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Photoacoustic in vitro flow cytometry for nanomaterial research.

ABSTRACT: Conventional flow cytometry is a versatile tool for drug research and cell characterization. However, it is poorly suited for quantification of non-fluorescent proteins and artificial nanomaterials without the use of additional labeling. The rapid growth of biomedical applications for small non-fluorescent nanoparticles (NPs) for drug delivery and contrast and therapy enhancement, as well as research focused on natural cell pigments and chromophores, demands high-throughput quantification methods for the non-fluorescent components. In this work, we present a novel photoacoustic (PA) fluorescence flow cytometry (PAFFC) platform that combines NP quantification though PA detection with conventional in vitro flow cytometry sample characterization using fluorescence labeling. PAFFC simplifies high-throughput analysis of cell-NP interactions, optimization of targeted nanodrugs, and NP toxicity assessment, providing a direct correlation between NP uptake and characterization of toxicity markers for every cell.

SUBMITTER: Nedosekin DA

PROVIDER: S-EPMC5387917 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Photoacoustic <i>in vitro</i> flow cytometry for nanomaterial research.

Nedosekin Dmitry A DA Fahmi Tariq T Nima Zeid A ZA Nolan Jacqueline J Cai Chengzhong C Sarimollaoglu Mustafa M Dervishi Enkeleda E Basnakian Alexei A Biris Alexandru S AS Zharov Vladimir P VP

Photoacoustics 20170327

Conventional flow cytometry is a versatile tool for drug research and cell characterization. However, it is poorly suited for quantification of non-fluorescent proteins and artificial nanomaterials without the use of additional labeling. The rapid growth of biomedical applications for small non-fluorescent nanoparticles (NPs) for drug delivery and contrast and therapy enhancement, as well as research focused on natural cell pigments and chromophores, demands high-throughput quantification method ...[more]

PMID: 28417068

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Project description:Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

Dataset Information

Photoacoustic in vitro flow cytometry for nanomaterial research.

Publications

Photoacoustic <i>in vitro</i> flow cytometry for nanomaterial research.

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