Bottom-up proteomics (E. coli, human)

Neurological diseases are on the rise – and as societies age, they affect an ever-increasing number of people, not only in Europe, but worldwide.

The Munich Cluster for Systems Neurology (SyNergy) investigates how complex neurological diseases such as Alzheimer's disease, stroke, and multiple sclerosis develop. Even though these diseases differ in their clinical manifestations, overlapping mechanisms are involved in their development. For example, the immune system gets activated in dementia, ...

This project serves as a centralized repository for omics datasets published by research groups within the SyNergy Cluster. It encompasses investigations such as proteomics and transcriptomics, which are further divided into individual studies led by SyNergy members. Each study is linked to relevant publications, assays and data files (with links to external repositories).

To explore investigations and their associated studies in more detail, please visit the 'Related items' tab on the Project ...

Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises serious privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived ...

Quantitative mass spectrometry has transformed proteomics by allowing the simultaneous quantification of thousands of proteins. To boost statistical power, it is necessary to increase sample sizes by combining patient-derived data from various institutions. However, this practice raises significant privacy concerns. We created this dataset comtaining 30 healthy donors and 30 patients suffering from focal segmental glomerulosclerosis - a rare kidney disease - and distributed them to three independent ...

Quantitative mass spectrometry has transformed proteomics by allowing the simultaneous quantification of thousands of proteins. To boost statistical power, it is necessary to increase sample sizes by combining patient-derived data from various institutions. However, this practice raises significant privacy concerns. We created a DIA-LFQ dataset containing 118 samples generated from Escherichia coli MG1655 (DSM 18039) cultures and distributed them to five independent proteomics centers. This ...