Microscopic imaging of live cells is routine in biology labs. Quantitative measurement requires precise recognition, labeling and tracking of each individual cell. This segmentation process is often quite challenging since images vary greatly in their features and qualities. Current segmentation methods rely on single boundary features, and are thus hardly applicable to different situations and shared between labs. In this issue, Ren et al. develop a robust and accurate cell [Detail] ...
We discuss the feasibility of using a nanopore sandwich device to implement the principle of kinetic proofreading to discriminate incorrect hybridizing oligonucleotides on a target DNA or RNA. We propose a method of sequencing DNA or RNA using this approach. The design parameters for such a DNA sequencer are estimated from the Hopfield-Ninio theory of kinetic proofreading and Schrödinger’s first-passage-time distribution function.
Background: Phase transition and phase separation as well as their tipping points are penetrating phenomena in biology and are intrinsic properties of biological systems ranging from basic molecule complexes to cells and all way up to entire ecosystems.
Results: For example, phase separation has been established as a key mechanism for biological molecules such as protein or RNA to form membraneless organelles to perform complex biological functions. Phase transitions are commonly observed during cellular differentiation, and generally, there are the tipping points or critical states just before the phase transitions. And the stability of ecosystem and extinction of species are systematic manifestation of phase transitions. All phase transition and phase separation phenomena display switch-like behavior and critical transitions.
Conclusion: Here we summarize the concepts regarding the epithelial-to-mesenchymal transition (EMT) as a type of phase changes and the implication of critical transitions in EMT, and discuss open questions and challenges in this fast-moving field.
Background: The single-molecular sequencing (SMS) is under rapid development and generating increasingly long and accurate sequences. De novo assembly of genomes from SMS sequences is a critical step for many genomic studies. To scale well with the developing trends of SMS, many de novo assemblers for SMS have been released. These assembly workflows can be categorized into two different kinds: the correction-and-assembly strategy and the assembly-and-correction strategy, both of which are gaining more and more attentions.
Results: In this article we make a discussion on the characteristics of errors in SMS sequences. We then review the currently widely applied de novo assemblers for SMS sequences. We also describe computational methods relevant to de novo assembly, including the alignment methods and the error correction methods. Benchmarks are provided to analyze their performance on different datasets and to provide use guides on applying the computation methods.
Conclusion: We make a detailed review on the latest development of de novo assembly and some relevant algorithms for SMS, including their rationales, solutions and results. Besides, we provide use guides on the algorithms based on their benchmark results. Finally we conclude the review by giving some developing trends of third generation sequencing (TGS).
Background: Immune evasion is a fundamental hallmark for cancer. At the early stages of tumor development, immune evasion strategies must be implemented by tumors to prevent attacks from the host immune systems. Blocking tumors’ immune evasion will re-activate the host immune systems to eliminate tumors. Immune-checkpoint therapy (ICT) which applies anti-PD-1/PD-L1 or anti-CTLA4 treatment has been a remarkable success in the past few years. However, ~70% of patients cannot gain any clinical benefits from ICT treatment due to the tumor-immunity system’s complexity. In the past, germline pathogenic variants have been thought to have only minor-heritable contributions to cancer.
Results: Emerging evidence has shown that germline genomes play a dominant-heritable contribution to cancer via encoding the host immune system. The functional components of the immune system are encoded by the host genome, thus the germline genome might have a profound impact on cancer immune evasion and immunotherapy response. Indeed, recent studies showed that germline pathogenic variants can influence immune capacity in cancer patients at a population level by (i) shaping tumor somatic mutations, altering methylation patterns and antigen-presentation capacity or (ii) influencing NK cell’s function to modulate lymphocyte infiltration in the tumor microenvironment. In addition, the HLA (types A, B or C) genotypes also shape the landscape of tumor somatic mutations.
Conclusion: These results highlight the indispensable roles of germline genome in immunity and cancer development and suggest that germline genomics should be integrated into the research field of cancer biology and cancer immunotherapy.
Background: ATP is the major energy source for myotube contraction, and is quickly produced to compensate ATP consumption and to maintain sufficient ATP level. ATP is consumed mainly in cytoplasm and produced in mitochondria during myotube contraction. To understand the mechanism of ATP homeostasis during myotube contraction, it is essential to monitor mitochondrial ATP at single-cell level, and examine how ATP is produced and consumed in mitochondria.
Methods: We established C2C12 cell line stably expressing fluorescent probe of mitochondrial ATP, and induced differentiation into myotubes. We gave electric pulse stimulation to the differentiated myotubes, and measured mitochondrial ATP. We constructed mathematical model of mitochondrial ATP at single-cell level, and analyzed kinetic parameters of ATP production and consumption.
Results: We performed hierarchical clustering analysis of time course of mitochondrial ATP, which resulted in two clusters. Cluster 1 showed strong transient increase, whereas cluster 2 showed weak transient increase. Mathematical modeling at single-cell level revealed that the ATP production rate of cluster 1 was larger than that of cluster 2, and that both regulatory pathways of ATP production and consumption of cluster 1 were faster than those of cluster 2. Cluster 1 showed larger mitochondrial mass than cluster 2, suggesting that cluster 1 shows the similar property of slow muscle fibers, and cluster 2 shows the similar property of fast muscle fibers.
Conclusion: Cluster 1 showed the stronger mitochondrial ATP increase by larger ATP production rate, but not smaller consumption. Cluster 1 might reflect the larger oxidative capacity of slow muscle fiber.
Background: Various models have been applied to predict the trend of the epidemic since the outbreak of COVID-19.
Methods: In this study, we designed a dynamic graph model, not for precisely predicting the number of infected cases, but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors.
Results: We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a city. We further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system, and external medical aids are critical to not only containing the further spread of the virus but also reducing fatality.
Conclusion: Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions. We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS-CoV-2 from spreading globally.
Background: Time-lapse live cell imaging of a growing cell population is routine in many biological investigations. A major challenge in imaging analysis is accurate segmentation, a process to define the boundaries of cells based on raw image data. Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging.
Methods: Combined with a multi-layer training set strategy, we developed a neural-network-based algorithm — Cellbow.
Results: Cellbow can achieve accurate and robust segmentation of cells in broad and general settings. It can also facilitate long-term tracking of cell growth and division. To facilitate the application of Cellbow, we provide a website on which one can online test the software, as well as an ImageJ plugin for the user to visualize the performance before software installation.
Conclusion: Cellbow is customizable and generalizable. It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed. For bright-field images, only a small set of sample images of the specific cell type from the user may be needed for training.
The complex pattern of cancer evolution poses a huge challenge to precision oncology. Longitudinal sequencing of tumor samples allows us to monitor the dynamics of mutations that occurred during this clonal evolution process. Here, we present a versatile toolbox, namely CELLO (Cancer EvoLution for LOngitudinal data), accompanied with a step-by-step tutorial, to exemplify how to profile, analyze and visualize the dynamic change of somatic mutational landscape using longitudinal genomic sequencing data. Moreover, we customize the hypermutation detection module in CELLO to adapt targeted-DNA and whole-transcriptome sequencing data, and verify the extensive applicability of CELLO in published longitudinal datasets from brain, bladder and breast cancers. The entire tutorial and reusable programs in MATLAB, R and docker versions are open access at https://github.com/WangLabHKUST/CELLO.
The Cistrome Data Browser (DB) at the website (cistrome.org/db) provides about 56,000 published human and mouse ChIP-seq, DNase-seq, and ATAC-seq chromatin profiles, which we have processed using uniform analysis and quality control pipelines. The Cistrome DB Toolkit at the website (dbtoolkit.cistrome.org) was developed to allow users to investigate fundamental questions using this data collection. In this tutorial, we describe how to use the Cistrome DB to search for publicly available chromatin profiles, to assess sample quality, to access peak results, to visualize signal intensities, to explore DNA sequence motifs, and to identify putative target genes. We also describe the use of the Toolkit module to seek the factors most likely to regulate a gene of interest, the factors that bind to a given genomic interval (enhancer, SNP, etc.), and samples that have significant peak overlaps with user-defined peak sets. This tutorial guides biomedical researchers in the use of Cistrome DB resources to rapidly obtain valuable insights into gene regulatory questions