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Prof. Dr. Anne-Kristin Kaster

+49 721 608-23005

anne-kristin kasterQzu6∂kit edu

Biotechnology and microbial genetics

Hunting the Microbial Dark Matter

Prokaryotic microorganisms are the oldest, most abundant, and particularly most diverse forms of life on earth and dominate many functions of the biosphere, including the productivity of the oceans and the global cycles of carbon, nitrogen, and other elements. Prokaryotes also harbor an enormous potential for novel natural product discovery, bioremediation and bioenergy production. However, it is estimated that over 99% of all microbial species from environmental microcosms remain uncultured, attempts to grow them under laboratory conditions fail or they grow too slowly to obtain sufficient biomass for analysis. Genome sequencing for the vast majority of Prokaryotes has therefore been inaccessible, obscuring the knowledge of microbial diversity, metabolic potentials and evolutionary histories. 

The ability to isolate individual microbes as single cells from a complex environmental sample and study their genomes provides a powerful tool to probe this biological “dark matter”. Single cell genomics (SCG) is therefore an essential complement to cultivation-based, metagenomic, and microbial community-focused research approaches. 

Please feel free to contact us, either for general information, collaborations or job applications. There is always a bench waiting for motivated students or postdoctoral fellows that are willing to apply for a fellowship.

 

 

Single Cell Genomics

Single cell genomics (SCG) consists of a series of integrated processes (Figure 1), enabling to read genetic information at the most basic level of biological organization and circumventing the problem of cultivation bias and selection during the isolation process. This allows direct access to genomic sequences and reference genomes of individual microorganisms from deep-branching phylogenetic groups that presently lack cultured representatives. Its integration and binning with environmental omics data provides unprecedented insights into microbial diversity and metabolic features that can not be detected by any of the individual techniques alone.

Fig 1: SCG pipeline established at the DSMZ.

(A) Unless analyzed immediately, environmental samples require deep-freezing in the presence of e.g. glycerol that preserves the integrity of the cell and its DNA. Cells are stained with a fluorescent dye, such as DAPI or SYBR® Green.

(B) Physical isolation of a single cell is performed by Fluorescent Activated Cell Sorting (FACS). Cells are suspended in a narrow stream of liquid such that they pass single-file through the path of multiple laser beams, each of a different wavelength. Optical detectors convert fluorescent light emitted from each cell into an electrical signal that can be processed to measure cell characteristics. The stream is then passed through a nozzle and broken into small droplets, each drop containing a single cell. The droplets acquire an electric charge and are then electrostatically deflected into e.g. 384 well plates.

C) After the separation the single cells need to be lysed to release their DNA. Most cell lysis in SCG today relies on an alkaline solution.

(D) Since a typical bacterial chromosome only contains a few femto- grams of of DNA, amplification is needed by a factor of about 106. Multiple Displacement amplification (MDA) is the most widely used reaction and relies on bacteriophage Φ29 DNA polymerase, an enzyme that is highly processive and has a strong strand displacement activity. The reaction is primed by random exonuclease-resistant hexanucleotides to synthesize overlapping amplicons. The amplification becomes exponential by a branching mechanism and up to >100 kb DNA products that can be obtained.

(E) PCR is used to screen for specific loci after the amplification, usually with broad eubacterial and archaeal 16S rRNA primers, followed by Sanger sequencing.

(F) After DNA library preparation, Next Generation Sequencing technologies like Illumina and PacBio are available for genome sequencing.

(G) After quality assessment, trimming and/or normalization of the sequencing reads, bioinformatics tools can conduct the assembly, orf calling and annotation of the genes, as well as pathway reconstruction and gene comparisons.