From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of ‘‘supercell statistics’’, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behc¸et’s disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behc¸et’s disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.Fil: Candia, Julian Marcelo. University of Maryland; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; ArgentinaFil: Maunu, Ryan. University of Maryland; Estados UnidosFil: Driscoll, Meghan. University of Maryland; Estados UnidosFil: Biancotto, Angélique. National Institutes of Health; Estados UnidosFil: Dagur, Pradeep. National Institutes of Health; Estados UnidosFil: McCoy Jr., J Philip. National Institutes of Health; Estados UnidosFil: Nida Sen, H.. National Institutes of Health; Estados UnidosFil: Wei, Lai. National Institutes of Health; Estados UnidosFil: Maritan, Amos. Università di Padova; ItaliaFil: Cao, Kan. University of Maryland; Estados UnidosFil: Nussenblatt, Robert B. National Institutes of Health; Estados UnidosFil: Banavar, Jayanth R.. University of Maryland; Estados UnidosFil: Losert, Wolfgang. University of Maryland; Estados Unido

From cellular characteristics to disease diagnosis: uncovering phenotypes with supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of "supercell statistics", a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçet's disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçet's disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.Instituto de Física de Líquidos y Sistemas Biológico

Constraints on the Ultra-High Energy Neutrino Flux from Gamma-Ray Bursts from a Prototype Station of the Askaryan Radio Array

We report on a search for ultra-high-energy (UHE) neutrinos from gamma-ray bursts (GRBs) in the data set collected by the Testbed station of the Askaryan Radio Array (ARA) in 2011 and 2012. From 57 selected GRBs, we observed no events that survive our cuts, which is consistent with 0.12 expected background events. Using NeuCosmA as a numerical GRB reference emission model, we estimate upper limits on the prompt UHE GRB neutrino fluence and quasi-diffuse flux from $10^{7}$ to $10^{10}$ GeV. This is the first limit on the prompt UHE GRB neutrino quasi-diffuse flux above $10^{7}$ GeV.Comment: 14 pages, 8 figures, Published in Astroparticle Physics Journa

First Constraints on the Ultra-High Energy Neutrino Flux from a Prototype Station of the Askaryan Radio Array

The Askaryan Radio Array (ARA) is an ultra-high energy ($>10^{17}$ eV) cosmic neutrino detector in phased construction near the South Pole. ARA searches for radio Cherenkov emission from particle cascades induced by neutrino interactions in the ice using radio frequency antennas ($\sim150-800$ MHz) deployed at a design depth of 200 m in the Antarctic ice. A prototype ARA Testbed station was deployed at $\sim30$ m depth in the 2010-2011 season and the first three full ARA stations were deployed in the 2011-2012 and 2012-2013 seasons. We present the first neutrino search with ARA using data taken in 2011 and 2012 with the ARA Testbed and the resulting constraints on the neutrino flux from $10^{17}-10^{21}$ eV.Comment: 26 pages, 15 figures. Since first revision, added section on systematic uncertainties, updated limits and uncertainty band with improvements to simulation, added appendix describing ray tracing algorithm. Final revision includes a section on cosmic ray backgrounds. Published in Astropart. Phys.

Neutrinos below 100 TeV from the southern sky employing refined veto techniques to IceCube data

Many Galactic sources of gamma rays, such as supernova remnants, are expected to produce neutrinos with a typical energy cutoff well below 100 TeV. For the IceCube Neutrino Observatory located at the South Pole, the southern sky, containing the inner part of the Galactic plane and the Galactic Center, is a particularly challenging region at these energies, because of the large background of atmospheric muons. In this paper, we present recent advancements in data selection strategies for track-like muon neutrino events with energies below 100 TeV from the southern sky. The strategies utilize the outer detector regions as veto and features of the signal pattern to reduce the background of atmospheric muons to a level which, for the first time, allows IceCube searching for point-like sources of neutrinos in the southern sky at energies between 100 GeV and several TeV in the muon neutrino charged current channel. No significant clustering of neutrinos above background expectation was observed in four years of data recorded with the completed IceCube detector. Upper limits on the neutrino flux for a number of spectral hypotheses are reported for a list of astrophysical objects in the southern hemisphere.Comment: 19 pages, 17 figures, 2 table

The IceCube Neutrino Observatory - Contributions to ICRC 2015 Part II: Atmospheric and Astrophysical Diffuse Neutrino Searches of All Flavors

Papers on atmospheric and astrophysical diffuse neutrino searches of all flavors submitted to the 34th International Cosmic Ray Conference (ICRC 2015, The Hague) by the IceCube Collaboration.Comment: 66 pages, 36 figures, Papers submitted to the 34th International Cosmic Ray Conference, The Hague 2015, v2 has a corrected author lis

A combined maximum-likelihood analysis of the high-energy astrophysical neutrino flux measured with IceCube

Evidence for an extraterrestrial flux of high-energy neutrinos has now been found in multiple searches with the IceCube detector. The first solid evidence was provided by a search for neutrino events with deposited energies $\gtrsim30$ TeV and interaction vertices inside the instrumented volume. Recent analyses suggest that the extraterrestrial flux extends to lower energies and is also visible with throughgoing, $\nu_\mu$-induced tracks from the Northern hemisphere. Here, we combine the results from six different IceCube searches for astrophysical neutrinos in a maximum-likelihood analysis. The combined event sample features high-statistics samples of shower-like and track-like events. The data are fit in up to three observables: energy, zenith angle and event topology. Assuming the astrophysical neutrino flux to be isotropic and to consist of equal flavors at Earth, the all-flavor spectrum with neutrino energies between 25 TeV and 2.8 PeV is well described by an unbroken power law with best-fit spectral index $-2.50\pm0.09$ and a flux at 100 TeV of $\left(6.7_{-1.2}^{+1.1}\right)\cdot10^{-18}\,\mathrm{GeV}^{-1}\mathrm{s}^{-1}\mathrm{sr}^{-1}\mathrm{cm}^{-2}$. Under the same assumptions, an unbroken power law with index $-2$ is disfavored with a significance of 3.8 $\sigma$ ($p=0.0066\%$) with respect to the best fit. This significance is reduced to 2.1 $\sigma$ ($p=1.7\%$) if instead we compare the best fit to a spectrum with index $-2$ that has an exponential cut-off at high energies. Allowing the electron neutrino flux to deviate from the other two flavors, we find a $\nu_e$ fraction of $0.18\pm0.11$ at Earth. The sole production of electron neutrinos, which would be characteristic of neutron-decay dominated sources, is rejected with a significance of 3.6 $\sigma$ ($p=0.014\%$).Comment: 16 pages, 10 figures; accepted for publication in The Astrophysical Journal; updated one referenc

The IceCube Neutrino Observatory - Contributions to ICRC 2015 Part III: Cosmic Rays

Papers on cosmic rays submitted to the 34th International Cosmic Ray Conference (ICRC 2015, The Hague) by the IceCube Collaboration.Comment: 83 pages, 52 figues, Papers submitted to the 34th International Cosmic Ray Conference, The Hague 2015, v2 has a corrected author lis