and proteins; RNAScope, RNA in situ hybridization visualization of single molecules; MERFISH, multiplexed error-robust fluorescence in situ hybridization; GeoMx, NanoString GeoMx digital spatial profiling; Visium, Visium 10x Genomics molecular profiling; Seq-Scope, a spatial barcoding technology with spatial resolution comparable to optical microscopy; Pixel-seq, polony-indexed library sequencing.

Fig. 4 |. Overview of the visualization tools to be developed to enable exploration of the senescent cell atlases produced by SenNet.

CCF exploration user interface (EUI) and Vitessce (a visual integration tool for exploring spatial single-cell datasets) will be integrated to enable seamless navigation across scales and queries of SenNet data. The CCF EUI enables registered tissue blocks from the registration user interface (RUI) to be explored spatially (via body browser in the left screenshot, center) and using ontology terms (via hierarchy in the left screenshot, on left) at anatomic scale. A click on a tissue dataset (left) leads to Vitessce (right), which supports the exploration of cellular and molecular scale distributions. EUI provides clinical and spatial context and ontology cross-links, while Vitessce supports details on demand at the molecular scale.

Fig. 5 |. Schematic of the SenNet Consortium goals.

SnC atlas building requires a framework for layering data. Data generated by the TMCs and TDA sites are input into the CODCC along with associated metadata. The datasets are organized and de-identified (curation), then analyzed and integrated. The goal is to create an atlas and public database of curated data that can be searched, analyzed and visualized as 3D images of organs using unified annotations. High-quality experimental data are needed to create a human reference atlas. The evolving reference atlas supports data standardization and federation, making it possible to integrate data from different specimens, laboratories and assay types. The atlas characterizes the healthy human—from the whole body down to the single-cell level; it can be compared across ages and diseases to understand differences, advance research and improve human health. Use case scenarios for different stakeholders (researchers, practitioners and students) guide atlas construction and usage but also experimental data acquisition and analysis. Of note, diversity in terms of human participant gender, race and socioeconomic status is emphasized in SenNet. However, these variables may impact SnC heterogeneity even further, meaning that, in the timeframe of the initial grants, statistically meaningful characterization of SnCs across diverse populations might not be achieved.