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.Comment: 25 pages, 7 figures; revised version with minor changes. To appear in J. Phys.: Cond. Mat

Approaches to hazard-oriented groundwater management based on multivariate analysis of groundwater quality

Drinking water extracted near rivers in alluvial aquifers is subject to potential microbial contamination due to rapidly infiltrating river water during high discharge events. The heterogeneity of river-groundwater interaction and hydrogeological characteristics of the aquifer renders a complex pattern of groundwater quality. The quality of the extracted drinking water can be managed using decision support and HACCP (Hazard Analysis and Critical Control Point) systems, but the detection of potential contamination remains a complex task to master. The methodology proposed herein uses a combination of high-resolution measurements and multivariate statistical analyses to characterise actual groundwater quality and detect potential contamination. The aim of this project was to improve the protection of riverine groundwater extraction wells and to increase the degrees of freedom available to the management of fluvial planes with drinking-water production and aquifer recharge by river-groundwater interaction. The monitoring network was set up in the Reinacherheide in North-west Switzerland and encompassed the depth-oriented installation of multiparameter instruments, a surface-water monitoring station and a flow-through cell with an automated sampler and high-precision measurement instruments. The parameters recorded included temperature, electrical conductivity, spectral absorption coefficient, particle density and turbidity. Two of the observation wells were equipped with a telemetry system and the flow cell could be controlled remotely. The well-field encompassed eight groundwater extraction wells. The optimal choice of observation wells and indicator parameters was assessed using principal component analysis of groundwater head, temperature and electrical conductivity time-series to detect the influence of, for example, river-water infiltration or river-stage fluctuations on the time-series recorded in the groundwater observation wells. Groundwater head was susceptible to pressure waves induced by both river-stage fluctuations and groundwater extraction. Temperature time-series showed only weak responses to high discharge events. Electrical conductivity, however, showed a distance-driven response pattern to high discharge events. To further assess the representative strength of individual groundwater quality indicator parameters for identifying microbial contamination, a bi-weekly and a high-resolution sampling campaign were carried out. The results showed high faecal-indicator bacteria densities (E. coli and Enterococcus sp.) at the beginning of high discharge events, followed by a rapid decrease, leading to a strong hit-and-miss characteristic in the bi-weekly sampling campaign. The third approach applied used the neural network-based combination of self-organizing maps and Sammon's projection (SOM-SM) to detect shifts in groundwater quality system states. The nonlinear analysis was carried out with groundwater head, temperature and electrical conductivity time-series from six observation wells. The subsequent shading of the projected trajectory of system states with independent time-series (spectral absorption coefficient and particle density) allowed the identification of critical system states, when actual groundwater quality decreased and contamination of the extraction wells was imminent. The time at which the changes in system state occurred and were detected were used as potential warning indicators for the water supplier. The effects of altered groundwater extraction (as a consequence of the SOM-SM warning) were then simulated using a groundwater flow model. The outcome of the SOM-SM analysis is, thus, proposed as an interface between the monitoring system and extraction-well management system. The proposed approach incorporates hydrogeological knowledge and the analysis of prevalent conditions concerning river-groundwater interaction with real-time telemetric data transfer, data-base management and nonlinear statistical analysis to detect deterioration in actual groundwater quality due to rapidly infiltrating river water. As the SOM-SM is not based on threshold values and independent of indicator parameters, the approach can be transferred to other sites with similar characteristics

Integrated Multi-Parameter Exploration Footprints of the Canadian Malartic Disseminated Au, McArthur River-Millennium Unconformity U, and Highland Valley Porphyry Cu Deposits: Preliminary Results from the NSERC-CMIC Mineral Exploration Footprints Research Network

Mineral exploration in Canada is increasingly focused on concealed and deeply buried targets, requiring more effective tools to detect large-scale ore-forming systems and to vector from their most distal margins to their high grade cores. A new generation of ore system models is required to achieve this. The Mineral Exploration Footprints Research Network is a consortium of 70 faculty, research associates, and students from 20 Canadian universities working with 30 mining, mineral exploration, and mining service providers to develop new approaches to ore system modelling based on more effective integration and visualization of multi-parameter geological-structural-mineralogical-lithogeochemical-petrophysical-geophysical exploration data. The Network is developing the next generation ore system models and exploration strategies at three sites based on integrated data visualization using self-consistent 3D Common Earth Models and geostatistical/machine learning technologies. Thus far over 60 footprint components and vectors have been identified at the Canadian Malartic stockwork-disseminated Au deposit, 20–30 at the McArthur-Millennium unconformity U deposits, and over 20 in the Highland Valley porphyry Cu system. For the first time, these are being assembled into comprehensive models that will serve as landmark case studies for data integration and analysis in the today’s challenging exploration environment

Identification of Seismo-Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier

We present an algorithm based on Self-Organizing Maps (SOM) and k-means clustering to recognize patterns in a continuous 12.5-year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications

Measurement and Modeling of Signaling at the Single-Cell Level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Fellowship)National Institutes of Health (U.S.) (grant R01-EB010246)National Institutes of Health (U.S.) (grant P50-GM68762)United States. Army Research Office (Institute for Collaborative Biotechnologies, Grant W911NF-09-0001

Luminescence lifetime imaging of three-dimensional biological objects

ABSTRACT A major focus of current biological studies is to fill the knowledge gaps between cell, tissue and organism scales. To this end, a wide array of contemporary optical analytical tools enable multiparameter quantitative imaging of live and fixed cells, three-dimensional (3D) systems, tissues, organs and organisms in the context of their complex spatiotemporal biological and molecular features. In particular, the modalities of luminescence lifetime imaging, comprising fluorescence lifetime imaging (FLI) and phosphorescence lifetime imaging microscopy (PLIM), in synergy with Förster resonance energy transfer (FRET) assays, provide a wealth of information. On the application side, the luminescence lifetime of endogenous molecules inside cells and tissues, overexpressed fluorescent protein fusion biosensor constructs or probes delivered externally provide molecular insights at multiple scales into protein–protein interaction networks, cellular metabolism, dynamics of molecular oxygen and hypoxia, physiologically important ions, and other physical and physiological parameters. Luminescence lifetime imaging offers a unique window into the physiological and structural environment of cells and tissues, enabling a new level of functional and molecular analysis in addition to providing 3D spatially resolved and longitudinal measurements that can range from microscopic to macroscopic scale. We provide an overview of luminescence lifetime imaging and summarize key biological applications from cells and tissues to organisms.</jats:p

Familiarization: A theory of repetition suppression predicts interference between overlapping cortical representations

Repetition suppression refers to a reduction in the cortical response to a novel stimulus that results from repeated presentation of the stimulus. We demonstrate repetition suppression in a well established computational model of cortical plasticity, according to which the relative strengths of lateral inhibitory interactions are modified by Hebbian learning. We present the model as an extension to the traditional account of repetition suppression offered by sharpening theory, which emphasises the contribution of afferent plasticity, by instead attributing the effect primarily to plasticity of intra-cortical circuitry. In support, repetition suppression is shown to emerge in simulations with plasticity enabled only in intra-cortical connections. We show in simulation how an extended ‘inhibitory sharpening theory’ can explain the disruption of repetition suppression reported in studies that include an intermediate phase of exposure to additional novel stimuli composed of features similar to those of the original stimulus. The model suggests a re-interpretation of repetition suppression as a manifestation of the process by which an initially distributed representation of a novel object becomes a more localist representation. Thus, inhibitory sharpening may constitute a more general process by which representation emerges from cortical re-organisation