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.
The origin of observed ultra-high energy cosmic rays (UHECRs, energies in excess of 1018.5 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 emission, analyses can be performed with very little atmospheric background. Previous searches have focused on detecting muon tracks from muon neutrino interactions from the 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.
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.
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. 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.
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