scholarly journals Single-cell CRISPR screening in drug resistance

2017 ◽  
Vol 33 (3) ◽  
pp. 207-210 ◽  
Author(s):  
William Wang ◽  
Xiangdong Wang
Keyword(s):  
The Analyst ◽  
2018 ◽  
Vol 143 (1) ◽  
pp. 164-174 ◽  
Author(s):  
Yong Zhang ◽  
Ludi Jin ◽  
Jingjing Xu ◽  
Yuezhou Yu ◽  
Lin Shen ◽  
...  

Drug resistance and heterogeneous characteristics of human gastric carcinoma cells (BGC823) under the treatment of paclitaxel (PTX) were investigated using single-cell Raman spectroscopy (RS).


2018 ◽  
Author(s):  
Clare Rebbeck ◽  
Florian Raths ◽  
Bassem Ben Cheik ◽  
Kenneth Gouin ◽  
Gregory J. Hannon ◽  
...  

AbstractMolecular barcoding has provided means to link genotype to phenotype, to individuate cells in single-cell analyses, to enable the tracking of evolving lineages, and to facilitate the analysis of complex mixtures containing phenotypically distinct lineages. To date, all existing approaches enable retrospective associations to be made between characteristics and the lineage harbouring them, but provide no path toward isolating or manipulating those lineages within the complex mixture. Here, we describe a strategy for creating functionalized barcodes that enable straightforward manipulation of lineages within complex populations of cells, either marking and retrieval of selected lineages, or modification of their phenotype within the population, including their elimination. These “SmartCodes” rely on a simple CRISPR-based, molecular barcode reader that can switch measurable, or selectable markers, on or off in a binary fashion. While this approach could have broad impact, we envision initial approaches to the study of tumour heterogeneity, focused on issues of tumour progression, metastasis, and drug resistance.


2019 ◽  
Author(s):  
Ahmet Acar ◽  
Daniel Nichol ◽  
Javier Fernandez-Mateos ◽  
George D. Cresswell ◽  
Iros Barozzi ◽  
...  

AbstractDrug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called ‘evolutionary herding’ that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 108–109cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.


2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangtian Yu ◽  
XiaoYong Pan ◽  
ShiQi Zhang ◽  
Yu-Hang Zhang ◽  
Lei Chen ◽  
...  

Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.


2019 ◽  
Vol 10 (47) ◽  
pp. 10958-10962 ◽  
Author(s):  
Jing Han ◽  
Xi Huang ◽  
Huihui Liu ◽  
Jiyun Wang ◽  
Caiqiao Xiong ◽  
...  

A single-cell MS approach for multiplexed glycan detection to investigate the relationship between drug resistance and glycans at a single-cell level and quantify multiple glycans, overcoming the limit of low ionization efficiency of glycans.


2013 ◽  
Vol 4 (6) ◽  
pp. 556-559 ◽  
Author(s):  
Jean-Marie Pagès ◽  
Slavka Kascàkovà ◽  
Laure Maigre ◽  
Anas Allam ◽  
Mickael Alimi ◽  
...  

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