scholarly journals Microfluidics 3D gel-island chip for single cell isolation and lineage-dependent drug responses study

Lab on a Chip ◽  
2016 ◽  
Vol 16 (13) ◽  
pp. 2504-2512 ◽  
Author(s):  
Zhixiong Zhang ◽  
Yu-Chih Chen ◽  
Yu-Heng Cheng ◽  
Yi Luan ◽  
Euisik Yoon
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Cell Tracking ◽  
Single Cells ◽  
Cell Isolation ◽  
Microfluidic Platform ◽  
Drug Responses ◽  
3D Microenvironment ◽  
Single Cell Isolation

This paper reports a novel gel-island microfluidic platform enabling single-cell tracking in biomimetic 3D microenvironment for investigating heterogeneous drug response of single cells.

A microfluidic device enabling deterministic single cell trapping and release

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Huichao Chai ◽  
Yongxiang Feng ◽  
Fei Liang ◽  
Wenhui Wang
Keyword(s):  
Chemical Analysis ◽  
Single Cell ◽  
Microfluidic Device ◽  
Single Cells ◽  
Cell Isolation ◽  
Cell Trapping ◽  
Analysis Techniques ◽  
Single Cell Trapping ◽  
Single Cell Isolation ◽  
Made In

Successful single-cell isolation is a pivotal technique for subsequent biological and chemical analysis of single cells. Although significant advances have been made in single-cell isolation and analysis techniques, most passive...

scholarly journals Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

2020 ◽  
Author(s):  
HARIPRIYA HARIKUMAR ◽  
Thomas P Quinn ◽  
Santu Rana ◽  
Sunil Gupta ◽  
Svetha Venkatesh
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Single Cells ◽  
Biological Data ◽  
Patient Specific ◽  
Expression Data ◽  
Drug Responses ◽  
Dual Channel ◽  
Cell Gene ◽  
Cell Networks

Abstract Background: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient.Methods: We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses.Conclusions: Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

scholarly journals Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Haripriya Harikumar ◽  
Thomas P. Quinn ◽  
Santu Rana ◽  
Sunil Gupta ◽  
Svetha Venkatesh
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Single Cells ◽  
Biological Data ◽  
Patient Specific ◽  
Expression Data ◽  
Drug Responses ◽  
Dual Channel ◽  
Cell Gene ◽  
Cell Networks

Abstract Background The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems. Our primary objective is to combine population-level drug-response data with patient-level single-cell expression data to predict how any gene will respond to any drug for any patient. Methods We take 2 approaches to benchmarking a “dual-channel” random walk with restart (RWR) for data integration. First, we evaluate how well RWR can predict known gene functions from single-cell gene co-expression networks. Second, we evaluate how well RWR can predict known drug responses from individual cell networks. We then present two exploratory applications. In the first application, we combine the Gene Ontology database with glioblastoma single cells from 5 individual patients to identify genes whose functions differ between cancers. In the second application, we combine the LINCS drug-response database with the same glioblastoma data to identify genes that may exhibit patient-specific drug responses. Conclusions Our manuscript introduces two innovations to the integration of heterogeneous biological data. First, we use a “dual-channel” method to predict up-regulation and down-regulation separately. Second, we use individualized single-cell gene co-expression networks to make personalized predictions. These innovations let us predict gene function and drug response for individual patients. Taken together, our work shows promise that single-cell co-expression data could be combined in heterogeneous information networks to facilitate precision medicine.

scholarly journals Deep Transfer Learning of Drug Sensitivity by Integrating Bulk and Single-cell RNA-seq data

2021 ◽  
Author(s):  
Junyi Chen ◽  
Ren Qi ◽  
Zhenyu Wu ◽  
Anjun Ma ◽  
Lang Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Transfer Learning ◽  
Large Scale ◽  
Drug Response ◽  
Single Cells ◽  
Cancer Drug ◽  
Sequencing Data ◽  
Drug Responses ◽  
Single Cell Rna Sequencing

Massively bulk RNA sequencing databases incorporating drug screening have opened up an avenue to inform the optimal clinical application of cancer drugs. Meanwhile, the growing single-cell RNA sequencing data contributes to improving therapeutic effectiveness by studying the heterogeneity of drug responses for cancer cell subpopulations. Yet, the drug response information for single-cell data is scarcely obtained. Thus, there is an urgent need to develop computational pipelines to infer and interpret cancer drug responses in single cells. Here, we developed scDEAL, a deep transfer learning framework integrating large-scale bulk and single-cell RNA sequencing drug response datasets. We benchmarked scDEAL on six single-cell RNA sequencing datasets and indicate its model interpretability by several case studies. scDEAL not only achieves accurate and robust performance in single-cell drug response predictions, but also can infer signature genes to reveal potential drug resistance mechanisms based on integrated gradient feature interpretation. This work may help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.

Impedance-Based Single-Cell Pipetting

2020 ◽  
Vol 25 (3) ◽  
pp. 222-233
Author(s):  
David Bonzon ◽  
Georges Muller ◽  
Jean-Baptiste Bureau ◽  
Nicolas Uffer ◽  
Nicolas Beuchat ◽  
...  
Keyword(s):  
Cell Viability ◽  
Single Cell ◽  
Gold Standard ◽  
Single Cells ◽  
Cell Isolation ◽  
Design Parameters ◽  
Cell Line Development ◽  
A Cell ◽  
Single Cell Isolation ◽  
Biological Methods

Many biological methods are based on single-cell isolation. In single-cell line development, the gold standard involves the dilution of cells by means of a pipet. This process is time-consuming as it is repeated over several weeks to ensure clonality. Here, we report the modeling, designing, and testing of a disposable pipet tip integrating a cell sensor based on the Coulter principle. We investigate, test, and discuss the effects of design parameters on the sensor performances with an analytical model. We also describe a system that enables the dispensing of single cells using an instrumented pipet coupled with the sensing tip. Most importantly, this system allows the recording of an impedance trace to be used as proof of single-cell isolation. We assess the performances of the system with beads and cells. Finally, we show that the electrical detection has no effect on cell viability.

scholarly journals Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jeremy A. Lombardo ◽  
Marzieh Aliaghaei ◽  
Quy H. Nguyen ◽  
Kai Kessenbrock ◽  
Jered B. Haun
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Processing Technologies ◽  
Microfluidic Platform ◽  
Tissue Processing ◽  
Single Cell Rna Sequencing ◽  
Culture Models ◽  
Tissue Digestion

AbstractTissues are complex mixtures of different cell subtypes, and this diversity is increasingly characterized using high-throughput single cell analysis methods. However, these efforts are hindered, as tissues must first be dissociated into single cell suspensions using methods that are often inefficient, labor-intensive, highly variable, and potentially biased towards certain cell subtypes. Here, we present a microfluidic platform consisting of three tissue processing technologies that combine tissue digestion, disaggregation, and filtration. The platform is evaluated using a diverse array of tissues. For kidney and mammary tumor, microfluidic processing produces 2.5-fold more single cells. Single cell RNA sequencing further reveals that endothelial cells, fibroblasts, and basal epithelium are enriched without affecting stress response. For liver and heart, processing time is dramatically reduced. We also demonstrate that recovery of cells from the system at periodic intervals during processing increases hepatocyte and cardiomyocyte numbers, as well as increases reproducibility from batch-to-batch for all tissues.

Application Of Isoraft Single Cell Isolation For Analysis Of Pediatric Bal Macrophages

2014 ◽  
Vol 133 (2) ◽  
pp. AB142
Author(s):  
Neil Alexis ◽  
Heather Wells ◽  
Yogesh Saini ◽  
Louisa Brighton ◽  
Nancy Allbritton ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Isolation ◽  
Single Cell Isolation

Grids from bands, or bands from grids? An examination of the effects of single unit contamination on grid cell firing fields

2016 ◽  
Vol 115 (2) ◽  
pp. 992-1002 ◽  
Author(s):  
Z. Navratilova ◽  
K. B. Godfrey ◽  
B. L. McNaughton
Keyword(s):  
Single Cell ◽  
Grid Cell ◽  
Cell Isolation ◽  
Recording Technology ◽  
Firing Patterns ◽  
Limiting Step ◽  
Isolation Methods ◽  
Single Cell Isolation ◽  
Cell Firing ◽  
Cell Data

Neural recording technology is improving rapidly, allowing for the detection of spikes from hundreds of cells simultaneously. The limiting step in multielectrode electrophysiology continues to be single cell isolation. However, this step is crucial to the interpretation of data from putative single neurons. We present here, in simulation, an illustration of possibly erroneous conclusions that may be reached when poorly isolated single cell data are analyzed. Grid cells are neurons recorded in rodents, and bats, that spike in equally spaced locations in a hexagonal pattern. One theory states that grid firing patterns arise from a combination of band firing patterns. However, we show here that summing the grid firing patterns of two poorly resolved neurons can result in spurious band-like patterns. Thus, evidence of neurons spiking in band patterns must undergo extreme scrutiny before it is accepted. Toward this aim, we discuss single cell isolation methods and metrics.

Sign in / Sign up

Export Citation Format

Share Document