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

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Multi-messenger Observations of a Binary Neutron Star Merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 {{s}} with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of {40}-8+8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 {M}ȯ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 {{Mpc}}) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.</p

A Search for Muon Neutrinos in Coincidence with Gamma-Ray Bursts in the Southern Hemisphere Sky Using the IceCube Neutrino Observatory

The origin of observed ultra-high energy cosmic rays (UHECRs, energies in excess of $10^{18.5}$ eV) remains unknown, as extragalactic magnetic fields deflect these charged particles from their true origin. Interactions of these UHECRs at their source would invariably produce high energy neutrinos. As these neutrinos are chargeless and nearly massless, their propagation through the universe is unimpeded and their detection can be correlated with the origin of UHECRs. Gamma-ray bursts (GRBs) are one of the few possible origins for UHECRs, observed as short, immensely bright outbursts of gamma-rays at cosmological distances. The energy density of GRBs in the universe is capable of explaining the measured UHECR flux, making them promising UHECR sources. Interactions between UHECRs and the prompt gamma-ray emission of a GRB would produce neutrinos that would be detected in coincidence with the GRB’s gamma-ray emission. The IceCube Neutrino Observatory can be used to search for these neutrinos in coincidence with GRBs, detecting neutrinos through the Cherenkov radiation emitted by secondary charged particles produced in neutrino interactions in the South Pole glacial ice. Restricting these searches to be in coincidence with GRB gamma-ray emis- sion, analyses can be performed with very little atmospheric background. Previous searches have focused on detecting muon tracks from muon neutrino interactions fromthe Northern Hemisphere, where the Earth shields IceCube’s primary background of atmospheric muons, or spherical cascade events from neutrinos of all flavors from the entire sky, with no compelling neutrino signal found. Neutrino searches from GRBs with IceCube have been extended to a search for muon tracks in the Southern Hemisphere in coincidence with 664 GRBs over five years of IceCube data in this dissertation. Though this region of the sky contains IceCube’s primary background of atmospheric muons, it is also where IceCube is most sensitive to neutrinos at the very highest energies as Earth absorption in the Northern Hemisphere becomes relevant. As previous neutrino searches have strongly constrained neutrino production in GRBs, a new per-GRB analysis is introduced for the first time to discover neutrinos in coincidence with possibly rare neutrino-bright GRBs. A stacked analysis is also performed to discover a weak neutrino signal distributed over many GRBs. Results of this search are found to be consistent with atmospheric muon backgrounds. Combining this result with previously published searches for muon neutrino tracks in the Northern Hemisphere, cascade event searches over the entire sky, and an extension of the Northern Hemisphere track search in three additional years of IceCube data that is consistent with atmospheric backgrounds, the most stringent limits yet can be placed on prompt neutrino production in GRBs, which increasingly disfavor GRBs as primary sources of UHECRs in current GRB models

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 \u201csupercell statistics\u201d, 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 Beh\ue7et'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 Beh\ue7et'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

Multicolor flow cytometry for healthy and uveitides patients

Multicolor flow cytometry datasets from 22 fresh peripheral blood patient samples. Please see attached ReadMe file

Summary of the supercell approach.

<p>(<b>a</b>) 2D synthetic data representing 7 single-cell patient samples in two categories. Due to cell heterogeneity, different phenotypes overlap and the data are non-separable. (<b>b</b>) A machine learning approach such as support vector machines is able to find the optimal decision boundary between two classes of datapoints. However, this method (and variants thereof) fail when the samples are strongly overlapping, as is the usual case for single-cell datasets (recall <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003215#pcbi-1003215-g001" target="_blank">Fig. 1A(i))</a>. (<b>c</b>) Sample means or higher-order moments of the cell multivariate distributions generally lead to poor, non-robust phenotypes. The solid line is the class boundary learnt using all datapoints; by removing either of the support vectors that define this boundary (marked by “I”, “II”, and “III”), the boundary changes as indicated by the dashed lines, thus leading to jackknife prediction failures. (<b>d</b>) Representing patient samples by supercell distributions, class separation becomes robust. Removing patient samples “I”, “II”, or “III”, the decision boundary changes as shown by the dashed lines. Departures from the boundary learnt using all patients (solid line) are less significant and do not cause any jackknife failed predictions.</p

Identifying diseases from heterogeneous single cells.

<p>A. Using standard methods of flow cytometry analysis, diseases such as sarcoidosis and Behçet's cannot be separated. (<b>i</b>) 2D scatter plot using markers CD3 and IL22. (<b>ii</b>) 2D Singular-Value decomposition analysis. Figs. A(i)–(ii) show CD8⁺ T cell subsamples from a cohort of 7 sarcoidosis patients and 6 patients diagnosed with Behçet's disease, but similar overlaps are also observed for other cell types and marker pairs. <b>B.</b> Cell ensembles carry the signatures of health and disease, despite heterogeneity at the single-cell level. (<b>i</b>)<b>–</b>(<b>iv</b>) Nuclear shapes of healthy and diseased (HGPS) cells can be classified as either blebbed or non-blebbed. Scale bar: 10 µ. Note that it is impossible to tell whether a person has the disease or not based on the analysis of a single cell. (<b>v</b>) Classifying nuclei as blebbed (red) or non-blebbed (black) based on just one shape parameter, which is automatically determined via custom image analysis software. Most cells in the ensemble of 30 randomly selected nuclei from a diseased cell line are labeled as blebbed. Scale bar: 50 µ. (<b>vi</b>) Conversely, analyzing nuclei from a healthy cell line, most cells are labeled as non-blebbed.</p

Quantitative multiparameter phenotyping of healthy and HGPS cells through cell averaging (“supercells”) and machine learning.

<p>A. Probability density distributions for one shape parameter (number of invaginations of the nuclear boundary) for healthy and diseased cell lines: (<b>i</b>) single cells; (<b>ii</b>) supercells of size 30. The cell averaging procedure removes the overlap between healthy and diseased cell line distributions. <b>B.</b> Distance from the perceptron boundary after machine learning, where positive (negative) distances correspond to the boundary side identified with the healthy (diseased) class: (<b>i</b>) single cells; (<b>ii</b>) supercells of size 30. Each cell line is shown separately along the horizontal axis. <b>C.</b> (<b>i</b>) Perceptron amplitudes: components of the vector normal to the classification hyperplane, each one associated with one of the shape parameters shown in the list. (<b>ii</b>) Fraction of cells correctly classified by the machine learning process as a function of the supercell size for a varying number of parameters used, as indicated. The top M measures are selected from the rank-ordered list based on the absolute values of the perceptron amplitudes.</p