scholarly journals Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome

2016 ◽  
Author(s):  
Andrew E Teschendorff
Keyword(s):  
Stem Cell ◽  
Cancer Stem Cell ◽  
Single Cell ◽  
Time Course ◽  
Single Cells ◽  
Interaction Network ◽  
Cellular Interaction ◽  
Accurate Estimation ◽  
Differentiation Potential ◽  
Cell Phenotypes

AbstractThe ability to quantify differentiation potential of single cells is a task of critical importance for single-cell studies. So far however, there is no robust general molecular correlate of differentiation potential at the single cell level. Here we show that differentiation potency of a single cell can be approximated by computing the signaling promiscuity, or entropy, of a cell’s transcriptomic profile in the context of a cellular interaction network, without the need for model training or feature selection. We validate signaling entropy in over 7,000 single cell RNA-Seq profiles, representing all main differentiation stages, including time-course data. We develop a novel algorithm called Single Cell Entropy (SCENT), which correctly identifies known cell subpopulations of varying potency, enabling reconstruction of cell-lineage trajectories. By comparing bulk to single cell data, SCENT reveals that expression heterogeneity within single cell populations is regulated, pointing towards the importance of cell-cell interactions. In the context of cancer, SCENT can identify drug resistant cancer stem-cell phenotypes, including those obtained from circulating tumor cells. In summary, SCENT can directly estimate the differentiation potency and plasticity of single-cells, allowing unbiased quantification of intercellular heterogeneity, and providing a means to identify normal and cancer stem cell phenotypes.Software AvailabilitySCENT is freely available as an R-package from github: https://github.com/aet21/SCENT

scholarly journals Crucial stem cell experiments? Stem cells, uncertainty, and single-cell experiments

2015 ◽  
Vol 30 (2) ◽  
pp. 183
Author(s):  
Melinda Fagan
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Single Cells ◽  
Single Cell Level ◽  
Epistemic Circularity ◽  
Cell Level ◽  
Differentiation Potential ◽  
Self Renewal

I have previously argued that stem cell experiments cannot in principle demonstrate that a single cell is a stem cell ([reference omitted for anonymous review]).  Laplane and others dispute this claim, citing experiments that identify stem cells at the single-cell level.  This paper rebuts the counterexample, arguing that these alleged ‘crucial stem cell experiments’ do not measure self-renewal for a single cell, do not establish a single cell’s differentiation potential, and, if interpreted as providing results about single cells, fall into epistemic circularity.  I then examine the source of the dispute, noting differences in philosophical and experimental perspectives.

scholarly journals Colorectal Cancer Stem Cell States Uncovered by Simultaneous Single‐Cell Analysis of Transcriptome and Telomeres

2021 ◽  
Vol 8 (8) ◽  
pp. 2004320
Author(s):  
Hua Wang ◽  
Peng Gong ◽  
Tong Chen ◽  
Shan Gao ◽  
Zhenfeng Wu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Stem Cell ◽  
Cancer Stem Cell ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis

scholarly journals Forced expression of Nanog with mRNA synthesized in vitro to evaluate the malignancy of HeLa cells through acquiring cancer stem cell phenotypes

2016 ◽  
Vol 35 (5) ◽  
pp. 2643-2650 ◽  
Author(s):  
YAN DING ◽  
AI QING YU ◽  
XIAO LI WANG ◽  
XING RONG GUO ◽  
YA HONG YUAN ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cancer Stem Cell ◽  
Hela Cells ◽  
Cell Phenotypes

scholarly journals Transformative opportunities for single-cell proteomics

2018 ◽  
Author(s):  
Harrison Specht ◽  
Nikolai Slavov
Keyword(s):  
Mass Spectrometry ◽  
Stem Cell ◽  
Cell Differentiation ◽  
Single Cell ◽  
Single Cells ◽  
Biological Systems ◽  
Stem Cell Differentiation ◽  
New Age ◽  
Technological Developments

Many pressing medical challenges - such as diagnosing disease, enhancing directed stem cell differentiation, and classifying cancers - have long been hindered by limitations in our ability to quantify proteins in single cells. Mass-spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.

scholarly journals Microfluidic guillotine for single-cell wound repair studies

2017 ◽  
Vol 114 (28) ◽  
pp. 7283-7288 ◽  
Author(s):  
Lucas R. Blauch ◽  
Ya Gai ◽  
Jian Wei Khor ◽  
Pranidhi Sood ◽  
Wallace F. Marshall ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Wound Repair ◽  
Time Course ◽  
Single Cells ◽  
Repair Process ◽  
Gene Knockdown ◽  
Viscous Stress ◽  
Repair Capacity ◽  
Stentor Coeruleus

Wound repair is a key feature distinguishing living from nonliving matter. Single cells are increasingly recognized to be capable of healing wounds. The lack of reproducible, high-throughput wounding methods has hindered single-cell wound repair studies. This work describes a microfluidic guillotine for bisecting single Stentor coeruleus cells in a continuous-flow manner. Stentor is used as a model due to its robust repair capacity and the ability to perform gene knockdown in a high-throughput manner. Local cutting dynamics reveals two regimes under which cells are bisected, one at low viscous stress where cells are cut with small membrane ruptures and high viability and one at high viscous stress where cells are cut with extended membrane ruptures and decreased viability. A cutting throughput up to 64 cells per minute—more than 200 times faster than current methods—is achieved. The method allows the generation of more than 100 cells in a synchronized stage of their repair process. This capacity, combined with high-throughput gene knockdown in Stentor, enables time-course mechanistic studies impossible with current wounding methods.

scholarly journals Cancer Stem Cell Phenotypes in ER+ Breast Cancer Models Are Promoted by PELP1/AIB1 Complexes

2018 ◽  
Vol 16 (4) ◽  
pp. 707-719 ◽  
Author(s):  
Thu H. Truong ◽  
Hsiangyu Hu ◽  
Nuri A. Temiz ◽  
Kyla M. Hagen ◽  
Brian J. Girard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Stem Cell ◽  
Cancer Stem Cell ◽  
Cancer Models ◽  
Cell Phenotypes

scholarly journals A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

2020 ◽  
Vol 117 (31) ◽  
pp. 18412-18423 ◽  
Author(s):  
Chia-Chen Hsu ◽  
Jiabao Xu ◽  
Bas Brinkhof ◽  
Hui Wang ◽  
Zhanfeng Cui ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Raman Spectra ◽  
Large Scale ◽  
Developmental Stages ◽  
Single Cells ◽  
Classification Model ◽  
Label Free ◽  
Machine Learning Classification

Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.

Abstract 2705: A computational algorithm to predict tumor growth and cancer stem cell proportion in-vitro and in-vivo from single-cell observations

2016 ◽  
Author(s):  
Alexander T. Pearson ◽  
Patrick Ingram ◽  
Shoumei Bai ◽  
Euisik Yoon ◽  
Trachette Jackson ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cancer Stem Cell ◽  
Tumor Growth ◽  
Single Cell ◽  
Computational Algorithm ◽  
Cell Proportion
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