A regression model approach to enable cell morphology correction in high-throughput flow cytometry

Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis

We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.

A flow cytometric journey into cell cycle analysis

Cell cycle involves a series of changes that lead to cell growth and division. Cell cycle analysis is crucial to understand cellular responses to changing environmental conditions. Since its inception, flow cytometry has been particularly useful for cell cycle analysis at single cell level due to its speed and precision. Previously, flow cytometric cell cycle analysis relied solely on the measurement of cellular DNA content. Later, methods were developed for multiparametric analysis. This review explains the journey of flow cytometry to understand different molecular and cellular events underlying cell cycle using various protocols. Recent advances in the field that overcome the shortcomings of traditional flow cytometry and expand its scope for cell cycle studies are also discussed.

DebarcodeR increases fluorescent cell barcoding capacity and accuracy

Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Adaptive regression modeling accounted for differential dye uptake between different cell types and Gaussian mixture modeling provided a robust method to probabilistically assign cells to samples. Assignment tolerance parameters are available to allow users to balance high cell recovery with accurate assignments. Improvements to experimental methods include: (1) inclusion of an "external standard" control where a pool of all cells was stained a single level of each barcoding dyes and (2) an "internal standard" where each cell is stained with a single level of a separate dye. DebarcodeR significantly improved speed, accuracy, and reproducibility of FCB while avoiding selective loss of unusual cell subsets when debarcoding microtiter plates of cell lines and heterogenous mixtures of primary cells. DebarcodeR is available on Github as an R package that works with flowCore and Cytoverse packages at github.com/cytolab/DebarcodeR.

Operating regimes in a single enzymatic cascade at ensemble-level

Single enzymatic cascade, ubiquitously found in cellular signaling networks, is a phosphorylation-dephosphorylation reaction cycle causing a transition between inactive and active states of a protein catalysed by kinase and phosphatase, respectively. Steady-state information processing ability of such a cycle (e.g., MAPK cascade) has been classified into four qualitatively different operating regimes, viz., hyperbolic (H), signal-transducing (ST), threshold-hyperbolic (TH) and ultrasensitive (U). These four regimes represent qualitatively different dose-response curves, that is, relationship between concentrations of input kinase (e.g., pMEK) and response activated protein (e.g., pERK). Regimes were identified using a deterministic model accounting for population-averaged behavior only. Operating regimes can be strongly influenced by the inherently present cell-to-cell variability in an ensemble of cells which is captured in the form of pMEK and pERK distributions using reporter-based single-cell experimentation. In this study, we show that such experimentally acquired snapshot pMEK and pERK distribution data of a single MAPK cascade can be directly used to infer the underlying operating regime even in the absence of a dose-response curve. This deduction is possible primarily due to the presence of a monotonic relationship between experimental observables R_IQR, ratio of the inter-quartile range of the pERK and pMEK distribution pairs and R_M, ratio of the medians of the distribution pair. We demonstrate this relationship by systematic analysis of a quasi-steady state approximated model superimposed with an input gamma distribution constrained by the stimulus strength specific pMEK distribution measured on Jurkat-T cells stimulated with PMA. As a first, we show that introduction of cell-to-cell variability only in the upstream kinase achieved by superimposition of an appropriate input pMEK distribution on the dose-response curve can predict bimodal response pERK distribution in ST regime. Implementation of the proposed method on the input-response distribution pair obtained in stimulated Jurkat-T cells revealed that while low-dosage PMA stimulation preserves the H regime observed in resting cells, high-dosage causes H to ST regime transition.

Single-cell epigenetics - Chromatin modification atlas unveiled by mass cytometry

Modifications of histone proteins are fundamental to the regulation of epigenetic phenotypes. Dysregulations of histone modifications have been linked to the pathogenesis of diverse human diseases. However, identifying differential histone modifications in patients with immune-mediated diseases has been challenging, in part due to the lack of a powerful analytic platform to study histone modifications in the complex human immune system. We recently developed a highly multiplexed platform, Epigenetic landscape profiling using cytometry by Time-Of-Flight (EpiTOF), to analyze the global levels of a broad array of histone modifications in single cells using mass cytometry. In this review, we summarize the development of EpiTOF and discuss its potential applications in biomedical research. We anticipate that this platform will provide new insights into the roles of epigenetic regulation in hematopoiesis, immune cell functions, and immune system aging, and reveal aberrant epigenetic patterns associated with immune-mediated diseases.

A cleavage product of Polycystin-1 is a mitochondrial matrix protein that affects mitochondria morphology and function when heterologously expressed

Recent studies have reported intrinsic metabolic reprogramming in Pkd1 knock-out cells, implicating dysregulated cellular metabolism in the pathogenesis of polycystic kidney disease. However, the exact nature of the metabolic changes and their underlying cause remains controversial. We show herein that Pkd1 k o /ko renal epithelial cells have impaired fatty acid utilization, abnormal mitochondrial morphology and function, and that mitochondria in kidneys of ADPKD patients have morphological alterations. We further show that a C-terminal cleavage product of polycystin-1 (CTT) translocates to the mitochondria matrix and that expression of CTT in Pkd1 ko/ko cells rescues some of the mitochondrial phenotypes. Using Drosophila to model in vivo effects, we find that transgenic expression of mouse CTT results in decreased viability and exercise endurance but increased CO2 production, consistent with altered mitochondrial function. Our results suggest that PC1 may play a direct role in regulating mitochondrial function and cellular metabolism and provide a framework to understand how impaired mitochondrial function could be linked to the regulation of tubular diameter in both physiological and pathological conditions.

Discovery of human cell selective effector molecules using single cell multiplexed activity metabolomics

Discovering bioactive metabolites within a metabolome is challenging because there is generally little foreknowledge of metabolite molecular and cell-targeting activities. Here, single-cell response profiles and primary human tissue comprise a response platform used to discover novel microbial metabolites with cell-type-selective effector properties in untargeted metabolomic inventories. Metabolites display diverse effector mechanisms, including targeting protein synthesis, cell cycle status, DNA damage repair, necrosis, apoptosis, or phosphoprotein signaling. Arrayed metabolites are tested against acute myeloid leukemia patient bone marrow and molecules that specifically targeted blast cells or nonleukemic immune cell subsets within the same tissue biopsy are revealed. Cell-targeting polyketides are identified in extracts from biosynthetically prolific bacteria, including a previously unreported leukemia blast-targeting anthracycline and a polyene macrolactam that alternates between targeting blasts or nonmalignant cells by way of light-triggered photochemical isomerization. High-resolution cell profiling with mass cytometry confirms response mechanisms and is used to validate initial observations.

Characterization of Peroxisomal Regulation Networks

Peroxisome proliferation involves signal recognition and computation by molecular networks that direct molecular events of gene expression, metabolism, membrane biogenesis, organelle proliferation, protein import, and organelle inheritance. Peroxisome biogenesis in yeast has served as a model system for exploring the regulatory networks controlling this process. Yeast is an outstanding model system to develop tools and approaches to study molecular networks and cellular responses and because the mechanisms of peroxisome biogenesis and key aspects of the transcriptional regulatory networks are remarkably conserved from yeast to humans. In this chapter, we focus on the complex regulatory networks that respond to environmental cues leading to peroxisome assembly and the molecular events of organelle assembly. Ultimately, understanding the mechanisms of the entire peroxisome biogenesis program holds promise for predictive modeling approaches and for guiding rational intervention strategies that could treat human conditions associated with peroxisome function.

One-Cell Doubling Evaluation by Living Arrays of Yeast, ODELAY!

Cell growth is a complex phenotype widely used in systems biology to gauge the impact of genetic and environmental perturbations. Due to the magnitude of genome-wide studies, resolution is often sacrificed in favor of throughput, creating a demand for scalable, time-resolved, quantitative methods of growth assessment. We present ODELAY (One-cell Doubling Evaluation by Living Arrays of Yeast), an automated and scalable growth analysis platform. High measurement density and single-cell resolution provide a powerful tool for large-scale multiparameter growth analysis based on the modeling of microcolony expansion on solid media. Pioneered in yeast but applicable to other colony forming organisms, ODELAY extracts the three key growth parameters (lag time, doubling time, and carrying capacity) that define microcolony expansion from single cells, simultaneously permitting the assessment of population heterogeneity. The utility of ODELAY is illustrated using yeast mutants, revealing a spectrum of phenotypes arising from single and combinatorial growth parameter perturbations.

Real-time monitoring of the sugar sensing in Saccharomyces cerevisiae indicates endogenous mechanisms for xylose signaling

Background

The sugar sensing and carbon catabolite repression in Baker’s yeast Saccharomyces cerevisiae is governed by three major signaling pathways that connect carbon source recognition with transcriptional regulation. Here we present a screening method based on a non-invasive in vivo reporter system for real-time, single-cell screening of the sugar signaling state in S. cerevisiae in response to changing carbon conditions, with a main focus on the response to glucose and xylose.

Results

The artificial reporter system was constructed by coupling a green fluorescent protein gene (yEGFP3) downstream of endogenous yeast promoters from the Snf3p/Rgt2p, SNF1/Mig1p and cAMP/PKA signaling pathways: HXT1p/2p/4p; SUC2p, CAT8p; TPS1p/2p and TEF4p respectively. A panel of eight biosensors strains was generated by single copy chromosomal integration of the different constructs in a W303-derived strain. The signaling biosensors were validated for their functionality with flow cytometry by comparing the fluorescence intensity (FI) response in the presence of high or nearly depleted glucose to the known induction/repression conditions of the eight different promoters. The FI signal correlated with the known patterns of the selected promoters while maintaining a non-invasive property on the cellular phenotype, as was demonstrated in terms of growth, metabolites and enzyme activity.

Conclusions

Once verified, the sensors were used to evaluate the signaling response to varying conditions of extracellular glucose, glycerol and xylose by screening in 96-well microtiter plates. We show that these yeast strains, which do not harbor any recombinant pathways for xylose utilization, are lacking a signaling response for extracellular xylose. However, for the HXT2p/4p sensors, a shift in the flow cytometry population dynamics indicated that internalized xylose does affect the signaling. These results suggest that the previously observed effects of this pentose on the S. cerevisiae physiology and gene regulation can be attributed to xylose and not only to a lack of glucose.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0580-x) contains supplementary material, which is available to authorized users.

Activation of RNase L by Murine Coronavirus in Myeloid Cells Is Dependent on Basal Oas Gene Expression and Independent of Virus-Induced Interferon

The oligoadenylate synthetase (OAS)-RNase L pathway is a potent interferon (IFN)-induced antiviral activity. Upon sensing double-stranded RNA, OAS produces 2',5'-oligoadenylates (2-5A), which activate RNase L. Murine coronavirus (mouse hepatitis virus [MHV]) nonstructural protein 2 (ns2) is a 2',5'-phosphodiesterase (PDE) that cleaves 2-5A, thereby antagonizing RNase L activation. PDE activity is required for robust replication in myeloid cells, as a mutant of MHV (ns2(H126R)) encoding an inactive PDE fails to antagonize RNase L activation and replicates poorly in bone marrow-derived macrophages (BMM), while ns2(H126R) replicates to high titer in several types of nonmyeloid cells, as well as in IFN receptor-deficient (Ifnar1(-/-)) BMM. We reported previously that myeloid cells express significantly higher basal levels of OAS transcripts than nonmyeloid cells. Here, we investigated the contributions of Oas gene expression, basal IFN signaling, and virus-induced IFN to RNase L activation. Infection with ns2(H126R) activated RNase L in Ifih1(-/-) BMM to a similar extent as in wild-type (WT) BMM, despite the lack of IFN induction in the absence of MDA5 expression. However, ns2(H126R) failed to induce RNase L activation in BMM treated with IFNAR1-blocking antibody, as well as in Ifnar1(-/-) BMM, both expressing low basal levels of Oas genes. Thus, activation of RNase L does not require virus-induced IFN but rather correlates with adequate levels of basal Oas gene expression, maintained by basal IFN signaling. Finally, overexpression of RNase L is not sufficient to compensate for inadequate basal OAS levels.

IMPORTANCE

The oligoadenylate synthetase (OAS)-RNase L pathway is a potent antiviral activity. Activation of RNase L during murine coronavirus (mouse hepatitis virus [MHV]) infection of myeloid cells correlates with high basal Oas gene expression and is independent of virus-induced interferon secretion. Thus, our data suggest that cells with high basal Oas gene expression levels can activate RNase L and thereby inhibit virus replication early in infection upon exposure to viral double-stranded RNA (dsRNA) before the induction of interferon and prior to transcription of interferon-stimulated antiviral genes. These findings challenge the notion that activation of the OAS-RNase L pathway requires virus to induce type I IFN, which in turn upregulates OAS gene expression, as well as to provide dsRNA to activate OAS. Our data further suggest that myeloid cells may serve as sentinels to restrict viral replication, thus protecting other cell types from infection.

Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry

Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneous relative growth and protein synthesis rates of multiplexed microbial populations across many conditions. These measurements generate quantitative profiles of dynamically evolving protein synthesis and degradation rates. We demonstrate this setup in the context of gene regulation of the unfolded protein response (UPR) of Saccharomyces cerevisiae and uncover a dynamic and complex landscape of gene expression, growth dynamics and proteolysis following perturbations.

Peroxisomes take shape

Peroxisomes carry out various oxidative reactions that are tightly regulated to adapt to the changing needs of the cell and varying external environments. Accordingly, they are remarkably fluid and can change dramatically in abundance, size, shape and content in response to numerous cues. These dynamics are controlled by multiple aspects of peroxisome biogenesis that are coordinately regulated with each other and with other cellular processes. Ongoing studies are deciphering the diverse molecular mechanisms that underlie biogenesis and how they cooperate to dynamically control peroxisome utility. These important challenges should lead to an understanding of peroxisome dynamics that can be capitalized upon for bioengineering and the development of therapies to improve human health.

Leukemia prediction using sparse logistic regression

We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.

Imaging flow cytometry: coping with heterogeneity in biological systems

Imaging flow cytometry (IFC) platforms combine features of flow cytometry and fluorescent microscopy with advances in data-processing algorithms. IFC allows multiparametric fluorescent and morphological analysis of thousands of cellular events and has the unique capability of identifying collected events by their real images. IFC allows the analysis of heterogeneous cell populations, where one of the cellular components has low expression (<0.03%) and can be described by Poisson distribution. With the help of IFC, one can address a critical question of statistical analysis of subcellular distribution of proteins in a cell. Here the authors review advantages of IFC in comparison with more traditional technologies, such as Western blotting and flow cytometry (FC), as well as new high-throughput fluorescent microscopy (HTFM), and discuss further developments of this novel analytical technique.