scholarly journals Current Trends of Microfluidic Single-Cell Technologies

2018 ◽  
Vol 19 (10) ◽  
pp. 3143 ◽  
Author(s):  
Pallavi Shinde ◽  
Loganathan Mohan ◽  
Amogh Kumar ◽  
Koyel Dey ◽  
Anjali Maddi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Physiological State ◽  
Cell Manipulation ◽  
Diagnostic Tools ◽  
Current Trends ◽  
Disease Mechanisms ◽  
Cell Technologies ◽  
Cell Measurement ◽  
Therapeutics Development

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.

scholarly journals Microfluidic Based Physical Approaches towards Single-Cell Intracellular Delivery and Analysis

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 631
Author(s):  
Kiran Kaladharan ◽  
Ashish Kumar ◽  
Pallavi Gupta ◽  
Kavitha Illath ◽  
Tuhin Subhra Santra ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Transfection Efficiency ◽  
Intracellular Delivery ◽  
Diagnostic Tools ◽  
Delivery Methods ◽  
Physical Methods ◽  
Cell Measurement ◽  
Therapeutic Development ◽  
Single Living

The ability to deliver foreign molecules into a single living cell with high transfection efficiency and high cell viability is of great interest in cell biology for applications in therapeutic development, diagnostics, and drug delivery towards personalized medicine. Various physical delivery methods have long demonstrated the ability to deliver cargo molecules directly to the cytoplasm or nucleus and the mechanisms underlying most of the approaches have been extensively investigated. However, most of these techniques are bulk approaches that are cell-specific and have low throughput delivery. In comparison to bulk measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. To elucidate distinct responses during cell genetic modification, methods to achieve transfection at the single-cell level are of great interest. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. This review article aims to cover various microfluidic-based physical methods for single-cell intracellular delivery such as electroporation, mechanoporation, microinjection, sonoporation, optoporation, magnetoporation, and thermoporation and their analysis. The mechanisms of various physical methods, their applications, limitations, and prospects are also elaborated.

scholarly journals Tailoring Cardiac Synthetic Transcriptional Modulation Towards Precision Medicine

2022 ◽  
Vol 8 ◽  
Author(s):  
Eric Schoger ◽  
Sara Lelek ◽  
Daniela Panáková ◽  
Laura Cecilia Zelarayán
Keyword(s):  
Gene Expression ◽  
Precision Medicine ◽  
Single Cell ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Regulatory Elements ◽  
Comprehensive Overview ◽  
Endogenous Gene ◽  
Cell Technologies ◽  
Organ Systems

Molecular and genetic differences between individual cells within tissues underlie cellular heterogeneities defining organ physiology and function in homeostasis as well as in disease states. Transcriptional control of endogenous gene expression has been intensively studied for decades. Thanks to a fast-developing field of single cell genomics, we are facing an unprecedented leap in information available pertaining organ biology offering a comprehensive overview. The single-cell technologies that arose aided in resolving the precise cellular composition of many organ systems in the past years. Importantly, when applied to diseased tissues, the novel approaches have been immensely improving our understanding of the underlying pathophysiology of common human diseases. With this information, precise prediction of regulatory elements controlling gene expression upon perturbations in a given cell type or a specific context will be realistic. Simultaneously, the technological advances in CRISPR-mediated regulation of gene transcription as well as their application in the context of epigenome modulation, have opened up novel avenues for targeted therapy and personalized medicine. Here, we discuss the fast-paced advancements during the recent years and the applications thereof in the context of cardiac biology and common cardiac disease. The combination of single cell technologies and the deep knowledge of fundamental biology of the diseased heart together with the CRISPR-mediated modulation of gene regulatory networks will be instrumental in tailoring the right strategies for personalized and precision medicine in the near future. In this review, we provide a brief overview of how single cell transcriptomics has advanced our knowledge and paved the way for emerging CRISPR/Cas9-technologies in clinical applications in cardiac biomedicine.

scholarly journals Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

2020 ◽  
Vol 117 (46) ◽  
pp. 28784-28794
Author(s):  
Sisi Chen ◽  
Paul Rivaud ◽  
Jong H. Park ◽  
Tiffany Tsou ◽  
Emeric Charles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Probabilistic Models ◽  
Single Cells ◽  
Measurement Techniques ◽  
Patient Specific ◽  
Cell Populations ◽  
Cell Measurement ◽  
The Impact

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

scholarly journals UCSC Cell Browser: Visualize Your Single-Cell Data

2020 ◽  
Author(s):  
Matthew L Speir ◽  
Aparna Bhaduri ◽  
Nikolay S Markov ◽  
Pablo Moreno ◽  
Tomasz J Nowakowski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Link Type ◽  
Multiple Datasets ◽  
Cell Technologies ◽  
Metadata Annotation ◽  
Cell Data ◽  
Python Package

AbstractSummaryAs the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets.Availability and implementationWe provide the UCSC Cell Browser as a free website where users can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://[email protected]

An EWOD-based microfluidic chip for single-cell isolation, mRNA purification and subsequent multiplex qPCR

Lab on a Chip ◽  
2014 ◽  
Vol 14 (19) ◽  
pp. 3739-3749 ◽  
Author(s):  
A. Rival ◽  
D. Jary ◽  
C. Delattre ◽  
Y. Fouillet ◽  
G. Castellan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Preparation ◽  
Single Cell ◽  
Microfluidic Chip ◽  
Gene Expression Analysis ◽  
Magnetic Beads ◽  
Cell Manipulation ◽  
Qrt Pcr ◽  
Single Cell Isolation ◽  
Digital Microfluidic

A compact EWOD digital microfluidic chip enables single cell manipulation, sample preparation using magnetic beads and gene expression analysis by qRT-PCR.

scholarly journals sciCAN: Single-cell chromatin accessibility and gene expression data integration via Cycle-consistent Adversarial Network

2021 ◽  
Author(s):  
Yang Xu ◽  
Edmon Begoli ◽  
Rachel Patton McCord
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Single Cell ◽  
Gene Expression Data ◽  
Chromatin Accessibility ◽  
Cellular Systems ◽  
Expression Data ◽  
Adversarial Network ◽  
Cell Technologies ◽  
Cell Data

The booming single-cell technologies bring a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge and gains more and more attention within the community. Here, we present a novel adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 3 state-of-the-art (SOTA) methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with better balance of mutual transferring between modalities than the other 3 SOTA methods. We further applied sciCAN to 10X Multiome data and confirmed the integrated representation preserves information of the hematopoietic hierarchy. Finally, we investigated CRSIPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

scholarly journals Cell population characterization and discovery using single-cell technologies in endocrine systems

2020 ◽  
Vol 65 (2) ◽  
pp. R35-R51
Author(s):  
Leonard Y M Cheung ◽  
Karine Rizzoti
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Reproductive Organs ◽  
Cellular Heterogeneity ◽  
Disease Mechanisms ◽  
Cell Technologies ◽  
Endocrine Organs ◽  
Source Materials

In the last 15 years, single-cell technologies have become robust and indispensable tools to investigate cell heterogeneity. Beyond transcriptomic, genomic and epigenome analyses, technologies are constantly evolving, in particular toward multi-omics, where analyses of different source materials from a single cell are combined, and spatial transcriptomics, where resolution of cellular heterogeneity can be detected in situ. While some of these techniques are still being optimized, single-cell RNAseq has commonly been used because the examination of transcriptomes allows characterization of cell identity and, therefore, unravel previously uncharacterized diversity within cell populations. Most endocrine organs have now been investigated using this technique, and this has given new insights into organ embryonic development, characterization of rare cell types, and disease mechanisms. Here, we highlight recent studies, particularly on the hypothalamus and pituitary, and examine recent findings on the pancreas and reproductive organs where many single-cell experiments have been performed.

scholarly journals Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

2018 ◽  
Author(s):  
Sisi Chen ◽  
Jong H. Park ◽  
Tiffany Tsou ◽  
Paul Rivaud ◽  
Emeric Charles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Probabilistic Models ◽  
Single Cells ◽  
Measurement Techniques ◽  
Patient Specific ◽  
Cell Populations ◽  
Cell Measurement ◽  
The Impact

AbstractSingle-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture, and tracks gene expression and cell abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals or physiological change.

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