http://www.cell.com/article/S0092867421015774/pdf

Mapping transcriptomic vector fields of single cells

The Epithelial-mesenchymal transition (EMT) is a cellular process implicated in embryonic development, wound healing, and pathological conditions such as cancer metastasis and fibrosis. Cancer cells undergoing EMT exhibit enhanced aggressive behavior characterized by drug resistance, tumor-initiation potential, and the ability to evade the immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells can stably acquire one or more hybrid epithelial/mesenchymal (E/M) phenotypes which often can be more aggressive than purely E or M cell populations. Thus, the EMT status of cancer cells can prove to be a critical estimate of patient prognosis. Recent attempts have employed different transcriptomics signatures to quantify EMT status in cell lines and patient tumors. However, a comprehensive comparison of these methods, including their accuracy in identifying cells in the hybrid E/M phenotype(s), is lacking. Here, we compare three distinct metrics that score EMT on a continuum, based on the transcriptomics signature of individual samples. Our results demonstrate that these methods exhibit good concordance among themselves in quantifying the extent of EMT in a given sample. Moreover, scoring EMT using any of the three methods discerned that cells can undergo varying extents of EMT across tumor types. Separately, our analysis also identified tumor types with maximum variability in terms of EMT and associated an enrichment of hybrid E/M signatures in these samples. Moreover, we also found that the multinomial logistic regression (MLR)-based metric was capable of distinguishing between "pure" individual hybrid E/M vs. mixtures of E and M cells. Our results, thus, suggest that while any of the three methods can indicate a generic trend in the EMT status of a given cell, the MLR method has two additional advantages: (a) it uses a small number of predictors to calculate the EMT score and (b) it can predict from the transcriptomic signature of a population whether it is comprised of "pure" hybrid E/M cells at the single-cell level or is instead an ensemble of E and M cell subpopulations.

/pdf/comparative-study-of-transcriptomics-based-scoring-metrics-22ppy1ze9t.pdf

Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum

https://europepmc.org/articles/pmc6781873?pdf=render

Learn to segment single cells with deep distance estimator and deep cell detector.

A major focus of current biological studies is to fill the knowledge gaps between cell, tissue and organism scales. To this end, a wide array of contemporary optical analytical tools enable multiparameter quantitative imaging of live and fixed cells, three-dimensional (3D) systems, tissues, organs and organisms in the context of their complex spatiotemporal biological and molecular features. In particular, the modalities of luminescence lifetime imaging, comprising fluorescence lifetime imaging (FLI) and phosphorescence lifetime imaging microscopy (PLIM), in synergy with Forster resonance energy transfer (FRET) assays, provide a wealth of information. On the application side, the luminescence lifetime of endogenous molecules inside cells and tissues, overexpressed fluorescent protein fusion biosensor constructs or probes delivered externally provide molecular insights at multiple scales into protein-protein interaction networks, cellular metabolism, dynamics of molecular oxygen and hypoxia, physiologically important ions, and other physical and physiological parameters. Luminescence lifetime imaging offers a unique window into the physiological and structural environment of cells and tissues, enabling a new level of functional and molecular analysis in addition to providing 3D spatially resolved and longitudinal measurements that can range from microscopic to macroscopic scale. We provide an overview of luminescence lifetime imaging and summarize key biological applications from cells and tissues to organisms.

Luminescence lifetime imaging of three-dimensional biological objects.

Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.

Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology

Recent advances in single cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide genome-wide snapshots of cell status but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long- term imaging. We present a live-cell imaging platform, Multiplex Trajectory Recording and Analysis of Cellular Kinetics, or M-TRACK, that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology and live cell imaging of high-dimensional cell morphological and texture features. To test the functionality of our platform, we used the A549 VIM-RFP EMT reporter line (ATCC® CCL-185EMT) to explore cell phenotypic transition in live cells. Live cell trajectories reveal parallel paths of epithelial-to-mesenchymal transition despite presence of heterogeneous single cell dynamics, emphasizing the importance of live cell studies. This framework, together with an accompanying computational package, is generally applicable to cell phenotypic transition processes.

https://www.biorxiv.org/content/biorxiv/early/2019/12/13/2019.12.12.874248.full.pdf

M-TRACK: a platform for live cell multiplex imaging reveals cell phenotypic transition dynamics inherently missing in snapshot data

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing techniques find applications in many fields, such as molecular biology, cancer biology, and disease modeling. In contrast to the knock-out procedure, a key step of CRISPR knock-in experiments is the homology-directed repair process that requires donor constructs as repair templates. Therefore, it is desirable to generate a series of donor templates efficiently and cost-effectively. In this study, we developed a new strategy that combines (i) Gibson assembly reaction, (ii) a linker pair composed of eight in silico screened restriction enzyme sites, and (iii) a hierarchical framework, to remarkably improve the efficiency of producing donor constructs for common genes as well as for the genes containing unbalanced guanine-cytosine content and requiring a selectable marker. Furthermore, the approach provides the ability of inserting additional elements into the donor templates, such as single guide RNA recognition sites that have been reported to enhance the efficiency of homology-directed repair. Conclusively, our modularized process is simple, fast, and cost-effective for making donor constructs and benefits the application of CRISPR knock-in methods.

Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in.

Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide genome-wide snapshots of cell status but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long- term imaging. We first developed a live-cell imaging platform that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology, and/or live cell imaging of high-dimensional cell morphological and texture features. With our platform and an A549 VIM-RFP EMT reporter line, live cell trajectories reveal parallel paths of epithelial-to-mesenchymal transition missing from snapshot data due to cell-cell heterogeneity. Our results emphasize the necessity of extracting dynamical information of phenotypic transitions from multiplex live cell imaging.

Live cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data

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M-TRACK: a platform for live cell multiplex imaging reveals cell phenotypic transition dynamics inherently missing in snapshot data

Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in.

Live cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data