H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry
Autoři:
Esteban Pardo aff001; Germán González aff002; Jason M. Tucker-Schwartz aff002; Shivang R. Dave aff002; Norberto Malpica aff001
Působiště autorů:
Medical Image Analysis and Biometry Lab, Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
aff001; Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, MA, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222265
Souhrn
Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called “cell astronomy” systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.
Klíčová slova:
Physical sciences – Mathematics – Applied mathematics – Algorithms – Astronomical sciences – Astronomy – Observational astronomy – Optical astronomy – Research and analysis methods – Simulation and modeling – Imaging techniques – Fluorescence imaging – Spectrum analysis techniques – Spectrophotometry – Cytophotometry – Flow cytometry – Biology and life sciences – Cell biology – Cellular types – Animal cells – Blood cells – White blood cells – Lymphocytes – Monocytes – Granulocytes – Immune cells – Cytometry – Medicine and health sciences – Immunology
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Článek vyšel v časopise
PLOS One
2019 Číslo 9
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