From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells

Building and analyzing metacells in single-cell genomics data

The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

In this work, an unsupervised volumetric semantic instance segmentation of the plasma membrane of HeLa cells as observed with serial block face scanning electron microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8192 × 8192 pixels each. The background was used to create a distance map, which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., the largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2000 × 2000 × 300 voxels that were treated as cell instances. Then, for each of these volumes, the nucleus was segmented, and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions of interest previously selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and the Jaccard similarity Index (JI): nucleus: JI =0.9665, AC =0.9975, cell including nucleus JI =0.8711, AC =0.9655, cell excluding nucleus JI =0.8094, AC =0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background. In samples with tightly packed cells, this may not be available. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data were released openly through GitHub, Zenodo and EMPIAR.

Machine learning based approach to pH imaging and classification of single cancer cells

The ability to identify different cell populations in a noninvasive manner and without the use of fluorescence labeling remains an important goal in biomedical research. Various techniques have been developed over the last decade, which mainly rely on fluorescent probes or nanoparticles. On the other hand, their applications to single-cell studies have been limited by the lengthy preparation and labeling protocols, as well as issues relating to reproducibility and sensitivity. Furthermore, some of these techniques require the cells to be fixed. Interestingly, it has been shown that different cell types exhibit a unique intracellular environment characterized by specific acidity conditions as a consequence of their distinct functions and metabolism. Here, we leverage a recently developed pH imaging modality and machine learning-based single-cell segmentation and classification to identify different cancer cell lines based on their characteristic intracellular pH. This simple method opens up the potential to perform rapid noninvasive identification of living cancer cells for early cancer diagnosis and further downstream analyses.

Lipidomic profiling of single mammalian cells by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI)

To better understand cell-to-cell heterogeneity, advanced analytical tools are in a growing demand for elucidating chemical compositions of each cell within a population. However, the progress of single-cell chemical analysis has been restrained by the limitations of small cell volumes and minute cellular concentrations. Here, we present a rapid and sensitive method for investigating the lipid profiles of isolated single cells using infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS). In this work, HeLa cells were dispersed onto a glass slide, and the cellular contents were ionized by IR-MALDESI and measured using a Q-Exactive HF-X mass spectrometer. Importantly, this approach does not require extraction and/or enrichment of analytes prior to MS analysis. Using this approach, 45 distinct lipid species, predominantly phospholipids, were detected and putatively annotated from the single HeLa cells. The proof-of-concept study demonstrates the feasibility and efficacy of IR-MALDESI-MS for rapid lipidomic profiling of single cells, which provides an important basis for future work on differentiation between normal and diseased cells at various developmental states, which can offer new insights into cellular metabolic pathways and pathological processes. Although not yet accomplished, we believe this approach can be readily used as an assessment tool to compare the number of identified species during source evolution and method optimization (intra-laboratory), and also disclose the complementary nature of different direct analytical approaches for the coverage of different types of endogenous analytes (inter-laboratory).Graphical abstract.

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%).

High throughput pSTAT signaling profiling by fluorescent cell barcoding and computational analysis

Fluorescent cell barcoding (FCB) is a multiplexing technique for high-throughput flow cytometry (FCM). Although powerful in minimizing staining variability, it remains a subjective FCM technique because of inter-operator variability and differences in data analysis. FCB was implemented by combining two-dye barcoding (DyLight 350 plus Pacific Orange) with five-color surface marker antibody and intracellular staining for phosphoprotein signaling analysis. We proposed a robust method to measure intra- and inter-assay variability of FCB in T/B cells and monocytes by combining range and ratio of variability to standard statistical analyses. Data analysis was carried out by conventional and semi-automated workflows and built with R software. Results obtained from both analyses were compared to assess feasibility and reproducibility of FCB data analysis by machine-learning methods. Our results showed efficient FCB using DyLight 350 and Pacific Orange at concentrations of 0, 15 or 30, and 250 μg/mL, and a high reproducibility of FCB in combination with surface marker and intracellular antibodies. Inter-operator variability was minimized by adding an internal control bridged across matrices used as rejection criterion if significant differences were present between runs. Computational workflows showed comparable results to conventional gating strategies. FCB can be used to study phosphoprotein signaling in T/B cells and monocytes with high reproducibility across operators, and the addition of bridge internal controls can further minimize inter-operator variability. This FCB protocol, which has high throughput analysis and low intra- and inter-assay variability, can be a powerful tool for clinical trial studies. Moreover, FCB data can be reliably analyzed using computational software.

Flow Plex-A tool for unbiased comprehensive flow cytometry data analysis

Introduction

The information content of multiparametric flow cytometry experiments is routinely underexploited given the paucity of adequate tools for unbiased comprehensive data analysis that can be applied successfully and independently by immunologists without computational training.

Methods

We aimed to develop a tool that allows straightforward access to the entire information content of any given flow cytometry panel for immunologists without special computational expertise. We used a data analysis approach which accounts for all mathematically possible combinations of markers in a given panel, coded the algorithm and applied the method to mined and self‐generated data sets.

Results

We developed Flow Plex, a straightforward computational tool that allows unrestricted access to the information content of a given flow cytometry panel, enables classification of human samples according to distinct immune phenotypes, such as different forms of autoimmune uveitis, acute myeloid leukemia vs “healthy”, “old” vs “young”, and facilitates the identification of cell populations with potential biologic relevance to states of disease and health.

Conclusions

We provide a tool that allows immunologists and other flow cytometry users with limited bioinformatics skills to extract comprehensive, unbiased information from flow cytometry data sets.

RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'

BACKGROUND

Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs.

RESULTS

Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample.

CONCLUSIONS

We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.

Re-programming immunosurveillance in persistent non-infectious ocular inflammation

Ocular function depends on a high level of anatomical integrity. This is threatened by inflammation, which alters the local tissue over short and long time-scales. Uveitis due to autoimmune disease, especially when it involves the retina, leads to persistent changes in how the eye interacts with the immune system. The normal pattern of immune surveillance, which for immune privileged tissues is limited, is re-programmed. Many cell types, that are not usually present in the eye, become detectable. There are changes in the tissue homeostasis and integrity. In both human disease and mouse models, in the most extreme cases, immunopathological findings consistent with development of ectopic lymphoid-like structures and disrupted angiogenesis accompany severely impaired eye function. Understanding how the ocular environment is shaped by persistent inflammation is crucial to developing novel approaches to treatment.

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Lamin A and microtubules collaborate to maintain nuclear morphology

Lamin A (LA) is a critical structural component of the nuclear lamina. Mutations within the LA gene (LMNA) lead to several human disorders, most striking of which is Hutchinson-Gilford Progeria Syndrome (HGPS), a premature aging disorder. HGPS cells are best characterized by an abnormal nuclear morphology known as nuclear blebbing, which arises due to the accumulation of progerin, a dominant mutant form of LA. The microtubule (MT) network is known to mediate changes in nuclear morphology in the context of specific events such as mitosis, cell polarization, nucleus positioning and cellular migration. What is less understood is the role of the microtubule network in determining nuclear morphology during interphase. In this study, we elucidate the role of the cytoskeleton in regulation and misregulation of nuclear morphology through perturbations of both the lamina and the microtubule network. We found that LA knockout cells exhibit a crescent shape morphology associated with the microtubule-organizing center. Furthermore, this crescent shape ameliorates upon treatment with MT drugs, Nocodazole or Taxol. Expression of progerin, in LA knockout cells also rescues the crescent shape, although the response to Nocodazole or Taxol treatment is altered in comparison to cells expressing LA. Together these results describe a collaborative effort between LA and the MT network to maintain nuclear morphology.

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 - S - G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues

Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and multi-parametric definitions of cell morphology. To associate cell morphology with cell-material interactions, we developed a shape phenotyping framework based on support vector machines. A feature selection procedure was implemented to select the most significant combination of cell shape metrics to build classifiers with both accuracy and stability to identify and predict microenvironment-driven morphological differences in heterogeneous cell populations. The analysis was conducted at a multi-cell level, where a "supercell" method used average shape measurements of small groups of single cells to account for heterogeneous populations and microenvironment. A subsampling validation algorithm revealed the range of supercell sizes and sample sizes needed for classifier stability and generalization capability. As an example, the responses of human bone marrow stromal cells (hBMSCs) to fibrous vs flat microenvironments were compared on day 1. Our analysis showed that 57 cells (grouped into supercells of size 4) are the minimum needed for phenotyping. The analysis identified that a combination of minor axis length, solidity, and mean negative curvature were the strongest early shape-based indicator of hBMSCs response to fibrous microenvironment.

Ring-Shaped Microlanes and Chemical Barriers as a Platform for Probing Single-Cell Migration

Quantification and discrimination of pharmaceutical and disease-related effects on cell migration requires detailed characterization of single-cell motility. In this context, micropatterned substrates that constrain cells within defined geometries facilitate quantitative readout of locomotion. Here, we study quasi-one-dimensional cell migration in ring-shaped microlanes. We observe bimodal behavior in form of alternating states of directional migration (run state) and reorientation (rest state). Both states show exponential lifetime distributions with characteristic persistence times, which, together with the cell velocity in the run state, provide a set of parameters that succinctly describe cell motion. By introducing PEGylated barriers of different widths into the lane, we extend this description by quantifying the effects of abrupt changes in substrate chemistry on migrating cells. The transit probability decreases exponentially as a function of barrier width, thus specifying a characteristic penetration depth of the leading lamellipodia. Applying this fingerprint-like characterization of cell motion, we compare different cell lines, and demonstrate that the cancer drug candidate salinomycin affects transit probability and resting time, but not run time or run velocity. Hence, the presented assay allows to assess multiple migration-related parameters, permits detailed characterization of cell motility, and has potential applications in cell biology and advanced drug screening.

High Content Imaging of Early Morphological Signatures Predicts Long Term Mineralization Capacity of Human Mesenchymal Stem Cells upon Osteogenic Induction

Human bone marrow-derived multipotent mesenchymal stromal cells, often referred to as mesenchymal stem cells (MSCs), represent an attractive cell source for many regenerative medicine applications due to their potential for multi-lineage differentiation, immunomodulation, and paracrine factor secretion. A major complication for current MSC-based therapies is the lack of well-defined characterization methods that can robustly predict how they will perform in a particular in vitro or in vivo setting. Significant advances have been made with identifying molecular markers of MSC quality and potency using multivariate genomic and proteomic approaches, and more recently with advanced techniques incorporating high content imaging to assess high-dimensional single cell morphological data. We sought to expand upon current methods of high dimensional morphological analysis by investigating whether short term cell and nuclear morphological profiles of MSCs from multiple donors (at multiple passages) correlated with long term mineralization upon osteogenic induction. Using the combined power of automated high content imaging followed by automated image analysis, we demonstrated that MSC morphology after 3 days was highly correlated with 35 day mineralization and comparable to other methods of MSC osteogenesis assessment (such as alkaline phosphatase activity). We then expanded on this initial morphological characterization and identified morphological features that were highly predictive of mineralization capacities (>90% accuracy) of MSCs from additional donors and different manufacturing techniques using linear discriminant analysis. Together, this work thoroughly demonstrates the predictive power of MSC morphology for mineralization capacity and motivates further studies into MSC morphology as a predictive marker for additional in vitro and in vivo responses.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach

Complex phenotypic differences among different acute leukemias cannot be fully captured by analyzing the expression levels of one single molecule, such as a miR, at a time, but requires systematic analysis of large sets of miRs. While a popular approach for analysis of such datasets is principal component analysis (PCA), this method is not designed to optimally discriminate different phenotypes. Moreover, PCA and other low-dimensional representation methods yield linear or non-linear combinations of all measured miRs. Global human miR expression was measured in AML, B-ALL, and TALL cell lines and patient RNA samples. By systematically applying support vector machines to all measured miRs taken in dyad and triad groups, we built miR networks using cell line data and validated our findings with primary patient samples. All the coordinately transcribed members of the miR-23a cluster (which includes also miR-24 and miR-27a), known to function as tumor suppressors of acute leukemias, appeared in the AML, B-ALL and T-ALL centric networks. Subsequent qRT-PCR analysis showed that the most connected miR in the B-ALL-centric network, miR-708, is highly and specifically expressed in B-ALLs, suggesting that miR-708 might serve as a biomarker for B-ALL. This approach is systematic, quantitative, scalable, and unbiased. Rather than a single signature, our approach yields a network of signatures reflecting the redundant nature of biological signaling pathways. The network representation allows for visual analysis of all signatures by an expert and for future integration of additional information. Furthermore, each signature involves only small sets of miRs, such as dyads and triads, which are well suited for in depth validation through laboratory experiments. In particular, loss-and gain-of-function assays designed to drive changes in leukemia cell survival, proliferation and differentiation will benefit from the identification of multi-miR signatures that characterize leukemia subtypes and their normal counterpart cells of origin.

Diagnosing sarcoidosis

PURPOSE OF REVIEW

The usual diagnostic criteria for sarcoidosis include the documentation of a compatible clinicoradiologic scenario, biopsy proof of granulomas, and exclusion of alternate causes for the findings. Establishing the presence of multisystem disease, and longitudinal assessment for the emergence of potential sarcoidosis mimics both strengthen the diagnosis. These principles have constituted a 'diagnostic modus operandi' for several decades, but the emergence of several new technologies has begun to transform their application.

RECENT FINDINGS

The widespread availability of high-resolution chest computed tomography, endobronchial ultrasound-guided transbronchial needle aspiration, rapid on-site cytopathology and to a lesser degree, fluorodeoxyglucose positron emission tomography have facilitated more convenient diagnosis. They also have identified possibilities for biopsy-less diagnosis in appropriate clinical contexts, similar to idiopathic pulmonary fibrosis, and allowed for more comprehensive assessment of the extent and activity of disease. Nonetheless, these new technologies cannot replace the central role of the clinician, whose judgment and circumspection are keys to accurate diagnosis.

SUMMARY

The diagnosis of sarcoidosis and the assessment of its extent should be tailored to the clinical situation. The application of new technologies may permit some evolution of the diagnostic approach in many patients.

A multigene array for measurable residual disease detection in AML patients undergoing SCT

AML is a diagnosis encompassing a diverse group of myeloid malignancies. Heterogeneous genetic etiology, together with the potential for oligoclonality within the individual patient, have made the identification of a single high-sensitivity marker of disease burden challenging. We developed a multiple gene measurable residual disease (MG-MRD) RQ-PCR array for the high-sensitivity detection of AML, retrospectively tested on 74 patients who underwent allo-SCT at the NHLBI in the period 1994-2012. MG-MRD testing on peripheral blood samples prior to transplantation demonstrated excellent concordance with traditional BM-based evaluation and improved risk stratification for post-transplant relapse and OS outcomes. Pre-SCT assessment by MG-MRD predicted all clinical relapses occurring in the first 100 days after allo-SCT compared with 57% sensitivity using WT1 RQ-PCR alone. Nine patients who were negative for WT1 prior to transplantation were correctly reclassified into a high-risk MG-MRD-positive group, associated with 100% post-transplant mortality. This study provides proof of principle that a multiple gene approach may be superior to the use of WT1 expression alone for AML residual disease detection.

On comparing heterogeneity across biomarkers

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

Overexpression of IL-17RC associated with ocular sarcoidosis

Background

Sarcoidosis is a chronic inflammatory disease with a systemic granulomatous disorder affecting multiple organs including the eye. Both CD4⁺ T cell and macrophage have been linked to the pathogenesis of the disease.

Methods

The expression of IL-17RC was measured using FACS,immunohistochemistry and real-time PCR. Serum level of IL-17 was detected using ELISA.

Results

An elevated expression of IL-17RC on CD8⁺ T cells in peripheral blood was found in patients with ocular sarcoidosis as compared to healthy controls. Interestingly, we found a significant increase in the serum level of IL-17 in patients with ocular sarcoidosis as compared to healthy controls, which may be responsible for the induction of IL-17RC on CD8⁺ cells. In addition, IL-17RC appeared only in the retinal tissue of the patient with clinically active sarcoidosis.

Conclusions

Our results suggested a potential involvement of IL-17RC⁺CD8⁺ T cells in pathogenesis of ocular sarcoidosis.

Autoimmune and autoinflammatory mechanisms in uveitis

The eye, as currently viewed, is neither immunologically ignorant nor sequestered from the systemic environment. The eye utilises distinct immunoregulatory mechanisms to preserve tissue and cellular function in the face of immune-mediated insult; clinically, inflammation following such an insult is termed uveitis. The intra-ocular inflammation in uveitis may be clinically obvious as a result of infection (e.g. toxoplasma, herpes), but in the main infection, if any, remains covert. We now recognise that healthy tissues including the retina have regulatory mechanisms imparted by control of myeloid cells through receptors (e.g. CD200R) and soluble inhibitory factors (e.g. alpha-MSH), regulation of the blood retinal barrier, and active immune surveillance. Once homoeostasis has been disrupted and inflammation ensues, the mechanisms to regulate inflammation, including T cell apoptosis, generation of T_reg cells, and myeloid cell suppression in situ, are less successful. Why inflammation becomes persistent remains unknown, but extrapolating from animal models, possibilities include differential trafficking of T cells from the retina, residency of CD8⁺ T cells, and alterations of myeloid cell phenotype and function. Translating lessons learned from animal models to humans has been helped by system biology approaches and informatics, which suggest that diseased animals and people share similar changes in T cell phenotypes and monocyte function to date. Together the data infer a possible cryptic infectious drive in uveitis that unlocks and drives persistent autoimmune responses, or promotes further innate immune responses. Thus there may be many mechanisms in common with those observed in autoinflammatory disorders.

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.

Studying the human immunome: the complexity of comprehensive leukocyte immunophenotyping

A comprehensive study of the cellular components of the immune system requires both deep and broad immunophenotyping of numerous cell populations in an efficient and practical manner. In this chapter, we describe the technical aspects of studying the human immunome using high-dimensional (15 color) fluorescence-based immunophenotyping. We focus on the technical aspects of polychromatic flow cytometry and the initial stages in developing a panel for comprehensive leukocyte immunophenotyping (CLIP). We also briefly discuss how this panel is being used and the challenges of encyclopedic analysis of these rich data sets.