# A New Workflow to Generate Monoclonal Antibodies against Microorganisms
## 1 Introduction  
Monoclonal antibodies (mAbs) are highly versatile biomolecules used manifold in analytical and diagnostic systems for the detection of targets ranging from low molecular weight substances to whole microorganisms [1, 2, 3]. In point of care (POC) systems such as for contaminated drinking water, antibody-based systems are indispensable tools to monitor contamination with pathogenic microorganisms [4]. For the establishment of these detection systems, highly specific mAbs recognizing the pathogen of interest are mandatory. In addition to the complexity of pathogens, the generation of specific mAbs is limited by the complex procedure of inducing strain-specific immune responses and the laborious selection of desired hybridoma cell lines [5]. With this objective, we performed an initial proof of concept study by combining bioinformatic epitope prediction with novel immunization and selection tools to identify antibody candidates that can discriminate _E. coli_ O157:H7 strain (EHEC) from _Legionella pneumophila_ and _Bacillus_ ssp. The strain _E. coli_ O157:H7 (EHEC) was used as a model target to show the feasibility of our workflow. The process of generating mAbs against certain bacterial strains is often initiated by using inactivated microorganism fractions for immunization. These fractions display numerous different cell surface structures not solely specific for the target strain. Therefore, the immunization with these fractions leads to antibodies with broad specificity to both, the desired bacterial strain but other strains, too. These undesired cross-reactivities represent amajor challenge in the process of antibody generation [6]. Furthermore, the immunization of chosen epitopes can carry high risks when no or only minor immune reactions take place. An appropriate immune reaction is often dependent on the used carrier proteins and the interval of immunizations. In addition to this, the quality of the underlying screening process after fusion is key for the selection of appropriate binders and the functionality in the later application. Especially, when microorganisms are used as antigenic targets, it is important to select suitable and unique epitope structures on the cell surface so that the antibody can detect these structures in the native conformation and assay environment [6]. When hybridoma technology is used, the screening process is mainly performed by limited dilution in combination with numerous enzyme-linked immunosorbent assay (ELISA) screenings, which makes it a time-consuming and expensive procedure. In addition, the presentation of the antigen in cell-based ELISA is often not related to the final environment in the final detection system. To circumvent these disadvantages and provide an optimized process, we chose the enterohemorrhagic _E. coli_ O157:H7 strain (EHEC) as a model organism to evaluate our workflow. The _E. coli_ O157:H7 serotype is a food-borne pathogen first identified in 1982 during an outbreak in Michigan [7]. It is now the dominant hemorrhagic serotype, infecting around 100,000 patients every year in the U.S. [8], inducing gastrointestinal indications up to life-threatening complications as Hemolytic Uremic Syndrome (HUS) or Hemorrhagic Colitis induced by the production of Shiga toxin (Stx). The state-of-the-art detection of EHEC infections is performed mainly by PCR and conventional plate counts [9]. These methods are reliable but they need both specialized equipment and time. Ready-to-use POC devices represents a fast and on-site alternative to monitor outbreaks in the field where no laboratory access is suitable. Specific antibodies are the centerpiece for POC applications. However, false-positive results may occur due to cross-reactivities of the antibody to other bacterial strains in the sample. Therefore, a focused antibody generation against a targeted structure located only on the desired bacterial strain is of great importance. Our new approach led to the generation of several mAbs against _E.