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Characterization of cell-free breast cancer patient-derived scaffolds using liquid chromatography-mass spectrometry/mass spectrometry data and RNA sequencing data.

ABSTRACT: Patient-derived scaffolds (PDSs) generated from primary breast cancer tumors can be used to model the tumor microenvironment in vitro. Patient-derived scaffolds are generated by repeated detergent washing, removing all cells. Here, we analyzed the protein composition of 15 decellularized PDSs using liquid chromatography-mass spectrometry/mass spectrometry. One hundred forty-three proteins were detected and their relative abundance was calculated using a reference sample generated from all PDSs. We performed heatmap analysis of all the detected proteins to display their expression patterns across different PDSs together with pathway enrichment analysis to reveal which processes that were connected to PDS protein composition. This protein dataset together with clinical information is useful to investigators studying the microenvironment of breast cancers. Further, after repopulating PDSs with either MCF7 or MDA-MB-231 cells, we quantified their gene expression profiles using RNA sequencing. These data were also compared to cells cultured in conventional 2D conditions, as well as to cells cultured as xenografts in immune-deficient mice. We investigated the overlap of genes regulated between these different culture conditions and performed pathway enrichment analysis of genes regulated by both PDS and xenograft cultures compared to 2D in both cell lines to describe common processes associated with both culture conditions. Apart from our described analyses of these systems, these data are useful when comparing different experimental model systems. Downstream data analyses and interpretations can be found in the research article "Patient-derived scaffolds uncover breast cancer promoting properties of the microenvironment" [1].

SUBMITTER: Landberg G

PROVIDER: S-EPMC7327418 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Characterization of cell-free breast cancer patient-derived scaffolds using liquid chromatography-mass spectrometry/mass spectrometry data and RNA sequencing data.

Landberg Göran G Jonasson Emma E Gustafsson Anna A Fitzpatrick Paul P Isakson Pauline P Karlsson Joakim J Larsson Erik E Svanström Andreas A Rafnsdottir Svanheidur S Persson Emma E Andersson Daniel D Rosendahl Jennifer J Petronis Sarunas S Ranji Parmida P Gregersson Pernilla P Magnusson Ylva Y Håkansson Joakim J Ståhlberg Anders A

Data in brief 20200616

Patient-derived scaffolds (PDSs) generated from primary breast cancer tumors can be used to model the tumor microenvironment <i>in vitro</i>. Patient-derived scaffolds are generated by repeated detergent washing, removing all cells. Here, we analyzed the protein composition of 15 decellularized PDSs using liquid chromatography-mass spectrometry/mass spectrometry. One hundred forty-three proteins were detected and their relative abundance was calculated using a reference sample generated from all ...[more]

PMID: 32637480

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Project description:BACKGROUND: Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms. RESULTS: We present LC-MSsim, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, LC-MSsim writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files. CONCLUSION: LC-MSsim generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that LC-MSsim will be useful to the wider community to perform benchmark studies and comparisons between computational tools.

Dataset Information

Characterization of cell-free breast cancer patient-derived scaffolds using liquid chromatography-mass spectrometry/mass spectrometry data and RNA sequencing data.

Publications

Characterization of cell-free breast cancer patient-derived scaffolds using liquid chromatography-mass spectrometry/mass spectrometry data and RNA sequencing data.

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