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 Multiplexed profiling of single-cell extracellular vesicles secretion

2019 ◽  
Vol 116 (13) ◽  
pp. 5979-5984 ◽  
Author(s):  
Yahui Ji ◽  
Dongyuan Qi ◽  
Linmei Li ◽  
Haoran Su ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Extracellular Vesicles ◽  
Single Cell Analysis ◽  
Comprehensive Evaluation ◽  
Single Cells ◽  
Deep Understanding ◽  
Principal Component ◽  
Cell Heterogeneity ◽  
Indispensable Tool

Extracellular vesicles (EVs) are important intercellular mediators regulating health and diseases. Conventional methods for EV surface marker profiling, which was based on population measurements, masked the cell-to-cell heterogeneity in the quantity and phenotypes of EV secretion. Herein, by using spatially patterned antibody barcodes, we realized multiplexed profiling of single-cell EV secretion from more than 1,000 single cells simultaneously. Applying this platform to profile human oral squamous cell carcinoma (OSCC) cell lines led to a deep understanding of previously undifferentiated single-cell heterogeneity underlying EV secretion. Notably, we observed that the decrement of certain EV phenotypes (e.g.,CD63+EV) was associated with the invasive feature of both OSCC cell lines and primary OSCC cells. We also realized multiplexed detection of EV secretion and cytokines secretion simultaneously from the same single cells to investigate the multidimensional spectrum of cellular communications, from which we resolved tiered functional subgroups with distinct secretion profiles by visualized clustering and principal component analysis. In particular, we found that different cell subgroups dominated EV secretion and cytokine secretion. The technology introduced here enables a comprehensive evaluation of EV secretion heterogeneity at single-cell level, which may become an indispensable tool to complement current single-cell analysis and EV research.

scholarly journals Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

2018 ◽  
Author(s):  
Mohammad Tanhaemami ◽  
Elaheh Alizadeh ◽  
Claire Sanders ◽  
Babetta L. Marrone ◽  
Brian Munsky’
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Lipid Accumulation ◽  
Time Course ◽  
Fluorescent Dyes ◽  
Single Cell Analysis ◽  
Learning Approaches ◽  
Label Free ◽  
Unlabeled Cell

Abstract—Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells(Picochlorum soloecismus)during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

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.

High-Throughput Production of Single-Cell Microparticles Using an Inkjet Printing Technology

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Tao Xu ◽  
Helen Kincaid ◽  
Anthony Atala ◽  
James J. Yoo
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Stem Cell Differentiation ◽  
Cell Number ◽  
High Throughput Drug Screening ◽  
Alginate Concentration ◽  
High Throughput Method ◽  
Normal Cellular Function

In this study, a novel biocompatible and inexpensive method for the rapid production of single-cell based microparticles is described. Using an HP DeskJet 550C printer, alginate microparticles containing one to several insulin-producing cells (beta-TC6) were fabricated by coprinting the cells and sodium alginate suspension into a CaCl2 solution. This method is able to generate microparticles of 30–60μm in diameter at a rate as high as 55,000particles∕s. Cell survival assays showed that more than 89% of printed cells survived the fabrication process. The printed beta-TC6 cells demonstrated continuous insulin secretion over a 6day period, which suggests that the printed cells are able to maintain normal cellular function within the microparticles. We show that the printing conditions, such as cell number, alginate concentration, and ionic strengths of CaCl2, influence cellular distribution and geometry of the printed particles. This study presents a simple and high-throughput method to encapsulate single cells, and this technique may be applied in various research investigations, including single-cell analysis, high-throughput drug screening, and stem cell differentiation at the single-cell level.

scholarly journals Droplet flow cytometry for single-cell analysis

RSC Advances ◽  
2021 ◽  
Vol 11 (34) ◽  
pp. 20944-20960
Author(s):  
Ming Li ◽  
Hangrui Liu ◽  
Siyuan Zhuang ◽  
Keisuke Goda
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Droplet Flow ◽  
Recent Advances

This work reviews recent advances in the integration of emulsion microdroplets and flow cytometry technologies, so-called droplet flow cytometry (DFC), for high-throughput single-cell analysis.

scholarly journals Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiao Dong ◽  
Fan Wang ◽  
Chuan Liu ◽  
Jing Ling ◽  
Xuebing Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Interacting Protein ◽  
Genetic Characteristics ◽  
Promising Therapeutic Target ◽  
Indispensable Tool

AbstractHepatocellular carcinoma (HCC) is a globally prevailing cancer with a low 5-year survival rate. Little is known about its intricate gene expression profile. Single-cell RNA sequencing is an indispensable tool to explore the genetic characteristics of HCC at a more detailed level. In this study, we profiled the gene expression of single cells from human HCC tumor and para-tumor tissues using the Smart-seq 2 sequencing method. Based on differentially expressed genes, we identified heterogeneous subclones in HCC tissues, including five HCC and two hepatocyte subclones. We then carried out hub-gene co-network and functional annotations analysis followed pseudo-time analysis with regulated transcriptional factor co-networks to determine HCC cellular trajectory. We found that MLX interacting protein like (MLXIPL) was commonly upregulated in the single cells and tissues and associated with a poor survival rate in HCC. Mechanistically, MLXIPL activation is crucial for promoting cell proliferation and inhibits cell apoptosis by accelerating cell glycolysis. Taken together, our work identifies the heterogeneity of HCC subclones, and suggests MLXIPL might be a promising therapeutic target for HCC.

scholarly journals Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel Kage ◽  
Kerstin Heinrich ◽  
Konrad v. Volkmann ◽  
Jenny Kirsch ◽  
Kristen Feher ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Flow Cytometry ◽  
High Throughput ◽  
Pulse Shape ◽  
Single Cells ◽  
Scattered Light ◽  
Free Cell ◽  
Hek293 Cells ◽  
Label Free ◽  
Functional Changes

AbstractFlow cytometers are robust and ubiquitous tools of biomedical research, as they enable high-throughput fluorescence-based multi-parametric analysis and sorting of single cells. However, analysis is often constrained by the availability of detection reagents or functional changes of cells caused by fluorescent staining. Here, we introduce MAPS-FC (multi-angle pulse shape flow cytometry), an approach that measures angle- and time-resolved scattered light for high-throughput cell characterization to circumvent the constraints of conventional flow cytometry. In order to derive cell-specific properties from the acquired pulse shapes, we developed a data analysis procedure based on wavelet transform and k-means clustering. We analyzed cell cycle stages of Jurkat and HEK293 cells by MAPS-FC and were able to assign cells to the G1, S, and G2/M phases without the need for fluorescent labeling. The results were validated by DNA staining and by sorting and re-analysis of isolated G1, S, and G2/M populations. Our results demonstrate that MAPS-FC can be used to determine cell properties that are otherwise only accessible by invasive labeling. This approach is technically compatible with conventional flow cytometers and paves the way for label-free cell sorting.

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