(4) Finally, the method is based round the open-source Micro-Manager platform , which is freely available. specificity in excess of 95%. Abstract Raman micro-spectroscopy is usually a powerful technique for the identification and classification of malignancy cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology medical center, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to accomplish automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder malignancy cell lines. The entire automation process is usually implemented, using the open source Micro-Manager 4-Aminohippuric Acid platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers. ; (ii) to analyse multi-well plates  for the purpose of developing a Raman-based cell viability assay; specifically, on the effect of doxorubicin concentration on monocytic THP-1 cells; and (iii) to investigate the effect of the targeted malignancy drug panitumumab on colorectal malignancy cell lines . Although not a target application of the system proposed Rabbit polyclonal to Caspase 4 here, the study of pathogens, and in particular, bacteria, has been another key application area of automated Raman cytology systems in recent years. The automated system described in the previous paragraph was adapted to record spectra of single isolated neutrophils from human peripheral blood , which were stimulated via an in vitro contamination model with heat-inactivated bacterial and fungi pathogens; the system captured 20,000 neutrophil spectra across numerous treatment groups, originating from three donors. Another system developed by Douet et al.  4-Aminohippuric Acid was designed to provide automated Raman spectroscopy of individual bacteria cells; once again, this automated system is based on image processing in order to automatically identify the bacterial cell position, followed by alignment of the cell with the excitation laser. In this case, the image processing component relies on the availability of an out-of-focus diffraction pattern facilitated by the use of a spatially coherent illumination source. The recorded image can be described as an in-line digital hologram, which can be subjected to numerical propagation  in order to obtain an in-focus image of the sample. The cell position can be recognized based only on image contrast, whereby the bacterial cell appears dark against a bright background. In this paper, we present an automated Raman cytology system with several contributions: (1) This system utilises a simpler image processing component than previous systems, which is based on a single step. It is shown that this approach can accurately identify the epithelial cell nucleus position, which, to the best of our knowledge, has not been a target area for previous automated systems. (2) The system can be applied to unlabelled phase-only adherent cells, which produce low image contrast. (3) The system is demonstrated to work with the ThinPrep standard [5,7,12,24], an established instrument and protocol used to prepare cytology samples in hospital settings, such as the cervical Pap smear. (4) Finally, the method is based around the open-source Micro-Manager platform , which is freely available. The associated code is supplied in 4-Aminohippuric Acid an online repository  and is described in detail in the supplementary information. This approach can be implemented easily on any existing RMS system that uses a motorised translation stage and a computer-controllable microscope lamp and excitation laser, which are commonplace in modern life-science microscopes and commercial RMS systems. 2. Automation 2.1. Principle of Automation: Identifying Cell Nucleus Position Using the Nucleus Microlens-Effect Central to the proposed automated Raman.