A high-throughput flow cytometry-on-a-CMOS platform for single-cell dielectric spectroscopy at microwave frequencies

Lab on a Chip ◽  
2018 ◽  
Vol 18 (14) ◽  
pp. 2065-2076 ◽  
Author(s):  
Jun-Chau Chien ◽  
Ali Ameri ◽  
Erh-Chia Yeh ◽  
Alison N. Killilea ◽  
Mekhail Anwar ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Dielectric Properties ◽  
Single Cell ◽  
High Throughput ◽  
Dielectric Spectroscopy ◽  
Free Flow ◽  
Label Free ◽  
Microwave Frequencies

This work presents a microfluidics-integrated label-free flow cytometry-on-a-CMOS platform for the characterization of the cytoplasm dielectric properties at microwave frequencies.

scholarly journals High-throughput label-free molecular fingerprinting flow cytometry

2019 ◽  
Vol 5 (1) ◽  
pp. eaau0241 ◽  
Author(s):  
Kotaro Hiramatsu ◽  
Takuro Ideguchi ◽  
Yusuke Yonamine ◽  
SangWook Lee ◽  
Yizhi Luo ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Fluorescent Labeling ◽  
Live Cells ◽  
Molecular Fingerprinting ◽  
Label Free ◽  
Indispensable Tool

Flow cytometry is an indispensable tool in biology for counting and analyzing single cells in large heterogeneous populations. However, it predominantly relies on fluorescent labeling to differentiate cells and, hence, comes with several fundamental drawbacks. Here, we present a high-throughput Raman flow cytometer on a microfluidic chip that chemically probes single live cells in a label-free manner. It is based on a rapid-scan Fourier-transform coherent anti-Stokes Raman scattering spectrometer as an optical interrogator, enabling us to obtain the broadband molecular vibrational spectrum of every single cell in the fingerprint region (400 to 1600 cm−1) with a record-high throughput of ~2000 events/s. As a practical application of the method not feasible with conventional flow cytometry, we demonstrate high-throughput label-free single-cell analysis of the astaxanthin productivity and photosynthetic dynamics ofHaematococcus lacustris.

scholarly journals Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry

2020 ◽  
Vol 11 (4) ◽  
pp. 1752 ◽  
Author(s):  
Kotaro Hiramatsu ◽  
Koji Yamada ◽  
Matthew Lindley ◽  
Kengo Suzuki ◽  
Keisuke Goda
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Euglena Gracilis ◽  
Large Scale ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Label Free

scholarly journals High-throughput label-free flow cytometry based on matched-filter compressive imaging

2018 ◽  
Vol 9 (12) ◽  
pp. 6145
Author(s):  
Cong Ba ◽  
William J. Shain ◽  
Thomas G. Bifano ◽  
Jerome Mertz
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Matched Filter ◽  
Free Flow ◽  
Label Free ◽  
Compressive Imaging

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

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 Abstract 2788: Deep characterization of tumor infiltrating leukocytes using a combination of flow cytometry and single-cell multiomics

2021 ◽  
Author(s):  
Xiaoshan Shi ◽  
Margaret Nakamoto ◽  
Aaron Middlebrook ◽  
Wei Huang ◽  
Evelyn Lo ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell

scholarly journals Label-free characterization of vitrification-induced morphology changes in single-cell embryos with full-field optical coherence tomography

2015 ◽  
Vol 20 (9) ◽  
pp. 096004 ◽  
Author(s):  
Livia Zarnescu ◽  
Michael C. Leung ◽  
Michael Abeyta ◽  
Helge Sudkamp ◽  
Thomas Baer ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Single Cell ◽  
Optical Coherence ◽  
Label Free ◽  
Full Field ◽  
Morphology Changes

scholarly journals Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis

2013 ◽  
Vol 15 (4) ◽  
pp. 363-372 ◽  
Author(s):  
Victoria Moignard ◽  
Iain C. Macaulay ◽  
Gemma Swiers ◽  
Florian Buettner ◽  
Judith Schütte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Progenitor Cells ◽  
High Throughput ◽  
Gene Expression Analysis ◽  
Transcriptional Networks ◽  
Stem And Progenitor Cells ◽  
Cell Gene Expression ◽  
Cell Gene
Sign in / Sign up

Export Citation Format

Share Document