High-throughput fluorescence imaging flow cytometry with light-sheet excitation and machine learning (Conference Presentation)

2018 ◽  
Author(s):  
Hideharu Mikami ◽  
Taichi Miura ◽  
Yasuyuki Ozeki ◽  
Keisuke Goda
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Fluorescence Imaging ◽  
High Throughput ◽  
Light Sheet ◽  
Imaging Flow Cytometry

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 Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning

2020 ◽  
Vol 48 (20) ◽  
pp. 11335-11346
Author(s):  
Nikolaos-Kosmas Chlis ◽  
Lisa Rausch ◽  
Thomas Brocker ◽  
Jan Kranich ◽  
Fabian J Theis
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Imaging Flow Cytometry ◽  
Myeloid Progenitor ◽  
Myeloid Progenitor Cells ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

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.

scholarly journals High-throughput multiparametric imaging flow cytometry: toward diffraction-limited sub-cellular detection and monitoring of sub-cellular processes

Cell Reports ◽  
2021 ◽  
Vol 34 (10) ◽  
pp. 108824
Author(s):  
Gregor Holzner ◽  
Bogdan Mateescu ◽  
Daniel van Leeuwen ◽  
Gea Cereghetti ◽  
Reinhard Dechant ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Multiparametric Imaging ◽  
Cellular Processes ◽  
Imaging Flow Cytometry

scholarly journals Infinity Flow: High-Throughput Single-Cell Quantification of 100s of Proteins Using Conventional Flow Cytometry and Machine Learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Quantification

scholarly journals High‐Throughput Particle Uptake Analysis by Imaging Flow Cytometry

2017 ◽  
Vol 80 (1) ◽  
Author(s):  
Asya Smirnov ◽  
Michael D. Solga ◽  
Joanne Lannigan ◽  
Alison K. Criss
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Particle Uptake ◽  
Imaging Flow Cytometry
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