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Additional file 1. Supplementary figures and tables cited in the text.

Metagenome-driven microbiome research is providing important new insights in fields as diverse as the pathogenesis of human disease, the metabolic interactions of complex microbial ecosystems involved in agriculture, and climate change. However, poor correlations typically observed between RNA and protein expression datasets even for single organisms make it hard to infer microbial protein expression with any accuracy from metagenomic data, thus restricting movement beyond microbial catalogues and into functional analysis of microbial effector molecules. By contrast, mass spectrometry analysis of microbiome data at the protein level in theory allows direct measurement of dynamic changes in microbial protein composition, localisation and modification that may mediate host/pathogen interactions in complex microbial ecosystems, but analysis of such metaproteomic datasets remains challenging. Here we describe a novel data analysis approach, MetaNovo , that searches complex datasets against the entire known protein universe, whilst still controlling false discovery rates, thus enabling metaproteomic data analyses without requiring prior expectation of likely sample composition or metagenomic data generation that classically inform construction of focussed, relatively small search libraries. MetaNovo directly identifies and quantifies the expressed metaproteomes, and estimates the microbial composition present in complex microbiome samples, using scalable de novo sequence tag matching and probabilistic optimization of very large, unbiased sequence databases prior to target-decoy search. We validated MetaNovo against the results obtained from the recently published MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications being found when searching relatively small databases. We then showed that using an unbiased search of the entire release of UniProt (ca. 90 million protein sequences 1 ) MetaNovo was able to identify a similar bacterial taxonomic distribution compared to that found using a small, focused matched metagenome database, but now also simultaneously identified proteins present in the samples that are derived from other organisms missed by 16S or shotgun sequencing and by previous metaproteomic methods. Using MetaNovo to analyze a set of single-organism human neuroblastoma cell-line samples ( SH-SY5Y ) against UniProt we achieved a comparable MS/MS identification rate during target-decoy search to using the UniProt human Reference proteome, with 22583 (85.99 %) of the total set of identified peptides shared in common. Taxonomic analysis of 612 peptides not found in the canonical set of human proteins yielded 158 peptides unique to the Chordata phylum as potential human variant identifications. Of these, 40 had previously been predicted and 9 identified using whole genome sequencing in a proteogenomic study of the same cell-line. By estimating taxonomic and peptide level information on microbiome samples directly from tandem mass spectrometry data, MetaNovo enables simultaneous identification of human, bacterial, helminth, fungal, viral and other eukaryotic proteins in a sample, thus allowing correlations between changes in microbial protein abundance and change in the host proteome to be drawn based on a single analysis. Data are available via ProteomeXchange with identifier PXD014214. The MetaNovo software is available from GitHub 2 and can be run as a standalone Singularity or Docker container available from the Docker Hub 3 .

Introduction Neurogenesis, the development of new neurons, starts soon after the formation of the neural tube and is largely completed by birth. Development of the brain after birth is mainly reliant on the formation of new connections between surviving neurons. However, adult neurogenesis does continue in the subgranular zone of the hippocampus from quiescent adult neural stem cells. Traditionally neural stem cells were cultured as neurospheres, a heterogeneous agglomeration of neural cells at various stages of differentiation. This heterogeneity prevented accurate quantitative analysis. In 2008 Sun et al produced the first non-immortalised human foetal neural stem (NS) cell line from nine week old human foetal cortex. These cells are cultured as monolayers, have a radial glia like appearance, self renew and form all three neural cell types, neurons, astrocytes and oligodendrocytes upon differentiation. More recently human foetal neuroepithelial like (NES) stem cells have been produced from five week old human foetal hind-brain, they resemble neuroepithelial cells, with characteristic rosettes, upon differentiation they appear to form a pure population of neurons. These homogeneous monolayer cultures enable quantitative proteomic analysis, to increase our understanding of early brain development Methods Three NES and two NS cell lines were available for analysis. They proliferate by stimulation from FGF and EGF, removal of these growth factors results in spontaneous differentiation. Proliferating NES and NS cells were compared using SILAC labelling. In addition, each cell line was differentiated for 12 days, 6 timepoints were taken and compared using label free quantitation. Results 4677 proteins were quantitated with 473 differentially expressed, revealing fundamental differences between NES and NS cells. NES cells are less differentiated, expressing SOX2 and LIN28, have active cell cycle processes, DNA elongation, histone modification and miRNA mediated gene silencing. Whereas NS cells are more developmentally defined, express multiple membrane proteins, have activated focal adhesion, thereby increasing their binding and interaction with their environment. NS metabolism is more oxidative, utilises lipid metabolism, the pentose phosphate pathway and produces creatine phosphate. Upon differentiation the cell cycle processes are downregulated and neurogenic and gliogenic processes increased. Conclusion This work represent a detailed in vitro characterisation of non immortalised human foetal neural stem cells, it describes the regulatory, metabolic and structural changes occurring within neural stem cells in early brain development. The information herein points towards de-differentiation potentially through LIN28-let7, as a means to produce more neurogenic neural stem cells in vitro thus aiding regenerative therapies, as well as provides a wealth of information for better understanding neurological developmental disorders.