scholarly journals A simple microdevice for single cell capture, array, release, and fast staining using oscillatory method

2018 ◽  
Vol 12 (3) ◽  
pp. 034105 ◽  
Author(s):  
Dantong Cheng ◽  
Yang Yu ◽  
Chao Han ◽  
Mengjia Cao ◽  
Guang Yang ◽  
...  
Keyword(s):  
2021 ◽  
Author(s):  
Rongxin Fu ◽  
Ya Su ◽  
Ruliang Wang ◽  
Xue Lin ◽  
Xiangyu Jin ◽  
...  

2019 ◽  
Vol 10 (5) ◽  
pp. 1506-1513 ◽  
Author(s):  
Min Li ◽  
Robbyn K. Anand

We present integration of selective single-cell capture at an array of wireless electrodes (bipolar electrodes, BPEs) with transfer into chambers, reagent exchange, fluidic isolation and rapid electrical lysis in a single platform, thus minimizing sample loss and manual intervention steps.


2019 ◽  
Author(s):  
Charlotte N. Stahlfeld ◽  
Jake J. Tokar ◽  
David Quigley ◽  
David Niles ◽  
Jamie M. Sperger ◽  
...  

Author(s):  
Jacob Amontree ◽  
Kangfu Chen ◽  
Jose Varillas ◽  
Z. Hugh Fan

The characterization of single cells within heterogeneous populations has great impact on both biomedical sciences and cancer research. By investigating cellular compositions on a broad scale, pertinent outliers may be lost in the sample set. Alternatively, an investigation focused on the behavior of specific cells, such as circulating tumor cells (CTCs), will reveal genetic biomarkers or phenotypic characteristics associated with cancer and metastasis. On average, CTC concentration in peripheral blood is extremely low, as few as one to two per billion of healthy blood cells. Consequently, the critical element lacking in many methods of CTC detection is accurate cell capture efficiency at low concentrations. To simulate CTC isolation, researchers usually spike small amounts of tumor cells to healthy blood for separation. However, spiking tumor cells at extremely low concentrations is challenging in a standard laboratory setting. We report our study on an innovative apparatus and method designed for low-cost, precise, and replicable single-cell spiking (SCS). Our SCS method operates solely from capillary aspiration without the reliance on external laboratory equipment. To ensure that our method does not affect the viability of each cell, we investigated the effects of surface membrane tensions induced by aspiration. Finally, we performed affinity-based CTC isolation using human acute lymphoblastic leukemia cells (CCRF-CEM) spiked into healthy whole blood with the SCS technique. The results of the isolation experiments demonstrate the reliability of our method in generating low-concentration cell samples.


2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.


2021 ◽  
Author(s):  
Saptarshi Bej ◽  
Anne-Marie Galow ◽  
Robert David ◽  
Markus Wolfien ◽  
Olaf Wolkenhauer

AbstractThe research landscape of single-cell and single-nuclei RNA sequencing is evolving rapidly, and one area that is enabled by this technology, is the detection of rare cells. An automated, unbiased and accurate annotation of rare subpopulations is challenging. Once rare cells are identified in one dataset, it will usually be necessary to generate other datasets to enrich the analysis (e.g., with samples from other tissues). From a machine learning perspective, the challenge arises from the fact that rare cell subpopulations constitute an imbalanced classification problem.We here introduce a Machine Learning (ML)-based oversampling method that uses gene expression counts of already identified rare cells as an input to generate synthetic cells to then identify similar (rare) cells in other publicly available experiments. We utilize single-cell synthetic oversampling (sc-SynO), which is based on the Localized Random Affine Shadowsampling (LoRAS) algorithm. The algorithm corrects for the overall imbalance ratio of the minority and majority class.We demonstrate the effectiveness of the method for two independent use cases, each consisting of two published datasets. The first use case identifies cardiac glial cells in snRNA-Seq data (17 nuclei out of 8,635). This use case was designed to take a larger imbalance ratio (∼1 to 500) into account and only uses single-nuclei data. The second use case was designed to jointly use snRNA-Seq data and scRNA-Seq on a lower imbalance ratio (∼1 to 26) for the training step to likewise investigate the potential of the algorithm to consider both single cell capture procedures and the impact of “less” rare-cell types. For validation purposes, all datasets have also been analyzed in a traditional manner using common data analysis approaches, such as the Seurat3 workflow.Our algorithm identifies rare-cell populations with a high accuracy and low false positive detection rate. A striking benefit of our algorithm is that it can be readily implemented in other and existing workflows. The code basis is publicly available at FairdomHub (https://fairdomhub.org/assays/1368) and can easily be transferred to train other customized approaches.


2018 ◽  
Author(s):  
Christopher S. McGinnis ◽  
Lyndsay M. Murrow ◽  
Zev J. Gartner

SUMMARYSingle-cell RNA sequencing (scRNA-seq) using droplet microfluidics occasionally produces transcriptome data representing more than one cell. These technical artifacts are caused by cell doublets formed during cell capture and occur at a frequency proportional to the total number of sequenced cells. The presence of doublets can lead to spurious biological conclusions, which justifies the practice of sequencing fewer cells to limit doublet formation rates. Here, we present a computational doublet detection tool – DoubletFinder – that identifies doublets based solely on gene expression features. DoubletFinder infers the putative gene expression profile of real doublets by generating artificial doublets from existing scRNA-seq data. Neighborhood detection in gene expression space then identifies sequenced cells with increased probability of being doublets based on their proximity to artificial doublets. DoubletFinder robustly identifies doublets across scRNA-seq datasets with variable numbers of cells and sequencing depth, and predicts false-negative and false-positive doublets defined using conventional barcoding approaches. We anticipate that DoubletFinder will aid in scRNA-seq data analysis and will increase the throughput and accuracy of scRNA-seq experiments.


2019 ◽  
Vol 31 (9) ◽  
pp. 2873
Author(s):  
Kensaku Hayashi ◽  
Momoko Kumemura ◽  
Shohei Kaneda ◽  
Vivek Menon ◽  
Laurent Jalabert ◽  
...  

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