26,762 research outputs found
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 CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - La Plata. Instituto de FÃsica de LÃquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de FÃsica de LÃquidos 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
Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model
Interference enhanced wide-field nanoparticle imaging is a highly sensitive
technique that has found numerous applications in labeled and label-free
sub-diffraction-limited pathogen detection. It also provides unique
opportunities for nanoparticle classification upon detection. More specif-
ically, the nanoparticle defocus images result in a particle-specific response
that can be of great utility for nanoparticle classification, particularly
based on type and size. In this work, we com- bine a model based supervised
learning algorithm with a wide-field common-path interferometric microscopy
method to achieve accurate nanoparticle classification. We verify our
classification schemes experimentally by using gold and polystyrene
nanospheres.Comment: 5 pages, 2 figure
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
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