Wet and dry methods to study surface and colloid biology

Colloid physics and colloid chemistry are well established fields within the colloid sciences. Both of these two disciplines are directed toward studying or engineering inanimate particles that are interacting in a physical way, such as electrostatics, rheology, stability, or their surfaces are either chemically modified or chemically reactive, respectively. In contrast, the new discipline the colloid biology is implementing the methods of colloid science on the living cells to either change their surface, cause aggregation, surface attachment as well entrapment or encapsulation. However, the biological properties of these particles are always adding emergent properties to the particles either through the constant adaptation of the cell surfaces, mass gaining, motility or other ways that cannot be predicted only by the physical properties of a particular cell. Oppositely, the method of modification of cells based on the colloid physics can likewise change the biological properties of the cell such as growth rate, expression profiles as well as interaction with other cells in the population or communities and can affect ecological relations, changing it from competitive to the collaborative or symbiotic coexistence. Therefore, it is a substantial need to establish the third pillar of colloid science, colloid biology. Therefore, the main objective of this chapter is to understand the basic concepts of colloid biology that are differentiating as well as linking with the colloid physics and colloid chemistry concepts and resulting in interdisciplinary science.

Literature:

1. Lapanje, A., Wimmersberger, C., Furrer, G., Brunner, I. and Frey, B., 2012. Pattern of elemental release during the granite dissolution can be changed by aerobic heterotrophic bacterial strains isolated from Damma Glacier (central Alps) deglaciated granite sand. Microbial ecology, 63, pp.865-882.

2. van Tatenhove-Pel, R.J., Rijavec, T., Lapanje, A., van Swam, I., Zwering, E., Hernandez-Valdes, J.A., Kuipers, O.P., Picioreanu, C., Teusink, B. and Bachmann, H., 2021. Microbial competition reduces metabolic interaction distances to the low µm-range. The ISME journal, 15(3), pp.688-701.

3. Rybkin, I., Gorin, D., Sukhorukov, G. and Lapanje, A., 2019. Thickness of polyelectrolyte layers of separately confined bacteria alters key physiological parameters on a single cell level. Frontiers in bioengineering and biotechnology, 7, p.378.

4. Gusev, A., Zakharova, O., Muratov, D.S., Vorobeva, N.S., Sarker, M., Rybkin, I., Bratashov, D., Kolesnikov, E., Lapanje, A., Kuznetsov, D.V. and Sinitskii, A., 2019. Medium-dependent antibacterial properties and bacterial filtration ability of reduced graphene oxide. Nanomaterials, 9(10), p.1454.

5. Gusev, A., Zakharova, O., Vasyukova, I., Muratov, D.S., Rybkin, I., Bratashov, D., Lapanje, A., Il'inikh, I., Kolesnikov, E. and Kuznetsov, D., 2019. Effect of GO on bacterial cells: Role of the medium type and electrostatic interactions. Materials Science and Engineering: C, 99, pp.275-281.

Prof. Dr. María Suárez, from Wageningen University & Research (WUR)

In the natural environment, bacteria can be found as free floating, forming flocs or attached to a surface forming biofilms. Genomics is an experimental technology that can be used to identify the types and amounts of DNA present in a sample. However obtaining good quality samples for genomics can be challenging and in this chapter we review some of the critical elements that need to be considered when preparing samples for genomics. The experimental protocol needs to be carefully optimized for the question at hand. In addition analysis of the obtained data requires the use of dedicated computational methods to identify and annotate genes.

After following this chapter you will be able to:

i) Design and apply sampling procedures.

ii) Discuss and evaluate sample pre-treatment methodologies for DNA sequencing using the Illumina system and adaptation for the Nanopore approach.

iii) Evaluate commercially available kits for nucleic acid extraction for specific applications.

iv) Quantify and analyze the integrity of purified DNA using electrophoretic and spectrometric approaches.

v) Explain key elements of state of the art DNA sequencing technologies.

vi) Use commonly used methods and computational tools for genome assembly and annotation.

Transcriptomics is the study of gene expression patterns in cells using RNA sequencing technologies. The main objectives of this chapters is to understand the basics of transcriptomics and how it can be used to study cell-cell and cell surface biology. With transcriptomics it is possible to identify the genes expressed on the surface of cells and studying their interactions with other cells or signaling pathways. There are several types of transcriptomics, including ribosomal transcriptomics, bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics. Ribosomal transcriptomics, also known as Ribo-seq or ribosome profiling, is a technique that combines RNA sequencing with ribosome profiling to study the translational landscape of cells. It provides a snapshot of the actively translated regions of mRNA within a cell, allowing researchers to identify which mRNAs are being translated into proteins and at what rate. Bulk RNA sequencing provides information about gene expression levels in each tissue, while single-cell RNA sequencing allows the identification of cell types and subtypes. Spatial transcriptomics combines RNA sequencing with spatial information, enabling the analysis of gene expression patterns within tissues. Transcriptomics is a powerful tool for identifying potential targets, understanding cellular differentiation and development, and improving diagnostics with specific biomarkers.

Recommended literature:

1. González-Plaza, J.J., Furlan, C., Rijavec, T., Lapanje, A., Barros, R., Tamayo-Ramos, J.A. and Suarez-Diez, M., 2022. Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels. Frontiers in Microbiology, 13.

2. de Vries, H.J., Kleibusch, E., Hermes, G.D., van den Brink, P. and Plugge, C.M., 2021. Biofouling control: the impact of biofilm dispersal and membrane flushing. Water Research, 198, p.117163.

3. Sinha, N., van Schothorst, E.M., Hooiveld, G.J., Keijer, J., Martins dos Santos, V.A. and Suarez-Diez, M., 2021. Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism. BMC bioinformatics, 22(1), pp.1-15.

4. Bharti, R. and Grimm, D.G., 2021. Current challenges and best-practice protocols for microbiome analysis. Briefings in bioinformatics, 22(1), pp.178-193.

5. Seneviratne, C.J., Suriyanarayanan, T., Widyarman, A.S., Lee, L.S., Lau, M., Ching, J., Delaney, C. and Ramage, G., 2020. Multi-omics tools for studying microbial biofilms: current perspectives and future directions. Critical Reviews in Microbiology, 46(6), pp.759-778.

Dr. Tomaz Rijavec, from Jozef Stefan Institute (JSI), Slovenia.

Metabolomics is the scientific study of chemical processes involving intracellular and extracellular metabolites, the small molecule substrates, intermediates and products of cell metabolism, helping us describe the molecular phanotype of bacteria. It is the highest level of omics, after genomics, transcriptomics and proteomics, giving us an actual insight into the metabolics activity of bacteria. Using different detection methods (e.g. NMR, MS, FTIR) we can characterize the primary and secondary metabolites and certain structural compounds. Using metabolic screening, targeted metabolomics and multi-level bioinformatic omics analyses we can start answering how the cell is sensing surfaces and how it is interacting with different substrates in the environment. It can further help us predict the metabolic traits of bacterial communities so that we can artificially manipulate them for our needs.

Literature

1. Bundy, J.G., Davey, M.P. and Viant, M.R., 2009. Environmental metabolomics: a critical review and future perspectives. Metabolomics, 5, pp.3-21.

2. Rubakhin, S.S., Lanni, E.J. and Sweedler, J.V., 2013. Progress toward single cell metabolomics. Current opinion in biotechnology, 24(1), pp.95-104.

3. Castro-Moretti, F.R., Gentzel, I.N., Mackey, D. and Alonso, A.P., 2020. Metabolomics as an emerging tool for the study of plant–pathogen interactions. Metabolites, 10(2), p.52.

4. Ye, D., Li, X., Shen, J. and Xia, X., 2022. Microbial metabolomics: From novel technologies to diversified applications. TrAC Trends in Analytical Chemistry, p.116540.

5. Johnson, C.H., Ivanisevic, J. and Siuzdak, G., 2016. Metabolomics: beyond biomarkers and towards mechanisms. Nature reviews Molecular cell biology, 17(7), pp.451-459.

Dr. Cristina Furlan, from Wageningen University & Research (WUR).

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others.

These molecules are then studied using high-throughput methods (omics) technologies and the choice of the -omic technique often depends on the specific research question, as each of them informs on a different level of the system. The study of colloid biology often involves combining multiple omics data sets for instance combining metagenomic data analysis informing on the relative abundances of microbes growing on a surface with metabolomics analysis characterizing the activity. There is a need for different approaches to data integration: i) horizontal for multiple datasets from the same type of data (i.e. multiple RNAseq datasets), ii) vertical for different data type on the same experiment and beyond (i.e. RNAseq+ metabolomics + physicochemical parameter dataset etc.)

Specific methods and tools have been developed addressing the forementioned problems, and often they are made accessible through user-friendly interfaces that facilitate the use of specialized tools such as Galaxy environments or Bioconductor packages.

In this video we will explore the challenges of data integration and mention some methods to achieve it.

Learning objectives

After successful completion of this video participants will be able to:

I.Demonstrate the need for data integration

II.Understand the characteristics of data integration of omics data: vertical and horizontal data integration.

III.Illustrate different methods for the integration of omics data.

Dr. Cristina Furlan, from Wageningen University & Research (WUR).

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archaeology, among others.

The need for structured data management has increased with the expansion of experimental tools and methods available. This is even more relevant for the study of surface and colloid biology. For instance, for the study of the development of microbial biofilms on a chosen surface, multiple technologies can be combined such as next generation sequencing (NGS) of DNA or RNA, GC-MS and LC-MS/MS, exploring the system at different levels (genes, transcripts, proteins, metabolites). Moreover, these can be complemented with techniques for analysis of the physical surface and its properties such as LC-HRMS or AFM-RAMAN. These are large and complex datasets that require deployment of tailored methods for multi-omics data integration.

Such integrative analyses are only possible when extensive information on the relationship between the samples and the datasets is preserved. Good data management strategies enacting the Findable, Accessible, Interoperable and Reusable (FAIR) principles have shown to greatly increase scientific reproducibility, discovery and innovation.

In this video we will explore what FAIR data management is and set the basis of its use.

Learning objectives

After successful completion of this video participants will be able to:

I.Identify the need for proper data management

II.Describe FAIR principles for data management

III.Discriminate minimal information requirements and standards for data deposition.

IV.Experiment on metadata format