The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Bernhard M Fuchs - One of the best experts on this subject based on the ideXlab platform.
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single Cell Identification in microbial communities by improved fluorescence in situ hybridization techniques
Nature Reviews Microbiology, 2008Co-Authors: Rudolf Amann, Bernhard M FuchsAbstract:Amann and Fuchs provide an update on recent methodological improvements to fluorescencein situhybridization protocols, with a particular focus on whether the original group-specific probes, which were mostly developed more than 10 years ago, are still valid. The ribosomal-RNA (rRNA) approach to microbial evolution and ecology has become an integral part of environmental microbiology. Based on the patchy conservation of rRNA, oligonucleotide probes can be designed with specificities that range from the species level to the level of phyla or even domains. When these probes are labelled with fluorescent dyes or the enzyme horseradish peroxidase, they can be used to identify single microbial Cells directly by fluorescence in situ hybridization. In this Review, we provide an update on the recent methodological improvements that have allowed more reliable quantification of microbial populations in situ in complex environmental samples, with a particular focus on the usefulness of group-specific probes in this era of ever-growing rRNA databases.
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single Cell Identification in microbial communities by improved fluorescence in situ hybridization techniques
Nature Reviews Microbiology, 2008Co-Authors: Rudolf Amann, Bernhard M FuchsAbstract:The ribosomal-RNA (rRNA) approach to microbial evolution and ecology has become an integral part of environmental microbiology. Based on the patchy conservation of rRNA, oligonucleotide probes can be designed with specificities that range from the species level to the level of phyla or even domains. When these probes are labelled with fluorescent dyes or the enzyme horseradish peroxidase, they can be used to identify single microbial Cells directly by fluorescence in situ hybridization. In this Review, we provide an update on the recent methodological improvements that have allowed more reliable quantification of microbial populations in situ in complex environmental samples, with a particular focus on the usefulness of group-specific probes in this era of ever-growing rRNA databases.
Tamim Abdelaal - One of the best experts on this subject based on the ideXlab platform.
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a comparison of automatic Cell Identification methods for single Cell rna sequencing data
Genome Biology, 2019Co-Authors: Tamim Abdelaal, Lieke Michielsen, Davy Cats, Dylan Hoogduin, Hailiang Mei, Marcel J T Reinders, Ahmed MahfouzAbstract:Single-Cell transcriptomics is rapidly advancing our understanding of the Cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine Cell identities, which are time-consuming and irreproducible. The exponential growth in the number of Cells and samples has prompted the adaptation and development of supervised classification methods for automatic Cell Identification. Here, we benchmarked 22 classification methods that automatically assign Cell identities including single-Cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-Cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified Cells, and computation time. We further evaluate the methods’ sensitivity to the input features, number of Cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. We present a comprehensive evaluation of automatic Cell Identification methods for single-Cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.
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a comparison of automatic Cell Identification methods for single Cell rna sequencing data
bioRxiv, 2019Co-Authors: Tamim Abdelaal, Lieke Michielsen, Davy Cats, Dylan Hoogduin, Hailiang Mei, Marcel J T Reinders, Ahmed MahfouzAbstract:Abstract Background Single Cell transcriptomics are rapidly advancing our understanding of the Cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine Cell identities, which are time-consuming and irreproducible. The exponential growth in the number of Cells and samples has prompted the adaptation and development of supervised classification methods for automatic Cell Identification. Results Here, we benchmarked 20 classification methods that automatically assign Cell identities including single Cell-specific and general-purpose classifiers. The methods were evaluated using eight publicly available single Cell RNA-sequencing datasets of different sizes, technologies, species, and complexity. The performance of the methods was evaluated based on their accuracy, percentage of unclassified Cells, and computation time. We further evaluated their sensitivity to the input features, their performance across different annotation levels and datasets. We found that most classifiers performed well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose SVM classifier has overall the best performance across the different experiments. Conclusions We present a comprehensive evaluation of automatic Cell Identification methods for single Cell RNA-sequencing data. All the code used for the evaluation is available on GitHub (https://github.com/tabdelaal/scRNAseq_Benchmark). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support extension of new methods and new datasets (https://github.com/tabdelaal/scRNAseq_Benchmark/tree/snakemake_and_docker).
Rudolf Amann - One of the best experts on this subject based on the ideXlab platform.
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single Cell Identification in microbial communities by improved fluorescence in situ hybridization techniques
Nature Reviews Microbiology, 2008Co-Authors: Rudolf Amann, Bernhard M FuchsAbstract:Amann and Fuchs provide an update on recent methodological improvements to fluorescencein situhybridization protocols, with a particular focus on whether the original group-specific probes, which were mostly developed more than 10 years ago, are still valid. The ribosomal-RNA (rRNA) approach to microbial evolution and ecology has become an integral part of environmental microbiology. Based on the patchy conservation of rRNA, oligonucleotide probes can be designed with specificities that range from the species level to the level of phyla or even domains. When these probes are labelled with fluorescent dyes or the enzyme horseradish peroxidase, they can be used to identify single microbial Cells directly by fluorescence in situ hybridization. In this Review, we provide an update on the recent methodological improvements that have allowed more reliable quantification of microbial populations in situ in complex environmental samples, with a particular focus on the usefulness of group-specific probes in this era of ever-growing rRNA databases.
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single Cell Identification in microbial communities by improved fluorescence in situ hybridization techniques
Nature Reviews Microbiology, 2008Co-Authors: Rudolf Amann, Bernhard M FuchsAbstract:The ribosomal-RNA (rRNA) approach to microbial evolution and ecology has become an integral part of environmental microbiology. Based on the patchy conservation of rRNA, oligonucleotide probes can be designed with specificities that range from the species level to the level of phyla or even domains. When these probes are labelled with fluorescent dyes or the enzyme horseradish peroxidase, they can be used to identify single microbial Cells directly by fluorescence in situ hybridization. In this Review, we provide an update on the recent methodological improvements that have allowed more reliable quantification of microbial populations in situ in complex environmental samples, with a particular focus on the usefulness of group-specific probes in this era of ever-growing rRNA databases.
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Identification of single bacterial Cells using digoxigenin labelled rrna targeted oligonucleotides
Microbiology, 1991Co-Authors: Boris Zarda, Rudolf Amann, Gunter Wallner, Karlheinz SchleiferAbstract:SUMMARY: Oligonucleotides were end-labelled with digoxigenin (DIG), chemically at the 5'-end or enzymically at the 3'-end. Following specific in situ hybridization of these probes to intraCellular rRNA molecules, the hybrids were detected with anti-DIG Fab fragments labelled with fluorescent dyes. The antibody fragments penetrated through the bacterial Cell periphery and specifically bound to their antigens. Probe-conferred and non-specific fluorescence per Cell were quantified by flow cytometry and compared to values obtained with end-labelled fluorescent probes. The DIG reporter molecules could also be detected in whole fixed Cells by antibodies labelled with either alkaline phosphatase or horseradish peroxidase. The penetration of the large antibody-enzyme complexes into the Cells required lysozyme/EDTA treatment prior to the hybridization and has so far only been achieved for Gramnegative bacteria. This technique has the potential for significant signal amplification as compared to the fluorescently end-labelled oligonucleotides hitherto used for single Cell Identification in microbial ecology. Moreover, it can be used instead of fluorescent assays in natural samples showing autofluorescence.
Jurgen Popp - One of the best experts on this subject based on the ideXlab platform.
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raman spectroscopy based Cell Identification on a microhole array chip
Micromachines, 2014Co-Authors: Ute Neugebauer, Christian M Kurz, Thomas Bocklitz, Tina Berger, Thomas Velten, Joachim H Clement, Christoph Krafft, Jurgen PoppAbstract:Circulating tumor Cells (CTCs) from blood of cancer patients are valuable prognostic markers and enable monitoring responses to therapy. The extremely low number of CTCs makes their isolation and characterization a major technological challenge. For label-free Cell Identification a novel combination of Raman spectroscopy with a microhole array platform is described that is expected to support high-throughput and multiplex analyses. Raman spectra were registered from regularly arranged Cells on the chip with low background noise from the silicon nitride chip membrane. A classification model was trained to distinguish leukocytes from myeloblasts (OCI-AML3) and breast cancer Cells (MCF-7 and BT-20). The model was validated by Raman spectra of a mixed Cell population. The high spectral quality, low destructivity and high classification accuracy suggests that this approach is promising for Raman activated Cell sorting.
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classification of raman spectra of single Cells with autofluorescence suppression by wavelength modulated excitation
Analytical Methods, 2013Co-Authors: Sebastian Dochow, Joachim H Clement, Christoph Krafft, Norbert Bergner, Michael Mazilu, Bavishna B Praveen, Praveen C Ashok, Robert F Marchington, Kishan Dholakia, Jurgen PoppAbstract:Wavelength modulated Raman spectroscopy has recently been shown to suppress the fluorescence background generated by the sample and the substrate. Here we apply this technique to collect wavelength modulated Raman spectra from 697 individual Cells for a model system of circulating tumour Cells that consists of leukocytes from patient's blood, acute myeloid leukaemia Cells (OCI-AML3), and breast tumour Cells BT-20 and MCF-7. We study the classification behaviour of wavelength modulated Raman spectra in comparison to a common background correction method in chemometrics. Classifications using a support vector machine with a radial based kernel function were compared for classical Raman spectra, average Raman spectra of each Cell and wavelength modulated Raman spectra. The dataset was divided into 80% training spectra and 20% independent validation spectra. The stability of the classification was tested by performing training and validation 200 times with randomly selected datasets. The results are displayed in box whisker plots. Cell Identification based on wavelength modulated Raman spectra gives similar classification rates than classical and averaged Raman spectra with a tendency of reduced accuracies and increased modelling variations. Possible explanations and strategies to further improve the wavelength modulated Raman spectroscopy are discussed.
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tumour Cell Identification by means of raman spectroscopy in combination with optical traps and microfluidic environments
Lab on a Chip, 2011Co-Authors: Sebastian Dochow, Ute Neugebauer, Thomas Bocklitz, Christoph Krafft, Thomas Henkel, Gunter Mayer, Jens Albert, Jurgen PoppAbstract:Raman spectroscopy has been recognized to be a powerful tool for label-free discrimination of Cells. Sampling methods are under development to utilize the unique capabilities to identify Cells in body fluids such as saliva, urine or blood. The current study applied optical traps in combination with Raman spectroscopy to acquire spectra of single Cells in microfluidic glass channels. Optical traps were realized by two 1070 nm single mode fibre lasers. Microflows were controlled by a syringe pump system. A novel microfluidic glass chip was designed to inject single Cells, modify the flow speed, accommodate the laser fibres and sort Cells after Raman based Identification. Whereas the integrated microchip setup used 514 nm for excitation of Raman spectra, a quartz capillary setup excited spectra with 785 nm laser wavelength. Classification models were trained using linear discriminant analysis to differentiate erythrocytes, leukocytes, acute myeloid leukaemia Cells (OCI-AML3), and breast tumour Cells BT-20 and MCF-7 with accuracies that are comparable with previous Raman experiments of dried Cells and fixed Cells in a Petri dish. Implementation into microfluidic environments enables a high degree of automation that is required to improve the throughput of the approach for Raman activated Cell sorting.
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Identification and differentiation of single Cells from peripheral blood by raman spectroscopic imaging
Journal of Biophotonics, 2010Co-Authors: Ute Neugebauer, Thomas Bocklitz, Joachim H Clement, Christoph Krafft, Jurgen PoppAbstract:Medical diagnosis can be improved significantly by fast, highly sensitive and quantitative Cell Identification from easily accessible body fluids. Prominent examples are disseminated tumor Cells circulating in the peripheral blood of cancer patients. These Cells are extremely rare and therefore difficult to detect. In this contribution we present the Raman spectroscopic characterization of different Cells that can be found in peripheral blood such as leukocytes, leukemic Cells and solid tumor Cells. Leukocytes were isolated from the peripheral blood from healthy donors. Breast carcinoma derived tumor Cells (MCF-7, BT-20) and myeloid leukaemia Cells (OCI-AML3) were prepared from Cell cultures. Raman images were collected from dried Cells on calcium fluoride slides using 785 nm laser excitation. Unsupervised statistical methods (hierarchical cluster analysis and principal component analysis) were used to visualize spectral differences and cluster formation according to the Cell type. With the help of supervised statistical methods (support vector machines) a classification model with 99.7% accuracy rates for the differentiation of the Cells was built. The model was successfully applied to identify single Cells from an independent mixture of Cells based on their vibrational spectra. The classification was confirmed by fluorescence staining of the Cells after the Raman measurement.
Ahmed Mahfouz - One of the best experts on this subject based on the ideXlab platform.
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a comparison of automatic Cell Identification methods for single Cell rna sequencing data
Genome Biology, 2019Co-Authors: Tamim Abdelaal, Lieke Michielsen, Davy Cats, Dylan Hoogduin, Hailiang Mei, Marcel J T Reinders, Ahmed MahfouzAbstract:Single-Cell transcriptomics is rapidly advancing our understanding of the Cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine Cell identities, which are time-consuming and irreproducible. The exponential growth in the number of Cells and samples has prompted the adaptation and development of supervised classification methods for automatic Cell Identification. Here, we benchmarked 22 classification methods that automatically assign Cell identities including single-Cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-Cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified Cells, and computation time. We further evaluate the methods’ sensitivity to the input features, number of Cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. We present a comprehensive evaluation of automatic Cell Identification methods for single-Cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.
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a comparison of automatic Cell Identification methods for single Cell rna sequencing data
bioRxiv, 2019Co-Authors: Tamim Abdelaal, Lieke Michielsen, Davy Cats, Dylan Hoogduin, Hailiang Mei, Marcel J T Reinders, Ahmed MahfouzAbstract:Abstract Background Single Cell transcriptomics are rapidly advancing our understanding of the Cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine Cell identities, which are time-consuming and irreproducible. The exponential growth in the number of Cells and samples has prompted the adaptation and development of supervised classification methods for automatic Cell Identification. Results Here, we benchmarked 20 classification methods that automatically assign Cell identities including single Cell-specific and general-purpose classifiers. The methods were evaluated using eight publicly available single Cell RNA-sequencing datasets of different sizes, technologies, species, and complexity. The performance of the methods was evaluated based on their accuracy, percentage of unclassified Cells, and computation time. We further evaluated their sensitivity to the input features, their performance across different annotation levels and datasets. We found that most classifiers performed well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose SVM classifier has overall the best performance across the different experiments. Conclusions We present a comprehensive evaluation of automatic Cell Identification methods for single Cell RNA-sequencing data. All the code used for the evaluation is available on GitHub (https://github.com/tabdelaal/scRNAseq_Benchmark). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support extension of new methods and new datasets (https://github.com/tabdelaal/scRNAseq_Benchmark/tree/snakemake_and_docker).