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Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides (https://github.com/MannLabs/alphapeptdeep). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition (https://github.com/MannLabs/PeptDeep-HLA).

Die Solidarische Landwirtschaft (SoLaWi) beschreibt eine direkte Verbindung von Erzeuger*innen und Verbraucher*innen ökologisch und regional hergestellter Produkte. Die SoLaWi-Prinzipien werden weltweit praktiziert; in Deutschland gibt es derzeit mindestens 166 SoLaWi-Betriebe und 113 Initiativen (SoLaWis in Gründung). In unserer Studie wurden acht Expert*inneninterviews mit Expert*innen von neu gegründeten SoLaWis in Deutschland durchgeführt, um die Motive, Voraussetzungen und Erfolgsfaktoren für die Gründung und das langfristige Gelingen von, (überwiegend) Gemüse produzierenden, SoLaWis zu untersuchen. Übergeordnete Erfolgsfaktoren sind das Vorhandensein eines landwirtschaftlichen und finanziellen Konzeptes, verfügbare Flächen und motivierte Menschen. Darüber hinaus ist es wichtig, die persönlichen Erwartungen von Anfang an klar zu kommunizieren, in der direkten Umgebung gut vernetzt (ideeller und materieller Austausch) und darüber hinaus an bundesweite Zusammenhänge wie z.B. das Netzwerk Solidarische Landwirtschaft angebunden zu sein. Basierend auf den Interviews wurden mögliche Handlungsstrategien für das Land Thüringen skizziert.