scholarly journals Integration of marker-free selection of single cells at a wireless electrode array with parallel fluidic isolation and electrical lysis

2019 ◽  
Vol 10 (5) ◽  
pp. 1506-1513 ◽  
Author(s):  
Min Li ◽  
Robbyn K. Anand
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Electrode Array ◽  
Cell Capture ◽  
Bipolar Electrodes ◽  
Manual Intervention ◽  
Electrical Lysis ◽  
Marker Free ◽  
Selection Of

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.

Capillary Force Driven Single-Cell Spiking Apparatus for Studying Circulating Tumor Cells

2018 ◽  
Author(s):  
Jacob Amontree ◽  
Kangfu Chen ◽  
Jose Varillas ◽  
Z. Hugh Fan
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Single Cells ◽  
Lymphoblastic Leukemia ◽  
Surface Membrane ◽  
Critical Element ◽  
Cell Capture ◽  
Biomedical Sciences ◽  
Low Concentrations

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.

scholarly journals Computational approaches towards reducing contamination in single-cell RNA-seq data

2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Real Life ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Sequence Analyses ◽  
Cell Functions ◽  
Biological Interpretation ◽  
Different Cell Types

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.

scholarly journals Bayesian inference of the gene expression states of single cells from scRNA-seq data

2019 ◽  
Author(s):  
Jérémie Breda ◽  
Mihaela Zavolan ◽  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Downstream Processing ◽  
Noise Removal ◽  
Rna Seq ◽  
Expression Of Genes ◽  
Normalization Methods ◽  
Quantify Gene Expression ◽  
Selection Of

AbstractIn spite of a large investment in the development of methodologies for analysis of single-cell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters.Comparison of Sanity with other recent normalization methods on a selection of scRNA-seq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

scholarly journals Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

2017 ◽  
Author(s):  
Junyue Cao ◽  
Jonathan S. Packer ◽  
Vijay Ramani ◽  
Darren A. Cusanovich ◽  
Chau Huynh ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Major Step ◽  
C Elegans

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

Integration of Dielectrophoretic Selective Single-Cell Capture at a Wireless Electrode Array with On-Chip Analysis

2021 ◽  
Author(s):  
Robbyn Anand ◽  
Darshna Pagariya ◽  
Joseph Banovetz ◽  
Han Chen ◽  
Sommer Osman ◽  
...  
Keyword(s):  
Single Cell ◽  
Electrode Array ◽  
Cell Capture ◽  
On Chip ◽  
Chip Analysis

scholarly journals Current techniques for single-cell lysis

2008 ◽  
Vol 5 (suppl_2) ◽  
Author(s):  
Robert B Brown ◽  
Julie Audet
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Laser Pulses ◽  
High Temporal Resolution ◽  
Cell Analysis ◽  
Careful Selection ◽  
Analysis Techniques ◽  
Cell Lysate ◽  
Selection Of

Owing to the small quantities of analytes and small volumes involved in single-cell analysis techniques, manipulation strategies must be chosen carefully. The lysis of single cells for downstream chemical analysis in capillaries and lab-on-a-chip devices can be achieved by optical, acoustic, mechanical, electrical or chemical means, each having their respective strengths and weaknesses. Selection of the most appropriate lysis method will depend on the particulars of the downstream cell lysate processing. Ultrafast lysis techniques such as the use of highly focused laser pulses or pulses of high voltage are suitable for applications requiring high temporal resolution. Other factors, such as whether the cells are adherent or in suspension and whether the proteins to be collected are desired to be native or denatured, will determine the suitability of detergent-based lysis methods. Therefore, careful selection of the proper lysis technique is essential for gathering accurate data from single cells.

Hydrodynamic shuttling for deterministic high-efficiency multiple single-cell capture in a microfluidic chip

Lab on a Chip ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1370-1377 ◽  
Author(s):  
Cheng-Kun He ◽  
Ya-Wen Chen ◽  
Ssu-Han Wang ◽  
Chia-Hsien Hsu
Keyword(s):  
Single Cell ◽  
Biomedical Research ◽  
Microfluidic Chip ◽  
High Efficiency ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Cell Capture

A new microfluidics technique for high-efficiency paring and analyzing multiple single cells can facilitate cellular heterogeneity studies important for biological and biomedical research.

Microfluidics Design for Single Cell Detection

2013 ◽  
Vol 562-565 ◽  
pp. 589-593
Author(s):  
Shao Bo Du ◽  
Sheng Bo Sang ◽  
Wen Dong Zhang ◽  
Jie Hu ◽  
Peng Wei Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Fluid Transport ◽  
Individual Cell ◽  
Single Cells ◽  
Cell Detection ◽  
Cell Capture ◽  
Single Cell Detection ◽  
Novel Structure ◽  
Mother And Daughter

Here we demonstrate a microfluidic-based analysis system based on single cell capture array, which can physically trap individual cell using micrometer-sized structures. A stable and in vivo-like microenvironment was built with the novel structure at the single-cell detection level. The microfluidic-based design can decouple single cells from fluid flow with the help of micropillars. The size and geometry of the cell jails are designed in order to discriminate between mother and daughter cells. It provides an experimental platform to efficiently monitor individual cell state for a long period of time. Furthermore, the parallel microfluidic array can ensure accuracy. In addition, finite element method (FEM) was employed to predict fluid transport properties for the most optimal fluid microenvironment.

scholarly journals Efficient analysis of a small number of cancer cells at the single-cell level using an electroactive double-well array

Lab on a Chip ◽  
2016 ◽  
Vol 16 (13) ◽  
pp. 2440-2449 ◽  
Author(s):  
Soo Hyeon Kim ◽  
Teruo Fujii
Keyword(s):  
Single Cell ◽  
Cancer Cells ◽  
Single Cells ◽  
Capture Efficiency ◽  
Single Cell Level ◽  
Cell Capture ◽  
Cell Level ◽  
Cell Trapping ◽  
Highly Efficient ◽  
Single Cell Trapping

The electroactive double well-array consists of trap-wells for highly efficient single-cell trapping using dielectrophoresis (cell capture efficiency of 96 ± 3%) and reaction-wells that confine cell lysates for analysis of intracellular materials from single cells.

scholarly journals BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanhua Huang ◽  
Guido Sanguinetti
Keyword(s):  
Alternative Splicing ◽  
Single Cell ◽  
Rna Splicing ◽  
Single Cells ◽  
Computational Method ◽  
Cell Level ◽  
Transcriptomic Data ◽  
Alternative Transcripts ◽  
Computational Identification ◽  
Selection Of

AbstractRNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

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