Oxidized alginate hydrogels with the GHK peptide enhance cord blood mesenchymal stem cell osteogenesis: A paradigm for metabolomics-based evaluation of biomaterial design

Oxidized alginate hydrogels are appealing alternatives to natural alginate due to their favourable biodegradability profiles and capacity to self-crosslink with amine containing molecules facilitating functionalization with extracellular matrix cues, which enable modulation of stem cell fate, achieve highly viable 3-D cultures, and promote cell growth. Stem cell metabolism is at the core of cellular fate (proliferation, differentiation, death) and metabolomics provides global metabolic signatures representative of cellular status, being able to accurately identify the quality of stem cell differentiation. Herein, umbilical cord blood mesenchymal stem cells (UCB MSCs) were encapsulated in novel oxidized alginate hydrogels functionalized with the glycine-histidine-lysine (GHK) peptide and differentiated towards the osteoblastic lineage. The ADA-GHK hydrogels significantly improved osteogenic differentiation compared to gelatin-containing control hydrogels, as demonstrated by gene expression, alkaline phosphatase activity and bone extracellular matrix deposition. Metabolomics revealed the high degree of metabolic heterogeneity in the gelatin-containing control hydrogels, captured the enhanced osteogenic differentiation in the ADA-GHK hydrogels, confirmed the similar metabolism between differentiated cells and primary osteoblasts, and elucidated the metabolic mechanism responsible for the function of GHK. Our results suggest a novel paradigm for metabolomics-guided biomaterial design and robust stem cell bioprocessing. STATEMENT OF SIGNIFICANCE: Producing high quality engineered bone grafts is important for the treatment of critical sized bone defects. Robust and sensitive techniques are required for quality assessment of tissue-engineered constructs, which result to the selection of optimal biomaterials for bone graft development. Herein, we present a new use of metabolomics signatures in guiding the development of novel oxidised alginate-based hydrogels with umbilical cord blood mesenchymal stem cells and the glycine-histidine-lysine peptide, demonstrating that GHK induces stem cell osteogenic differentiation. Metabolomics signatures captured the enhanced osteogenesis in GHK hydrogels, confirmed the metabolic similarity between differentiated cells and primary osteoblasts, and elucidated the metabolic mechanism responsible for the function of GHK. In conclusion, our results suggest a new paradigm of metabolomics-driven design of biomaterials

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

The impact of acute nutritional interventions on the plasma proteome - Supplementary data

Plasma proteomics was used to characterise the impact of caloric restriction, refeeding or oral glucose tolerance test to the physiology.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets