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Biology subjects

Jayaram, S.

Publications and source records attributed to Jayaram, S..

2 recordsLinked to original sources

The asynchrony in the exit from naive pluripotency cannot be explained by differences in the cell cycle phase

Development is characterized by consecutive cell state transitions that build on each other and ultimately lead to the generation of the numerous different cell types found in the organism. During each of these transitions, cells change their gene expression profiles and take on new identities. Cell state transitions have to be tightly coordinated with proliferation to ensure simultaneous growth and differentiation. The exit from naive pluripotency is an ideal model system for studying the temporal coordination of proliferation and differentiation. Individual cells initiate differentiation earlier compared to others, thereby leading to an asynchronous exit from naive pluripotency. One of the major differences among the cells of the starting population of mouse embryonic stem cells (mESCs) is the cell cycle status, and could therefore be an underlying cause of the differences in the onset of the exit from naive pluripotency. However, through comprehensive analysis including single cell RNA sequencing (scRNA-seq), cell cycle synchronization, and perturbation experiments, we demonstrate here that the cell cycle phase at the initiation of differentiation does not influence the timing of the exit from naive pluripotency.

developmental biology↗

A proteomics sample metadata representation for multiomics integration, and big data analysis.

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGETAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

bioinformatics↗