Cell Tree Age as a new evolutionary model for representing age-associated somatic mutation burden

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological age. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of ‘Cell Tree Age’, it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials. Encoding accumulating somatic mutation burden in humans via evolutionary Cell Trees and representing the overall snapshot somatic mutation burden numerically as a Cell Tree Age provides a new model to summarize the most fundamental hallmark of aging, using perhaps the most well established quantitative methodology in biology, phylogenetics

The Mental Landscape of Imagining Life Beyond the Current Life Span : Implications for Construal and Self-Continuity

© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. Funding Funding for Mechanical Turk Participants was provided by Seattle Pacific University School of Psychology, Family and Community. Acknowledgments Life-extension supporters were recruited by A. Csordas at the Undoing Aging conference held in Berlin, Germany, on March 15–17, 2018. No incentives were offered for participation. Data from the present manuscript were not published in any form prior to the submission of the manuscript for publication.Peer reviewedPublisher PD

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.publishedVersio

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data

PRIDE Inspector: a tool to visualize and validate MS proteomics data

PRIDE Inspector thus provides a user-friendly, comprehensive tool for the browsing, inspection, and evaluation of data in the PRIDE database, or in a compatible standard file format. As such, we believe that PRIDE Inspector will substantially increase the ability of researchers, editors and peer-reviewers to explore, review, evaluate, and reuse proteomics data.This work was supported by the Wellcome Trust (grant number WT085949MA) and EMBL core funding. R.G.C. is supported by EU FP7 grant SLING (grant number 226073). J.A.V. is supported by the EU FP7 grants LipidomicNet (grant number 202272) and ProteomeXchange (grant number 260558). A.F. was partially supported by the Spanish network COMBIOMED (RD07/0067/0006, ISCIII-FIS). L.M. would like to acknowledge support from the EU FP7 PRIME-XS grant (grant number 262067)

The PRoteomics IDEntification (PRIDE) Converter 2 Framework: An Improved Suite of Tools to Facilitate Data Submission to the PRIDE Database and the ProteomeXchange Consortium

The original PRIDE Converter tool greatly simplified the process of submitting mass spectrometry (MS)-based proteomics data to the PRIDE database. However, after much user feedback, it was noted that the tool had some limitations and could not handle several user requirements that were now becoming commonplace. This prompted us to design and implement a whole new suite of tools that would build on the successes of the original PRIDE Converter and allow users to generate submission-ready, well-annotated PRIDE XML files. The PRIDE Converter 2 tool suite allows users to convert search result files into PRIDE XML (the format needed for performing submissions to the PRIDE database), generate mzTab skeleton files that can be used as a basis to submit quantitative and gel-based MS data, and post-process PRIDE XML files by filtering out contaminants and empty spectra, or by merging several PRIDE XML files together. All the tools have both a graphical user interface that provides a dialog-based, user-friendly way to convert and prepare files for submission, as well as a command-line interface that can be used to integrate the tools into existing or novel pipelines, for batch processing and power users. The PRIDE Converter 2 tool suite will thus become a cornerstone in the submission process to PRIDE and, by extension, to the ProteomeXchange consortium of MS-proteomics data repositories.publishedVersio

Intact mitochondria migrate in membrane tubular network connections formed between human stem cells

The hypothesis of mitochondrial transfer between eukaryotic animal cells is intriguing, although its route of action and physiological role is unknown. Our goal was to examine intercellular connections among several types of stem cells and to observe whether intact functional mitochondria may travel via these connections. Time-lapse laser scanning confocal microscopy has shown that human amnion-derived stem cells as well as bone marrow derived mouse and human mesenchymal stem cells form cell-to-cell connections via a tubular membrane network. The maximal length of these micrometer-thick tubes is around 180 ?m. Interestingly, freshly isolated amniotic epithelial stem cells did not form these connections, only after several passages when the morphology of the cells is significantly altered. Large area cell-cell contacts can be retained as long thin membrane bridges after the cells depart and de novo tube formation is also observed. Using MitoTracker red staining we observed that intact mitochondria are moving in these tubes by 20 – 60 nm/s velocity, suggesting that mitochondria can leave one cell via the membrane tubes and can enter into another cell. These results suggest that specific types of stem cells form comprehensive tubular networks among each other. One physiological role of these networks may be that mitochondria can migrate from one cell to the other, which may be a novel way of communication among stem cells

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data