Cell therapies based on inducible pluripotent stem cells offer promising new treatments for a variety of different illnesses. However, the sensitivity of stem cells to hydrodynamic stress makes developing reliable stem cell production processes challenging. Understanding hydrodynamic stress conditions experienced by stem cells during early-stage process development is important to guide scale-up and design scale-down experiments. We characterize the hydrodynamic stresses in a 125 mL shake flask using Lattice-Boltzmann implicit large eddy simulations (LB-ILES). First, we validated the LB-ILES shake flask simulations using volumetric power input measurements and experimental liquid distribution data showing good overall agreement, while also numerical challenges of the LB-ILES method regarding grid and time step dependencies are discussed. The mean shear stress in the shake flask increases from 0.01 to 0.24 Pa when increasing the shaking frequency from 55 to 250 rpm, and the mean Kolmogorov length scale decreases from 185 to 51 μm. Furthermore, time-averaged distributions of the shear stress and Kolmogorov length scales were evaluated and compared to reported stress thresholds for stem cells. Based on the shear stress and Kolmogorov length scale distributions, our developed shake flask CFD model can help to design small-scale experiments to characterize stem cell cultures in terms of their hydrodynamic stress tolerance, and ultimately guide scale-up stem cell cultures to larger cultivation systems.

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases.

The tremendous progress in sequencing technologies has made DNA sequencing routine for microbiome studies. Additionally, advances in mass spectrometric techniques have extended conventional proteomics into the field of microbial ecology. However, systematic studies that provide a better understanding of the complementary nature of these 'omics' approaches, particularly for complex environments such as wastewater treatment sludge, are urgently needed. Here, we describe a comparative metaomics study on aerobic granular sludge from three different wastewater treatment plants. For this, we employed metaproteomics, whole metagenome, and 16S rRNA amplicon sequencing to study the same granule material with uniform size. We furthermore compare the taxonomic profiles using the Genome Taxonomy Database (GTDB) to enhance the comparability between the different approaches. Though the major taxonomies were consistently identified in the different aerobic granular sludge samples, the taxonomic composition obtained by the different omics techniques varied significantly at the lower taxonomic levels, which impacts the interpretation of the nutrient removal processes. Nevertheless, as demonstrated by metaproteomics, the genera that were consistently identified in all techniques cover the majority of the protein biomass. The established metaomics data and the contig classification pipeline are publicly available, which provides a valuable resource for further studies on metabolic processes in aerobic granular sludge.