A chromatic explosion: the development and future of multiparameter flow cytometry

Flow Cytometry: Impact on Early Drug Discovery

Modern flow cytometers can make optical measurements of 10 or more parameters per cell at tens of thousands of cells per second and more than five orders of magnitude dynamic range. Although flow cytometry is used in most drug discovery stages, "sip-and-spit" sampling technology has restricted it to low-sample-throughput applications. The advent of HyperCyt sampling technology has recently made possible primary screening applications in which tens of thousands of compounds are analyzed per day. Target-multiplexing methodologies in combination with extended multiparameter analyses enable profiling of lead candidates early in the discovery process, when the greatest numbers of candidates are available for evaluation. The ability to sample small volumes with negligible waste reduces reagent costs, compound usage, and consumption of cells. Improved compound library formatting strategies can further extend primary screening opportunities when samples are scarce. Dozens of targets have been screened in 384- and 1536-well assay formats, predominantly in academic screening lab settings. In concert with commercial platform evolution and trending drug discovery strategies, HyperCyt-based systems are now finding their way into mainstream screening labs. Recent advances in flow-based imaging, mass spectrometry, and parallel sample processing promise dramatically expanded single-cell profiling capabilities to bolster systems-level approaches to drug discovery.

Computationally efficient multidimensional analysis of complex flow cytometry data using second order polynomial histograms

Many methods have been described for automated clustering analysis of complex flow cytometry data, but so far the goal to efficiently estimate multivariate densities and their modes for a moderate number of dimensions and potentially millions of data points has not been attained. We have devised a novel approach to describing modes using second order polynomial histogram estimators (SOPHE). The method divides the data into multivariate bins and determines the shape of the data in each bin based on second order polynomials, which is an efficient computation. These calculations yield local maxima and allow joining of adjacent bins to identify clusters. The use of second order polynomials also optimally uses wide bins, such that in most cases each parameter (dimension) need only be divided into 4-8 bins, again reducing computational load. We have validated this method using defined mixtures of up to 17 fluorescent beads in 16 dimensions, correctly identifying all populations in data files of 100,000 beads in <10 s, on a standard laptop. The method also correctly clustered granulocytes, lymphocytes, including standard T, B, and NK cell subsets, and monocytes in 9-color stained peripheral blood, within seconds. SOPHE successfully clustered up to 36 subsets of memory CD4 T cells using differentiation and trafficking markers, in 14-color flow analysis, and up to 65 subpopulations of PBMC in 33-dimensional CyTOF data, showing its usefulness in discovery research. SOPHE has the potential to greatly increase efficiency of analysing complex mixtures of cells in higher dimensions.

Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument compatible probability state model

BACKGROUND

Leuko64™ (Trillium Diagnostics) is a flow cytometric assay that measures neutrophil CD64 expression and serves as an in vitro indicator of infection/sepsis or the presence of a systemic acute inflammatory response. Leuko64 assay currently utilizes QuantiCALC, a semiautomated software that employs cluster algorithms to define cell populations. The software reduces subjective gating decisions, resulting in interanalyst variability of <5%. We evaluated a completely automated approach to measuring neutrophil CD64 expression using GemStone™ (Verity Software House) and probability state modeling (PSM).

METHODS

Four hundred and fifty-seven human blood samples were processed using the Leuko64 assay. Samples were analyzed on four different flow cytometer models: BD FACSCanto II, BD FACScan, BC Gallios/Navios, and BC FC500. A probability state model was designed to identify calibration beads and three leukocyte subpopulations based on differences in intensity levels of several parameters. PSM automatically calculates CD64 index values for each cell population using equations programmed into the model. GemStone software uses PSM that requires no operator intervention, thus totally automating data analysis and internal quality control flagging. Expert analysis with the predicate method (QuantiCALC) was performed. Interanalyst precision was evaluated for both methods of data analysis.

RESULTS

PSM with GemStone correlates well with the expert manual analysis, r(2) = 0.99675 for the neutrophil CD64 index values with no intermethod bias detected. The average interanalyst imprecision for the QuantiCALC method was 1.06% (range 0.00-7.94%), which was reduced to 0.00% with the GemStone PSM. The operator-to-operator agreement in GemStone was a perfect correlation, r(2) = 1.000.

CONCLUSION

Automated quantification of CD64 index values produced results that strongly correlate with expert analysis using a standard gate-based data analysis method. PSM successfully evaluated flow cytometric data generated by multiple instruments across multiple lots of the Leuko64 kit in all 457 cases. The probability-based method provides greater objectivity, higher data analysis speed, and allows for greater precision for in vitro diagnostic flow cytometric assays.

Deep profiling of multitube flow cytometry data

MOTIVATION

Deep profiling the phenotypic landscape of tissues using high-throughput flow cytometry (FCM) can provide important new insights into the interplay of cells in both healthy and diseased tissue. But often, especially in clinical settings, the cytometer cannot measure all the desired markers in a single aliquot. In these cases, tissue is separated into independently analysed samples, leaving a need to electronically recombine these to increase dimensionality. Nearest-neighbour (NN) based imputation fulfils this need but can produce artificial subpopulations. Clustering-based NNs can reduce these, but requires prior domain knowledge to be able to parameterize the clustering, so is unsuited to discovery settings.

RESULTS

We present flowBin, a parameterization-free method for combining multitube FCM data into a higher-dimensional form suitable for deep profiling and discovery. FlowBin allocates cells to bins defined by the common markers across tubes in a multitube experiment, then computes aggregate expression for each bin within each tube, to create a matrix of expression of all markers assayed in each tube. We show, using simulated multitube data, that flowType analysis of flowBin output reproduces the results of that same analysis on the original data for cell types of >10% abundance. We used flowBin in conjunction with classifiers to distinguish normal from cancerous cells. We used flowBin together with flowType and RchyOptimyx to profile the immunophenotypic landscape of NPM1-mutated acute myeloid leukemia, and present a series of novel cell types associated with that mutation.

High-resolution cytometry for high-content cell cycle analysis

One of the major limitations of flow cytometry (FCM) is the absence of an intracellular view. Automated microscopy and image analysis, together with technological developments, led to new approaches in cytometry that bypass the above limitation, introducing high resolution, high content, and large statistical sampling. However, few attempts have been made, until now, to translate the wide repertoire of FCM assays into high-content image screening. This unit describes the implementation of an acquisition and analysis protocol for evaluation of the cell cycle by automated microscopy. The approach grants the possibility to perform simultaneous analysis of a high number of different parameters. A large part of this unit is devoted to the description of hardware features that can optimize the recorded information together with the acquisition and analysis procedures employed to produce good-quality data.

Inductively coupled plasma mass spectrometry-based immunoassay: a review

The last 10 years witnessed the emerging and growing up of inductively coupled plasma mass spectrometry (ICPMS)-based immunoassay. Its high sensitivity and multiplex potential have made ICPMS a revolutionary technique for bioanalyte quantification after element-tagged immunoassay. This review focuses on the major developments and the applications of ICPMS-based immunoassay, with emphasis on methodological innovations. The ICPMS-based immunoassay with elemental tags of metal ions, nanoparticles, and metal containing polymers was discussed in detail. The recent development of multiplex assay, mass cytometry, suspension array, and surface analysis demonstrated the versatility and great potential of this technique. ICPMS-based immunoassay has become one of the key methods in bioanalysis.

Cell population identification using fluorescence-minus-one controls with a one-class classifying algorithm

MOTIVATION

The tried and true approach of flow cytometry data analysis is to manually gate on each biomarker separately, which is feasible for a small number of biomarkers, e.g. less than five. However, this rapidly becomes confusing as the number of biomarker increases. Furthermore, multivariate structure is not taken into account. Recently, automated gating algorithms have been implemented, all of which rely on unsupervised learning methodology. However, all unsupervised learning outputs suffer the same difficulties in validation in the absence of external knowledge, regardless of application domain.

RESULTS

We present a new semi-automated algorithm for population discovery that is based on comparison to fluorescence-minus-one controls, thus transferring the problem into that of one-class classification, as opposed to being an unsupervised learning problem. The novel one-class classification algorithm is based on common principal components and can accommodate complex mixtures of multivariate densities. Computational time is short, and the simple nature of the calculations means the algorithm can easily be adapted to process large numbers of cells (10(6)). Furthermore, we are able to find rare cell populations as well as populations with low biomarker concentration, both of which are inherently hard to do in an unsupervised learning context without prior knowledge of the samples' composition.

AVAILABILITY AND IMPLEMENTATION

R scripts are available via https://fccf.mpiib-berlin.mpg.de/daten/drfz/bioinformatics/with{username,password}={bioinformatics,Sar=Gac4}.

Small sample sorting of primary adherent cells by automated micropallet imaging and release

Primary patient samples are the gold standard for molecular investigations of tumor biology yet are difficult to acquire, heterogeneous in nature and variable in size. Patient-derived xenografts (PDXs) comprised of primary tumor tissue cultured in host organisms such as nude mice permit the propagation of human tumor samples in an in vivo environment and closely mimic the phenotype and gene expression profile of the primary tumor. Although PDX models reduce the cost and complexity of acquiring sample tissue and permit repeated sampling of the primary tumor, these samples are typically contaminated by immune, blood, and vascular tissues from the host organism while also being limited in size. For very small tissue samples (on the order of 10(3) cells) purification by fluorescence-activated cell sorting (FACS) is not feasible while magnetic activated cell sorting (MACS) of small samples results in very low purity, low yield, and poor viability. We developed a platform for imaging cytometry integrated with micropallet array technology to perform automated cell sorting on very small samples obtained from PDX models of pancreatic and colorectal cancer using antibody staining of EpCAM (CD326) as a selection criteria. These data demonstrate the ability to automate and efficiently separate samples with very low number of cells.

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation

A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. © 2014 The Authors. Published by Wiley Periodicals Inc.

Analysis of individual mitochondria via fluorescent immunolabeling with Anti-TOM22 antibodies

Mitochondria are responsible for maintaining a variety of cellular functions. One such function is the interaction and subsequent import of proteins into these organelles via the translocase of outer membrane (TOM) complex. Antibodies have been used to analyze the presence and function of proteins comprising this complex, but have not been used to investigate variations in the abundance of TOM complex in mitochondria. Here, we report on the feasibility of using capillary cytometry with laser-induced fluorescence to detect mitochondria labeled with antibodies targeting the TOM complex and to estimate the number of antibodies that bind to these organelles. Mitochondria were fluorescently labeled with DsRed2, while antibodies targeting the TOM22 protein, one of nine proteins comprising the TOM complex, were conjugated to the Atto-488 fluorophore. At typical labeling conditions, 94% of DsRed2 mitochondria were also immunofluorescently labeled with Atto-488 Anti-TOM22 antibodies. The calculated median number of Atto-488 Anti-TOM22 antibodies bound to the surface of mitochondria was ∼2,000 per mitochondrion. The combination of fluorescent immunolabeling and capillary cytometry could be further developed to include multicolor labeling experiments, which enable monitoring several molecular targets at the same time in the same or different organelle types.

DNA-directed assembly of antibody-fluorophore conjugates for quantitative multiparametric flow cytometry

Multiparametric flow cytometry offers a powerful approach to single-cell analysis with broad applications in research and diagnostics. Despite advances in instrumentation, progress in methodology has lagged. Currently there is no simple and efficient method for antibody labeling or quantifying the number of antibodies bound per cell. Herein, we describe a DNA-directed assembly approach to fluorescent labeling that overcomes these barriers. Oligonucleotide-tagged antibodies and microparticles can be annealed to complementary oligonucleotides bearing fluorophores to create assay-specific labeling probes and controls, respectively. The ratio of the fluorescence intensity of labeled cells to the control particles allows direct conversion of qualitative data to quantitative units of antibody binding per cell. Importantly, a single antibody can be labeled with any fluorophore by using a simple mix-and-match labeling strategy. Thus, any antibody can provide a quantitative probe in any fluorescent channel, thus overcoming major barriers to the use of flow cytometry as a technique for systems biology and clinical diagnostics.

Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data

We present a significantly improved version of the flowType and RchyOptimyx BioConductor-based pipeline that is both 14 times faster and can accommodate multiple levels of biomarker expression for up to 96 markers. With these improvements, the pipeline is positioned to be an integral part of data analysis for high-throughput experiments on high-dimensional single-cell assay platforms, including flow cytometry, mass cytometry and single-cell RT-qPCR.

Quantitative measurement of proteolytic rates with quantum dot-peptide substrate conjugates and Förster resonance energy transfer

An important challenge in biology is the development of probes for visualizing and quantitatively tracking enzyme activity. Proteases are an important class of enzyme with value as both diagnostic and therapeutic targets. In this chapter, we describe the preparation of quantum dot (QD)-peptide substrate conjugates as probes for measuring proteolytic activity. QDs have several highly advantageous optical properties that make these materials especially well suited for applications in bioanalysis and bioimaging. Further, peptide substrates for proteases can be controllably self-assembled to QDs and this capability, in combination with Förster resonance energy transfer (FRET), enables the design of quantitative in vitro assays capable of directly reporting on proteolytic activity. We present a detailed method for the preparation, calibration, and application of such QD probes, along with methods of analysis to generate progress curves for the proteolytic digestion of substrate. Representative data are illustrated for two different proteases and two different QD-fluorescent dye FRET pairs. The general methodology is likely to be applicable with other hydrolytic enzymes in addition to proteases. Overall, the method is straightforward to implement with commercially available materials and does not require specialized expertise.

Flow cytometry bioinformatics

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.

Concise review: guidance in developing commercializable autologous/patient-specific cell therapy manufacturing

Cell therapy is poised to play an enormous role in regenerative medicine. However, little guidance is being made available to academic and industrial entities in the start-up phase. In this technical review, members of the International Society for Cell Therapy provide guidance in developing commercializable autologous and patient-specific manufacturing strategies from the perspective of process development. Special emphasis is placed on providing guidance to small academic or biotech researchers as to what simple questions can be addressed or answered at the bench in order to make their cell therapy products more feasible for commercial-scale production. We discuss the processes that are required for scale-out at the manufacturing level, and how many questions can be addressed at the bench level. The goal of this review is to provide guidance in the form of topics that can be addressed early in the process of development to better the chances of the product being successful for future commercialization.

Aptamers: versatile probes for flow cytometry

Aptamers are nucleic acid oligomers with distinct conformational shapes that allow them to bind targets with high affinity and specificity. Aptamers are selected from a random oligonucleotide library by their capability to bind a certain molecular target. A variety of targets ranging from small molecules like amino acids to complex targets and whole cells have been used to select aptamers. These characteristics and the ability to create specific aptamers against virtually any cell type in a process termed "systematic evolution by exponential enrichment" make them interesting tools for flow cytometry. In this contribution, we review the application of aptamers as probes for flow cytometry, especially cell-phenotyping and detection of various cancer cell lines and virus-infected cells and pathogens. We also discuss the potential of aptamers combined with nanoparticles such as quantum dots for the generation of new multivalent detector molecules with enhanced affinity and sensitivity. With regard to recent advancements in aptamer selection and the decreasing costs for oligonucleotide synthesis, aptamers may rise as potent competitors for antibodies as molecular probes in flow cytometry.

Accelerating next-generation vaccine development for global disease prevention

Vaccines are among the greatest successes in the history of public health. However, past strategies for vaccine development are unlikely to succeed in the future against major global diseases such as AIDS, tuberculosis, and malaria. For such diseases, the correlates of protection are poorly defined and the pathogens evade immune detection and/or exhibit extensive genetic variability. Recent advances have heralded in a new era of vaccine discovery. However, translation of these advances into vaccines remains impeded by lack of understanding of key vaccinology principles in humans. We review these advances toward vaccine discovery and suggest that for accelerating successful vaccine development, new human immunology-based clinical research initiatives be implemented with the goal of elucidating and more effectively generating vaccine-induced protective immune responses.

Photophysical properties and electronic structure of bacteriochlorin-chalcones with extended near-infrared absorption

Synthetic bacteriochlorins enable systematic tailoring of substituents about the bacteriochlorin chromophore and thereby provide insights concerning the native bacteriochlorophylls of bacterial photosynthesis. Nine free-base bacteriochlorins (eight prepared previously and one prepared here) have been examined that bear diverse substituents at the 13- or 3,13-positions. The substituents include chalcone (3-phenylprop-2-en-1-onyl) derivatives with groups attached to the phenyl moiety, a "reverse chalcone" (3-phenyl-3-oxo-1-enyl), and extended chalcones (5-phenylpenta-2,4-dien-1-onyl, retinylidenonyl). The spectral and photophysical properties (τs, Φf, Φ(ic), Φ(isc), τT, k(f), k(ic), k(isc)) of the bacteriochlorins have been characterized. The bacteriochlorins absorb strongly in the 780-800 nm region and have fluorescence quantum yields (Φf) in the range 0.05-0.11 in toluene and dimethylsulfoxide. Light-induced electron promotions between orbitals with predominantly substituent or macrocycle character or both may give rise to some net macrocycle ↔ substituent charge-transfer character in the lowest and higher singlet excited states as indicated by density functional theory (DFT) and time-dependent DFT calculations. Such calculations indicated significant participation of molecular orbitals beyond those (HOMO - 1 to LUMO + 1) in the Gouterman four-orbital model. Taken together, the studies provide insight into the fundamental properties of bacteriochlorins and illustrate designs for tuning the spectral and photophysical features of these near-infrared-absorbing tetrapyrrole chromophores.

A computational platform for robotized fluorescence microscopy (I): high-content image-based cell-cycle analysis

Hardware automation and software development have allowed a dramatic increase of throughput in both acquisition and analysis of images by associating an optimized statistical significance with fluorescence microscopy. Despite the numerous common points between fluorescence microscopy and flow cytometry (FCM), the enormous amount of applications developed for the latter have found relatively low space among the modern high-resolution imaging techniques. With the aim to fulfill this gap, we developed a novel computational platform named A.M.I.CO. (Automated Microscopy for Image-Cytometry) for the quantitative analysis of images from widefield and confocal robotized microscopes. Thanks to the setting up of both staining protocols and analysis procedures, we were able to recapitulate many FCM assays. In particular, we focused on the measurement of DNA content and the reconstruction of cell-cycle profiles with optimal parameters. Standard automated microscopes were employed at the highest optical resolution (200 nm), and white-light sources made it possible to perform an efficient multiparameter analysis. DNA- and protein-content measurements were complemented with image-derived information on their intracellular spatial distribution. Notably, the developed tools create a direct link between image-analysis and acquisition. It is therefore possible to isolate target populations according to a definite quantitative profile, and to relocate physically them for diffraction-limited data acquisition. Thanks to its flexibility and analysis-driven acquisition, A.M.I.CO. can integrate flow, image-stream and laser-scanning cytometry analysis, providing high-resolution intracellular analysis with a previously unreached statistical relevance.

The Pharmacogenomic HLA Biomarker Associated to Adverse Abacavir Reactions: Comparative Analysis of Different Genotyping Methods

Many pharmacogenomic biomarkers (PGBM) were identified and translated into clinical practice, affecting the usage of drugs via label updates. In this context, abacavir is one of the most brilliant examples of pharmacogenetic studies translated into clinical practice. Pharmacogenetic studies have revealed that abacavir HSRs are highly associated with the major histocompatibility complex class I. Large studies established the effectiveness of prospective HLA-B*57:01 screening to prevent HSRs to abacavir. Accordingly to these results the abacavir label has been modified: the European Medicines Agency (EMA) and the FDA recommend/suggested that the administration of abacavir must be preceded by a specific genotyping test. The HLA locus is extremely polymorphic, exhibiting many closely related alleles, making it difficult to discriminate HLA-B*57:01 from other related alleles, and a number of different molecular techniques have been developed recently to detect the presence of HLA-B*57:01. In this review, we provide a summary of the available techniques used by laboratories to genotype HLA-B*57:01, outlining the scientific and pharmacoeconomics pros and cons.

Flow cytometry: a flexible tool for biomarker research

Flow cytometry is increasingly recognized as an invaluable technology in biomarker research. Owing to its multiparametric nature it can provide highly detailed information on any single cell in a heterogeneous population. Its versatility means it can be conducted in both the preclinical and clinical setting, generating biomarker data that can drive decisions pertaining to dose selection in clinical trials, treatment options for cancer sufferers and even suitability of patients to receive transplants. Most tissue types can be utilized by the flow cytometrist, allowing the technology to be applied to many fields of research, yet consensus still needs to be reached on standardization, regulation and validation of multiparametric flow cytometry assays. In parallel, continual innovation in analysis software to manage the huge datasets that can be generated is also needed. Nevertheless, the flexibility of flow cytometry means that it remains at the forefront of both routine and exploratory biomarker studies.

Consortium biology in immunology: the perspective from the Immunological Genome Project

Although the field has a long collaborative tradition, immunology has made less use than genetics of 'consortium biology', wherein groups of investigators together tackle large integrated questions or problems. However, immunology is naturally suited to large-scale integrative and systems-level approaches, owing to the multicellular and adaptive nature of the cells it encompasses. Here, we discuss the value and drawbacks of this organization of research, in the context of the long-running 'big science' debate, and consider the opportunities that may exist for the immunology community. We position this analysis in light of our own experience, both positive and negative, as participants of the Immunological Genome Project.

Single cell analysis using surface enhanced Raman scattering (SERS) tags

Fluorescence is a mainstay of bioanalytical methods, offering sensitive and quantitative reporting, often in multiplexed or multiparameter assays. Perhaps the best example of the latter is flow cytometry, where instruments equipped with multiple lasers and detectors allow measurement of 15 or more different fluorophores simultaneously, but increases beyond this number are limited by the relatively broad emission spectra. Surface enhanced Raman scattering (SERS) from metal nanoparticles can produce signal intensities that rival fluorescence, but with narrower spectral features that allow a greater degree of multiplexing. We are developing nanoparticle SERS tags as well as Raman flow cytometers for multiparameter single cell analysis of suspension or adherent cells. SERS tags are based on plasmonically active nanoparticles (gold nanorods) whose plasmon resonance can be tuned to give optimal SERS signals at a desired excitation wavelength. Raman resonant compounds are adsorbed on the nanoparticles to confer a unique spectral fingerprint on each SERS tag, which are then encapsulated in a polymer coating for conjugation to antibodies or other targeting molecules. Raman flow cytometry employs a high resolution spectral flow cytometer capable of measuring the complete SERS spectra, as well as conventional flow cytometry measurements, from thousands of individual cells per minute. Automated spectral unmixing algorithms extract the contributions of each SERS tag from each cell to generate high content, multiparameter single cell population data. SERS-based cytometry is a powerful complement to conventional fluorescence-based cytometry. The narrow spectral features of the SERS signal enables more distinct probes to be measured in a smaller region of the optical spectrum with a single laser and detector, allowing for higher levels of multiplexing and multiparameter analysis.

Quantitative comparison between full-spectrum and filter-based imaging in hyperspectral fluorescence microscopy

We implement a filterless illumination scheme on a hyperspectral fluorescence microscope to achieve full-range spectral imaging. The microscope employs polarisation filtering, spatial filtering and spectral unmixing filtering to replace the role of traditional filters. Quantitative comparisons between full-spectrum and filter-based microscopy are provided in the context of signal dynamic range and accuracy of measured fluorophores' emission spectra. To show potential applications, a five-colour cell immunofluorescence imaging experiment is theoretically simulated. Simulation results indicate that the use of proposed full-spectrum imaging technique may result in three times improvement in signal dynamic range compared to that can be achieved in the filter-based imaging.

Brilliant violet fluorophores: a new class of ultrabright fluorescent compounds for immunofluorescence experiments

The Nobel Prize in Chemistry was awarded in 2000 for the discovery of conductive organic polymers, which have subsequently been adapted for applications in ultrasensitive biological detection. Here, we report the first use of this new class of fluorescent probes in a diverse range of cytometric and imaging applications. We demonstrate that these "Brilliant Violet" reporters are dramatically brighter than other UV-violet excitable dyes, and are of similar utility to phycoerythrin (PE) and allophycocyanin (APC). They are thus ideally suited for cytometric assays requiring high sensitivity, such as MHC-multimer staining or detection of intracellular antigens. Furthermore, these reporters are sensitive and spectrally distinct options for fluorescence imaging, two-photon microscopy and imaging cytometry. These ultra-bright materials provide the first new high-sensitivity fluorescence probes in over 25 years and will have a dramatic impact on the design and implementation of multicolor panels for high-sensitivity immunofluorescence assays.

Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays

MOTIVATION

Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.

RESULTS

To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis).

AVAILABILITY

The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CONTACT

rbrinkman@bccrc.ca.

Cytometry: today's technology and tomorrow's horizons

Flow cytometry has been the premier tool for single cell analysis since its invention in the 1960s. It has maintained this position through steady advances in technology and applications, becoming the main force behind interrogating the complexities of the immune system. Technology development was a three-pronged effort, including the hardware, reagents, and analysis algorithms to allow measurement of as many as 20 independent parameters on each cell, at tens of thousands of cells per second. In the coming years, cytometry technology will integrate with other techniques, such as transcriptomics, metabolomics, and so forth. Ongoing efforts are aimed at algorithms to analyse these aggregated datasaets over large numbers of samples. Here we review the development efforts heralding the next stage of flow cytometry.

Amyloid-specific T-cells differentiate Alzheimer's disease from Lewy body dementia

Alzheimer's disease and dementia with Lewy bodies are the most common neurodegenerative dementias in old age. Accurate diagnosis of these conditions has important clinical implications because they tend to be confounded. In the brain of Alzheimer's disease patients amyloid-beta is produced in excess and deposited as plaques, forming the hallmark of this condition. Lymphocytes have been implicated in the process of amyloid-beta removal and inflammation occurrence. Here we investigated peripheral amyloid-beta1-42-specific T-cells by multicolor flow cytometry to simultaneously detect and characterize activation markers and cell signaling proteins (phospho-protein kinase C) in patients with Alzheimer's disease or Lewy body dementia and in healthy controls. Results indicate that only Alzheimer's disease patients display small subsets of peripheral amyloid-beta1-42-specific T-cells, characterized by bright expression of phosphorylated-protein kinase C-delta or -zeta whose significance although discussed, is far from being understood. The identification of such subsets, anyhow, may strongly contribute to distinguish Alzheimer's disease from dementia with Lewy bodies, opening possible new routes to early therapeutic strategies.

Multiparametric cytometry for exploration of complex cellular dynamics

The development of polychromatic cytometry has contributed to significant progress in the field of human immunology. Although numerous functional studies of rare cell populations have been performed using this technology, here we used polychromatic cytometry to explore the dynamics of complex cellular systems implicated in innate immunity. We used PBMC stimulated with live influenza virus as an experimental model. We studied the time course of activation of PBMC, which contain DC, monocytes, and NK cells, all of which are, in addition to their innate immune properties, susceptible to Flu infection. We developed 12 color panels to investigate intracellular expression of IFN-α, TNF-α, IL-12, IL-6, IFN-γ, CD107, and influenza virus nucleoprotein simultaneously in these cell populations. These panels allowed reproducible determination of activation markers induced in DC after their direct exposure to various stimulations or in NK cells by indirect DC-mediated activation within the complex cellular environment. The ability to use a low number of cells and reduced quantities of reagents permitted us to perform kinetic experiments. The power of polychromatic cytometry associated with bioinformatic tools allowed us to analyze the multiple functional data generated as dynamic clustering maps. These maps present a readily understandable view of activation events induced in different populations of PBMC. In addition, it reveals new information on the coordination of the complex pathways induced and on the cellular interactions that sustained indirect DC-mediated NK cell activation. Our work shows that polychromatic cytometry is a tool for discoveries in unexplored complex cell systems, at the crossroads of immunology and virology. © 2012 International Society for Advancement of Cytometry.

Polychromatic flow cytometry in the clinical laboratory

Technological advances in flow cytometry include increasingly sophisticated instruments and an expanding range of fluorochromes. These advances are making it possible to detect an increasing number of markers on a single cell. The term polychromatic flow cytometry applies to such systems that detect five or more markers simultaneously. This review provides an overview of the current and future impact of polychromatic flow cytometry in the clinical laboratory. The use of multiple markers has several advantages in the diagnosis and monitoring of haematological malignancies. Cell populations can be analysed more comprehensively and efficiently, and abnormal populations can be distinguished more readily when normal counterparts are present. Polychromatic flow cytometry is particularly useful in the evaluation of plasma cells, and the role of flow cytometry in the assessment of plasma cell disorders is reviewed in depth. There is improved sensitivity in the assessment of small populations, which is critical in the evaluation of minimal residual disease. Flow cytometry can also play a role in assessment of circulating tumour cells in carcinoma. Introduction of polychromatic flow cytometry is a complex process with many challenges including design of antibody panels and instrument compensation. Developments in data analysis are required to realise the full benefits of the other technical advances. Standardisation of protocols may reduce inter-laboratory variation. While the complexity of polychromatic flow cytometry creates challenges, it has substantial potential to improve clinical analysis.

Age-related differences in naturally acquired T cell memory to Plasmodium falciparum merozoite surface protein 1

Naturally acquired immunity to Plasmodium falciparum malaria in malaria holoendemic areas is characterized by the gradual, age-related development of protection against high-density parasitemia and clinical malaria. Animal studies, and less commonly, observations of humans with malaria, suggest that T-cell responses are important in the development and maintenance of this immunity, which is mediated primarily by antibodies that slow repeated cycles of merozoites through erythrocytes. To advance our rather limited knowledge on human T-cell immunity to blood stage malaria infection, we evaluated CD4 and CD8 T-cell effector memory subset responses to the 42 kDa C-terminal fragment of Merozoite Surface Protein 1 (MSP1₄₂), a malaria vaccine candidate, by 49 healthy 0.5 to ≥18 year old residents of a holoendemic area in western Kenya. The proportion of individuals with peripheral blood mononuclear cell MSP1₄₂ driven IFN-γ ELISPOT responses increased from 20% (2/20) among 0.5–1 year old children to 90% (9/10) of adults ≥18 years (P = 0.01); parallel increases in the magnitude of IFN-γ responses were observed across all age groups (0.5, 1, 2, 5 and ≥18 years, P = 0.001). Less than 1% of total CD4 and CD8 T-cells from both children and adults produced IFN-γ in response to MSP1₄₂. However, adults had higher proportions of MSP1₄₂ driven IFN-γ secreting CD4 and CD8 effector memory (CD45RA⁻ CD62L⁻) T-cells than children (CD4: 50.9% vs. 28.8%, P = 0.036; CD8: 52.1% vs. 18.3%, respectively P = 0.009). In contrast, MSP1₄₂ driven IFN-γ secreting naïve-like, transitional (CD45RA⁺ CD62L⁺) CD4 and CD8 cells were the predominant T-cell subset among children with significantly fewer of these cells in adults (CD4: 34.9% vs. 5.1%, P = 0.002; CD8: 47.0% vs. 20.5%, respectively, P = 0.030). These data support the concept that meaningful age-related differences exist in the quality of T-cell immunity to malaria antigens such as MSP1.

Data-driven compensation for flow cytometry of solid tissues

Propidium Iodide is a fluorochrome that is used to measure the DNA content of individual cells, taken from solid tissues, with a flow cytometer. Compensation for spectral cross-over of this fluorochrome still leads to compensation results that are depending on operator experience. We present a data-driven compensation (DDC) algorithm that is designed to automatically compensate combined DNA phenotype flow cytometry acquisitions. The generated compensation values of the DDC algorithm are validated by comparison with manually determined compensation values. The results show that (1) compensation of two-color flow cytometry leads to comparable results using either manual compensation or the DDC method; (2) DDC can calculate sample-specific compensation trace lines; (3) the effects of two different approaches to calculate compensation values can be visualized within one sample. We conclude that the DDC algorithm contributes to the standardization of compensation for spectral cross-over in flow cytometry of solid tissues.

Photophysical properties and electronic structure of stable, tunable synthetic bacteriochlorins: extending the features of native photosynthetic pigments

Bacteriochlorins, which are tetrapyrrole macrocycles with two reduced pyrrole rings, are Nature's near-infrared (NIR) absorbers (700-900 nm). The strong absorption in the NIR region renders bacteriochlorins excellent candidates for a variety of applications including solar light harvesting, flow cytometry, molecular imaging, and photodynamic therapy. Natural bacteriochlorins are inherently unstable due to oxidative conversion to the chlorin (one reduced pyrrole ring) or the porphyrin. The natural pigments are also only modestly amenable to synthetic manipulation, owing to a nearly full complement of substituents on the macrocycle. Recently, a new synthetic methodology has afforded access to stable synthetic bacteriochlorins wherein a wide variety of substituents can be appended to the macrocycle at preselected locations. Herein, the spectroscopic and photophysical properties of 33 synthetic bacteriochlorins are investigated. The NIR absorption bands of the chromophores range from ∼700 to ∼820 nm; the lifetimes of the lowest excited singlet state range from ∼2 to ∼6 ns; the fluorescence quantum yields range from ∼0.05 to ∼0.25; and the yield of the lowest triplet excited state is ∼0.5. The spectroscopic/photophysical studies of the bacteriochlorins are accompanied by density functional theory (DFT) calculations that probe the characteristics of the frontier molecular orbitals. The DFT calculations indicate that the impact of substituents on the spectral properties of the molecules derives primarily from effects on the lowest unoccupied molecular orbital. Collectively, the studies show how the palette of synthetic bacteriochlorins extends the properties of the native photosynthetic pigments (bacteriochlorophylls). The studies have also elucidated design principles for tuning the spectral and photophysical characteristics as required for a wide variety of photochemical applications.

Analysis of Aspergillus nidulans germination, initial growth and carbon source response by flow cytometry

In this work, flow cytometry was utilized to analyze the initial vegetative growth of the model fungus Aspergillus nidulans as measured by the number of events increasing size and internal complexity. It was established the ideal parameters for the analysis of conidial populations, whose growth was followed after germination in glucose or sucrose. While glucose in culture increased growth several magnitudes in comparison to control cultures in saline, growth was less intense in cultures amended with sucrose. Results indicated that flow cytometry could be a useful tool to study fungal germination and initial growth since it allowed rapid identification of different populations by means of their increasing in size and granularity with good reproducibility and without the need for direct observation and count of individual cells.

Cytomegalovirus viral load and virus-specific immune reconstitution after peripheral blood stem cell versus bone marrow transplantation

Peripheral blood stem cell (PBSC) products contain more T cells and monocytes when compared with bone marrow (BM), leading to fewer bacterial and fungal infections. Cytomegelovirus (CMV) viral load and disease as well as CMV-specific immune reconstitution were compared in patients enrolled in a randomized trial comparing PSBC and BM transplantation. There was a higher rate of CMV infection and disease during the first 100 days after transplantation among PBSC recipients (any antigenemia/DNAemia: PBSC, 63% vs BM, 42%, P = .04; CMV disease: PBSC, 17% vs BM, 4%, P = .03). By 2 years, CMV disease rates were similar. The early increase in CMV events correlated temporarily with lower CMV-specific CD4(+) T helper and CD8(+) cytotoxic T lymphocyte function at 30 days after transplantation in PBSC recipients. By 3 months after transplantation and thereafter, CMV-specific immune responses were similar between BM and PBSC recipients. In conclusion, higher CMV infection and disease rates occurred in PBSC transplant recipients early after transplantation. These differences may be because of a transient delay in CMV-specific immune reconstitution following PBSC transplantation.