Biology subjects

Miguel Trabajo, T.

Publications and source records attributed to Miguel Trabajo, T..

4 recordsLinked to original sources

bioRxiv2024

Inferring Bacterial Interspecific Interactions from Microcolony Growth Expansion

Interactions between species are thought to be crucial for modulating their growth and behaviour within communities, and determinant for the emergence of community functions. Several different interaction concepts exist, but there is no consensus on how interactions should be quantified and integrated in community growth theory. Here we expand on existing concepts of real-time measurements of pure culture microcolony growth to develop and benchmark coculture microcolony experiments, and show how these can both parametrize growth kinetic and interspecific interaction effects. We follow surface growth by time-lapse microscopy of fluorescently tagged Pseudomonas putida and Pseudomonas veronii under substrate competition with succinate, or under substrate indifference with D-mannitol and putrescine. Monoculture-grown microcolonies showed substrate concentration dependent expansion rates as expected from Monod relations, whereas individual microcolony yields were strongly dependent on densities and spatial positioning of founder cells. Maximum specific growth rates in cocultures under substrate competition were diminished by ca. 15%, which was seeding-density independent. The collective P. putida population dominated growth over that of P. veronii, but with 27% yield loss under competition compared to monoculture growth; and 90% for that of P. veronii. Incidental local reversal of competition was observed where P. veronii microcolonies profited at the detriment of P. putida, and between 9 and 43% of P. veronii microcolonies grew bigger than expected from bulk competition, depending on seeding density. Simulations with a cell-agent Monod surface growth model suggested that colony expansion rate decrease in competitive coculture is caused by metabolite cross-feeding, which was supported by exometabolite analysis during and after growth of the strains on their individual or swapped supernatant. Coculture microcolony growth experiments thus provide a flexible platform for analysis of kinetic and interspecific interactions, expanding from individual microcolony phenotypic effects to averaged behaviour across all microcolony pairs. The system in theory is scalable to follow real-time growth of multiple species simultaneously into communities.

microbiology↗

bioRxiv2024

Dimalis: A complete standalone pipeline to analyse prokaryotic cell growth from time-lapse imaging

Real-time imaging of bacterial cell division, population growth and behaviour is essential for our understanding of microbial-catalyzed processes at the microscale. However, despite the relative ease by which high resolution imaging data can be acquired, the extraction of relevant cell features from images remains cumbersome. Here we present a versatile pipeline for automated extraction of bacterial cell features from standalone or time-resolved image series, with standardized data output for easy downstream processing. The input consist of phase-contrast images with or without additional fluorescence details, which are denoised to account for potential out-of-focus regions, and segmented to outline the morphologies of individual cells. Cells are then tracked over subsequent time frame images to provide genealogy or microcolony spatial information. We test the pipeline with eight different bacterial strains, cultured in microfluidics systems with or without nutrient flow, or on agarose miniature surfaces to follow microcolony growth. Examples of downstream processing in form of extraction of growth kinetic parameters or bistable cell differentiation are provided. The pipeline is wrapped in a Docker to facilitate installation, consistent processing and avoiding constant software updates.

microbiology↗

bioRxiv2024

Bacttle: a microbiology educational board game for lay public and schools

1.Inspired by the positive impact of serious games on science understanding and motivated by personal interests in scienti[fi]c outreach, we developed Bacttle, an easy-to-play microbiology board game with adaptive difficulty, targeting any player from 7 years old onward. Bacttle addresses both the lay public and teachers for use in classrooms as a way of introducing microbiology concepts. The layout of the game and its mechanism are the result of multiple rounds of trial, feedback and re-design. The [fi]nal version consists of a deck of cards, a 3D-printed board and tokens (with a paper-based alternative), with all digital content open source. Players in Bacttle take on the character of a bacterial species. The aim for each species is to proliferate under the environmental conditions of the board and the interactions with the board and with other players, which vary as the play evolves. Players start with a given number of lives that will increase or decrease based on the traits they play for different environmental scenarios. Such bacterial traits come in the form of cards that can be deployed strategically. In order to assess the impact of the game on microbiological knowledge, we scored differences in the understanding of general concepts before and after playing the game. We assessed a total of 169 visitors at two different university open day science fairs. Players were asked to [fi]ll a brief survey before and after the game with questions targeting conceptual advances. Results show that Bacttle increases general microbiology knowledge on players as young as 5 years old, and with the highest impact on those who have no a priori microbiology comprehension.

scientific communication and education↗

bioRxiv2022

STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images

Bacterial growth can be studied at the single cell-level through time-lapse microscopy imaging. Technical advances in microscopy lead to increasing image quality, which in turn allows to visualize larger areas of growth, containing more and more cells. In this context, the use of automated computational tools becomes essential. In this paper, we present STrack, a tool that allows to track cells in time-lapse images in a fast and efficient way. We compared it to three recently published tracking tools on images ranging over six different bacterial strains, and STrack showed to be the most consistent tracking tool, returning more than 80% of correct cell lineages on average. The python implementation of STrack, a docker structure, and a tutorial on how to download and use the tool can be found on the following github page: https://github.com/Helena-todd/STrack

bioinformatics↗