quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data.

The volume of public proteomics data is rapidly increasing, causing a computational challenge for large-scale reanalysis. Here, we introduce quantms ( https://quant,ms.org/ ), an open-source cloud-based pipeline for massively parallel proteomics data analysis. We used quantms to reanalyze 83 public ProteomeXchange datasets, comprising 29,354 instrument files from 13,132 human samples, to quantify 16,599 proteins based on 1.03 million unique peptides. quantms is based on standard file formats improving the reproducibility, submission and dissemination of the data to ProteomeXchange.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis.

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.

Knockdown of FOXA1 enhances the osteogenic differentiation of human bone marrow mesenchymal stem cells partly via activation of the ERK1/2 signalling pathway.

BACKGROUND: The available therapeutic options for large bone defects remain extremely limited, requiring new strategies to accelerate bone healing. Genetically modified bone mesenchymal stem cells (BMSCs) with enhanced osteogenic capacity are recognised as one of the most promising treatments for bone defects. METHODS: We performed differential expression analysis of miRNAs between human BMSCs (hBMSCs) and human dental pulp stem cells (hDPSCs) to identify osteogenic differentiation-related microRNAs (miRNAs). Furthermore, we identified shared osteogenic differentiation-related miRNAs and constructed an miRNA-transcription network. The Forkhead box protein A1 (FOXA1) knockdown strategy with a lentiviral vector was used to explore the role of FOXA1 in the osteogenic differentiation of MSCs. Cell Counting Kit-8 was used to determine the effect of the knockdown of FOXA1 on hBMSC proliferation; real-time quantitative reverse transcription PCR (qRT-PCR) and western blotting were used to investigate target genes and proteins; and alkaline phosphatase (ALP) staining and Alizarin Red staining (ARS) were used to assess ALP activity and mineral deposition, respectively. Finally, a mouse model of femoral defects was established in vivo, and histological evaluation and radiographic analysis were performed to verify the therapeutic effects of FOXA1 knockdown on bone healing. RESULTS: We identified 22 shared and differentially expressed miRNAs between hDPSC and hBMSC, 19 of which were downregulated in osteogenically induced samples. The miRNA-transcription factor interaction network showed that FOXA1 is the most significant and novel osteogenic differentiation biomarker among more than 300 transcription factors that is directly targeted by 12 miRNAs. FOXA1 knockdown significantly promoted hBMSC osteo-specific genes and increased mineral deposits in vitro. In addition, p-ERK1/2 levels were upregulated by FOXA1 silencing. Moreover, the increased osteogenic differentiation of FOXA1 knockdown hBMSCs was partially rescued by the addition of ERK1/2 signalling inhibitors. In a mouse model of femoral defects, a sheet of FOXA1-silencing BMSCs improved bone healing, as detected by microcomputed tomography and histological evaluation. CONCLUSION: These findings collectively demonstrate that FOXA1 silencing promotes the osteogenic differentiation of BMSCs via the ERK1/2 signalling pathway, and silencing FOXA1 in vivo effectively promotes bone healing, suggesting that FOXA1 may be a novel target for bone healing.

Aucubin promotes bone-fracture healing via the dual effects of anti-oxidative damage and enhancing osteoblastogenesis of hBM-MSCs.

BACKGROUND: Aucubin (AU), an iridoid glucoside isolated from many traditional herbal medicines, has anti-osteoporosis and anti-apoptosis bioactivities. However, the effect of AU on the treatment of bone-fracture remains unknown. In the present study, the aims were to investigate the roles and mechanisms of AU not only on osteoblastogenesis of human bone marrow-derived mesenchymal stromal cells (hBM-MSCs) and anti-oxidative stress injury in vitro, but also on bone-fracture regeneration by a rat tibial fracture model in vivo. METHODS: CCK-8 assay was used to assess the effect of AU on the viability and proliferation of hBM-MSCs. The expression of specific genes and proteins on osteogenesis, apoptosis and signaling pathways was measured by qRT-PCR, western blotting and immunofluorescence analysis. ALP staining and quantitative analysis were performed to evaluate ALP activity. ARS and quantitative analysis were performed to evaluate calcium deposition. DCFH-DA staining was used to assess the level of reactive oxygen species (ROS). A rat tibial fracture model was established to validate the therapeutic effect of AU in vivo. Micro-CT with quantitative analysis and histological evaluation were used to assess the therapeutic effect of AU locally injection at the fracture site. RESULTS: Our results revealed that AU did not affect the viability and proliferation of hBM-MSCs. Compared with control group, western blotting, PCR, ALP activity and calcium deposition proved that AU-treated groups promoted osteogenesis of hBM-MSCs. The ratio of phospho-Smad1/5/9 to total Smad also significantly increased after treatment of AU. AU-induced expression of BMP2 signaling target genes BMP2 and p-Smad1/5/9 as well as of osteogenic markers COL1A1 and RUNX2 was downregulated after treating with noggin and LDN193189. Furthermore, AU promoted the translocation of Nrf2 from cytoplasm to nucleus and the expression level of HO1 and NQO1 after oxidative damage. In a rat tibial fracture model, local injection of AU promoted bone regeneration. CONCLUSIONS: Our study demonstrates the dual effects of AU in not only promoting bone-fracture healing by regulating osteogenesis of hBM-MSCs partly via canonical BMP2/Smads signaling pathway but also suppressing oxidative stress damage partly via Nrf2/HO1 signaling pathway.

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

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.