Dataset Information

Single-cell RNA sequencing of zebrafish beta-cells from various stages

ABSTRACT: Age-associated deterioration of cellular physiology leads to pathological conditions, and detection of premature aging could provide a window for preventive therapies against age-related diseases. For this, methods that accurately evaluate cellular age are required. However, such techniques are currently limited and based on post hoc evaluation using a limited set of histological markers. Development of a technique capable of predicting cellular age, and its modifiers, requires a framework that can robustly handle the noise associated with single-cell sampling protocols. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displayed greater than 90% accuracy in predicting the chronological stage of zebrafish beta-cells and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS enabled the evaluation of differences in calorie intake and body mass index on the aging of zebrafish and human cells, respectively. We further harnessed the predictive power of GERAS to identify genome-wide molecular factors that correlate with aging. We show that one of these factors, junb, which declines in expression with aging, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to predict the chronological stage of heterogeneous cell populations. The study demonstrates the utility of stage classifiers in assessing pro-aging factors, and uncovering candidate genes associated with premature aging.

ORGANISM(S): Danio rerio

PROVIDER: GSE109881 | GEO | 2018/08/11

REPOSITORIES: GEO

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Project description:Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of bio-active peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools towards improved identification of bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, we identified 48 sORF-encoded peptides originating from presumed non-coding locations, next to identifying 66 known neuropeptides from within 22 different families. Altogether, this robust pipeline fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.

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Single-cell RNA sequencing of zebrafish beta-cells from various stages

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