Flow Cytometry In Samples Research Articles

Cellular RNA levels define heterotrophic substrate-uptake rate sub-guilds in activated sludge microbial communities.

A heterotrophic-specialist model was proposed previously to divide wastewater treatment plant (WWTP) heterotrophs into sub-guilds of consumers of readily or slowly degradable substrates (RDS or SDS, respectively). The substrate degradation rate model coupled to metabolic considerations predicted that RNA and polyhydroxyalkanoate (PHA) levels would be positively correlated in the activated sludge communities with high RNA and PHA occurring in RDS-consumers, and low RNA with no PHA accumulation occurring in SDS-consumers because their external substrates are always present. This prediction was verified in previous studies and in the current one. Thus, RNA and PHA levels were used as biomarkers of the RDS- and SDS-consumer sub-guilds for cell sorting using flow cytometry of samples from three WWTPs. Subsequently, 16S rRNA gene amplicon sequencing revealed that the sorted groups were highly similar over time and among WWTPs, and demonstrated a clear segregation by RNA levels. Predicted ecophysiological traits based on 16S rRNA phylogeny suggested that the high-RNA population showed RDS-consumer traits such as higher rrn copy numbers per genome. Using a mass-flow immigration model, it appeared that the high-RNA populations exhibited high immigration rates more frequently than low-RNA populations, but the differences in frequencies were less with increasing solids residence times.

Variation in Platelet Activation State in Pre-Donation Whole Blood: Effect of Time of Day and ABO Blood Group

BackgroundWhilst there has been investigation into the effect of time of the day on platelet activation and function in healthy individuals, there is a lack of studies in the literature to examine this relationship among platelet donors.MethodsWe assessed the extent of platelet activation by percentage of platelets with surface-expressed P-selectin and flow cytometry in samples of whole blood from a group of qualified platelet donors (n = 84).ResultsThe mean (SD) percentage of activated platelets in the pre-donation blood samples was 1.85 ± 1.57% (range 0.2–7.5%). In univariate analyses, the percentage of activated platelets was significantly and inversely correlated with the collection time (ie, the time of day blood samples were collected) (r = –0.35, p = 0.001) and positively correlated to mean platelet volume (MPV) (r = 0.29, p = 0.008). A weaker positive correlation was also observed with ABO blood group (r = 0.228, p = 0.036). Analysis of the collection time as a categorical variable showed a greater degree of activated platelets in samples collected between 8:00 h and 10:00 h than in samples collected during the hours of >10:00 h ≤14:00 h (2.5 ± 1.8 versus 1.1 ± 0.74, p < 0.001). In the adjusted linear regression model, collection time was a significant independent predictor of platelet activation state in whole blood (β = –0.26; p < 0.001), as did ABO blood group (β = 0.55; p = 0.019).ConclusionOur results show that collection time is the most important predictor of platelet activation state in pre-donation whole blood among platelet donors. This work may have implications for optimizing the timing of platelet donation.

UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia.

Simple SummaryAcute myeloid leukemia (AML) is the second most frequent leukemia entity in children and adolescents, and definitely the most aggressive variant. Multiparameter flow-cytometry is one of the methodologies most useful to monitor the number of remaining leukemic cells in bone marrow (minimal residual disease, MRD) in AML patients, because it is widely available and applicable to most patients. However, AML flow cytometry data show very complex patterns and identifying leukemic cells in the data is subjective, time-consuming and requires experienced operators who are not available world-wide. In this paper, we approach automatic assessment of AML flow cytometry samples with a novel semi-supervised machine learning model, leveraging implicit expert knowledge stored in a collection of manually assessed samples. Because AML data exhibit a high degree of variability in the patterns of blast cell populations that is difficult to model, the model detects anomalies starting from the appearance of normal cell populations.Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median -score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML.

Flow Cytometry-Based Single Cell Analyses of Bacterial Adaptation to Intracellular Environments.

Since decades, flow cytometry (FC) is a powerful technique to perform single cell analyses with high accuracy and throughput. Moreover, FC is the method of choice to study bacterial cell heterogeneity and complements single-cell imaging techniques. The complex experimental approaches for FC sample preparation and the subsequent FC adjustment and gating strategy demand careful considerations to be successful when analyzing complex microbial populations, especially when liberated populations of intracellular bacterial pathogens, or bacterial pathogens inside intact host cells are analyzed. Here, we provide a set of experimental protocols for FC sample preparation of (1) in vitro cultured bacterial cells, (2) liberated intracellular bacteria from host cells, or (3) preparation of intact infected phagocytic or epithelial cells commonly used as host cells in infection biology. Since sample preparation, cytometer adjustment, and gating strategy are essential for experimental success, we aim to provide our expertise to support application of FC by other researchers.

An Artificial Intelligence Approach for B Lymphoblastic Leukemia Minimal Residual Disease Detection and Clinical Prognosis Prediction Using Flow Cytometry Data

IntroductionDetection of minimal residual disease (MRD) of B-cell acute lymphoblastic leukemia (B-ALL) measured by flow cytometry (FC) has been shown to be an important prognostic indicator in B-ALL. However, the current analysis is based on the manual gating by the reviewers; therefore, objectivity could be compromised, and decentralization of the B-ALL MRD detection is difficult to be established. We then aimed to develop an objective diagnostic tool via artificial intelligence (AI) technique for FC data interpretation in order to provide efficient and objective B-ALL MRD detection.MethodsRetrospective FC data of patients with adult B-ALL from 2009 to 2016 in National Taiwan University Hospital were enrolled. A total of 1,073 unique bone marrow FC samples were collected for analysis. Each of the samples had a 10-tube test done on 100,000 cells - each cell was measured in 6 fluorescent channels (FSC, SSC, FITC, PE, PerCP, APC) within one tube.The flow conclusion of each FC sample was categorized into “positive MRD” and “no MRD” groups according to previous manual interpretation, and the whole dataset was randomly divided into 3 groups: Training set (n=718), Accuracy set (n=141) and Validation set (n=178). The Training set was used to train the algorithm, and the Accuracy set was used to evaluate the accuracies achieved and optimize algorithmic parameters. The final optimized algorithm was then evaluated blindly in Validation set, with the concordance rate comparing the manual and the AI conclusions in distinguishing “positive MRD” from “no MRD”.For the algorithm development, the recorded numerical values of the 6 fluorescent channels (FSC, SSC, FITC, PE, PerCP, APC) of each tube (100,000 cells) were considered as raw feature attributes. The probabilistic distributions of these attributes were first modeled as multivariate Gaussian mixture model using a sub-dictionary learning approach. Then a probabilistic derivation was exploited to compute per-sample L2-normalized vectorized representations. Lastly, representations of each tube were then concatenated to be the final feature input to the supervised machine learning classifier, in this case, a support vector machine with linear kernel. In addition, ANOVA-based feature selection was also conducted throughout the experiments.For outcome correlation analysis, we included 35 adult B-ALL patients with available FC data after induction therapy, record their clinical parameters and measured their overall survival (OS) and progression-free survival (PFS), with median follow-up 40.0 months. The Kaplan-Meier curves were constructed to estimated OS and RFS.ResultsThe primary algorithm concordance estimated using the Accuracy set was 90.8% and the area under the receiver operating characteristic curve (AUC) is 0.944 (Table1). The calibrated algorithm, adjusted with training in Accuracy set, can produce a concordance rate of 88.2% with manual interpretation on Validation set, and the AUC is 0.919 (Table 1).It is noteworthy that it only took 7 seconds to analyze one FC sample using the AI algorithm while it took approximately 15 to 30 minutes for manual gating by trained professionals. Furthermore, the concordance of the algorithm developed from 10 tube-data remained almost unchanged when the tube number decreased. In fact, algorithm from 1 tube still yields an AUC of 0.874 (vs. 0.919 from 12 tubes), indicating that AI algorithm may reduce laboratory testing currently applied in standard FC processing.For outcome correlation, B-ALL patients with no MRD by AI algorithm had significant longer OS compared to those with positive MRD (NR vs 20.0 months, p=0.0051). Moreover, patients with no MRD also had significant longer RFS compared to those with positive MRD (8.9 vs 5.2 months, p=0.019), (Figure.1).ConclusionsThis is the first study using AI approach to develop FC data diagnostic algorithms with prognosis implications for B-ALL, demonstrating the advantages of reliability, high efficacy and time saving. Incorporating other test results into AI algorithm may lead to more precise prognostic prediction in the future and potentially guide therapeutic strategy for B-ALL. [Display omitted] DisclosuresKo:Celgene International Sàrl: Research Funding. Li:Celgene International Sàrl: Research Funding. Tien:Celgene International Sàrl: Research Funding. Tang:Celgene International Sàrl: Research Funding.

Isolation, in vitro study, and stem cell markers for type A spermatogonia in a Characiformes species.

The objective of this study was to establish a protocol for the characterization, isolation, and culture of type A spermatogonia using specific molecular markers for these cells in fish. To this end, adult Prochilodus lineatus testes were collected and digested enzymatically and the resulting testicular suspension was separated using a discontinuous Percoll gradient, followed by differential plating. The cell cultures obtained were monitored for 15 days and analyzed using the immunofluorescence method with anti-Vasa, anti-GFRα1, and anti-OCT4 antibodies. Spermatogonial enrichment was also performed using flow cytometry. Although discontinuous Percoll gradient centrifugation followed by differential plating enabled the removal of differentiated germ cells and somatic cells, enriching the pool of type A spermatogonia, the enrichment of type A spermatogonia through flow cytometry of samples without Percoll proved to be more efficient. Prominent cell agglomerates that were characterized according to different stem cell markers as type A spermatogonia were observed during the 15 days of the cell culture. The use of immunoperoxidase and western blot analysis methods confirmed the specificity of the markers for type A spermatogonia of P. lineatus. When combined with specific cell culture conditions, the positive characterization of these molecular markers clarified certain aspects of spermatogonial regulation, such as survival and proliferation. Finally, understanding the regulation of the in vitro germ cell maintenance process may contribute to the enhancement of in vivo and in vitro reproduction techniques of endangered or aquaculture fish species.

Simultaneous quantification of natural and inducible regulatory T-cell subsets during interferon-\u03b2 therapy of multiple sclerosis patients

BackgroundThe mechanisms underlying the therapeutic activity of interferon-β in multiple sclerosis are still not completely understood. In the present study, we evaluated the short and long-term effects of interferon-β treatment on different subsets of regulatory T cells in relapsing–remitting multiple sclerosis patients biologically responsive to treatment because of mixovirus resistance protein A inducibility.MethodsIn this prospective longitudinal study, subsets of natural regulatory T cells (naïve, central memory and effector memory) and inducible regulatory T cells (Tr1), as well as in vitro-induced regulatory T cells (Tr1-like cells), were simultaneously quantified by flow cytometry in samples prepared from 148 therapy-naïve multiple sclerosis patients obtained before and after 6, 12, 18, and 24 months of interferon-β-1a treatment. mRNA for interleukin-10 and Tr1-related genes (CD18, CD49b, and CD46, together with Cyt-1 and Cyt-2 CD46-associated isoforms) were quantified in Tr1-like cells.ResultsDespite profound inter-individual variations in the modulation of all regulatory T-cell subsets, the percentage of natural regulatory T cells increased after 6, 12, and 24 months of interferon-β treatment. This increase was characterized by the expansion of central and effector memory regulatory T-cell subsets. The percentage of Tr1 significantly enhanced at 12 months of therapy and continued to be high at the subsequent evaluation points. Patients experiencing relapses displayed a higher percentage of naïve regulatory T cells and a lower percentage of central memory regulatory T cells and of Tr1 before starting interferon-β therapy. In addition, an increase over time of central memory and of Tr1 was observed only in patients with stable disease. However, in vitro-induced Tr1-like cells, prepared from patients treated for 24 months, produced less amount of interleukin-10 mRNA compared with pre-treatment Tr1-like cells.ConclusionInterferon-β induces the expansion of T regulatory subsets endowed with a high suppressive activity, especially in clinically stable patients. The overall concurrent modulation of natural and inducible regulatory T-cell subsets might explain the therapeutic effects of interferon-β in multiple sclerosis patients.

Multicenter flow cytometry proficiency testing of canine blood and lymph node samples.

Flow cytometry (FC) is used increasingly in veterinary medicine for further characterization of hematolymphoid cells. Guidelines for optimizing assay performance and interpretation of results are limited, and concordance of results across laboratories is unknown. This study aimed to determine inter-investigator agreement on the interpretation of FC results from split samples analyzed in different laboratories using various protocols, cytometers, and software; and on the interpretation of archived FC standard (FCS) data files contributed by the different investigators. This was a multicenter observational cross-sectional study. Anticoagulated blood or lymph node aspirate samples from nine client-owned dogs were aliquoted and shipped to participating laboratories. Samples were analyzed with individual laboratory-developed protocols. In addition, FCS files from a set of separate samples from 11 client-owned dogs were analyzed by participating investigators. A person not associated with the study tabulated the results and interpretations. Agreement of interpretations was assessed with Fleiss' kappa statistic. Prolonged transit times affected sample quality for some laboratories. Overall agreement among investigators regarding the FC sample interpretation was strong (κ=0.86±0.19, P<.001), and for specific categories, ranged from moderate to perfect. Agreement of the lymphoproliferation or other leukocyte sample category from the analysis of the FCS files was weak (κ=0.58±0.05, P<.001). Lymphoproliferations were readily identified by FC, but identification of the categories of hematolymphoid neoplasia in fresh samples or archived files was variable. There is a need for a more standardized approach to maximize the enormous potential of FC in veterinary medicine.

Identification of Novel Human Monocyte Subsets and Evidence for Phenotypic Groups Defined by Interindividual Variations of Expression of Adhesion Molecules

Monocytes contribute to immune responses as a source for subsets of dendritic cells and macrophages. Human blood monocytes are classified as classical, non-classical and intermediate cells. However, the particular functions of these subsets have been hard to define, with conflicting results and significant overlaps. One likely reason for these ambiguities is in the heterogeneity of these monocyte subsets regrouping cells with divergent functions. To better define monocyte populations, we have analysed expression of 17 markers by multicolour flow cytometry in samples obtained from 28 control donors. Data acquisition was tailored to detect populations present at low frequencies. Our results reveal the existence of novel monocyte subsets detected as larger CD14+ cells that were CD16+ or CD16neg. These large monocytes differed from regular, smaller monocytes with respect to expression of various cell surface molecules, such as FcR, chemokine receptors, and adhesion molecules. Unsupervised multidimensional analysis confirmed the existence of large monocytes and revealed interindividual variations that were grouped according to unique patterns of expression of adhesion molecules CD62L, CD49d, and CD43. Distinct inflammatory responses to TLR agonists were found in small and large monocytes. Overall, refining the definition of monocyte subsets should lead to the identification of populations with specific functions.

Disease detection methodologies in relapsed B-cell acute lymphoblastic leukemia: Opportunities for improvement.

Accurate disease detection is integral to risk stratification in B-cell acute lymphoblastic leukemia (ALL). The gold standard used to evaluate response in the United States includes morphologic evaluation and minimal residual disease (MRD) testing of aspirated bone marrow (BM) by flow cytometry (FC). This MRD assessment is usually made on a single aspirate sample that is subject to variability in collection techniques and sampling error. Additionally, central nervous system (CNS) assessments for ALL include evaluations of cytopathology and cell counts, which can miss subclinical involvement. We retrospectively compared BM biopsy, aspirate, and FC samples obtained from children and young adults with relapsed/refractory ALL to identify the frequency and degree of disease discrepancies in this population. We also compared CNS FC and cytopathology techniques. Sixty of 410 (14.6%) BM samples had discrepant results, 41 (10%) of which were clinically relevant as they resulted in a change in the assignment of marrow status. Discrepant BM results were found in 28 of 89 (31.5%) patients evaluated. Additionally, cerebrospinal fluid (CSF) FC identified disease in 9.7% of cases where cytopathology was negative. These results support further investigation of the role of concurrent BM biopsy, with aspirate and FC evaluations, and the addition of FC to CSF evaluations, to fully assess disease status and response, particularly in patients with relapsed/refractory ALL. Prospective studies incorporating more comprehensive analysis to evaluate the impact on clinical outcomes are warranted.

Learning a Cytometric Deep Phenotype Embedding for Automatic Hematological Malignancies Classification.

Identification of minimal residual disease (MRD) is important in assessing the prognosis of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). The current best clinical practice relies heavily on Flow Cytometry (FC) examination. However, the current FC diagnostic examination requires trained physicians to perform lengthy manual interpretation on high-dimensional FC data measurements of each specimen. The difficulty in handling idiosyncrasy between interpreters along with the time-consuming diagnostic process has become one of the major bottlenecks in advancing the treatment of hematological diseases. In this work, we develop an automatic MRD classifications (AML, MDS, normal) algorithm based on learning a deep phenotype representation from a large cohort of retrospective clinical data with over 2000 real patients' FC samples. We propose to learn a cytometric deep embedding through cell-level autoencoder combined with specimen-level latent Fisher-scoring vectorization. Our method achieves an average AUC of 0.943 across four different hematological malignancies classification tasks, and our analysis further reveals that with only half of the FC markers would be sufficient in obtaining these high recognition accuracies.

Prognostic Value of DNA Index By Flow Cytometry for Minimal Residual Disease in Childhood B-Cell Acute Lymphoblastic Leukemia

Background: Prognostic factors in B-ALL represent a critical role for treatment stratification. High hyperdiploids (>50 chromosomes) have been related to better outcomes, and hypodiploids to worse prognosis. DNA Index (DI) quantifies the DNA from leukemic blast by flow cytometry and has been correlated to the number of chromosomes. Our aim was to demonstrate the prognostic value of DI for minimal residual disease in childhood B-ALL.Methods: 26 blood samples from newly diagnosed childhood B-ALL cases were analyzed between November 2017 and February 2018. DI was evaluated by flow cytometry in samples with Propidium Iodide and leukemic blasts were identified by CD19/CD22/CD10/CD20 antibodies. DI was calculated as the ratio of mean fluorescence of pathologic B blasts and normal cells (T, NK lymphocytes, neutrophils and monocytes) in G0/G1 phase. We stablished three categories: diploid (0.95 - 1.05, 46 chromosomes), hypodiploid (<0.95) and hyperdiploid (>1.05). 8-colors FacsCanto II BD flow cytometer was also used to evaluate minimal residual disease with 0.0025% threshold of detection.Results: 26 cases were evaluated and 2 died during induction treatment. Median age was 8 years (4mo - 16years) and 54% were males. At diagnosis, 62% showed DI diploid and 38% DI hyperdiploid, no hypodiploids cases were detected. Clinical characteristics were similar between both groups. Median DI in the hyperdiploids cases was 1.25 (R: 1.12 - 1.64). We only detected two hyperdiploids cases by conventional karyotype. All patients received the same BFM-based protocol. After induction, all cases achieved complete remission and 46% had MRD negative at the 28th day. DI diploid and hyperdiploid cases achieved 47% and 44% MRD negative, respectively. At the end of consolidation (R: 6-8 Mo), 77% cases achieved MRD negative, and between categories, 62% of DI diploid cases had MRD negative and 100% of DI hyperdiploid cases were negative for MRD detection by flow (p=0.02).Conclusions: The hyperdiploid DNA index by flow cytometry is associated with minimal residual disease negative at the end of consolidation.Flow cytometry offers an alternative over the conventional karyotype to detect good prognosis groups among B-ALL cases. DisclosuresNo relevant conflicts of interest to declare.

A retrospective study of flow cytometric characterization of suspected extranodal lymphomas in dogs.

Flow cytometry (FC) is widely applied to characterize and stage nodal lymphomas in dogs because it has a short turnaround time, requires minimally invasive sampling, and allows contemporary evaluation of neoplastic cells in the primary lesion and of blood and marrow involvement. We investigated advantages and limitations of FC in suspected extranodal lymphomas in dogs. The likelihood of obtaining a suitable FC sample was significantly lower for aspirates of extranodal lesions than for lymph node aspirates. However, we noted no differences among different extranodal lesion sites. We also describe FC results for 39 samples compatible with extranodal lymphoma. A dominant population of large cells was easily identified on morphologic FC scattergrams in many cases. Phenotypic aberrancies were frequently present, mainly in T-cell lymphomas. Lymphoma cells were distinguishable from normal residual lymphocytes in >85% of cases, facilitating the quantification of putative blood and marrow involvement by FC. Despite the high percentage of non-diagnostic samples (32 of 73, >40%), we support the inclusion of FC in the diagnostic workup of suspected extranodal lymphomas in dogs, in conjunction with histopathology. Histopathology is the gold standard for diagnosing lymphoma, provides relevant information, including tissue invasion and epitheliotropism, but has a longer turnaround time.

A Scalable Pipeline for High-Throughput Flow Cytometry

Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.

Issue Highlight - July 2018.

Issue Highlight - July 2018.

Nuclear genome size variation in the allopolyploidOnosma arenaria–O. pseudoarenariaspecies group: methodological issues and revised data

We used flow cytometry (FCM) to investigate genome size variation in two polymorphic allopolyploids, Onosma arenaria Waldst. and Kit. and O. pseudoarenaria Schur, in Central Europe and the Balkan Peninsula. An intercalating DNA stain, propidium iodide (PI), and internal standardization were used. Our data showed that cytosolic compounds may be present in FCM samples and could inhibit, or more frequently promote, PI intercalation. In the absence of PI intercalation interference, leaf-based genome size estimates were observed to be lower than seed-based ones in O. pseudoarenaria, whereas no difference was recorded in O. arenaria. In incubation tests, genome size values frequently increase after a longer staining period. For final genome size measurements, we applied the FCM protocol based on seed material using a ∼150 min incubation period, and provide evidence of mean genome size variation among populations of both species. Two and four natural genome size groups were revealed in O. arenaria and O. pseudoarenaria respectively. Group mean genome sizes varied considerably in both O. arenaria (5.36–5.76 pg) and O. pseudoarenaria (5.98–6.58 pg). This extensive genome size variation is attributed to unexplored taxonomic heterogenity in both taxa. Future taxonomic revision of the group may be supported with genome size measurements obtained using an appropriately standardized methodology.

Utility and proposed algorithm of CSF flow cytometry in hematologic malignancies.

In patients with hematologic malignancies, multiparameter flow cytometry (FCM) offers greater sensitivity than cytology in detecting malignant cells in the initial cerebrospinal fluid (CSF) specimen. However, the role of FCM in assessment of subsequent specimens is unclear. We developed an algorithm to reduce the number of low-yield FCM tests without significant impact on clinically meaningful results. Patients with hematologic malignancies were studied in a derivation cohort, and the following algorithm was developed: (1) cytology and FCM on all initial samples, (2) cytology on all subsequent samples, and (3) FCM on subsequent samples only if previous FCM was positive. A separate population served as the validation cohort. The derivation cohort included 197 patients representing 1157 cytology and 543 FCM samples. Common malignancies were B-Cell ALL (25.3%), diffuse large B cell lymphoma (29.4%), and Burkitt lymphoma (7.7%). In the derivation cohort, the algorithm yielded a sensitivity of 90.0% (95% CI, 81.2-95.6%) and a specificity of 100% (95% CI, 98.9-100.0%). The validation cohort included 132 patients with 563 cytology and 602 FCM samples. In the validation cohort, the testing algorithm yielded a sensitivity of 87.5% (95% CI, 75.9-94.8%) and a specificity of 100% (95% CI, 99.1-100.0%). Of the 15 samples that were missed by the algorithm, FCM findings did not impact patients' management because of known CNS disease (seven patients) or they were responding to treatment (eight patients). CSF testing in hematologic malignancies using the proposed algorithm presents an evidence-based approach to reduce the number of unnecessary FCM tests of CSF without compromising patient care.

Application of Flow Cytometry to the Detection of Pathogenic Bacteria.

Outbreaks of infections have emphasized the necessity for rapid and economic detection methods for pathogens in samples ranging from those of clinical origin to food products during production and retail storage, and increasingly, in environmental samples. Flow cytometry (FCM) allows the rapid acquisition of multi-parametric data regarding cell populations within fluidised samples. However, the application of FCM to pathogen detection depends on the availability of specific fluorescent probes such as antibodies and RNA probes capable of detecting and isolating pathogens from these diverse samples. A particular issue for FCM methodology is the ability to recover and discriminate bacteria from the sample matrix which may pose a major technical hurdle towards accurate and sensitive analysis. This review article focuses on detection of pathogens using FCM in samples originating from food, water, environmental and clinical sources and outlines the current state of the art and potential future applications.

Clinical Outcome Prediction Using Single-Cell Data.

Single-cell technologies like flow cytometry (FCM) provide valuable biological data for knowledge discovery in complex cellular systems like tissues and organs. FCM data contains multi-dimensional information about the cellular heterogeneity of intricate cellular systems. It is possible to correlate single-cell markers with phenotypic properties of those systems. Cell population identification and clinical outcome prediction from single-cell measurements are challenging problems in the field of single cell analysis. In this paper, we propose a hybrid learning approach to predict clinical outcome using samples' single-cell FCM data. The proposed method is efficient in both i) identification of cellular clusters in each sample's FCM data and ii) predict clinical outcome (healthy versus unhealthy) for each subject. Our method is robust and the experimental results indicate promising performance.

FlowVS: channel-specific variance stabilization in flow cytometry

BackgroundComparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances.ResultsWe present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett’s likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test.With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays.ConclusionsThe homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett’s likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.