scholarly journals Single cell high-throughput qRT-PCR protocol

2021 ◽  
Author(s):  
Sirisha Achanta ◽  
Rajanikanth Vadigepalli
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Reverse Transcription ◽  
Single Cells ◽  
Rna Extraction ◽  
High Sensitivity ◽  
Single Experiment ◽  
Tissue Samples ◽  
Qrt Pcr ◽  
Cell Sample

Abstract Single cell high-throughput qRT-PCR protocol combines high sensitivity technique of single cell qPCR with high-throughput qPCR technology that can generate data from 96 samples and 96 genes in a single experiment. It can be adapted for various sample types- cell culture, tissue samples and extracted RNA (10 pg) and measured on traditional qPCR and high-throughput qPCR platforms. The workflow is comprised of four steps – cell lysis, reverse transcription, pre-amplification and qPCR. Key features of this protocol are; processing low input samples directly to reverse transcription without RNA extraction which minimizes sample loss, pre-amplification enables amplification of cDNA from single cells to detectable levels for qPCR and measuring up to 400 genes from a single cell sample/10 pg of RNA (starting material). Robust, reproducible and versatile this protocol can be adapted to several upstream and downstream techniques.

scholarly journals Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cells ◽  
Cellular Heterogeneity ◽  
Tumour Heterogeneity ◽  
Fresh Tissue ◽  
Human Cancers ◽  
Cryopreserved Cell ◽  
Single Cell Rna Sequencing

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals scHiCSRS: A Self-Representation Smoothing Method with Gaussian Mixture Model for Imputing single-cell Hi-C Data

2021 ◽  
Author(s):  
Shili Lin ◽  
Qing Xie
Keyword(s):  
Single Cell ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Single Cells ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Smoothing Method ◽  
Intermediate Step ◽  
Downstream Analysis ◽  
Structural Zeros

Motivation: Single-cell Hi-C techniques make it possible to study cell-to-cell variability in genomic features. However, excess zeros are commonly seen in single-cell Hi-C (scHi-C) data, making scHi-C matrices extremely sparse and bringing extra difficulties in downstream analysis. The observed zeros are a combination of two events: structural zeros for which the loci never inter- act due to underlying biological mechanisms, and dropouts or sampling zeros where the two loci interact but are not captured due to insufficient sequencing depth. Although quality improvement approaches have been proposed as an intermediate step for analyzing scHi-C data, little has been done to address these two types of zeros. We believe that differentiating between structural zeros and dropouts would benefit downstream analysis such as clustering. Results: We propose scHiCSRS, a self-representation smoothing method that improves the data quality, and a Gaussian mixture model that identifies structural zeros among observed zeros. scHiCSRS not only takes spatial dependencies of a scHi-C 2D data structure into account but also borrows information from similar single cells. Through an extensive set of simulation studies, we demonstrate the ability of scHiCSRS for identifying structural zeros with high sensitivity and for accurate imputation of dropout values in sampling zeros. Downstream analysis for three real datasets show that data improved from scHiCSRS yield more accurate clustering of cells than simply using observed data or improved data from several comparison methods.

scholarly journals Microfluidic guillotine for single-cell wound repair studies

2017 ◽  
Vol 114 (28) ◽  
pp. 7283-7288 ◽  
Author(s):  
Lucas R. Blauch ◽  
Ya Gai ◽  
Jian Wei Khor ◽  
Pranidhi Sood ◽  
Wallace F. Marshall ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Wound Repair ◽  
Time Course ◽  
Single Cells ◽  
Repair Process ◽  
Gene Knockdown ◽  
Viscous Stress ◽  
Repair Capacity ◽  
Stentor Coeruleus

Wound repair is a key feature distinguishing living from nonliving matter. Single cells are increasingly recognized to be capable of healing wounds. The lack of reproducible, high-throughput wounding methods has hindered single-cell wound repair studies. This work describes a microfluidic guillotine for bisecting single Stentor coeruleus cells in a continuous-flow manner. Stentor is used as a model due to its robust repair capacity and the ability to perform gene knockdown in a high-throughput manner. Local cutting dynamics reveals two regimes under which cells are bisected, one at low viscous stress where cells are cut with small membrane ruptures and high viability and one at high viscous stress where cells are cut with extended membrane ruptures and decreased viability. A cutting throughput up to 64 cells per minute—more than 200 times faster than current methods—is achieved. The method allows the generation of more than 100 cells in a synchronized stage of their repair process. This capacity, combined with high-throughput gene knockdown in Stentor, enables time-course mechanistic studies impossible with current wounding methods.

scholarly journals RT-dPCR in Mosquito Samples for ZIKV Detection: Effects of RNA Extraction and Reverse Transcription in Target Concentration

Viruses ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 827
Author(s):  
Paula Rodrigues de Almeida ◽  
Ana Karolina Antunes Eisen ◽  
Meriane Demoliner ◽  
Fernando Rosado Spilki
Keyword(s):  
Reverse Transcription ◽  
Tissue Concentration ◽  
Critical Role ◽  
Rna Extraction ◽  
High Sensitivity ◽  
Clinical Samples ◽  
Assay Sensitivity ◽  
Robust Detection ◽  
Detection Techniques ◽  
Mosquito Pool

Zika virus (ZIKV) is an important arbovirus, responsible for recent outbreaks of Guillain Barré Syndrome and Congenital Zika Syndrome (CZS). After thousands of CZS cases, ZIKV is under constant surveillance in Brazil. Reliable and robust detection techniques are required to minimize the influence of host inhibitors from clinical samples and mosquito pool samples. Reverse transcription Digital Polymerase Chain Reaction (RT-dPCR) is a technique that allows the accurate quantification of DNA targets with high sensitivity, and it is usually less affected by inhibitors than RT-qPCR. This study aimed to assess the influence of mosquito tissue, RNA extraction and cDNA synthesis in ZIKV PCR detection. Samples containing 0, 3 and 10 mosquitoes were spiked with ZIKV MR766 and serially diluted prior to RNA extraction and RT-dPCR for ZIKV. Two reverse transcription protocols were tested. Assay sensitivity allowed the detection of 1.197 copies/µL. A higher correlation between dilution factor and target quantification was observed in 10 mosquito pool samples. The lower quantification in samples diluted without mosquitoes highlights the critical role of the reverse transcription step in RNA detection, since it could be attributed to reverse transcriptase variable performance in samples with low overall RNA concentration. The results in mosquito pools indicate that mosquito tissues do not inhibit ZIKV RT-dPCR, and the RT-dPCR technique has good sensitivity and robustness for ZIKV detection in mosquito pool samples regardless of mosquito tissue concentration.

scholarly journals Performance Comparison of Reverse Transcriptases for Single-Cell Studies

2019 ◽  
Vol 66 (1) ◽  
pp. 217-228 ◽  
Author(s):  
Daniel Zucha ◽  
Peter Androvic ◽  
Mikael Kubista ◽  
Lukas Valihrach
Keyword(s):  
Single Cell ◽  
Reverse Transcription ◽  
Cell Model ◽  
Single Cells ◽  
Performance Comparison ◽  
Reaction Yield ◽  
Reverse Transcriptases ◽  
Reverse Transcription Pcr ◽  
Cell Experiment ◽  
Almost All

Abstract BACKGROUND Recent advances allowing quantification of RNA from single cells are revolutionizing biology and medicine. Currently, almost all single-cell transcriptomic protocols rely on reverse transcription (RT). However, RT is recognized as a known source of variability, particularly with low amounts of RNA. Recently, several new reverse transcriptases (RTases) with the potential to decrease the loss of information have been developed, but knowledge of their performance is limited. METHODS We compared the performance of 11 RTases in quantitative reverse transcription PCR (RT-qPCR) on single-cell and 100-cell bulk templates, using 2 priming strategies: a conventional mixture of random hexamers with oligo(dT)s and a reduced concentration of oligo(dT)s mimicking common single-cell RNA-sequencing protocols. Depending on their performance, 2 RTases were further tested in a high-throughput single-cell experiment. RESULTS All tested RTases demonstrated high precision (R2 > 0.9445). The most pronounced differences were found in their ability to capture rare transcripts (0%–90% reaction positivity rate) and in their absolute reaction yield (7.3%–137.9%). RTase performance and reproducibility were compared with Z scores. The 2 best-performing enzymes were Maxima H− and SuperScript IV. The validity of the obtained results was confirmed in a follow-up single-cell model experiment. The better-performing enzyme (Maxima H−) increased the sensitivity of the single-cell experiment and improved resolution in the clustering analysis over the commonly used RTase (SuperScript II). CONCLUSIONS Our comprehensive comparison of 11 RTases in low RNA input conditions identified 2 best-performing enzymes. Our results provide a point of reference for the improvement of current single-cell quantification protocols.

High-throughput single-cell analysis of low copy number β-galactosidase by a laboratory-built high-sensitivity flow cytometer

2013 ◽  
Vol 48 ◽  
pp. 49-55 ◽  
Author(s):  
Lingling Yang ◽  
Tianxun Huang ◽  
Shaobin Zhu ◽  
Yingxing Zhou ◽  
Yunbin Jiang ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Copy Number ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Flow Cytometer ◽  
Cell Analysis ◽  
Low Copy Number

scholarly journals An optimised direct lysis method for gene expression studies on low cell numbers

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Anh Viet-Phuong Le ◽  
Dexing Huang ◽  
Tony Blick ◽  
Erik W. Thompson ◽  
Alexander Dobrovic
Keyword(s):  
Gene Expression ◽  
Reverse Transcription ◽  
Single Cells ◽  
Quantitative Polymerase Chain Reaction ◽  
Rna Extraction ◽  
Cost Effective ◽  
Breast Cancer Cell Lines ◽  
Cell Numbers ◽  
Ionic Detergent ◽  
Gene Expression Studies

Abstract There is increasing interest in gene expression analysis of either single cells or limited numbers of cells. One such application is the analysis of harvested circulating tumour cells (CTCs), which are often present in very low numbers. A highly efficient protocol for RNA extraction, which involves a minimal number of steps to avoid RNA loss, is essential for low input cell numbers. We compared several lysis solutions that enable reverse transcription (RT) to be performed directly on the cell lysate, offering a simple rapid approach to minimise RNA loss for RT. The lysis solutions were assessed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in low cell numbers isolated from four breast cancer cell lines. We found that a lysis solution containing both the non-ionic detergent (IGEPAL CA-630, chemically equivalent to Nonidet P-40 or NP-40) and bovine serum albumin (BSA) gave the best RT-qPCR yield. This direct lysis to reverse transcription protocol outperformed a column-based extraction method using a commercial kit. This study demonstrates a simple, reliable, time- and cost-effective method that can be widely used in any situation where RNA needs to be prepared from low to very low cell numbers.

scholarly journals High-Throughput Single-Cell Cultivation on Microfluidic Streak Plates

2016 ◽  
Vol 82 (7) ◽  
pp. 2210-2218 ◽  
Author(s):  
Cheng-Ying Jiang ◽  
Libing Dong ◽  
Jian-Kang Zhao ◽  
Xiaofang Hu ◽  
Chaohua Shen ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Single Cells ◽  
Response Analysis ◽  
Bacterial Strains ◽  
Content Type ◽  
Chemical Gradients ◽  
Droplet Array ◽  
Polycyclic Aromatic ◽  
Single Cell Isolation

ABSTRACTThis paper describes the microfluidic streak plate (MSP), a facile method for high-throughput microbial cell separation and cultivation in nanoliter sessile droplets. The MSP method builds upon the conventional streak plate technique by using microfluidic devices to generate nanoliter droplets that can be streaked manually or robotically onto petri dishes prefilled with carrier oil for cultivation of single cells. In addition, chemical gradients could be encoded in the droplet array for comprehensive dose-response analysis. The MSP method was validated by using single-cell isolation ofEscherichia coliand antimicrobial susceptibility testing ofPseudomonas aeruginosaPAO1. The robustness of the MSP work flow was demonstrated by cultivating a soil community that degrades polycyclic aromatic hydrocarbons. Cultivation in droplets enabled detection of the richest species diversity with better coverage of rare species. Moreover, isolation and cultivation of bacterial strains by MSP led to the discovery of several species with high degradation efficiency, including fourMycobacteriumisolates and a previously unknown fluoranthene-degradingBlastococcusspecies.

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