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Single-Cell Analysis in Cancer
Published in Inna Kuperstein, Emmanuel Barillot, Computational Systems Biology Approaches in Cancer Research, 2019
Inna Kuperstein, Emmanuel Barillot
With the development of single-cell sequencing techniques in recent years, it is now possible to gain unprecedented insights into the differentiation dynamics from stem cells to mature cell types and resolve cellular heterogeneity of mature cell types, as was shown for the intestine,5 hippocampus6 or hematopoietic compartment.7 Cells from a system of interest are dissociated into single-cell suspensions in most single-cell sequencing protocols and RNA, DNA and/or proteins are barcoded on a single-cell basis using DNA oligonucleotides.8 Although these single-cell suspension approaches currently offer more options for downstream processing, they come with the downside of erasing spatial information and capturing cell states only at one particular time point. Nevertheless, various computational methods can be used to infer differentiation dynamics and trajectories using pseudo-temporal ordering of these snapshot data, given that enough intermediate states have been sampled during data acquisition. Many of the existing computational methods make use of dimensional reduction approaches such as principal component analysis (PCA), classical multidimensional scaling or diffusion maps to reduce complexity prior to differentiation topology inference. Some of these methods are tree-based while others are graph-based.9,10 Another group of lineage inference methods, comprising tools such as STEMNET11 and FateID,2 uses a probabilistic approach to classify every cell in a dataset according to its probability to differentiate towards a mature cell state without using cluster partition information. In the following section we are going to present the algorithms StemID for lineage inference and stem cell identification and FateID for fate bias inference in progenitor populations.
Two modified density gradient centrifugation methods facilitate the isolation of mouse Leydig cells
Published in Preparative Biochemistry & Biotechnology, 2023
Jiayang Jiang, Xiaoman Zhou, Chunliu Gao, Rongqin Ke, Qiwei Guo
In the present study, approximately 108 interstitial cells were obtained from two testes of a mouse and 1.7 × 105, 3.9 × 105, and 11.9 × 105 LCs were extracted using the regular method, modified method 1, and modified method 2, respectively. When compared to those of previous studies based on continuous and discontinuous Percoll gradients, the purities of the regular method and modified method 1 were comparatively satisfactory, but the yields of our methods were lower. Two reasons could be attributed to this finding. First, the volume of the LC-concentrated solution collected from Percoll gradients was relatively small (e.g. 0.5 mL). Secondly, the wash steps in our methods could decrease the yields.[11] Nevertheless, the yields of LCs in the present study were sufficient for further analysis with most molecular technologies, including single-cell sequencing,[25] assay for transposase-accessible chromatin using sequencing,[26] and chromatin immunoprecipitation assay.[27]
Pathogen contamination of groundwater systems and health risks
Published in Critical Reviews in Environmental Science and Technology, 2023
Yiran Dong, Zhou Jiang, Yidan Hu, Yongguang Jiang, Lei Tong, Ying Yu, Jianmei Cheng, Yu He, Jianbo Shi, Yanxin Wang
Fast scientific and technological advances make it possible to overcome the technical hurdles in groundwater pathogen surveillance and analyses. For example, the widely applied paper-based test kits during COVID-19 pandemic are exemplary for pathogen detection in an easy and economical manner. Although in their infancy, emerging synthetic biology enables genetical modulation of cells with desired functionalities to construct pathogen-responsive biosensors (Cesewski & Johnson, 2020). Meanwhile, robotics and laboratory automation can improve scalability, safety and reproducibility over traditional surveillance strategies (Ko et al., 2022). Multi-omic techniques, including single-cell sequencing, will facilitate discovery and understandings of novel pathogens and their etiological mechanisms (Carr & Chaguza, 2021; Avital et al., 2022). When portable sequencing is coupled with appropriate apps, high-throughput genomic analyses will facilitate pathogen detection in areas with limited healthcare facilities and encourage broader participation of citizen scientists (Palatnick et al., 2020; Kovaka et al., 2021). In addition, the rapid development of information technologies such as artificial intelligence (AI) can translate big data to predict the effect of climate change on pathogen emergence (Lake & Barker, 2018). Groundwater biosafety-oriented dashboard and databases will provide real-time, reliable and accessible information about emerging outbreaks and the explosive growth of multi-omic data for the public, policymakers, scientists, and healthcare professionals worldwide. These multidisciplinary technologies will significantly enhance the efficacy of surveillance and fundamental studies on groundwater pathogens, which will be instructive for diagnosis and mitigation of the related contamination and diseases.