Biology subjects

Schattenberg, F.

Publications and source records attributed to Schattenberg, F..

5 recordsLinked to original sources

bioRxiv2026

Water Kefir as a Model for Aggregative Development of Microbial Multicellularity within a Multispecies Biofilm

Water kefir is a traditional fermented beverage and a non-dairy alternative to milk kefir. Water kefir grains are cauliflower-like aggregates, ranging in size from 1-20 mm. They are composed of an exopolysaccharide matrix derived from the fermentation substrate sucrose. The microbial community on and within the grains is a complex, highly stable multicellular aggregate typically consisting of 10-15 core species that primarily belong to three major groups: lactic acid bacteria, acetic acid bacteria, and yeasts. Remarkably, the structural organisation, stability, and growth of kefir grains cannot be readily explained by their constituent species alone, and attempts to reconstitute them from the isolated species have so far been unsuccessful. Here, we combined a comprehensive structural investigation of the macro- and micromorphology of water kefir grains with culture-dependent and -independent analyses of its microbial biodiversity. The dynamics and interactions within this complex microbial community were studied by combining flow cytometry, fluorescence-activated cell sorting, and amplicon sequencing of selected subpopulations. Furthermore, the growth of water kefir grains was spatiotemporally resolved and quantitatively characterised, thereby unravelling an emergent multicellular growth function, documented for the first time. Our combined evidence provides an unprecedented understanding of a highly structured and metabolically synergistic multicellular microbial aggregate and lets us propose water kefir as a model system for the aggregative development of microbial multicellularity from multiple species. It should lay the foundation for future mechanistic studies aimed at understanding how multispecies microbial communities self-organise into stable, spatially structured multicellular aggregates.

microbiology↗

bioRxiv2025

biscot: an Optimal Transport framework for multimodal bacterial single-cell data analysis

Computational optimal transport-based approaches have emerged as promising tools for the integration and interpretation of complex single-cell data. In this study, we introduce an integrative Optimal Transport (OT) framework for spatiotemporal and multi-omics bacterial single-cell analysis using Gaussian Mixture Model (GMM) OT, termed biscot (bacterial integrative single-cell optimal transport). We show that biscot, equipped with a novel global-to-local GMM initialization, outperforms classical OT and entropically-regularized OT methods both in terms of speed and accuracy for disentangling complex bacterial communities mixtures from single-cell flow cytometry data. When applied to time-series flow cytometry data from Bacillus subtilis, our framework delivers robust and biologically meaningful results, effectively capturing subtle phenotypic shifts in spore populations transitioning from inactive to active growth states. biscot also allows multi-omics integration of flow cytometry and unpaired bacterial single-cell RNA sequencing (scRNA-seq) data, enabling the alignment of individual gene expression profiles to the cytometric data. For an unpaired flow cytometry/scRNA-seq dataset of Bacillus subtilis cells, we validate the biological plausibility of inferred gene expression patterns with relevant marker genes, including spoVID and nin, closely aligning with observed cellular states. Overall, our framework thus provides not only dynamic tracking of phenotypic cell states but aligns cell states with detailed transcriptomic information from scRNA-seq, demonstrating its potential to advance microbial single-cell research. biscot will be made publicly available on GitHub.

bioinformatics↗

bioRxiv2025

Type VI secretion phenotypic heterogeneity ensures trade-off between antibacterial activity and resistance in Enteroaggregative E. coli

The type VI secretion system (T6SS) is a nanoweapon deployed by bacteria to deliver effectors into target cells, allowing niche colonization or host invasion. In entero-aggregative Escherichia coli (EAEC), the expression of the T6SS gene cluster is responsive to iron starvation by the Fur regulator, and to Dam-dependent methylation. However, fluorescence microscopy recordings showed that the T6SS displays heterogeneous expression in a clonal population. The population is composed of three types of cells in a stable equilibrium: non producers (OFF), producers, and assemblers (ON). Single-cell analyses further showed that the activation state is transmitted to the progeny, but the overall distribution respects the equilibrium suggesting a genetic control of heterogeneity. Mutagenesis of Fur-binding boxes and GATC sites revealed that specific promoter elements control this heterogeneity. Phenotypic characterization of the ON and OFF subpopulations demonstrated that ON cells eliminate susceptible target cells, whereas OFF cells survive attacks from T6SS-defensive bacteria. The heterogeneity of EAEC T6SS is therefore an attenuation strategy conferring a trade-off between antibacterial efficiency and survival in polymicrobial environments.

microbiology↗

bioRxiv2021

Stabilising microbial communities by looped mass transfer

Creating structurally and functionally stable microbiomes would be greatly beneficial to biotechnology and human health but so far has proven challenging. Here, we propose a looped mass transfer design that keeps microbiomes constant over long periods of time. The effluent of five parallel reactors that began with the same inoculum, was mixed in a reactor that represented a regional pool. Part of this pool was transferred back to the five reactors. Community dynamics were monitored and visualized by quantitative microbial flow cytometry and selected taxonomic sequencing of whole communities and sorted subcommunities. The rescue effect, known from metacommunity theory, was the main stabilizing mechanism that led to the survival of subcommunities with zero netgrowth, especially at high mass transfer rates. The looped mass transfer approach promises to overcome notorious stochastic structural fluctuations in bioreactors and has the potential to design and stabilize communities that can perform desired functions.

microbiology↗

bioRxiv2021

Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting

Microbiome management research and applications rely on temporally-resolved measurements of community composition. Current technologies to assess community composition either make use of cultivation or sequencing of genomic material, which can become time consuming and/or laborious in case high-throughput measurements are required. Here, using data from a shrimp hatchery as an economically relevant case study, we combined 16S rRNA gene amplicon sequencing and flow cytometry data to develop a computational workflow that allows the prediction of taxon abundances based on flow cytometry measurements. The first stage of our pipeline consists of a classifier to predict the presence or absence of the taxon of interest, with yields an average accuracy of 88.13{+/-}4.78 % across the top 50 OTUs of our dataset. In the second stage, this classifier was combined with a regression model to predict the relative abundances of the taxon of interest, which yields an average R2 of 0.35{+/-}0.24 across the top 50 OTUs of our dataset. Application of the models on flow cytometry time series data showed that the generated models can predict the temporal dynamics of a large fraction of the investigated taxa. Using cell-sorting we validated that the model correctly associates taxa to regions in the cytometric fingerprint where they are detected using 16S rRNA gene amplicon sequencing. Finally, we applied the approach of our pipeline on two other datasets of microbial ecosystems. This pipeline represents an addition to the expanding toolbox for flow cytometry-based monitoring of bacterial communities and complements the current plating- and marker gene-based methods. ImportanceMonitoring of microbial community composition is crucial for both microbiome management research and applications. Existing technologies, such as plating and amplicon sequencing, can become laborious and expensive when high-throughput measurements are required. Over the recent years, flow cytometry-based measurements of community diversity have been shown to correlate well to those derived from 16S rRNA gene amplicon sequencing in several aquatic ecosystems, suggesting there is a link between the taxonomic community composition and phenotypic properties as derived through flow cytometry. Here, we further integrated 16S rRNA gene amplicon sequencing and flow cytometry survey data in order to construct models that enable the prediction of both the presence and the abundance of individual bacterial taxa in mixed communities using flow cytometric fingerprinting. The developed pipeline holds great potential to be integrated in routine monitoring schemes and early warning systems for biotechnological applications.

microbiology↗