Dataset Information

Proteomic profiles of synovial fluids derived from autologous chondrocyte implantation surgery

ABSTRACT: The pathology of failure in currently used biological treatments (cell therapies and microfracture) for cartilage repair or early osteoarthritis (OA) is poorly understood. We aimed to identify a reliable panel of biological predictors, present in synovial fluid, which can be used in the preoperative setting to optimise patient selection and therapy efficacy. The-long term aim is to reduce the number of patients who progress to end-stage OA and require knee replacement by making earlier, less invasive treatments more effective. In this first stage, we have taken synovial fluid aspirates from patients undergoing autologous chondrocyte implantation (ACI) at our centre in Oswestry, UK. Synovial fluids (SFs) were obtained from 14 ACI responders (mean Lysholm improvement of 33 (17-54)) and 13 non-responders (mean Lysholm decrease of 14 (4-46)) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Dynamic range compression of synovial fluids coupled with label-free quantification mass spectrometry (MS), was used in an unbiased approach to identify predictive markers of ACI success or failure and to investigate the biological pathways involved in the clinical response to ACI.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Synovial Tissue

DISEASE(S): Osteoarthritis

SUBMITTER: Emma Wilson

LAB HEAD: Mandy Peffers

PROVIDER: PXD005220 | Pride | 2017-07-04

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	220216_LF10_5fold_2h.mzid.gz	Mzid
	220216_LF10_5fold_2h.pride.mztab.gz	Mztab
	220216_LF11_5fold_2h.mzid.gz	Mzid
	220216_LF11_5fold_2h.pride.mztab.gz	Mztab
	220216_LF12_5fold_2h.mgf	Mgf

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Publications

Autologous chondrocyte implantation-derived synovial fluids display distinct responder and non-responder proteomic profiles.

Hulme Charlotte H CH Wilson Emma L EL Peffers Mandy J MJ Roberts Sally S Simpson Deborah M DM Richardson James B JB Gallacher Pete P Wright Karina T KT

Arthritis research & therapy 20170630 1

<h4>Background</h4>Autologous chondrocyte implantation (ACI) can be used in the treatment of focal cartilage injuries to prevent the onset of osteoarthritis (OA). However, we are yet to understand fully why some individuals do not respond well to this intervention. Identification of a reliable and accurate biomarker panel that can predict which patients are likely to respond well to ACI is needed in order to assign the patient to the most appropriate therapy. This study aimed to compare the base ...[more]

PMID: 28666451

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Project description:Background: Results obtained from our previous study using a label-free quantitation (LF) proteomic approach in which dynamic range compression was used to profile lower abundance proteins, demonstrated few proteins that were differentially abundant within the synovial fluid (SF) when comparing Autologous Chondrocyte Implantation (ACI) responders and non-responders at baseline (1). This study builds upon our previous findings by assessing higher abundance proteins within these SFs; providing a more global proteome analysis from which we can understand more fully the biology underlying ACI success or failure. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomics was used to assess SFs from ACI responders (mean Lysholm improvement of 33; n=14) and non-responders (mean Lysholm decrease of 14; n=13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins were investigated using pathway analyses and the iTRAQ proteomic dataset was combined with our published proteomic dataset of dynamically compressed SFs, from which an interactome network model of systemic protein interactions was generated. Results: iTRAQ proteomics has confirmed our previous finding that there is a marked proteome shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥2.0 fold change between Stages I and II in responders and non-responders, respectively) and has highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, that were not found in the LF study; 16 of which were altered at baseline. Two protein abundance changes (Complement C1S subcomponent and Matrix metalloproteinase 3 (MMP3), have been biochemically validated. Combination of the iTRAQ and LF proteomic datasets has generated in-depth SF proteome information that has been used to generate interactome networks representing ACI success or failure, from which functional pathways that are dysregulated in ACI non-responders have been identified. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome have been identified. A holistic overview of the SF proteome in responders and non-responders to ACI has been profiled providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.