Diversity training for signal transduction: leveraging cell-to-cell variability to dissect cellular signaling, differentiation and death

Dynamic variability in SHP-1 abundance determines natural killer cell responsiveness

[Figure: see text].

Constitutive Activation of the B Cell Receptor Underlies Dysfunctional Signaling in Chronic Lymphocytic Leukemia

In cancer biology, the functional interpretation of genomic alterations is critical to achieve the promise of genomic profiling in the clinic. For chronic lymphocytic leukemia (CLL), a heterogeneous disease of B-lymphocytes maturing under constitutive B cell receptor (BCR) stimulation, the functional role of diverse clonal mutations remains largely unknown. Here, we demonstrate that alterations in BCR signaling dynamics underlie the progression of B cells toward malignancy. We reveal emergent dynamic features—bimodality, hypersensitivity, and hysteresis—in the BCR signaling pathway of primary CLL B cells. These signaling abnormalities in CLL quantitatively derive from BCR clustering and constitutive signaling with positive feedback reinforcement, as demonstrated through single-cell analysis of phospho-responses, computational modeling, and super-resolution imaging. Such dysregulated signaling segregates CLL patients by disease severity and clinical presentation. These findings provide a quantitative framework and methodology to assess complex and heterogeneous leukemia pathology and to inform therapeutic strategies in parallel with genomic profiling.

Using phospho-flow cytometry and computational modeling, Ziegler et al. find that B cell receptor clustering and positive feedback through SYK and LYN drive signaling hypersensitivity, bistability, and hysteresis in chronic lymphocytic leukemic B cells. Super-resolution microscopy confirms membrane auto-aggregation in leukemic B cells, and variability in signaling dysfunction predicts disease severity.

Quantifying the Dynamics of Hematopoiesis by In Vivo IdU Pulse-Chase, Mass Cytometry, and Mathematical Modeling

We present a new method to directly quantify the dynamics of differentiation of multiple cellular subsets in unperturbed mice. We combine a pulse-chase protocol of 5-iodo-2'-deoxyuridine (IdU) injections with subsequent analysis by mass cytometry (CyTOF) and mathematical modeling of the IdU dynamics. Measurements by CyTOF allow for a wide range of cells to be analyzed at once, due to the availability of a large staining panel without the complication of fluorescence spillover. These are also compatible with direct detection of integrated iodine signal, with minimal impact on immunophenotyping based on the surface markers. Mathematical modeling beyond a binary classification of surface marker abundance allows for a continuum of cellular states as the cells transition from one state to another. Thus, we present a complete and robust method for directly quantifying differentiation at the systemic level, allowing for system-wide comparisons between different mouse strains and/or experimental conditions. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Environments Tune and Select Cellular Diversity

Technical advances in single-cell sequencing data and their application to greater samples is revealing substantial cell-to-cell variation in expression levels and propagation of this variation between molecules across cells. New quantitative approaches that apply mechanistic and statistical models in a systems-wide approach are illuminating the drivers of phenotypic diversity.

Dichotomy of cellular inhibition by small-molecule inhibitors revealed by single-cell analysis

Despite progress in drug development, a quantitative and physiological understanding of how small-molecule inhibitors act on cells is lacking. Here, we measure the signalling and proliferative response of individual primary T-lymphocytes to a combination of antigen, cytokine and drug. We uncover two distinct modes of signalling inhibition: digital inhibition (the activated fraction of cells diminishes upon drug treatment, but active cells appear unperturbed), versus analogue inhibition (the activated fraction is unperturbed whereas activation response is diminished). We introduce a computational model of the signalling cascade that accounts for such inhibition dichotomy, and test the model predictions for the phenotypic variability of cellular responses. Finally, we demonstrate that the digital/analogue dichotomy of cellular response as revealed on short (signal transduction) timescales, translates into similar dichotomy on longer (proliferation) timescales. Our single-cell analysis of drug action illustrates the strength of quantitative approaches to translate in vitro pharmacology into functionally relevant cellular settings.

Many drugs are small molecule inhibitors of cell signalling. Through single cell analysis and mathematical modelling here the authors show that cell-to-cell variability diversifies inhibition response into digital and analogue, and that the two translate into distinct long-term functional responses.

Novel robust biomarkers for human bladder cancer based on activation of intracellular signaling pathways

We recently proposed a new bioinformatic algorithm called OncoFinder for quantifying the activation of intracellular signaling pathways. It was proved advantageous for minimizing errors of high-throughput gene expression analyses and showed strong potential for identifying new biomarkers. Here, for the first time, we applied OncoFinder for normal and cancerous tissues of the human bladder to identify biomarkers of bladder cancer. Using Illumina HT12v4 microarrays, we profiled gene expression in 17 cancer and seven non-cancerous bladder tissue samples. These experiments were done in two independent laboratories located in Russia and Canada. We calculated pathway activation strength values for the investigated transcriptomes and identified signaling pathways that were regulated differently in bladder cancer (BC) tissues compared with normal controls. We found, for both experimental datasets, 44 signaling pathways that serve as excellent new biomarkers of BC, supported by high area under the curve (AUC) values. We conclude that the OncoFinder approach is highly efficient in finding new biomarkers for cancer. These markers are mathematical functions involving multiple gene products, which distinguishes them from “traditional” expression biomarkers that only assess concentrations of single genes.

Non-monotonic cellular responses to heterogeneity in talin protein expression-level

Talin is a key cell-matrix adhesion component with a central role in regulating adhesion complex maturation, and thereby various cellular properties including adhesion and migration. However, knockdown studies have produced inconsistent findings regarding the functional influence of talin in these processes. Such discrepancies may reflect non-monotonic responses to talin expression-level variation that are not detectable via canonical "binary" comparisons of aggregated control versus knockdown cell populations. Here, we deployed an "analogue" approach to map talin influence across a continuous expression-level spectrum, which we extended with sub-maximal RNAi-mediated talin depletion. Applying correlative imaging to link live cell and fixed immunofluorescence data on a single cell basis, we related per cell talin levels to per cell measures quantitatively defining an array of cellular properties. This revealed both linear and non-linear correspondences between talin expression and cellular properties, including non-monotonic influences over cell shape, adhesion complex-F-actin association and adhesion localization. Furthermore, we demonstrate talin level-dependent changes in networks of correlations among adhesion/migration properties, particularly in relation to cell migration speed. Importantly, these correlation networks were strongly affected by talin expression heterogeneity within the natural range, implying that this endogenous variation has a broad, quantitatively detectable influence. Overall, we present an accessible analogue method that reveals complex dependencies on talin expression-level, thereby establishing a framework for considering non-linear and non-monotonic effects of protein expression-level heterogeneity in cellular systems.

Unraveling liver complexity from molecular to organ level: challenges and perspectives

Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.

Multidimensional single-cell analysis of BCR signaling reveals proximal activation defect as a hallmark of chronic lymphocytic leukemia B cells

Purpose

Chronic Lymphocytic Leukemia (CLL) is defined by a perturbed B-cell receptor-mediated signaling machinery. We aimed to model differential signaling behavior between B cells from CLL and healthy individuals to pinpoint modes of dysregulation.

Experimental Design

We developed an experimental methodology combining immunophenotyping, multiplexed phosphospecific flow cytometry, and multifactorial statistical modeling. Utilizing patterns of signaling network covariance, we modeled BCR signaling in 67 CLL patients using Partial Least Squares Regression (PLSR). Results from multidimensional modeling were validated using an independent test cohort of 38 patients.

Results

We identified a dynamic and variable imbalance between proximal (pSYK, pBTK) and distal (pPLCγ2, pBLNK, ppERK) phosphoresponses. PLSR identified the relationship between upstream tyrosine kinase SYK and its target, PLCγ2, as maximally predictive and sufficient to distinguish CLL from healthy samples, pointing to this juncture in the signaling pathway as a hallmark of CLL B cells. Specific BCR pathway signaling signatures that correlate with the disease and its degree of aggressiveness were identified. Heterogeneity in the PLSR response variable within the B cell population is both a characteristic mark of healthy samples and predictive of disease aggressiveness.

Conclusion

Single-cell multidimensional analysis of BCR signaling permitted focused analysis of the variability and heterogeneity of signaling behavior from patient-to-patient, and from cell-to-cell. Disruption of the pSYK/pPLCγ2 relationship is uncovered as a robust hallmark of CLL B cell signaling behavior. Together, these observations implicate novel elements of the BCR signal transduction as potential therapeutic targets.