Standardization of flow cytometry and cell sorting to enable a transcriptomic analysis in a multi-site sarcoidosis study
Roman E. MagallonLaura D. HarmacekN. ArgerPineet GrewalLinda S. PowersBrenda R. WernerBriana BarkesLi LiKristyn MacPhailMay GillespieElizabeth K. WhiteSarah E. CollinsTalyor BrownJessica CardenasEdward S. ChenLisa A. MaierSonia M. LeachNabeel HamzehLaura L. KothBrian P. O’Connor
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The contribution and regulation of various CD4 + T cell lineages that occur with remitting vs progressive courses in sarcoidosis are poorly understood. We developed a multiparameter flow cytometry panel to sort these CD4 + T cell lineages followed by measurement of their functional potential using RNA-sequencing analysis at six-month intervals across multiple study sites. To obtain good quality RNA for sequencing, we relied on chemokine receptor expression to identify and sort lineages. To minimize gene expression changes induced by perturbations of T cells and avoid protein denaturation caused by freeze/thaw cycles, we optimized our protocols using freshly isolated samples at each study site. To accomplish this study, we had to overcome significant standardization challenges across multiple sites. Here, we detail standardization considerations for cell processing, flow staining, data acquisition, sorting parameters, and RNA quality control analysis that were performed as part of the NIH-sponsored, multi-center study, BRonchoscopy at Initial sarcoidosis diagnosis Targeting longitudinal Endpoints (BRITE). After several rounds of iterative optimization, we identified the following aspects as critical for successful standardization: 1) alignment of PMT voltages across sites using CS&T/rainbow bead technology; 2) a single template created in the cytometer program that was used by all sites to gate cell populations during data acquisition and cell sorting; 3) use of standardized lyophilized flow cytometry staining cocktails to reduce technical error during processing; 4) development and implementation of a standardized Manual of Procedures. After standardization of cell sorting, we were able to determine the minimum number of sorted cells necessary for next generation sequencing through analysis of RNA quality and quantity from sorted T cell populations. Overall, we found that implementing a multi-parameter cell sorting with RNA-seq analysis clinical study across multiple study sites requires iteratively tested standardized procedures to ensure comparable and high-quality results.Keywords:
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The article reviews applications of flow cytometry sorting in manufacturing of pharmaceuticals. Flow cytometry sorting is an extremely powerful tool for monitoring, screening and separating single cells based on any property that can be measured by flow cytometry. Different applications of flow cytometry sorting are classified into groups and discussed in separate sections as follows: (a) isolation of cell types, (b) high throughput screening, (c) cell surface display, (d) droplet fluorescent-activated cell sorting (FACS). Future opportunities are identified including: (a) sorting of particular fractions of the cell population based on a property of interest for generating inoculum that will result in improved outcomes of cell cultures and (b) the use of population balance models in combination with FACS to design and optimize cell cultures.
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Experimental results in fungal biology research are usually obtained as average measurements across whole populations of cells, whilst ignoring what is happening at the single cell level. Microscopy has allowed us to study single-cell behavior, but it has low throughput and cannot be used to select individual cells for downstream experiments. Here we present a method that allows for the analysis and selection of single fungal cells in high throughput by flow cytometry and fluorescence activated cell sorting (FACS), respectively. This protocol can be adapted for every fungal species that produces cells of up to 70 microns in diameter. After initial setting of the flow cytometry gates, which takes a single day, accurate single cell analysis and sorting can be performed. This method yields a throughput of thousands of cells per second. Selected cells can be subjected to downstream experiments to study single-cell behavior.
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Introduction: Flow cytometry and cell sorting are powerful technologies for examining the molecular, genetic, and physiological properties of individual cells.Objective: The objective of this article is to provide a historical survey of the development of flow cytometry and cell sorting for use with higher plants, a summary of the state of art at the present day, and a prediction of where the field might progress over the coming years.Methods: Adapting flow cytometry and sorting for use with higher plants requires the production of single cell suspensions, or suspensions of subcellular organelles. It also requires identification of methods for fluorescence labeling of the cells or organelles of interest, such that they can be usefully analyzed and sorting. These methods are identified and outlined.Results and conclusions: Recent advances in molecular and biotechnological methods, platforms, and instrumentation, combined with flow cytometry and sorting, provide increasingly powerful analytical tools for exploring the components and structure of regulatory networks governing plant growth and development, and the interactions of plants with their environments. They also will be invaluable in cataloguing the individual species that comprise the biological diversity of flowering plants.
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This chapter contains sections titled: Introduction Methods, Technologies, and Results Current Methods for Global Analysis of Gene Expression Methods Based on Hybridization Methods Based on Sequencing Emerging Sequencing Technologies Other -omics Disciplines and Technologies Using Flow Cytometry to Monitor Gene Expression and Cellular States Transgenic Markers Suitable for Flow Cytometry and Sorting Subcellular Targeting as a Means for Transgenic Analysis Using Flow Sorting to Measure Gene Expression and Define Cellular States Protoplast and Cell Sorting Based on Endogenous Properties Protoplast Sorting Based on Transgenic Markers Sorting of Nuclei Based on Transgenic Markers Prospects Combining Flow and Image Cytometry Use of Protoplasts for Confirmatory Studies Analysing Noise in Gene Expression
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Background: A stream-in-air cell sorter enables rapid sorting to a high purity, but it is not well suited for sorting of infectious material due to the risk of airborne spread to the surroundings. Methods: A FACS Vantage cell sorter was modified for safe use with potentially HIV infected cells. Safety tests with bacteriophages were performed to evaluate the potential spread of biologically active material during cell sorting. Cells transduced with a retroviral vector carrying the gene for GFP were sorted on the basis of their GFP fluorescence, and GFP expression was followed during subsequent culture. Results: The bacteriophage sorting showed that the biologically active material was confined to the sorting chamber. A failure mode simulating a nozzle blockage resulted in detectable droplets inside the sorting chamber, but no droplets could be detected when an additional air suction from the sorting chamber had been put on. The GFP transduced cells were sorted to 99% purity. Cells not expressing GFP at the time of sorting did not turn on the gene during subsequent culture. Un-sorted cells and cells sorted to be positive for GFP showed a decrease in the fraction of GFP positive cells during culture. Conclusions: Sorting of live infected cells can be performed safely and with no deleterious effects on vector expression using the modified FACS Vantage instrument. Cytometry 37: 284–290, 1999. © 1999 Wiley-Liss, Inc.
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Microarrays are commonly applied to study the transcriptome of specific species. However, many available microarrays are restricted to model organisms, and the design of custom microarrays for other species is often not feasible. Hence, transcriptomics approaches of non-model organisms as well as comparative transcriptomics studies among two or more species often make use of cost-intensive RNAseq studies or, alternatively, by hybridizing transcripts of a query species to a microarray of a closely related species. When analyzing these cross-species microarray expression data, differences in the transcriptome of the query species can cause problems, such as the following: (i) lower hybridization accuracy of probes due to mismatches or deletions, (ii) probes binding multiple transcripts of different genes, and (iii) probes binding transcripts of non-orthologous genes. So far, methods for (i) exist, but these neglect (ii) and (iii). Here, we propose an approach for comparative transcriptomics addressing problems (i) to (iii), which retains only transcript-specific probes binding transcripts of orthologous genes. We apply this approach to an Arabidopsis lyrata expression data set measured on a microarray designed for Arabidopsis thaliana, and compare it to two alternative approaches, a sequence-based approach and a genomic DNA hybridization-based approach. We investigate the number of retained probe sets, and we validate the resulting expression responses by qRT-PCR. We find that the proposed approach combines the benefit of sequence-based stringency and accuracy while allowing the expression analysis of much more genes than the alternative sequence-based approach. As an added benefit, the proposed approach requires probes to detect transcripts of orthologous genes only, which provides a superior base for biological interpretation of the measured expression responses.
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Summary— Flow cytometry (FMC) is an analytical and preparative technique whereas image analysis is only applied to cell analysis. Recently, image analysis has been adapted as a preparative method using a new technique: image cytometry for analysis and sorting (ICAS). FCM and ICAS are complementary. Flow cytometry allows rapid, quantitative and precise study of fluorescence and light scattering in a large number of cells in suspension, while ICAS analyses fewer cells (adherent cells or tissue) on the basis of fluorescence, morphology and size. ICAS can use these criteria to destroy unwanted cells and hence sort selected cells. ICAS can also be used for confocal microscopy and laser surgery.
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Sorting is a common problem in computer science. There are lot of well-known sorting algorithms created for sequential execution on a single processor. Recently, hardware platforms enable to create wide parallel algorithms. We have standard processors consist of multiple cores and hardware accelerators like GPU. The graphic cards with their parallel architecture give new possibility to speed up many algorithms. In this paper we describe results of implementation of a few different sorting algorithms on GPU cards and multicore processors. Then hybrid algorithm will be presented which consists of parts executed on both platforms, standard CPU and GPU.
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