In collaboration with TNO scientific team (led by Dr. Armand Paauw), we have developed framework to enable reliable identification of microorganisms, potentially causing human infectious disease. The identification is based on analysis of the bacterial proteome.
The developed application relies on a two-step workflow: a genus-level search followed by a species-level search. This strategy enables the rapid identification of microorganisms based on the analyzed proteome. Thus far, this method has mainly been validated using bacteria from simulated blood culture flasks (Berendsen et al. IJMM, 2019, Berendsen et al. Future Micro. 2017). The list of microorganisms in the database can be found in our publication (Berendsen et al. IJMM, 2019). Proteomes from bacterial colonies derived from agar-plates are also suited for analyses. Future releases of the webtool will include antimicrobial resistance detection.
High-end proteome analytical tools are realizing a quantum leap in medical diagnostics. The potential of these tools is also demonstrated for microbiological diagnostics. Whereas several technologies are being applied for genomic analyses, proteomics offer additional and largely complementary information. Having a high feasibility for automation, robust proteome analyzers have a huge potential to become used routinely for microbiology diagnostic purposes. The challenge is to create easy-to-use instruments and accompanying software. Creating methods with a fixed number of procedures and integrated data algorithms for the analysis of the MS-data tailored to the diagnostic questions relevant for clinical microbiology diagnostics are likely to be the next game changer in this area.
Identification of pathogens
Current analyzers are able to rapidly assess the proteome in a sample. With a few developmental steps in the field of simplification and standardization, introduction in clinical microbiology is only hampered by the lack of automated data interpretation. Until now proteome analysis of micro-organisms is cumbersome because existing data analysis software is developed from the premise that the proteome of one particular type of eukaryotic cell is studied. Therefore, it was our goal to realize a microbiology tuned proteome interpreter. To help you interpret complex microbiologic proteomics data and provide trustworthy answers to the (research) question that preceded the analysis. Our first achievement is the developed web application on this website, which is tailored to identify human bacterial pathogens. We can imagine that a similar approach in other fields of microbiology (e.g. food-, water-, plant-microbiology or contamination control) precise identification of bacteria can be crucial, as well. Or you may have another research question or application in mind, which can perhaps be analyzed with a similar proteomic analysis approach. TNO and HORAIZON are open to all types of collaborations and / or co-developing project in the field of proteomics and identification of micro-organisms.