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Journal articles on the topic 'Single cell quantification'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Reiter, M., B. Kirchner, H. Muller, C. Holzhauer, W. Mann, and M. W. Pfaffl. "Quantification noise in single cell experiments." Nucleic Acids Research 39, no. 22 (2011): 9834. http://dx.doi.org/10.1093/nar/gkr1136.

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2

Reiter, M., B. Kirchner, H. Muller, C. Holzhauer, W. Mann, and M. W. Pfaffl. "Quantification noise in single cell experiments." Nucleic Acids Research 39, no. 18 (2011): e124-e124. http://dx.doi.org/10.1093/nar/gkr505.

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3

Jonasson, Emma, Lisa Andersson, Soheila Dolatabadi, Salim Ghannoum, Pierre Åman, and Anders Ståhlberg. "Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity." Cells 9, no. 3 (2020): 759. http://dx.doi.org/10.3390/cells9030759.

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Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription

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4

Loeffler, Dirk, and Timm Schroeder. "Understanding cell fate control by continuous single-cell quantification." Blood 133, no. 13 (2019): 1406–14. http://dx.doi.org/10.1182/blood-2018-09-835397.

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Abstract Cells and the molecular processes underlying their behavior are highly dynamic. Understanding these dynamic biological processes requires noninvasive continuous quantitative single-cell observations, instead of population-based average or single-cell snapshot analysis. Ideally, single-cell dynamics are measured long-term in vivo; however, despite progress in recent years, technical limitations still prevent such studies. On the other hand, in vitro studies have proven to be useful for answering long-standing questions. Although technically still demanding, long-term single-cell imagin

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5

Junkin, Michael, Alicia J. Kaestli, Zhang Cheng, et al. "High-Content Quantification of Single-Cell Immune Dynamics." Cell Reports 15, no. 2 (2016): 411–22. http://dx.doi.org/10.1016/j.celrep.2016.03.033.

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6

Ivask, Angela, Andrew J. Mitchell, Christopher M. Hope, Simon C. Barry, Enzo Lombi, and Nicolas H. Voelcker. "Single Cell Level Quantification of Nanoparticle–Cell Interactions Using Mass Cytometry." Analytical Chemistry 89, no. 16 (2017): 8228–32. http://dx.doi.org/10.1021/acs.analchem.7b01006.

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7

Xu, Liyi, Ilana L. Brito, Eric J. Alm, and Paul C. Blainey. "Virtual microfluidics for digital quantification and single-cell sequencing." Nature Methods 13, no. 9 (2016): 759–62. http://dx.doi.org/10.1038/nmeth.3955.

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8

Qiu, Xiaojie, Andrew Hill, Jonathan Packer, Dejun Lin, Yi-An Ma, and Cole Trapnell. "Single-cell mRNA quantification and differential analysis with Census." Nature Methods 14, no. 3 (2017): 309–15. http://dx.doi.org/10.1038/nmeth.4150.

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9

Schroeder, Timm. "Long-term single cell quantification: new tools for old questions." Experimental Hematology 43, no. 9 (2015): S32. http://dx.doi.org/10.1016/j.exphem.2015.06.025.

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10

Chen, Liang, and Sika Zheng. "BCseq: accurate single cell RNA-seq quantification with bias correction." Nucleic Acids Research 46, no. 14 (2018): e82-e82. http://dx.doi.org/10.1093/nar/gky308.

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11

Schroeder, Timm. "Long-term Live Single Cell Quantification of Transcription Factor Dynamics." Blood 128, no. 22 (2016): SCI—4—SCI—4. http://dx.doi.org/10.1182/blood.v128.22.sci-4.sci-4.

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Abstract Hematopoiesis is highly complex and dynamic, and consist of large numbers of different cells expressing many molecules. Despite intensive research, many long-standing questions in hematopoiesis research remain disputed. One major reason is the fact that we usually only analyze populations of cells - rather than individual cells - at very few time points of an experiment. Tracking of individual cells would be an extremely powerful approach to improve our understanding of molecular cell fate control. We are therefore developing imaging systems to follow the fate of single cells over man

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12

Edwards, Erin Elizabeth, Katherine Gayle Birmingham, Meghan Jeanne O’Melia, Jaeho Oh, and Susan Napier Thomas. "Fluorometric Quantification of Single-Cell Velocities to Investigate Cancer Metastasis." Cell Systems 7, no. 5 (2018): 496–509. http://dx.doi.org/10.1016/j.cels.2018.10.005.

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13

Ren, Jifeng, Yongshu Li, Shuhuan Hu, et al. "Nondestructive quantification of single-cell nuclear and cytoplasmic mechanical properties based on large whole-cell deformation." Lab on a Chip 20, no. 22 (2020): 4175–85. http://dx.doi.org/10.1039/d0lc00725k.

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14

Weder, G., O. Guillaume-Gentil, N. Matthey, et al. "The quantification of single cell adhesion on functionalized surfaces for cell sheet engineering." Biomaterials 31, no. 25 (2010): 6436–43. http://dx.doi.org/10.1016/j.biomaterials.2010.04.068.

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15

Iturri, Jagoba, Andreas Weber, María d. M. Vivanco, and José L. Toca-Herrera. "Single-Cell Probe Force Studies to Identify Sox2 Overexpression-Promoted Cell Adhesion in MCF7 Breast Cancer Cells." Cells 9, no. 4 (2020): 935. http://dx.doi.org/10.3390/cells9040935.

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The replacement of the cantilever tip by a living cell in Atomic Force Microscopy (AFM) experiments permits the direct quantification of cell–substrate and cell–cell adhesion forces. This single-cell probe force measurement technique, when complemented by microscopy, allows controlled manipulation of the cell with defined location at the area of interest. In this work, a setup based on two glass half-slides, a non-fouling one with bacterial S-layer protein SbpA from L. sphaericus CMM 2177 and the second with a fibronectin layer, has been employed to measure the adhesion of MCF7 breast cancer c

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16

Potthoff, Eva, Dario Ossola, Tomaso Zambelli, and Julia A. Vorholt. "Bacterial adhesion force quantification by fluidic force microscopy." Nanoscale 7, no. 9 (2015): 4070–79. http://dx.doi.org/10.1039/c4nr06495j.

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Fluidic force microscopy demonstrates the potential to quantify bacterial adhesion by single-cell force spectroscopy, achieving higher immobilization forces than state-of-the-art cell-cantilever interactions. Reversible cell fixation on the tip allows for serial measurements of many cells in the nN range using a single cantilever.

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17

Rashkow, Jason T., Sunny C. Patel, Ryan Tappero, and Balaji Sitharaman. "Quantification of single-cell nanoparticle concentrations and the distribution of these concentrations in cell population." Journal of The Royal Society Interface 11, no. 94 (2014): 20131152. http://dx.doi.org/10.1098/rsif.2013.1152.

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Quantification of nanoparticle uptake into cells is necessary for numerous applications in cellular imaging and therapy. Herein, synchrotron X-ray fluorescence (SXRF) microscopy, a promising tool to quantify elements in plant and animal cells, was employed to quantify and characterize the distribution of titanium dioxide (TiO 2 ) nanosphere uptake in a population of single cells. These results were compared with average nanoparticle concentrations per cell obtained by widely used inductively coupled plasma mass spectrometry (ICP-MS). The results show that nanoparticle concentrations per cell q

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18

Li, Xiufeng, Beiyuan Fan, Shanshan Cao, et al. "A microfluidic flow cytometer enabling absolute quantification of single-cell intracellular proteins." Lab on a Chip 17, no. 18 (2017): 3129–37. http://dx.doi.org/10.1039/c7lc00546f.

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19

NIKAIDO, Itoshi. "Comprehensive Quantification of Gene Expression Fluctuation by Single-cell RNA-seq." Seibutsu Butsuri 56, no. 6 (2016): 330–33. http://dx.doi.org/10.2142/biophys.56.330.

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20

Wang, Xiaolei, Yi Cui, and Joseph Irudayaraj. "Single-Cell Quantification of Cytosine Modifications by Hyperspectral Dark-Field Imaging." ACS Nano 9, no. 12 (2015): 11924–32. http://dx.doi.org/10.1021/acsnano.5b04451.

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21

Chen, Huanhuan, Xihong Xie, and Tai-Yen Chen. "Single-molecule microscopy for in-cell quantification of protein oligomeric stoichiometry." Current Opinion in Structural Biology 66 (February 2021): 112–18. http://dx.doi.org/10.1016/j.sbi.2020.10.022.

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22

Skylaki, Stavroula, Oliver Hilsenbeck, and Timm Schroeder. "Challenges in long-term imaging and quantification of single-cell dynamics." Nature Biotechnology 34, no. 11 (2016): 1137–44. http://dx.doi.org/10.1038/nbt.3713.

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23

Park, Seung-min, Jae Young Lee, Soongweon Hong, et al. "Dual transcript and protein quantification in a massive single cell array." Lab on a Chip 16, no. 19 (2016): 3682–88. http://dx.doi.org/10.1039/c6lc00762g.

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24

Yu, Tian, Chong Tang, Ying Zhang, Ruirui Zhang, and Wei Yan. "Microfluidics-based digital quantitative PCR for single-cell small RNA quantification†." Biology of Reproduction 97, no. 3 (2017): 490–96. http://dx.doi.org/10.1093/biolre/iox102.

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25

FUNATSU, Takashi. "Single-molecule Imaging and Quantification of mRNAs in a Living Cell." YAKUGAKU ZASSHI 129, no. 3 (2009): 265–72. http://dx.doi.org/10.1248/yakushi.129.265.

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26

Schroeder, Timm. "1025 - LONG-TERM SINGLE-CELL QUANTIFICATION: NEW TOOLS FOR OLD QUESTIONS." Experimental Hematology 76 (August 2019): S35. http://dx.doi.org/10.1016/j.exphem.2019.06.263.

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27

Moore, J. L., Z. Du, and Z. Bao. "Systematic quantification of developmental phenotypes at single-cell resolution during embryogenesis." Development 140, no. 15 (2013): 3266–74. http://dx.doi.org/10.1242/dev.096040.

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28

Kint, Sam, Wim Van Criekinge, Linos Vandekerckhove, et al. "Single cell epigenetic visualization assay." Nucleic Acids Research 49, no. 8 (2021): e43-e43. http://dx.doi.org/10.1093/nar/gkab009.

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Abstract Characterization of the epigenetic status of individual cells remains a challenge. Current sequencing approaches have limited coverage, and it is difficult to assign an epigenetic status to the transcription state of individual gene alleles in the same cell. To address these limitations, a targeted microscopy-based epigenetic visualization assay (EVA) was developed for detection and quantification of epigenetic marks at genes of interest in single cells. The assay is based on an in situ biochemical reaction between an antibody-conjugated alkaline phosphatase bound to the epigenetic ma

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29

Di Caprio, Fabrizio, Francesca Pagnanelli, Rene H. Wijffels, and Douwe Van der Veen. "Quantification ofTetradesmus obliquus(Chlorophyceae) cell size and lipid content heterogeneity at single-cell level." Journal of Phycology 54, no. 2 (2018): 187–97. http://dx.doi.org/10.1111/jpy.12610.

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30

Cui, Xiaonan, Lihui Ren, Yufei Shan, et al. "Smartphone-based rapid quantification of viable bacteria by single-cell microdroplet turbidity imaging." Analyst 143, no. 14 (2018): 3309–16. http://dx.doi.org/10.1039/c8an00456k.

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31

Shiga, Mikio, Shigeto Seno, Makoto Onizuka, and Hideo Matsuda. "SC-JNMF: single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization." PeerJ 9 (August 27, 2021): e12087. http://dx.doi.org/10.7717/peerj.12087.

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Single-cell RNA-sequencing is a rapidly evolving technology that enables us to understand biological processes at unprecedented resolution. Single-cell expression analysis requires a complex data processing pipeline, and the pipeline is divided into two main parts: The quantification part, which converts the sequence information into gene-cell matrix data; the analysis part, which analyzes the matrix data using statistics and/or machine learning techniques. In the analysis part, unsupervised cell clustering plays an important role in identifying cell types and discovering cell diversity and su

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32

Winkelmeier, Petra, Bernd Glauner, and Toni Lindl. "Quantification of Cytotoxicity by Cell Volume and Cell Proliferation." Alternatives to Laboratory Animals 21, no. 2 (1993): 269–80. http://dx.doi.org/10.1177/026119299302100222.

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A fast and sensitive method for the quantification of cytotoxicity, using the cell counter and analyser system, CASY 1, was established. This system has a high resolution and a large dynamic range of volume determination, permitting the volume changes caused by cytotoxic effects to be measured in a reproducible and standardised manner. As a first approach, eight cytotoxic compounds with different modes of action were investigated. For seven of the compounds, changes in the mean cell volume could be demonstrated after three hours. All eight compounds showed a dramatic decrease in mean cell volu

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33

Qiu, Ping, George J. Soder, Vincent J. Sanfiorenzo, et al. "Quantification of single nucleotide polymorphisms by automated DNA sequencing." Biochemical and Biophysical Research Communications 309, no. 2 (2003): 331–38. http://dx.doi.org/10.1016/j.bbrc.2003.08.008.

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34

Xie, Hui, Munan Yin, Weibin Rong, and Lining Sun. "In Situ Quantification of Living Cell Adhesion Forces: Single Cell Force Spectroscopy with a Nanotweezer." Langmuir 30, no. 10 (2014): 2952–59. http://dx.doi.org/10.1021/la500045q.

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35

Francis, Andrew T., Melanie J. Shears, Sean C. Murphy, and Dan Fu. "Direct Quantification of Single Red Blood Cell Hemoglobin Concentration with Multiphoton Microscopy." Analytical Chemistry 92, no. 18 (2020): 12235–41. http://dx.doi.org/10.1021/acs.analchem.0c01609.

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36

Jin, Xin, Xuejun Guo, Deshu Xu, Yanna Zhao, Xinghui Xia, and Fan Bai. "Single-Cell Real-Time Visualization and Quantification of Perylene Bioaccumulation in Microorganisms." Environmental Science & Technology 51, no. 11 (2017): 6211–19. http://dx.doi.org/10.1021/acs.est.7b02070.

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37

Komatsubara, Akira T., Yuhei Goto, Yohei Kondo, Michiyuki Matsuda, and Kazuhiro Aoki. "Single-cell quantification of the concentrations and dissociation constants of endogenous proteins." Journal of Biological Chemistry 294, no. 15 (2019): 6062–72. http://dx.doi.org/10.1074/jbc.ra119.007685.

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38

Sawtell, N. M. "Comprehensive quantification of herpes simplex virus latency at the single-cell level." Journal of virology 71, no. 7 (1997): 5423–31. http://dx.doi.org/10.1128/jvi.71.7.5423-5431.1997.

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39

Distelmaier, Felix, Werner J. H. Koopman, Epifania R. Testa, et al. "Life cell quantification of mitochondrial membrane potential at the single organelle level." Cytometry Part A 73A, no. 2 (2008): 129–38. http://dx.doi.org/10.1002/cyto.a.20503.

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40

Tyrpak, David R., Yaocun Li, Siqi Lei, Hugo Avila, and John Andrew MacKay. "Single-Cell Quantification of the Transition Temperature of Intracellular Elastin-like Polypeptides." ACS Biomaterials Science & Engineering 7, no. 2 (2021): 428–40. http://dx.doi.org/10.1021/acsbiomaterials.0c01117.

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41

Zheng, Ling-Na, Liu-Xing Feng, Jun-Wen Shi, et al. "Single-Cell Isotope Dilution Analysis with LA–ICP–MS: A New Approach for Quantification of Nanoparticles in Single Cells." Analytical Chemistry 92, no. 21 (2020): 14339–45. http://dx.doi.org/10.1021/acs.analchem.0c01775.

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42

Zucha, Daniel, Peter Androvic, Mikael Kubista, and Lukas Valihrach. "Performance Comparison of Reverse Transcriptases for Single-Cell Studies." Clinical Chemistry 66, no. 1 (2019): 217–28. http://dx.doi.org/10.1373/clinchem.2019.307835.

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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

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43

Park, Min Cheol, Jae Young Hur, Hye Sung Cho, Sang-Hyun Park, and Kahp Y. Suh. "High-throughput single-cell quantification using simple microwell-based cell docking and programmable time-course live-cell imaging." Lab Chip 11, no. 1 (2011): 79–86. http://dx.doi.org/10.1039/c0lc00114g.

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44

Soneson, Charlotte, Avi Srivastava, Rob Patro, and Michael B. Stadler. "Preprocessing choices affect RNA velocity results for droplet scRNA-seq data." PLOS Computational Biology 17, no. 1 (2021): e1008585. http://dx.doi.org/10.1371/journal.pcbi.1008585.

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Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a ‘direction of change’ and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on

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45

Matula, Petr, Anil Kumar, Ilka Wörz, et al. "Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection." Cytometry Part A 75A, no. 4 (2009): 309–18. http://dx.doi.org/10.1002/cyto.a.20662.

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46

Geng, Jiefei, and Daniel J. Klionsky. "Direct quantification of autophagic flux by a single molecule-based probe." Autophagy 13, no. 4 (2017): 639–41. http://dx.doi.org/10.1080/15548627.2017.1280646.

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47

Kim, Seon, Ying Hsu, and Andreas Linninger. "Interpretation of Cellular Imaging and AQP4 Quantification Data in a Single Cell Simulator." Processes 2, no. 1 (2014): 218–37. http://dx.doi.org/10.3390/pr2010218.

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48

Patel, Tapan P., Karen Man, Bonnie L. Firestein, and David F. Meaney. "Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging." Journal of Neuroscience Methods 243 (March 2015): 26–38. http://dx.doi.org/10.1016/j.jneumeth.2015.01.020.

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49

Hilsenbeck, Oliver, Michael Schwarzfischer, Stavroula Skylaki, et al. "Software tools for single-cell tracking and quantification of cellular and molecular properties." Nature Biotechnology 34, no. 7 (2016): 703–6. http://dx.doi.org/10.1038/nbt.3626.

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50

Koo, Hyunseo, Ikbum Park, Yoonhee Lee, et al. "Visualization and Quantification of MicroRNA in a Single Cell Using Atomic Force Microscopy." Journal of the American Chemical Society 138, no. 36 (2016): 11664–71. http://dx.doi.org/10.1021/jacs.6b05048.

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