Nolwenn LeMeur

Bio: Nolwenn LeMeur is an academic researcher from Fred Hutchinson Cancer Research Center. The author has contributed to research in topics: Flow Cytometry Standard & Bioconductor. The author has an hindex of 1, co-authored 1 publications receiving 358 citations. Previous affiliations of Nolwenn LeMeur include University of Rennes.

flowCore: a Bioconductor package for high throughput flow cytometry.

Abstract: Background: Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates. Results: We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry. Conclusion: The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.

Critical assessment of automated flow cytometry data analysis techniques

Abstract: Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Web-based Analysis and Publication of Flow Cytometry Experiments

Abstract: Cytobank is a Web-based application for storage, analysis, and sharing of flow cytometry experiments. Researchers use a Web browser to log in and use a wide range of tools developed for basic and advanced flow cytometry. In addition to providing access to standard cytometry tools from any computer, Cytobank creates a platform and community for developing new analysis and publication tools. Figure layouts created on Cytobank are designed to allow transparent access to the underlying experiment annotation and data processing steps. Since all flow cytometry files and analysis data are stored on a central server, experiments and figures can be viewed or edited by anyone with the proper permission, from any computer with Internet access. Once a primary researcher has performed the initial analysis of the data, collaborators can engage in experiment analysis and make their own figure layouts using the gated, compensated experiment files. Cytobank is available to the scientific community at http://www.cytobank.org.

Computational flow cytometry: helping to make sense of high-dimensional immunology data

Abstract: Recent advances in flow cytometry allow scientists to measure an increasing number of parameters per cell, generating huge and high-dimensional datasets. To analyse, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community. Computational flow cytometry is emerging as an important new field at the intersection of immunology and computational biology; it allows new biological knowledge to be extracted from high-throughput single-cell data. This Review provides non-experts with a broad and practical overview of the many recent developments in computational flow cytometry.

Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry.

Abstract: Microglia, the specialized innate immune cells of the CNS, play crucial roles in neural development and function. Different phenotypes and functions have been ascribed to rodent microglia, but little is known about human microglia (huMG) heterogeneity. Difficulties in procuring huMG and their susceptibility to cryopreservation damage have limited large-scale studies. Here we applied multiplexed mass cytometry for a comprehensive characterization of postmortem huMG (103 - 104 cells). We determined expression levels of 57 markers on huMG isolated from up to five different brain regions of nine donors. We identified the phenotypic signature of huMG, which was distinct from peripheral myeloid cells but was comparable to fresh huMG. We detected microglia regional heterogeneity using a hybrid workflow combining Cytobank and R/Bioconductor for multidimensional data analysis. Together, these methodologies allowed us to perform high-dimensional, large-scale immunophenotyping of huMG at the single-cell level, which facilitates their unambiguous profiling in health and disease.