Single-cell proteomics (SCP) has emerged as a powerful approach to quantify protein expression variability at cellular resolution, yet most state-of-the-art workflows are tailored to eukaryotic cells with only one study exploring how single bacteria can be analyzed by mass spectrometry. Here, we established bacSCP, a protocol extending SCP to bacterial cells, facing analytical challenges such as the thick bacterial cell wall hampering lysis, the extremely small cell size and resultant low protein content, and the comparatively high level of contaminating proteins from external sources. Using this bacSCP pipeline, we quantified more than 50 bacterial proteins from single
Bacillus subtilis
and
Escherichia coli
cells. Upon heat stress, we reproducibly observed up to 8-fold upregulation of chaperones including GroEL, GroES, and ClpC for a
B. subtilis ΔmcsB
strain. Importantly, single-cell measurements revealed potential heterogeneity within the heat-stressed subpopulation, enabling interrogation of stress-response variability at the proteome level. These results demonstrate the feasibility of bacSCP and provide a foundation for studying bacterial stress adaptation and phenotypic diversity with single-cell proteomic resolution.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.