Fluorescence imaging flow cytometry with ultrahigh sensitivity enabled by virtual motion freezing (Conference Presentation)

2019 ◽  
Author(s):  
Hideharu Mikami ◽  
Makoto Kawaguchi ◽  
Chun-Jung Huang ◽  
Cheng Lei ◽  
Shunnosuke Ueno ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Fluorescence Imaging ◽  
Imaging Flow Cytometry ◽  
Ultrahigh Sensitivity

scholarly journals On-chip light-sheet fluorescence imaging flow cytometry at a high flow speed of 1 m/s

2018 ◽  
Vol 9 (7) ◽  
pp. 3424 ◽  
Author(s):  
Taichi Miura ◽  
Hideharu Mikami ◽  
Akihiro Isozaki ◽  
Takuro Ito ◽  
Yasuyuki Ozeki ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Fluorescence Imaging ◽  
Light Sheet ◽  
Flow Speed ◽  
High Flow ◽  
Imaging Flow Cytometry ◽  
On Chip

scholarly journals Virtual-freezing fluorescence imaging flow cytometry

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hideharu Mikami ◽  
Makoto Kawaguchi ◽  
Chun-Jung Huang ◽  
Hiroki Matsumura ◽  
Takeaki Sugimura ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Fluorescence Imaging ◽  
Imaging Flow Cytometry

Single-Cell Analysis of Morphological and Metabolic Heterogeneity in Euglena gracilis by Fluorescence-Imaging Flow Cytometry

2018 ◽  
Vol 90 (19) ◽  
pp. 11280-11289 ◽  
Author(s):  
Hector E. Muñoz ◽  
Ming Li ◽  
Carson T. Riche ◽  
Nao Nitta ◽  
Eric Diebold ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Fluorescence Imaging ◽  
Euglena Gracilis ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Imaging Flow Cytometry ◽  
Metabolic Heterogeneity

scholarly journals Accurate classification of microalgae by intelligent frequency-division-multiplexed fluorescence imaging flow cytometry

OSA Continuum ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 430 ◽  
Author(s):  
Jeffrey Harmon ◽  
Hideharu Mikami ◽  
Hiroshi Kanno ◽  
Takuro Ito ◽  
Keisuke Goda
Keyword(s):  
Flow Cytometry ◽  
Fluorescence Imaging ◽  
Frequency Division ◽  
Imaging Flow Cytometry ◽  
Multiplexed Fluorescence Imaging

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

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