Data analysis in flow cytometry: the future just started

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data

Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms

Background:

Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described.

Subjects and Methods:

Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system.

Results:

Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912.

Conclusions:

The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

Single Cell and Population Level Analysis of HCA Data

High Content Analysis instrumentation has undergone tremendous hardware advances in recent years. It is now possible to obtain images of hundreds of thousands to millions of individual objects, across multiple wells, channels, and plates, in a reasonable amount of time. In addition, it is possible to extract dozens, or hundreds, of features per object using commonly available software tools. Analyzing this data provides new challenges to the scientists. The magnitude of these numbers is reminiscent of flow cytometer, where practitioners have long been taking what effectively amounted to very low resolution, multi-parametric measurements from individual cells for many decades. Flow cytometrists have developed a wide range of tools to effectively analyze and interpret these types of data. This chapter will review the techniques used in flow cytometry and show how they can easily and effectively be applied to High Content Analysis.

MicroRNA-31 inhibits RhoA-mediated tumor invasion and chemotherapy resistance in MKN-45 gastric adenocarcinoma cells

microRNAs are small single-stranded non-coding RNA molecules which modify gene expression by silencing potential target genes. The aberrant expression of RhoA, a small GTPase protein of Rho family, is involved in gastric cancer tumorigenesis. Since miR-31 is a pleomorphic molecule, we evaluated the miR-31/RhoA axis in inducing the malignant phenotype of gastric cancer cells MKN-45. Also, the clinicopathological significance of RhoA was investigated in a well-defined collection of gastric carcinomas which were embedded in tissue microarray blocks. Induction of miR-31 in MKN-45 followed by suppression of RhoA expression resulted in increased sensitivity to 5-fluorouracil, inhibition of cell proliferation, and invasion compared to the control groups. Immunohistochemical analysis in gastric adenocarcinoma patients' samples showed significantly higher expression of RhoA in diffuse versus intestinal subtype tumors ( P = 0.009), poorly differentiated versus well and moderately differentiated tumors ( P = 0.03) and the presence of vascular invasion versus the absence of vascular invasion ( P = 0.04). Our findings suggest a critical role for miR-31, as a tumor suppressor gene, in gastric cancer tumorigenesis by targeting the RhoA. Impact statement Gastric cancer ranks as the third leading cause of cancer-associated deaths worldwide. The RhoA gene encodes a small GTPase protein of Rho family (RhoA) that its dysregulation is associated with cell motility and invasion. A strong line of evidence supports the regulation of RhoA by a number of miRs, including miR-31 in tumors. Our findings revealed that miR-31 is involved in gastric cancer tumorigenesis as a tumor suppressor gene. Through down-regulation of RhoA, miR-31 decreased cell proliferation, migration, and invasion in gastric cancer cells. In addition, induction of miR-31 increased sensitivity to 5-FU; thus, increasing its tissue concentrations could be a potential target for treatment of gastric cancer in the future.

Novel data analysis method for multicolour flow cytometry links variability of multiple markers on single cells to a clinical phenotype

Multicolour Flow Cytometry (MFC) produces multidimensional analytical data on the quantitative expression of multiple markers on single cells. This data contains invaluable biomedical information on (1) the marker expressions per cell, (2) the variation in such expression across cells, (3) the variability of cell marker expression across samples that (4) may vary systematically between cells collected from donors and patients. Current conventional and even advanced data analysis methods for MFC data explore only a subset of these levels. The Discriminant Analysis of MultiAspect CYtometry (DAMACY) we present here provides a comprehensive view on health and disease responses by integrating all four levels. We validate DAMACY by using three distinct datasets: in vivo response of neutrophils evoked by systemic endotoxin challenge, the clonal response of leukocytes in bone marrow of acute myeloid leukaemia (AML) patients, and the complex immune response in blood of asthmatics. DAMACY provided good accuracy 91–100% in the discrimination between health and disease, on par with literature values. Additionally, the method provides figures that give insight into the marker expression and cell variability for more in-depth interpretation, that can benefit both physicians and biomedical researchers to better diagnose and monitor diseases that are reflected by changes in blood leukocytes.

High-dimensional CyTOF analysis of dengue virus-infected human DCs reveals distinct viral signatures

Dengue virus (DENV) is the most prevalent mosquito-borne virus causing human disease. Of the 4 DENV serotypes, epidemiological data suggest that DENV-2 secondary infections are associated with more severe disease than DENV-4 infections. Mass cytometry by time-of-flight (CyTOF) was used to dissect immune changes induced by DENV-2 and DENV-4 in human DCs, the initial targets of primary infections that likely affect infection outcomes. Strikingly, DENV-4 replication peaked earlier and promoted stronger innate immune responses, with increased expression of DC activation and migration markers and increased cytokine production, compared with DENV-2. In addition, infected DCs produced higher levels of inflammatory cytokines compared with bystander DCs, which mainly produced IFN-induced cytokines. These high-dimensional analyses during DENV-2 and DENV-4 infections revealed distinct viral signatures marked by different replication strategies and antiviral innate immune induction in DCs, which may result in different viral fitness, transmission, and pathogenesis.

Analysis of S1P Receptor Expression by Uterine Immune Cells Using Standardized Multi-parametric Flow Cytometry

Flow cytometry is a powerful tool for phenotypic and functional analyses of single immune cells. The increasing capability of flow cytometry technology has driven a more detailed understanding of immune cell subsets and functions in complex cellular systems such as the developing human decidua/placenta. We propose a standardized procedure for the isolation and analysis of human decidual natural killer (dNK) cells and this method can be extended to investigation of other uterine lymphocytes. Here this platform is used to examine the expression of sphingosine-1-phosphate (S1P) receptor and functional growth factors by dNK cells.

Phenotyping Multiple Subsets of Immune Cells In Situ in FFPE Tissue Sections: An Overview of Methodologies

The recent clinical success of new cancer immunotherapy agents and methods is driving the need to understand the role of immune cells in solid tissues, especially tumors. Immune cell phenotyping via flow cytometry, while a cornerstone of immunology, is not spatially resolved and cannot analyze immune cell subsets in situ in clinical biopsy sections or to determine their interrelationships. To address this problem, a number of methodologies have been developed in attempts to phenotype immune and other cells in images acquired from tissue sections and to assess their organization in the tumor and its microenvironment. This chapter review the staining and multiplex image analysis methods that have been developed for phenotyping immune and other cells in formalin-fixed, paraffin-embedded (FFPE) tissue sections.

Anti-TNF-α Drugs Differently Affect the TNFα-sTNFR System and Monocyte Subsets in Patients with Psoriasis

TNF-α has a central role in the development and maintenance of psoriatic plaques, and its serum levels correlate with disease activity. Anti-TNF-α drugs are, however, ineffective in a relevant percentage of patients for reasons that are currently unknown. To understand whether the response to anti-TNF-α drugs is influenced by the production of anti-drug antibodies or by the modulation of the TNFα-TNFα receptor system, and to identify changes in monocyte phenotype and activity, we analysed 119 psoriatic patients who either responded or did not respond to different anti-TNF-α therapies (adalimumab, etanercept or infliximab), and measured plasma levels of TNF-α, TNF-α soluble receptors, drug and anti-drug antibodies. Moreover, we analyzed the production of TNF-α and TNF-α soluble receptors by peripheral blood mononuclear cells (PBMCs), and characterized different monocyte populations. We found that: i) the drug levels varied between responders and non-responders; ii) anti-infliximab antibodies were present in 15% of infliximab-treated patients, while anti-etanercept or anti-adalimumab antibodies were never detected; iii) plasma TNF-α levels were higher in patients treated with etanercept compared to patients treated with adalimumab or infliximab; iv) PBMCs from patients responding to adalimumab and etanercept produced more TNF-α and sTNFRII in vitro than patients responding to infliximab; v) PBMCs from patients not responding to infliximab produce higher levels of TNF-α and sTNFRII than patients responding to infliximab; vi) anti- TNF-α drugs significantly altered monocyte subsets. A complex remodelling of the TNFα-TNFα receptor system thus takes place in patients treated with anti-TNF-α drugs, that involves either the production of anti-drug antibodies or the modulation of monocyte phenotype or inflammatory activity.

iNKT Cells in Secondary Progressive Multiple Sclerosis Patients Display Pro-inflammatory Profiles

BACKGROUND

Multiple sclerosis (MS), an autoimmune disease with neurodegeneration and inflammation is characterized by several alterations of different T cell subsets. However, few data exist on the role of iNKT lymphocytes.

OBJECTIVE

To identify possible changes in the phenotype of iNKT cells in patients with different clinical forms of MS and find alterations in their polyfunctionality [i.e., ability to produce simultaneously up to four cytokines such as IL-17, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, and IL-4].

METHODS

We studied a total of 165 patients, 91 with a relapsing-remitting form [RR; 31 were treated with interferon (IFN)1a-β, 25 with natalizumab (NAT), 29 with glatiramer acetate; 17 were newly diagnosed RR without treatment, 19 not-active RR without treatment]. Forty-four patients had a progressive MS: 20 primary progressive (PP) and 24 secondary progressive (SP). A total of 55 age- and sex-matched subjects represented healthy controls (CTR). Among fresh peripheral blood mononuclear cells, iNKT cells were identified by flow cytometry. Moreover, the capability of iNKT cells to produce different cytokines (IL-17, TNF-α, IFN-γ, and IL-4) after in vitro stimulation were evaluated in 18 RR (11 treated with NAT and 7 with IFN), 4 PP, 6 SP, and 16 CTR.

RESULTS

No main differences were found in iNKT cell phenotype among MS patients with different MS forms or during different treatments. However, the polyfunctional response of iNKT cells showed Th1 and Th17 profiles. This was well evident in patients with SP form, who are characterized by high levels of inflammation and neurodegeneration, and exhibited a sustained increase in the production of Th17 cytokines. Patients treated with NAT displayed lower levels of iNKT cells producing IL-17, TNF-α, and IFN-γ.

CONCLUSION

Our data suggest that the progressive phase of the disease is characterized by permanent iNKT activation and a skewing towards an inflammatory phenotype. Compared to other treatments, NAT was able to modulate iNKT cell function.

Review: imaging technologies for flow cytometry

High-throughput single cell imaging is a critical enabling and driving technology in molecular and cellular biology, biotechnology, medicine and related areas. Imaging flow cytometry combines the single-cell imaging capabilities of microscopy with the high-throughput capabilities of conventional flow cytometry. Recent advances in imaging flow cytometry are remarkably revolutionizing single-cell analysis. This article describes recent imaging flow cytometry technologies and their challenges.

Th1 and Th17 proinflammatory profile characterizes invariant natural killer T cells in virologically suppressed HIV+ patients with low CD4+/CD8+ ratio

INTRODUCTION

Scanty data exist on the phenotype and functionality of invariant natural killer T (iNKT) cells in HIV-infected (HIV+) patients.

METHODS

By flow cytometry, we studied iNKT cells from 54 HIV+ patients who started combined antiretroviral therapy and had undetectable viral load for more than 1 year. Twenty-five maintained a CD4/CD8 ratio less than 0.4, whereas 29 reached a ratio more than 1.1; 32 age-matched and sex-matched patients were healthy controls (CTR).

RESULTS

Patients with low ratio had lower percentage of CD4 iNKT cells compared with patients with high ratio and higher CD8 iNKT cell percentage; double-negative iNKT cells were lower in HIV+ patients compared with CTR. Patients with low ratio had higher percentage of CD4 and double-negative iNKT cells expressing CD38 and HLA-DR compared with patients with high ratio. CD4 iNKT cells expressing PD-1 were higher in patients with CD4/CD8 ratio less than 0.4, whereas double-negative iNKT cells expressing PD-1 were lower compared with patients with ratio more than 1.1. Patients with low ratio had higher CD4 iNKT cells producing IL-17, CD8 iNKT cells producing IFN-γ, TNF-α or IFN-γ and TNF-α, and double-negative iNKT cells producing IL-17 or IL-17 and IFN-γ compared with CTR. Activated CD4 (or CD8) T cells correlated with activated CD4 (or CD8) iNKT cells, as well as the percentages of CD4 (or CD8) T cells expressing PD-1 was correlated to that of CD4 (or CD8) iNKT cells expressing PD-1.

CONCLUSION

Low CD4/CD8 ratio despite effective combined antiretroviral therapy is associated with altered iNKT cell subsets, enhanced activation, and prominent Th1/Th17 proinflammatory profile.

HD Flow Cytometry: An Improved Way to Quantify Cellular Interactions with Nanoparticles

Histogram deconvolution flow cytometry enables improved quantification of nanomaterial-cell interactions. The algorithm identifies the positive cells in highly overlapped populations and calculates the fluorescence intensity of the positive population. This technique performs better than commercially available methods with the additional benefit of visualizing the output.

Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at www.bioconductor.org. It has been downloaded nearly 6,000 times since 2014.

Inherent low Erk and p38 activity reduce Fas Ligand expression and degranulation in T helper 17 cells leading to activation induced cell death resistance

Activation Induced Cell Death of T helper cells is central to maintaining immune homeostasis and a perturbation often manifests in aberrant T helper cells that is associated with immunopathologies. Significant presence of T cells positive for IL-17A (Th17) and dual positive for IFN-γ/IL-17A (Th1/Th17) in both effector (CD45RA+RO+) and memory (CD45RA-RO+) compartments with differential FasL protein in RA peripheral blood suggested their differential TCR AICD sensitivity. Lowered active caspase-3 in Th17 and Th1/Th17 over Th1 cells confirmed their capability to resist AICD and pointed to early upstream events. Differential MAPK activities, FasL protein and downstream caspase-3 activities in murine Th1 and Th17 cells established distinct TCR mediated signaling pathways and suggested low Erk and p38 activity as pivotal for AICD sensitivity. We extrapolated our mouse and human data and report that Fas-FasL is the preferred death pathway for both Th1 and Th17 and that inherently low Erk2 activity protected Th17 cells from TCR AICD. The presence of significantly higher numbers of aberrant T helper cells in RA also suggest an inflammatory cytokine milieu and AICD insensitive T cell link to sustained inflammation. Re sensitization to apoptosis by targeting MAPK activity especially Erk2 in RA might be of therapeutic value.

Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data

OBJECTIVE

Flow cytometry (FC) is a widely acknowledged technology in diagnosis of acute myeloid leukemia (AML) and has been indispensable in determining progression of the disease. Although FC plays a key role as a posttherapy prognosticator and evaluator of therapeutic efficacy, the manual analysis of cytometry data is a barrier to optimization of reproducibility and objectivity. This study investigates the utility of our recently introduced nonparametric Bayesian framework in accurately predicting the direction of change in disease progression in AML patients using FC data.

METHODS

The highly flexible nonparametric Bayesian model based on the infinite mixture of infinite Gaussian mixtures is used for jointly modeling data from multiple FC samples to automatically identify functionally distinct cell populations and their local realizations. Phenotype vectors are obtained by characterizing each sample by the proportions of recovered cell populations, which are, in turn, used to predict the direction of change in disease progression for each patient.

RESULTS

We used 200 diseased and nondiseased immunophenotypic panels for training and tested the system with 36 additional AML cases collected at multiple time points. The proposed framework identified the change in direction of disease progression with accuracies of 90% (nine out of ten) for relapsing cases and 100% (26 out of 26) for the remaining cases.

CONCLUSIONS

We believe that these promising results are an important first step toward the development of automated predictive systems for disease monitoring and continuous response evaluation.

SIGNIFICANCE

Automated measurement and monitoring of therapeutic response is critical not only for objective evaluation of disease status prognosis but also for timely assessment of treatment strategies.

Cellular Microbiology of Mycoplasma canis

Mycoplasma canis can infect many mammalian hosts but is best known as a commensal or opportunistic pathogen of dogs. The unexpected presence of M. canis in brains of dogs with idiopathic meningoencephalitis prompted new in vitro studies to help fill the void of basic knowledge about the organism's candidate virulence factors, the host responses that it elicits, and its potential roles in pathogenesis. Secretion of reactive oxygen species and sialidase varied quantitatively (P < 0.01) among strains of M. canis isolated from canine brain tissue or mucosal surfaces. All strains colonized the surface of canine MDCK epithelial and DH82 histiocyte cells and murine C8-D1A astrocytes. Transit through MDCK and DH82 cells was demonstrated by gentamicin protection assays and three-dimensional immunofluorescence imaging. Strains further varied (P < 0.01) in the extents to which they influenced the secretion of tumor necrosis factor alpha (TNF-α) and the neuroendocrine regulatory peptide endothelin-1 by DH82 cells. Inoculation with M. canis also decreased major histocompatibility complex class II (MHC-II) antigen expression by DH82 cells (P < 0.01), while secretion of gamma interferon (IFN-γ), interleukin-6 (IL-6), interleukin-10 (IL-10), and complement factor H was unaffected. The basis for differences in the responses elicited by these strains was not obvious in their genome sequences. No acute cytopathic effects on any homogeneous cell line, or consistent patterns of M. canis polyvalent antigen distribution in canine meningoencephalitis case brain tissues, were apparent. Thus, while it is not likely a primary neuropathogen, M. canis has the capacity to influence meningoencephalitis through complex interactions within the multicellular and neurochemical in vivo milieu.

Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics

Biofilms serve essential ecosystem functions and are used in different technical applications. Studies from stream ecology and waste-water treatment have shown that biofilm functionality depends to a great extent on community structure. Here we present a fast and easy-to-use method for individual cell-based analysis of stream biofilms, based on stain-free flow cytometry and visualization of the high-dimensional data by viSNE. The method allows the combined assessment of community structure, decay of phototrophic organisms and presence of abiotic particles. In laboratory experiments, it allows quantification of cellular decay and detection of survival of larger cells after temperature stress, while in the field it enables detection of community structure changes that correlate with known environmental drivers (flow conditions, dissolved organic carbon, calcium) and detection of microplastic contamination. The method can potentially be applied to other biofilm types, for example, for inferring community structure for environmental and industrial research and monitoring.

Cell separation: Potentials and pitfalls

Cell separation techniques play an indispensable part in numerous basic biological studies and even clinical settings. Although various cell isolation methods with diverse applications have been devised so far, not all of them have been able to gain widespread popularity among researchers and clinicians. There is not a single method known to be advantageous over all cell isolation techniques, and in fact, it is the researcher's aim in performing a study that determines the most suitable method. A perfect method for one study might not be necessarily a proper choice for another and likewise, expensive and complex isolation methods might not always be the best choices. There are several criteria such as cell purity, viability, activation status, and frequency that need to be given serious thought before selecting an isolation technique. Moreover, time and cost are two of the key elements that should be taken into consideration before implementing a project. Hence, here we provide a succinct description of six more popular cell separation methods with respect to their principles, advantages, and disadvantages as well as their most common applications. We further provide several key features of each technique so that it helps the researchers to take the first step toward opting for the best method that fits well into their projects.

A flow cytometric approach to analyzing mature and progenitor endothelial cells following traumatic brain injury

BACKGROUND

Traumatic brain injury (TBI) continues to be a major source of death and disability worldwide, and one of the earliest and most profound deficits comes from vascular damage and breakdown of the blood-brain barrier (BBB). Cerebral vascular endothelial cells (cvECs) and endothelial progenitor cells (EPCs) have been shown to play essential roles in vessel repair and BBB stability, although their individual contributions remain poorly defined.

NEW METHOD

We employ TruCount beads with flow cytometry to precisely quantify cvECs, EPCs, and peripheral leukocytes in the murine cortex after controlled cortical impact (CCI) injury.

RESULTS

We found a significant reduction in the number of cvECs at 3 days post-injury (dpi), whereas the EPCs and invading peripheral leukocytes were significantly increased compared with sham controls. Proliferation studies demonstrate that both cvECs and EPCs are undergoing cell expansion in the first week post-injury. Furthermore, analysis of protein expression using mean fluorescence intensity found increases in PECAM-1, VEGFR-2, and VE-Cadherin expression per cell at 3 dpi, which is consistent with western blot analysis.

COMPARISON WITH EXISTING METHODS

Classic methods of cell analysis, such as histological cell counts, in the traumatic injured brain are labor intensive, time-consuming, and potentially biased; whereas flow cytometry provides an efficient, non-biased approach to simultaneously quantify multiple cell types. However, conventional flow cytometry that employs capped events can provide misleading results in CNS injured tissues.

CONCLUSIONS

We demonstrate that TruCount quantification using flow cytometry is a powerful tool for quantifying mature and progenitor endothelial cell changes after TBI.

Toward harmonized phenotyping of human myeloid-derived suppressor cells by flow cytometry: results from an interim study

There is an increasing interest for monitoring circulating myeloid-derived suppressor cells (MDSCs) in cancer patients, but there are also divergences in their phenotypic definition. To overcome this obstacle, the Cancer Immunoguiding Program under the umbrella of the Association of Cancer Immunotherapy is coordinating a proficiency panel program that aims at harmonizing MDSC phenotyping. After a consultation period, a two-stage approach was designed to harmonize MDSC phenotype. In the first step, an international consortium of 23 laboratories immunophenotyped 10 putative MDSC subsets on pretested, peripheral blood mononuclear cells of healthy donors to assess the level of concordance and define robust marker combinations for the identification of circulating MDSCs. At this stage, no mandatory requirements to standardize reagents or protocols were introduced. Data analysis revealed a small intra-laboratory, but very high inter-laboratory variance for all MDSC subsets, especially for the granulocytic subsets. In particular, the use of a dead-cell marker altered significantly the reported percentage of granulocytic MDSCs, confirming that these cells are especially sensitive to cryopreservation and/or thawing. Importantly, the gating strategy was heterogeneous and associated with high inter-center variance. Overall, our results document the high variability in MDSC phenotyping in the multicenter setting if no harmonization/standardization measures are applied. Although the observed variability depended on a number of identified parameters, the main parameter associated with variation was the gating strategy. Based on these findings, we propose further efforts to harmonize marker combinations and gating parameters to identify strategies for a robust enumeration of MDSC subsets.

PD-1 Blockade Expands Intratumoral Memory T Cells

Tumor responses to programmed cell death protein 1 (PD-1) blockade therapy are mediated by T cells, which we characterized in 102 tumor biopsies obtained from 53 patients treated with pembrolizumab, an antibody to PD-1. Biopsies were dissociated, and single-cell infiltrates were analyzed by multicolor flow cytometry using two computational approaches to resolve the leukocyte phenotypes at the single-cell level. There was a statistically significant increase in the frequency of T cells in patients who responded to therapy. The frequency of intratumoral B cells and monocytic myeloid-derived suppressor cells significantly increased in patients' biopsies taken on treatment. The percentage of cells with a regulatory T-cell phenotype, monocytes, and natural killer cells did not change while on PD-1 blockade therapy. CD8(+) memory T cells were the most prominent phenotype that expanded intratumorally on therapy. However, the frequency of CD4(+) effector memory T cells significantly decreased on treatment, whereas CD4(+) effector T cells significantly increased in nonresponding tumors on therapy. In peripheral blood, an unusual population of blood cells expressing CD56 was detected in two patients with regressing melanoma. In conclusion, PD-1 blockade increases the frequency of T cells, B cells, and myeloid-derived suppressor cells in tumors, with the CD8(+) effector memory T-cell subset being the major T-cell phenotype expanded in patients with a response to therapy.

PD-1 Blockade Expands Intratumoral Memory T Cells

Tumor responses to programmed cell death protein 1 (PD-1) blockade therapy are mediated by T cells, which we characterized in 102 tumor biopsies obtained from 53 patients treated with pembrolizumab, an antibody to PD-1. Biopsies were dissociated, and single-cell infiltrates were analyzed by multicolor flow cytometry using two computational approaches to resolve the leukocyte phenotypes at the single-cell level. There was a statistically significant increase in the frequency of T cells in patients who responded to therapy. The frequency of intratumoral B cells and monocytic myeloid-derived suppressor cells significantly increased in patients' biopsies taken on treatment. The percentage of cells with a regulatory T-cell phenotype, monocytes, and natural killer cells did not change while on PD-1 blockade therapy. CD8(+) memory T cells were the most prominent phenotype that expanded intratumorally on therapy. However, the frequency of CD4(+) effector memory T cells significantly decreased on treatment, whereas CD4(+) effector T cells significantly increased in nonresponding tumors on therapy. In peripheral blood, an unusual population of blood cells expressing CD56 was detected in two patients with regressing melanoma. In conclusion, PD-1 blockade increases the frequency of T cells, B cells, and myeloid-derived suppressor cells in tumors, with the CD8(+) effector memory T-cell subset being the major T-cell phenotype expanded in patients with a response to therapy.

Adenosine A1 receptors contribute to immune regulation after neonatal hypoxic ischemic brain injury

Neonatal brain hypoxic ischemia (HI) often results in long-term motor and cognitive impairments. Post-ischemic inflammation greatly effects outcome and adenosine receptor signaling modulates both HI and immune cell function. Here, we investigated the influence of adenosine A1 receptor deficiency (A1R(-/-)) on key immune cell populations in a neonatal brain HI model. Ten-day-old mice were subjected to HI. Functional outcome was assessed by open locomotion and beam walking test and infarction size evaluated. Flow cytometry was performed on brain-infiltrating cells, and semi-automated analysis of flow cytometric data was applied. A1R(-/-) mice displayed larger infarctions (+33%, p < 0.05) and performed worse in beam walking tests (44% more mistakes, p < 0.05) than wild-type (WT) mice. Myeloid cell activation after injury was enhanced in A1R(-/-) versus WT brains. Activated B lymphocytes expressing IL-10 infiltrated the brain after HI in WT, but were less activated and did not increase in relative frequency in A1R(-/-). Also, A1R(-/-) B lymphocytes expressed less IL-10 than their WT counterparts, the A1R antagonist DPCPX decreased IL-10 expression whereas the A1R agonist CPA increased it. CD4(+) T lymphocytes including FoxP3(+) T regulatory cells, were unaffected by genotype, whereas CD8(+) T lymphocyte responses were smaller in A1R(-/-) mice. Using PCA to characterize the immune profile, we could discriminate the A1R(-/-) and WT genotypes as well as sham operated from HI-subjected animals. We conclude that A1R signaling modulates IL-10 expression by immune cells, influences the activation of these cells in vivo, and affects outcome after HI.

Algorithmic Tools for Mining High-Dimensional Cytometry Data

The advent of mass cytometry has led to an unprecedented increase in the number of analytes measured in individual cells, thereby increasing the complexity and information content of cytometric data. Although this technology is ideally suited to the detailed examination of the immune system, the applicability of the different methods for analyzing such complex data is less clear. Conventional data analysis by manual gating of cells in biaxial dot plots is often subjective, time consuming, and neglectful of much of the information contained in a highly dimensional cytometric dataset. Algorithmic data mining has the promise to eliminate these concerns, and several such tools have been applied recently to mass cytometry data. We review computational data mining tools that have been used to analyze mass cytometry data, outline their differences, and comment on their strengths and limitations. This review will help immunologists to identify suitable algorithmic tools for their particular projects.

Multidimensional Clusters of CD4+ T Cell Dysfunction Are Primarily Associated with the CD4/CD8 Ratio in Chronic HIV Infection

HIV infection provokes a myriad of pathological effects on the immune system where many markers of CD4+ T cell dysfunction have been identified. However, most studies to date have focused on single/double measurements of immune dysfunction, while the identification of pathological CD4+ T cell clusters that is highly associated to a specific biomarker for HIV disease remain less studied. Here, multi-parametric flow cytometry was used to investigate immune activation, exhaustion, and senescence of diverse maturation phenotypes of CD4+ T cells. The traditional method of manual data analysis was compared to a multidimensional clustering tool, FLOw Clustering with K (FLOCK) in two cohorts of 47 untreated HIV-infected individuals and 21 age and sex matched healthy controls. In order to reduce the subjectivity of FLOCK, we developed an "artificial reference", using 2% of all CD4+ gated T cells from each of the HIV-infected individuals. Principle component analyses demonstrated that using an artificial reference lead to a better separation of the HIV-infected individuals from the healthy controls as compared to using a single HIV-infected subject as a reference or analyzing data manually. Multiple correlation analyses between laboratory parameters and pathological CD4+ clusters revealed that the CD4/CD8 ratio was the preeminent surrogate marker of CD4+ T cells dysfunction using all three methods. Increased frequencies of an early-differentiated CD4+ T cell cluster with high CD38, HLA-DR and PD-1 expression were best correlated (Rho = -0.80, P value = 1.96×10-11) with HIV disease progression as measured by the CD4/CD8 ratio. The novel approach described here can be used to identify cell clusters that distinguish healthy from HIV infected subjects and is biologically relevant for HIV disease progression. These results further emphasize that a simple measurement of the CD4/CD8 ratio is a useful biomarker for assessment of combined CD4+ T cell dysfunction in chronic HIV disease.

Non-Classical monocytes display inflammatory features: Validation in Sepsis and Systemic Lupus Erythematous

Given the importance of monocytes in pathogenesis of infectious and other inflammatory disorders, delineating functional and phenotypic characterization of monocyte subsets has emerged as a critical requirement. Although human monocytes have been subdivided into three different populations based on surface expression of CD14 and CD16, published reports suffer from contradictions with respect to subset phenotypes and function. This has been attributed to discrepancies in reliable gating strategies for flow cytometric characterization and purification protocols contributing to significant changes in receptor expression. By using a combination of multicolour flow cytometry and a high-dimensional automated clustering algorithm to confirm robustness of gating strategy and analysis of ex-vivo activation of whole blood with LPS we demonstrate the following: a. ‘Classical’ monocytes are phagocytic with no inflammatory attributes, b. ‘Non-classical’ subtype display ‘inflammatory’ characteristics on activation and display properties for antigen presentation and c. ‘Intermediate’ subtype that constitutes a very small percentage in circulation (under physiological conditions) appear to be transitional monocytes that display both phagocytic and inflammatory function. Analysis of blood from patients with Sepsis, a pathogen driven acute inflammatory disease and Systemic Lupus Erythmatosus (SLE), a chronic inflammatory disorder validated the broad conclusions drawn in the study.

Aberrant Expression of MHC Class II in Melanoma Attracts Inflammatory Tumor-Specific CD4+ T- Cells, Which Dampen CD8+ T-cell Antitumor Reactivity

In the absence of a local inflammatory response, expression of MHC class II molecules is restricted mainly to hematopoietic cells and thymus epithelium. However, certain tumors, such as melanoma, may acquire aberrant constitutive expression of MHC class II. In a set of primary melanoma cell populations and correspondingly expanded autologous tumor-infiltrating lymphocytes (TIL), we show how MHC class II expression on melanoma cells associates with strong MHC class II-restricted CD4(+) T-cell responses that are specific for tumors. Notably, we found that tumor-specific CD4(+) T-cell responses were dominated by TNF production. TNF reduced CD8(+) T-cell activation in IFNγ-rich environments resembling a tumor site. Conversely, direct CD4(+) T-cell responses had no influence on either the proliferation or viability of melanoma cells. Taken together, our results illustrate a novel immune escape mechanism that can be activated by aberrant expression of MHC class II molecules, which by attracting tumor-specific CD4(+) T cells elicit a local inflammatory response dominated by TNF that, in turn, inhibits cytotoxic CD8(+) T-cell responses

Facilitated gate setting by sequential dot plot scanning

Microbial communities comprising thousands of unknown organisms can be studied flow cytometrically by applying just one fluorescent parameter and using scatter characteristics of cells. Resulting 2D-plots need to represent high numbers of cells to detect the many subcommunities, even rare ones that might be present in the sample. Evaluation of such data can be faulty and subjective due to the low number of parameters available for data discrimination and the high numbers of overlaying events. Here, we describe a procedure that helps to evaluate such data using facilitated gate setting by sequential dot-plot scanning.

Complexity and challenges in defining myeloid-derived suppressor cells

Study of myeloid cells endowed with suppressive activity is an active field of research which has particular importance in cancer, in view of the negative regulatory capacity of these cells to the host's immune response. The expansion of these cells, called myeloid-derived suppressor cells (MDSCs), has been documented in many models of tumor-bearing mice and in patients with tumors of various origin, and their presence is associated with disease progression and reduced survival. For this reason, monitoring this type of cell expansion is of clinical importance, and flow cytometry is the technique of choice for their identification. Over the years, it has been demonstrated that MDSCs comprise a group of immature myeloid cells belonging both to monocytic and granulocytic lineages, with several stages of differentiation; their occurrence depends on tumor-derived soluble factors, which guide their expansion and determine their block of differentiation. Because of their heterogeneous composition, accurate phenotyping of these cells requires a multicolor approach, so that the expansion of all MDSC subsets can be appreciated. This review article focuses on identifying MDSCs and discusses problems associated with phenotyping circulating and tumor-associated MDSCs in humans and in mouse models.

High Reproducibility of ELISPOT Counts from Nine Different Laboratories

The primary goal of immune monitoring with ELISPOT is to measure the number of T cells, specific for any antigen, accurately and reproducibly between different laboratories. In ELISPOT assays, antigen-specific T cells secrete cytokines, forming spots of different sizes on a membrane with variable background intensities. Due to the subjective nature of judging maximal and minimal spot sizes, different investigators come up with different numbers. This study aims to determine whether statistics-based, automated size-gating can harmonize the number of spot counts calculated between different laboratories. We plated PBMC at four different concentrations, 24 replicates each, in an IFN-γ ELISPOT assay with HCMV pp65 antigen. The ELISPOT plate, and an image file of the plate was counted in nine different laboratories using ImmunoSpot® Analyzers by (A) Basic Count™ relying on subjective counting parameters set by the respective investigators and (B) SmartCount™, an automated counting protocol by the ImmunoSpot® Software that uses statistics-based spot size auto-gating with spot intensity auto-thresholding. The average coefficient of variation (CV) for the mean values between independent laboratories was 26.7% when counting with Basic Count™, and 6.7% when counting with SmartCount™. Our data indicates that SmartCount™ allows harmonization of counting ELISPOT results between different laboratories and investigators.

WITHDRAWN: Systems Biology and Immune Aging

The Publisher regrets that this article is an accidental duplication of anarticle that has already been published, http://dx.doi.org/10.1016/j.imlet.2014.09.009. The duplicate article has therefore been withdrawn.

MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data

Background

Biological data often originate from samples containing mixtures of subpopulations, corresponding e.g. to distinct cellular phenotypes. However, identification of distinct subpopulations may be difficult if biological measurements yield distributions that are not easily separable.

Results

We present Multiresolution Correlation Analysis (MCA), a method for visually identifying subpopulations based on the local pairwise correlation between covariates, without needing to define an a priori interaction scale. We demonstrate that MCA facilitates the identification of differentially regulated subpopulations in simulated data from a small gene regulatory network, followed by application to previously published single-cell qPCR data from mouse embryonic stem cells. We show that MCA recovers previously identified subpopulations, provides additional insight into the underlying correlation structure, reveals potentially spurious compartmentalizations, and provides insight into novel subpopulations.

Conclusions

MCA is a useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

Quantitative characterization of cellular membrane-receptor heterogeneity through statistical and computational modeling

Cell population heterogeneity can affect cellular response and is a major factor in drug resistance. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in vascular endothelial growth factor receptor (VEGFR) membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (>35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy.

T-cell activation discriminates subclasses of symptomatic primary humoral immunodeficiency diseases in adults

Background

Symptomatic Primary Humoral Immunodeficiency Diseases (PHID) constitute a highly heterogeneous group of diseases characterized by a shared hypogammaglobulinemia, resulting in increased risk of recurrent or severe infections. Associations have been described with a variety of immunological abnormalities involving B and T-cell differentiation, T-cell activation and innate immunity. However, PHID discrimination remains based on B-lymphocyte abnormalities and other components of the immune system have not been sufficiently taken into account. We carried out unsupervised and supervised methods for classification in a cohort of 81 symptomatic PHID patients to evaluate the relative importance of 23 immunological parameters and to select relevant markers that may be useful for diagnosis and prognosis.

Results

We identified five groups of patients, among which the percentage of PHID complications varied substantially. Combining the set of markers involved in PHID supported the existence of two distinct mechanisms associated with complications. Switched memory B-cell attrition and CD8+ HLA-DR + activated T-cell increase were the prominent abnormalities observed in PHID complications. Furthermore, in a subgroup of 57 patients with common variable immunodeficiency, the classification that added CD8+ HLA-DR + to the consensual EUROclass classification was better than the EUROclass model in predicting complications.

Conclusion

These results highlight the importance of T-cell activation that may improve discrimination of PHID patients in specific subgroups and help to identify patients with different clinical outcomes.

Understanding health and disease with multidimensional single-cell methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.

Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow

Accelerating imaging speed in optical microscopy is often realized at the expense of image contrast, image resolution, and detection sensitivity--a common predicament for advancing high-speed and high-throughput cellular imaging. We here demonstrate a new imaging approach, called asymmetric-detection time-stretch optical microscopy (ATOM), which can deliver ultrafast label-free high-contrast flow imaging with well delineated cellular morphological resolution and in-line optical image amplification to overcome the compromised imaging sensitivity at high speed. We show that ATOM can separately reveal the enhanced phase-gradient and absorption contrast in microfluidic live-cell imaging at a flow speed as high as ~10 m/s, corresponding to an imaging throughput of ~100,000 cells/sec. ATOM could thus be the enabling platform to meet the pressing need for intercalating optical microscopy in cellular assay, e.g. imaging flow cytometry--permitting high-throughput access to the morphological information of the individual cells simultaneously with a multitude of parameters obtained in the standard assay.

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.