Rediscovering Bacteria through Single-Molecule Imaging in Living Cells.

Bacteria are microorganisms central to health and disease, serving as important model systems for our understanding of molecular mechanisms and for developing new methodologies and vehicles for biotechnology. In the past few years, our understanding of bacterial cell functions has been enhanced substantially by powerful single-molecule imaging techniques. Using single fluorescent molecules as a means of breaking the optical microscopy limit, we can now reach resolutions of ∼20 nm inside single living cells, a spatial domain previously accessible only by electron microscopy. One can follow a single bacterial protein complex as it performs its functions and directly observe intricate cellular structures as they move and reorganize during the cell cycle. This toolbox enables the use of in vivo quantitative biology by counting molecules, characterizing their intracellular location and mobility, and identifying functionally distinct molecular distributions. Crucially, this can all be achieved while imaging large populations of cells, thus offering detailed views of the heterogeneity in bacterial communities. Here, we examine how this new scientific domain was born and discuss examples of applications to bacterial cellular mechanisms as well as emerging trends and applications.

Generalized energy equipartition in harmonic oscillators driven by active baths.

We study experimentally and numerically the dynamics of colloidal beads confined by a harmonic potential in a bath of swimming E. coli bacteria. The resulting dynamics is well approximated by a Langevin equation for an overdamped oscillator driven by the combination of a white thermal noise and an exponentially correlated active noise. This scenario leads to a simple generalization of the equipartition theorem resulting in the coexistence of two different effective temperatures that govern dynamics along the flat and the curved directions in the potential landscape.

Daily life in the Open Biologist's second job, as a Data Curator.

Background: Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications. Methods: Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to maximize data reusability, while maintaining research productivity. Conclusion: We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

Researchers should openly share data associated with their publications unless there is a valid reason not to. Additionally, datasets have to be described with enough detail to ensure that they are reproducible and reusable by others. Since most research institutions offer limited professional support in this area, the responsibility for data sharing largely falls to researchers themselves. However, many research groups still struggle to follow data reusability principles in practice. In this work, we describe our data curation (data organization and management) efforts working directly with the researchers who create the data. We show the steps we took to organize, standardize, and share several datasets in biological sciences, pointing out the main challenges we faced. Finally, we suggest simple and practical data management actions, as well as tools that experimentalists can integrate into their daily work, to make sharing data easier and more effective.

Quantification of very low-abundant proteins in bacteria using the HaloTag and epi-fluorescence microscopy.

Cell biology is increasingly dependent on quantitative methods resulting in the need for microscopic labelling technologies that are highly sensitive and specific. Whilst the use of fluorescent proteins has led to major advances, they also suffer from their relatively low brightness and photo-stability, making the detection of very low abundance proteins using fluorescent protein-based methods challenging. Here, we characterize the use of the self-labelling protein tag called HaloTag, in conjunction with an organic fluorescent dye, to label and accurately count endogenous proteins present in very low numbers (<7) in individual Escherichia coli cells. This procedure can be used to detect single molecules in fixed cells with conventional epifluorescence illumination and a standard microscope. We show that the detection efficiency of proteins labelled with the HaloTag is ≥80%, which is on par or better than previous techniques. Therefore, this method offers a simple and attractive alternative to current procedures to detect low abundance molecules.