OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects

Counting circular objects such as cell colonies is an important source of information for biologists. Although this task is often time-consuming and subjective, it is still predominantly performed manually. The aim of the present work is to provide a new tool to enumerate circular objects from digital pictures and video streams. Here, I demonstrate that the created program, OpenCFU, is very robust, accurate and fast. In addition, it provides control over the processing parameters and is implemented in an in- tuitive and modern interface. OpenCFU is a cross-platform and open-source software freely available at http://opencfu.sourceforge.net

Intelligent computational system for colony-forming-unit enumeration and differentiation

Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in a wide range of fields. The enumeration processes of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular pathologies require an urgent and accurate response for the therapy to be effective. To reduce time limitations and perhaps discrepancies, this work introduces an intelligent image processing software capable of automatically quantifying the number of Colony Forming Units (CFUs) in Petri-plates. This rapid enumeration enables the technician to provide an expeditious assessment of the microbial load. Moreover, an auxiliary system is able to differentiate among colony images of Echerichia coli, Pseudomonas aeruginosa and Staphylococcus aureus via Machine Learning, based on a Convolutional Neural Network in a process of cross-validation. For testing and validation of the system, the three bacterial groups were cultured, and a significant labeled database was created, exercising suited microbiological laboratory methodologies and subsequent image acquisition. The system demonstrated acceptable accuracy measures; the mean values of precision, recall and F-measure were 95%, 95% and 0.95, for E. coli, 91%, 91% and 0.90 for P. aeruginosa, and 84%, 86% and 0.85 for S. aureus. The adopted deep learning approach accomplished satisfactory results, manifesting 90.31% of accuracy. Ultimately, evidence related to the time-saving potential of the system was achieved; the time spent on the quantification of plates with a high number of colonies might be reduced to a half and occasionally to a third.A análise quantitativa de microrganismos é uma ferramenta essencial na aferição da segurança e qualidade numa ampla variedade de áreas. O processo de enumeração de microrganismos viáveis através das técnicas de cultura tradicionais é económica e metodologicamente adequado, conferindo lhe alta aplicabilidade a nível mundial. Contudo, a contagem manual pode ser morosa, laboriosa e imprecisa. Em adição, certas patologias requerem uma urgente e precisa resposta de modo a que a terapia seja eficaz. De forma a reduzir limitações e discrepâncias, este trabalho apresenta um software inteligente de processamento de imagem capaz de quantificar automaticamente o número de Unidades Formadoras de Colónias (UFCs) em placas de Petri. Esta rápida enumeração, possibilita ao técnico uma expedita avaliação da carga microbiana. Adicionalmente, um sistema auxiliar tem a capacidade de diferenciar imagens de colónias de Echerichia coli, Pseudomonas aeruginosa e Staphylococcus aureus recorrendo a Machine Learning, através de uma Rede Neuronal Convolucional num processo de validação cruzada. Para testar e validar o sistema, os três grupos bacterianos foram cultivados e uma significativa base de dados foi criada, recorrendo às adequadas metodologias microbiológicas laboratoriais e subsequente aquisição de imagens. O sistema demonstrou medidas de precisão aceitáveis; os valores médios de precisão, recall e F-measure, foram 95%, 95% e 0.95, para E. coli, 91%, 91% e 0.90 para P. aeruginosa, e 84%, 86% e 0.85 para S. aureus. A abordagem deep learning obteve resultados satisfatórios, manifestando 90.31% de precisão. O sistema revelou potencial em economizar tempo; a duração de tarefas afetas à quantificação de placas com elevado número de colónias poderá ser reduzida para metade e ocasionalmente para um terço

An Integrated Analytical Approach for the Characterization of Probiotic Strains in Food Supplements

Research surrounding health benefits from probiotics is becoming popular because of the increasing demand for safer products with protective and therapeutic effects. Proven benefits are species- or genus-specific; however, no certified assays are available for their characterization and quantification at the strain level in the food supplement industry. The objective of this study was to develop a strain-specific Real-time quantitative polymerase chain reaction (RT-qPCR)-based method to be implemented in routine tests for the identification and quantification of Bifidobacterium longum, Bifidobacterium animalis spp. lactis, Lactobacillus paracasei, Lactobacillus rhamnosus, Lactobacillus casei, Bifidobacterium breve, Lactobacillus acidophilus, Lactobacillus plantarum, and Lactobacillus helveticus, starting from a powder mixture of food supplements. The method optimization was carried out in combination with flow cytometry to compare results between the two strategies and implement the analytical workflow with the information also regarding cell viability. These assays were validated in accordance with the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) criteria using the plate count enumeration as the gold standard reference. Briefly, probiotic DNAs were extracted from two powder food supplements. Strain-specific primers targeting unique sequence regions of 16S RNA were identified and amplified by RT-qPCR. Primers were tested for specificity, sensitivity, and efficiency. Both RT-qPCR and flow-cytometry methods described in our work for the quantification and identification of Lactobacillus and Bifidobacterium strains were specific, sensitive, and precise, showing better performances with respect to the morphological colony identification. This work demonstrated that RT-qPCR can be implemented in the quality control workflow of commercial probiotic products giving more standardized and effective results regarding species discrimination

Whole Slide Imaging for High-Throughput Monitoring of Microbial Growth and Its Application to Rapid Antimicrobial Susceptibility Testing

Archival abstract submitte

Principal component-based image segmentation: a new approach to outline in vitro cell colonies

publishedVersio

Optimization of an automatic counting system for the quantification of Staphylococcus epidermidis cells in biofilms

Biofilm formation is recognized as the main virulence factor in a variety of chronic infections. In vitro evaluation of biofilm formation is often achieved by quantification of viable or total cells. However, these methods depend on biofilm disruption, which is often achieved by vortexing or sonication. In this study, we investigated the effects of sonication on the elimination of Staphylococcus epidermidis cell clusters from biofilms grown over time, and quantification was performed by three distinct analytical techniques. Even when a higher number of sonication cycles was used, some stable cell clusters remained in the samples obtained from 48- and 72-h-old biofilms, interfering with the quantification of sessile bacteria by plate counting. On the other hand, the fluorescence microscopy automatic counting system allowed proper quantification of biofilm samples that had undergone any of the described sonication cycles, suggesting that this is a more accurate method for assessing the cell concentration in S. epidermidis biofilms, especially in mature biofilms.This work was funded by the Fundacao para a Ciencia e a Tecnologia (FCT) and the COMPETE grants PTDC/BIA-MIC/113450/2009 and FCOMP-01-0124-FEDER-014309

IND-enabling studies for a clinical trial to genetically program a persistent cancer-targeted immune system

Purpose: To improve persistence of adoptively transferred T-cell receptor (TCR)–engineered T cells and durable clinical responses, we designed a clinical trial to transplant genetically-modified hematopoietic stem cells (HSCs) together with adoptive cell transfer of T cells both engineered to express an NY-ESO-1 TCR. Here, we report the preclinical studies performed to enable an investigational new drug (IND) application. Experimental Design: HSCs transduced with a lentiviral vector expressing NY-ESO-1 TCR and the PET reporter/suicide gene HSV1-sr39TK and T cells transduced with a retroviral vector expressing NY-ESO-1 TCR were coadministered to myelodepleted HLA-A2/K^b mice within a formal Good Laboratory Practice (GLP)–compliant study to demonstrate safety, persistence, and HSC differentiation into all blood lineages. Non-GLP experiments included assessment of transgene immunogenicity and in vitro viral insertion safety studies. Furthermore, Good Manufacturing Practice (GMP)–compliant cell production qualification runs were performed to establish the manufacturing protocols for clinical use. Results: TCR genetically modified and ex vivo–cultured HSCs differentiated into all blood subsets in vivo after HSC transplantation, and coadministration of TCR-transduced T cells did not result in increased toxicity. The expression of NY-ESO-1 TCR and sr39TK transgenes did not have a detrimental effect on gene-modified HSC's differentiation to all blood cell lineages. There was no evidence of genotoxicity induced by the lentiviral vector. GMP batches of clinical-grade transgenic cells produced during qualification runs had adequate stability and functionality. Conclusions: Coadministration of HSCs and T cells expressing an NY-ESO-1 TCR is safe in preclinical models. The results presented in this article led to the FDA approval of IND 17471

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny objects (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end CNN model with encoder to decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. It addresses the limitation of contour conglutination of dense objects while counting. Evaluation was done using classical segmentation metrics (Dice, Jaccard, Hausdorff distance) as well as counting metrics. Experimental result shows that the proposed PID-Net has the best performance and potential for dense tiny objects counting tasks, which achieves 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches like Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment the dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting tasks