Single-Cell Omics (SCO)

Single-cell omics has enabled us to better understand the diversity and functional heterogeneity of complex microbial communities, which is otherwise difficult with meta-omics methods. However, this field of research is still relatively new and there remain many methodological challenges that make it difficult to analyze single cells, especially from environmental samples. Our goal is to improve on currently existing methods in single cell genomics (SCG) and transcriptomics (SCT) so that these methods are more feasible for researchers to use and encourages the discovery of novel, uncultured prokaryotes (microbial dark matter).

Integration and binning of single cell omics (SCO) with environmental data provide unprecedented insights into microbial diversity and metabolic features.

We currently host the first prokaryotic SCO pipeline in Europe, that

  • enables the targeted selection of rare taxa for a deep investigation of microbial dark matter
  • allows to sort prokaryotic cells under anoxic conditions
  • improves cost-efficiency

As the majority of environmental microbial organisms still evade cultivation attempts, genomic insights into many taxa are limited to cultivation-independent approaches. However, current methods of metagenomic binning and single cell genome sequencing have individual drawbacks, which can limit the quality as well as completeness of the reconstructed genomes. Current attempts to combine both approaches still use whole genome amplification techniques which are known to be prone to bias. We established a novel approach for the purpose of metagenomic genome reconstructions, that utilizes the potential of fluorescence-activated cell sorting (FACS) for targeted enrichment and depletion of different cell types to create distinct cell fractions of sufficient size circumvent amplification. By distributing sequencing efforts over these fractions as well as the original sample, co-assemblies become highly optimized for co-abundance variation based binning approaches. “Midi-metagenomics” enables accurate metagenome assembled genome (MAG) reconstruction from individual sorted samples with higher quality than co-assembly of multiple distinct samples and has potential for the targeted enrichment and sequencing of uncultivated organism of interest.

This method has been published as a pre-print in bioRxiv

MDMcleaner is a pipeline for reliable contig classification of metagenome assembled (MAG) and single-cell amplified (SAG) genomes that is optimized for highly fragmented “microbial dark matter” genomes and is aware of potential reference database contaminations.
It uses the GTDB taxonomic system and GTDB representative genomes, as well as SILVA SSU and LSU and RefSeq eukaryotic and viral datasets as references. Classification is based on a “lowest common ancestor” (LCA) approach, that is implemented in a way that can recognize potential contaminants not only in the analyzed genome, but also in the underlying reference datasets. Furthermore each contig is classified only up to taxlevels that are actually supported by the corresponding alignment identities, thereby avoiding overclassification for organisms that are underrepresented in the reference database.

For more detailed information please refer to the corresponding publication at Nucleic Acids Research

The most recent source code is available at github sourcecode.

The tool is installable via pip PyPI version and bioconda install with bioconda.

While humans have a gut microbiome improving their immune system, plants have a rhizobiom that promotes immunity against biotic stress. Based on changes in abundance of roots exudates, some  Prokaryotes in the rhizosphere (a few millimeters interface between plant roots and surrounding soil) secrete polyketides – either killing pathogens or triggering plant defence.​
In the M4F project, together with Prof. Peter Nick – Botanical Institute of KIT, click here, we look for rhizobacteria promoting grapevines defence signalling against trunk diseases (Esca syndrome), which is an increasing problem for many vineyards due to global warming.​
Here, we study the soil microbiome of vineyards and terra preta by shotgun-metagenomics and metatranscriptomics to identify candidate phyla with functional potential to boost grapevines immunity against wood-decaying fungi.

This project is funded by KIT strategy fund.

If you want to visit the sampling spots you’ll find it here Gutshof Menges and in the area around.

Natural and constructed wetlands attenuate aquatic pollutants through an intricate relationship between microorganisms and plants, thereby safeguarding our water resources. We are using molecular tools to better understand microbial activities in the rhizosphere of wetlands as relevant driver of pollutant transformation. We are also investigating the fate of antimicrobial resistance in these and other (semi)-aquatic ecosystems. Overall, our work contributes to the exploration, integration, and protection of wetlands as nature-based solutions for sustainable living.

Further information can be found at the project webpages nature and RootWayS, and on Researchgate.

Briefly, our goal here is to label low abundant species from environmental samples using live-Fluorescence In Situ Hybridization (FISH) and isolate them from the rest of their microbial community in order to get axenic cultures of biotechnologically relevant Prokaryotes.

Strategy and workflow for isolation and cultivation of so far not-culturable microorganisms. (1) Investigation of an environmantal sample via meta-omics analysis. (2) After sequencing and bioinformatic analysis (3) target genes associated with desired function/property will be identified. (4) Designing specific probes enables (5) fluorescence-based single cell sorting. (6) Isolated cells are going to be cultivated in a specialalized incubation chamber (7) where medium(-components) are provided based on the environmental model. Genome analyses will also be used to identify media components as well as matrix composition like sugar-polymers or protein to provide tailor-made culture conditions (8).

The project “Microbial culture based on Meta-omics Assisted, Targeted Sorting, and Isolation in a Customized Matrix (MicroMATRIX)” is funded by the BMBF. You can check out further information about the project and our collaboration partners here:

Antibiotic resistance genes (ARGs) are one of the most challenging contaminants of emerging concern (CECs). Instead of being directly produced by human activity, ARGs emerge as consequence of antibiotic use in clinical settings, and residual antibiotic contamination. ARGs spread through horizontal gene transfer and conjugative plasmids, because their ability to cross inter-species barriers, are key in this process. Recent findings revealed the existence of marine plasmids (MAPS) of global distribution and broad host range. These MAPS can transmit ARGs across oceanic distances, and may reintroduce them to human food chains via marine products. They are, however, different
to classical plasmids from clinical settings. MAPMAR uses metagenomics, data science and single-cell sequencing to obtain a catalog of most prevalent and transmissible MAPs. By testing methods to block their transmission, MAPMAR explores strategies to curtail the risk of oceans acting as highways for ARG propagation.

During the last decade, single-cell omics technologies such as single-cell transcriptomics (SCT) have rapidly emerged. As a complement to cultivation-based meta-transcriptomic approaches, SCT allows direct access to information on gene expression from individual cells rather than bulk samples, and thus tremendously improves our understanding of numerous biological processes.

While single-cell technologies are mainly applied to and consequently vastly advanced for eukaryotes, adapting these methods to prokaryotic organisms has been proven challenging for several reasons. First, the stiff but yet highly diverse cell wall architecture among bacterial species hampers effective permeabilization and lysis. Furthermore, the low RNA amounts are insufficient to be directly analyzed via current sequencing technologies. Hence, most protocols comprise an essential amplification step before sequencing. However, this step might introduce a bias to the data set veiling the genuine expression profiles by incomplete or uneven amplification, chimera formation, and biases against templates with high GC content. Although various experimental techniques have been developed to deal with these issues, no systematic approach has been taken to evaluate this bias. Nonetheless, studying the microbial world at single-cell level is of fundamental importance, particularly regarding the enormous potential of (yet unknown) microbial species for biotechnological applications, but also in the context of infectious diseases and cancer progression and therapy.

Therefore, this project aims at addressing the issues of amplification bias in SCT. To do so, prokaryotic single-cell RNA-seq data will be generated and used to train suitable regression models. By using modern methods from machine learning, technical noise and biological variation will systematically be discriminated to eventually correct for the bias.

Click also here to learn more about the project!

This project mainly focuses on the microbial community and metabolic potential of Chloroflexi in hot springs. FISH combined with FACS is used to target and sort single cells of Chloroflexi in samples, followed by single cell sequencing. The microbial community of Chloroflexi in hot springs was analyzed to explore its diversity. Based on single amplified genomes (SAGs), the metabolic potentials of Chloroflexi is investigated.