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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.
Flow cytometry is a widely used analytical technique for examining microscopic particles, such as cells. The Flow Cytometry Standard (FCS) was developed in 1984 for storing flow data and it is supported by all instrument and third party software vendors. However, FCS does not capture the full scope of flow cytometry (FCM)-related data and metadata, and data standards have recently been developed to address this shortcoming.
Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.
By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.
Liposomes are promising carriers for drugs and bioactive compounds. Size and structure are their crucial parameters. Thus, it is essential to assess individual vesicles as prepared. Currently available techniques fail to measure liposome's size and structure simultaneously, with a high throughput. To solve this problem, we have developed a novel, flow cytometric method quantifying liposomes.
The understanding of how different cell types adapt their metabolism in the face of challenges has been attracting the attention of researchers for many years. Recently, immunologists also started to focus on how the metabolism of immune cells can impact the way that immunity drives its responses. The presence of a pathogen or damage in a tissue changes severely the way that the immune cells need to respond. When activated, immune cells usually shift their metabolism from a high energy demanding status using mitochondria respiration to a glycolytic based rapid ATP production. The diminished amount of respiration leads to changes in the mitochondrial membrane potential and, consequently, generation of reactive oxygen species. Here, we show how flow cytometry can be used to track changes in mitochondrial mass, membrane potential and superoxide (ROS) production in live immune cells. ● This protocol suggests a quick way of evaluating mitochondrial fitness using flow cytometry. We propose using the probes MitoTraker Green and MitoTracker Red/ MitoSOX at the same time. This way, it is possible to evaluate different parameters of mitochondrial biology in living cells. ● Flow cytometry is a highly used tool by immunologists. With the advances of studies focusing on the metabolism of immune cells, a simplified application of flow cytometry for mitochondrial studies and screenings is a helpful clarifying method for immunology.
High-dimensional single-cell data has become an important tool in unraveling the complexity of the immune system and its involvement in homeostasis and a large array of pathologies. As technological tools are developed, researchers are adopting them to answer increasingly complex biological questions. Up until recently, mass cytometry (MC) has been the main technology employed in cytometric assays requiring more than 29 markers. Recently, however, with the introduction of full spectrum flow cytometry (FSFC), it has become possible to break the fluorescence barrier and go beyond 29 fluorescent parameters. In this study, in collaboration with the Stanford Human Immune Monitoring Center (HIMC), we compared five patient samples using an established immune panel developed by the HIMC using their MC platform. Using split samples and the same antibody panel, we were able to demonstrate highly comparable results between the two technologies using multiple data analysis approaches. We report here a direct comparison of two technology platforms (MC and FSFC) using a 32-marker flow cytometric immune monitoring panel that can identify all the previously described and anticipated immune subpopulations defined by this panel.
Extracellular vesicles (EVs), including exosomes and microvesicles, are 30-800 nm vesicles that are released by most cell types, as biological packages for intercellular communication. Their importance in cancer and inflammation makes EVs and their cargo promising biomarkers of disease and cell-free therapeutic agents. Emerging high-resolution cytometric methods have created a pressing need for efficient fluorescent labeling procedures to visualize and detect EVs. Suitable labels must be bright enough for one EV to be detected without the generation of label-associated artifacts. To identify a strategy that robustly labels individual EVs, we used nanoFACS, a high-resolution flow cytometric method that utilizes light scattering and fluorescence parameters along with sample enumeration, to evaluate various labels. Specifically, we compared lipid-, protein-, and RNA-based staining methods and developed a robust EV staining strategy, with the amine-reactive fluorescent label, 5-(and-6)-Carboxyfluorescein Diacetate Succinimidyl Ester, and size exclusion chromatography to remove unconjugated label. By combining nanoFACS measurements of light scattering and fluorescence, we evaluated the sensitivity and specificity of EV labeling assays in a manner that has not been described for other EV detection methods. Efficient characterization of EVs by nanoFACS paves the way towards further study of EVs and their roles in health and disease.
Persisters and viable but non-culturable (VBNC) cells are two phenotypic variants known to be highly tolerant to antibiotics. Although both cell types are stained as live and often appear as nongrowing during antibiotic treatment, the only distinguishing feature is the ability of persisters to recolonize in standard culture media in the absence of antibiotics. Despite considerable progress in the characterization of persister formation mechanisms, their resuscitation mechanisms remain unclear due to technical limitations in detecting and isolating these cell types in culture environments that are highly heterogeneous.
The quantitative assessment of cellular DNA and RNA content by flow cytometry to provide useful information for both diagnosis and prognosis of patients with hematologic malignancies is reviewed. While the characterization of cell surface antigens seems to be more germane to questions of the normal cell counterpart (stage) of malignant transformation and the biology of regulation of proliferation and differentiation by cell-cell contact and humoral factors, DNA-derived and RNA-derived parameters were surprisingly sensitive in the distinction of major morphologic groups, drug sensitivity and long-term prognosis. Our findings to date in the study of leukemias, lymphomas and myelomas are summarized.
Multiple stress factors in honey bees are causing loss of bee colonies worldwide. Several infectious agents of bees are believed to contribute to this problem. The mechanisms of honey bee immunity are not completely understood, in part due to limited information about the types and abundances of hemocytes that help bees resist disease. Our study utilized flow cytometry and microscopy to examine populations of hemolymph particulates in honey bees. We found bee hemolymph includes permeabilized cells, plasmatocytes, and acellular objects that resemble microparticles, listed in order of increasing abundance. The permeabilized cells and plasmatocytes showed unexpected differences with respect to properties of the plasma membrane and labeling with annexin V. Both permeabilized cells and plasmatocytes failed to show measurable mitochondrial membrane potential by flow cytometry using the JC-1 probe. Our results suggest hemolymph particulate populations are dynamic, revealing significant differences when comparing individual hive members, and when comparing colonies exposed to diverse conditions. Shifts in hemocyte populations in bees likely represent changing conditions or metabolic differences of colony members. A better understanding of hemocyte profiles may provide insight into physiological responses of honey bees to stress factors, some of which may be related to colony failure.
Measuring differences in cell cycle progression is often essential to understand cell behavior under different conditions, treatments and environmental changes. Cell synchronization is widely used for this purpose, but unfortunately, there are many cases where synchronization is not an option. Many cell lines, patient samples or primary cells cannot be synchronized, and most synchronization methods involve exposing the cells to stress, which makes the method incompatible with the study of stress responses such as DNA damage. The use of dual-pulse labelling using EdU and BrdU can potentially overcome these problems, but the need for individual sample processing may introduce a great variability in the results and their interpretation. Here, we describe a method to analyze cell proliferation and cell cycle progression by double staining with thymidine analogues in combination with fluorescent cell barcoding, which allows one to multiplex the study and reduces the variability due to individual sample staining, reducing also the cost of the experiment.
Flow cytometry is an indispensable tool in biology and medicine for counting and analyzing cells in large heterogeneous populations. It identifies multiple characteristics of every single cell, typically via fluorescent probes that specifically bind to target molecules on the cell surface or within the cell. However, flow cytometry has a critical limitation: the color barrier. The number of chemical traits that can be simultaneously resolved is typically limited to several due to the spectral overlap between fluorescence signals from different fluorescent probes. Here, we present color-scalable flow cytometry based on coherent Raman flow cytometry with Raman tags to break the color barrier. This is made possible by combining a broadband Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS) flow cytometer, resonance-enhanced cyanine-based Raman tags, and Raman-active dots (Rdots). Specifically, we synthesized 20 cyanine-based Raman tags whose Raman spectra are linearly independent in the fingerprint region (400 to 1,600 cm-1). For highly sensitive detection, we produced Rdots composed of 12 different Raman tags in polymer nanoparticles whose detection limit was as low as 12 nM for a short FT-CARS signal integration time of 420 µs. We performed multiplex flow cytometry of MCF-7 breast cancer cells stained by 12 different Rdots with a high classification accuracy of 98%. Moreover, we demonstrated a large-scale time-course analysis of endocytosis via the multiplex Raman flow cytometer. Our method can theoretically achieve flow cytometry of live cells with >140 colors based on a single excitation laser and a single detector without increasing instrument size, cost, or complexity.
Diffuse in vivo flow cytometry (DiFC) is an emerging technique for enumerating rare fluorescently labeled circulating cells noninvasively in the bloodstream. Thus far, we have reported red and blue-green versions of DiFC. Use of near-infrared (NIR) fluorescent light would in principle allow use of DiFC in deeper tissues and would be compatible with emerging NIR fluorescence molecular contrast agents.
Riboswitches regulate gene expression through direct, small molecule-mRNA interactions. The creation of new synthetic riboswitches from in vitro selected aptamers benefits from rapid, high-throughput methods for identifying switches capable of triggering dramatic changes in gene expression in the presence of a desired ligand. Here we present a flow cytometry-based screen for identifying synthetic riboswitches that induce robust increases in gene expression in the presence of theophylline. The performance characteristics of our newly identified riboswitches exceed those of previously described natural and synthetic riboswitches. Sequencing data and structure probing experiments reveal the ribosome binding site to be an important determinant of how well a switch performs and may provide insights into the design of new synthetic riboswitches.
Antibacterial drugs are the most consumed group of drugs in the modern hospitals. Standard methods of antibiotic sensitivity are labour and time-consuming, taking up to 24 hours after the pure culture is isolated (the analysis typically lasts up to 72 hours). Working out express diagnostic methods is of importance, and studies are made in various directions. Flow cytometry in detecting resistant E. coli strains was used. Flow cytometry fluorescent dyes were used to stain viable and dead cells. For method validation, relative accuracy, relative susceptibility, relative specificity and Cohen's kappa test were determined compared to the delusion test. Cytometry method showed acceptable results on the model of E.coli. Relative accuracy comprised 88.8%, sensitivity - 85.7%, specificity was 88.8%, Cohen's kappa test showed value 0.524, which is a medium agreement between the measurements by different methods.
Förster resonance energy transfer (FRET) continues to be a useful tool to study movement and interaction between proteins within living cells. When FRET as an optical technique is measured with flow cytometry, conformational changes of proteins can be rapidly measured cell-by-cell for the benefit of screening and profiling. We exploit FRET to study the extent of activation of α4β1 integrin dimers expressed on the surface of leukocytes. The stalk-like transmembrane heterodimers when not active lay bent and upon activation extend outward. Integrin extension is determined by changes in the distance of closest approach between an FRET donor and acceptor, bound at the integrin head and cell membrane, respectively. Time-resolved flow cytometry analysis revealed donor emission increases up to 17%, fluorescence lifetime shifts over 1.0 ns during activation, and FRET efficiencies of 37% and 26% corresponding to the inactive and active integrin state, respectively. Last, a graphical phasor analysis, including population clustering, gating, and formation of an FRET trajectory, added precision to a comparative analysis of populations undergoing FRET, partial donor recovery, and complete donor recovery. This work establishes a quantitative cytometric approach for profiling fluorescence donor decay kinetics during integrin conformational changes on a single-cell level.
Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.
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