## 20. Gene expression and phenotypic heterogeneity during bacterial swarming

Hannah Jeckel¹ and Knut Drescher²

¹ Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States of America

² Biozentrum, University of Basel, 4056 Basel, Switzerland

### Status

Motile active matter systems range in scale from macroscopic flocks of animals to microscopic communities of self-propelled eukaryotic cells, bacterial cells, and abiotic motile Janus particles. Non-living active matter systems are typically composed of a fixed number of particles, which may change their behavior as a direct consequence of their local environment. In contrast, living systems typically contain a varying number of agents whose morphology and behavior, referred to as their phenotype, is not only influenced by their current local environment, but also influenced by the previous environments that each agent has experienced during different times of its organismal development. The information of the current and previous environments that an agent has experienced during its development is transferred into the current phenotype of the agent through gene regulatory mechanisms. As a result of this complexity of motile active matter systems comprising living agents, there can be several distinct phenotypes of genetically identical agents present at the same time in the same local environment, leading to a rich diversity of interactions between phenotypic subpopulations that may give rise to emergent behaviors of the active matter system.

Even for the biologically simplest motile active matter systems, such as bacterial swarms, it is unclear how gene expression causes the formation of different phenotypic subpopulations, and how the interactions of these subpopulations influence emergent behaviors, such as swarm expansion and susceptibility to antibiotics. This section discusses bacterial swarming as a model system to study spatiotemporal phenotypic heterogeneity in motile active matter. Furthermore, this section highlights recent advances as well as future challenges for characterizing the phenotypic subpopulations that arise in bacterial swarm communities and how subpopulation behaviors and interactions at the microscopic scale connect to the macroscale collective dynamics.

### Current and future challenges

During bacterial swarming, a densely packed community of self-propelled bacteria rapidly expands across macroscopic distances on a surface (figure 28). While this community expands, the number of cells in the community grows, so that the community expansion is driven by motility, growth, and the underlying metabolism that the cells perform to fuel growth and motility [184, 185]. Using microscopy-based observations, it has been comprehensively documented that the cellular behavior and cellular morphology varies drastically between different temporal stages of the swarm expansion

process, between different spatial regions within the swarm, and even within the same spatiotemporal location in the swarm [185, 186]: in some regions cells form highly motile rafts, in some regions cells primarily move as isolated cells, or form non-motile cell clusters or cell chains, and in other regions these different cellular phenotypes co-occur (figure 29). A major research challenge is to understand how the collective dynamics of the swarm emerge from the microscopic activities of the phenotypically different bacterial cells.

On short time scales, i.e. time scales in which cellular phenotypes do not change and on which the swarm community does not significantly expand, mechanical interactions between the cells are sufficient to qualitatively and quantitatively describe the collective dynamics of the bacterial swarm [185].

On longer time scales, i.e. time scales on which the cellular phenotypes change, which are also the time scales on which the swarm expands significantly and the cell number of the swarm changes significantly, it is unclear how the microscopic cellular activities generate the emergent collective dynamics of the swarm. To develop an understanding of the system dynamics on these longer time scales, an important challenge is that new experimental technologies are needed: While brightfield microscopy methods have been useful in the past for characterizing cellular motility and morphology, they cannot capture the underlying biological reprogramming of cells that results in changes in cellular phenotypes. Adapting or developing methodologies for comprehensively characterizing the gene expression at the cellular level in bacterial swarms are therefore required. Once these methods are available, another important challenge will be the development of data analysis strategies that incorporate temporal and spatial information into existing statistical frameworks, to utilize these spatiotemporal gene expression datasets for formulating models that incorporate gene expression, changes of cellular phenotypes, as well as cell morphology and cell motility. Such models could potentially establish a scale-bridging understanding of how gene expression, microscopic activities of the bacterial cells, and interactions between the phenotypically different cell subpopulations cause the collective dynamics during bacterial swarm expansion.

### Advances in science and technology to meet challenges

To understand how gene expression leads to phenotypic heterogeneity during swarming, and how the microscopic activities of phenotypically heterogeneous cells cause the emergent collective properties of bacterial swarms, experimental data needs to be recorded at multiple layers of information. While it is important to record the motility dynamics and cell morphology of all cells in the swarm (which is typically achieved using brightfield live-cell microscopy), information of the gene expression needs to also be recorded for every cell during the swarming dynamics. Currently, this is only possible using fluorescent protein-based gene expression reporters, which enable live imaging at the single cell level. However, this fluorescent-reporter-based approach is limited by the time-consuming requirement of having to genetically engineer a separate bacterial strain for each gene that needs
<figure><img src="image_30.png" /><figcaption>**Figure 28.** Macroscopic swarming patterns created by *B. subtilis* wildtype (leftmost) and different mutants.</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>**Figure 29.** Microscope images acquired at different positions within a *B. subtilis* swarm. This figure shows that the shape of individual cells changes dramatically between the swarm center and the swarm front.</figcaption></figure>

to be read out. Furthermore, due to the spectral limitations of fluorescent proteins, it is typically not possible to read out more than 3–5 different fluorescent proteins at the same time. Despite these limitations, studies using fluorescent reporters for studying gene expression during bacterial swarming have already yielded important insights into swarm development [187].

To overcome the limitations of fluorescent reporters for measuring gene expression during swarming, techniques for measuring gene expression of all genes simultaneously are promising, such as transcriptome or proteome measurements. A decade ago, these techniques required >10⁵ cells for a single measurement, which meant that they only allowed bulk measurements of gene expression. However, spatial resolution can still be achieved when these bulk technologies are combined with spatial sampling [188], and a spatiotemporal characterization of large-scale gene expression patterns during swarming was recently accomplished using robotic sampling at a spatial resolution of ~100 µm [189]. Significant advances of sequencing techniques have furthermore made it possible to measure the expression level of 5%–20% of the genes (i.e. hundreds of genes) in a single bacterial cell, for example by using multiplexed fluorescence *in-situ* hybridization techniques based on imaging [190] or the rapidly improving single-cell RNA-seq techniques [191]. These technological developments for measuring genome-wide gene expression levels at the single-cell level could provide the necessary toolkits to enable the simultaneous spatiotemporal measurements of gene expression, cellular motility dynamics, and collective dynamics during swarm development.

## Concluding remarks

Understanding the dynamics of biological motile active matter systems comes with unique challenges associated with the phenotypic heterogeneity of living agents, their ability to change phenotypes over time based on changes in gene expression, and their ability to proliferate. To obtain an understanding such motile active matter systems over time scales on which the cells grow and change their phenotypes, we first need a characterization of the system in terms of gene expression, activity of individual cells in terms of motility and growth, as well as the collective cell activity, to provide the basis for developing models that connect processes between these three length scales. Bacterial communities, such as swarms, are particularly suited for achieving such a scale-spanning understanding of motile active matter, because bacteria are relatively simple organisms (compared to eukaryotes), which can be easily genetically manipulated, with fast replication times, which can be well-controlled in laboratory experiments. Furthermore, the availability of detailed annotations of genomes of model organisms for bacterial swarming, such as *Bacillus subtilis* or *P. aeruginosa*, can enable interpretations of which intracellular processes influence the motility and morphology of cells, and ultimately the collective dynamics of the whole swarm.

## Acknowledgments

We are grateful for the following funders for enabling our work on this topic: Swiss National Science Foundation (SNSF Consolidator Grant TMCG-3_213801 to K.D.), Deutsche
Forschungsgemeinschaft (DR 982/6-1 to K.D., as part of the Priority Programme SPP 2389).

## Data availability statement

The data that support the findings of this study are available upon reasonable request from the authors.

## ORCID iDs

Gerhard Gompper https://orcid.org/0000-0002-8904-0986

Howard A Stone https://orcid.org/0000-0002-9670-0639

Christina Kurzthaler https://orcid.org/0000-0002-5516-1464

David Saintillan https://orcid.org/0000-0001-9948-708X

Fernado Peruani https://orcid.org/0000-0003-2985-8935

Dmitry A Fedosov https://orcid.org/0000-0001-7469-9844

Thorsten Auth https://orcid.org/0000-0002-6618-2316

Cecile Cottin-Bizonne https://orcid.org/0000-0001-5807-9215

Eric Clément https://orcid.org/0000-0002-2068-9699

Anke Lindner https://orcid.org/0000-0002-5007-9568

Raymond E Goldstein https://orcid.org/0000-0003-2645-0598

Benno Liebchen https://orcid.org/0000-0002-7647-6430

Anton Souslov https://orcid.org/0000-0001-7735-100X

Lucio Isa https://orcid.org/0000-0001-6731-9620

Giacomo Frangipane https://orcid.org/0000-0002-1533-4754

Fridtjof Brauns https://orcid.org/0000-0002-6108-9278

M Cristina Marchetti https://orcid.org/0000-0003-3583-4999

Frank Cichos https://orcid.org/0000-0002-9803-4975

Veit-Lorenz Heuthe https://orcid.org/0000-0003-1248-4770

Clemens Bechinger https://orcid.org/0000-0002-5496-5268

Hannah Jeckel https://orcid.org/0000-0002-7080-4907

Knut Drescher https://orcid.org/0000-0002-7340-2444

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