scholarly journals Corrigendum: Initiation of mtDNA transcription is followed by pausing, and diverges across human cell types and during evolution

2019 ◽  
Vol 29 (4) ◽  
pp. 710.1-710.1
Author(s):  
Amit Blumberg ◽  
Edward J. Rice ◽  
Anshul Kundaje ◽  
Charles G. Danko ◽  
Dan Mishmar
Keyword(s):  
Human Cell ◽  
Cell Types ◽  
Mtdna Transcription

scholarly journals Initiation of mtDNA transcription is followed by pausing, and diverges across human cell types and during evolution

2017 ◽  
Vol 27 (3) ◽  
pp. 362-373 ◽  
Author(s):  
Amit Blumberg ◽  
Edward J. Rice ◽  
Anshul Kundaje ◽  
Charles G. Danko ◽  
Dan Mishmar
Keyword(s):  
Human Cell ◽  
Cell Types ◽  
Mtdna Transcription

scholarly journals Initiation of mtDNA transcription is followed by pausing, and diverge across human cell types and during evolution

2016 ◽  
Author(s):  
Amit Blumberg ◽  
Edward J. Rice ◽  
Anshul Kundaje ◽  
Charles G. Danko ◽  
Dan Mishmar
Keyword(s):  
Cell Lines ◽  
Human Cell ◽  
Transcription Initiation ◽  
De Novo ◽  
Cell Types ◽  
Reference Sequence ◽  
Sequence Motif ◽  
Mtdna Sequence ◽  
Mtdna Transcription

AbstractMitochondrial DNA (mtDNA) genes are long known to be co-transcribed in polycistrones, yet it remains impossible to study nascent mtDNA transcripts quantitatively in vivo using existing tools. To this end we used deep sequencing (GRO-seq and PRO-seq) and analyzed nascent mtDNA-encoded RNA transcripts in diverse human cell lines and metazoan organisms. Surprisingly, accurate detection of human mtDNA transcription initiation sites (TIS) in the heavy and light strands revealed a novel conserved transcription pausing site near the light strand TIS, upstream to the transcription-replication transition region. This pausing site correlated with the presence of a bacterial pausing sequence motif, yet the transcription pausing index varied quantitatively among the cell lines. Analysis of non-human organisms enabled de novo mtDNA sequence assembly, as well as detection of previously unknown mtDNA TIS, pausing, and transcription termination sites with unprecedented accuracy. Whereas mammals (chimpanzee, rhesus macaque, rat, and mouse) showed a human-like mtDNA transcription pattern, the invertebrate pattern (Drosophila and C. elegans) profoundly diverged. Our approach paves the path towards in vivo, quantitative, reference sequence-free analysis of mtDNA transcription in all eukaryotes.

scholarly journals Profiling Bioactivity of the ToxCast Chemical Library Using BioMAP Primary Human Cell Systems

2009 ◽  
Vol 14 (9) ◽  
pp. 1054-1066 ◽  
Author(s):  
Keith A. Houck ◽  
David J. Dix ◽  
Richard S. Judson ◽  
Robert J. Kavlock ◽  
Jian Yang ◽  
...  
Keyword(s):  
Human Cell ◽  
Regulatory Networks ◽  
Cell Types ◽  
Environmental Chemicals ◽  
Data Sets ◽  
Chemical Library ◽  
Data Set ◽  
Cell Systems ◽  
Disease Biology ◽  
Nfκb Pathway

The complexity of human biology has made prediction of health effects as a consequence of exposure to environmental chemicals especially challenging. Complex cell systems, such as the Biologically Multiplexed Activity Profiling (BioMAP) primary, human, cell-based disease models, leverage cellular regulatory networks to detect and distinguish chemicals with a broad range of target mechanisms and biological processes relevant to human toxicity. Here the authors use the BioMAP human cell systems to characterize effects relevant to human tissue and inflammatory disease biology following exposure to the 320 environmental chemicals in the Environmental Protection Agency’s (EPA’s) ToxCast phase I library. The ToxCast chemicals were assayed at 4 concentrations in 8 BioMAP cell systems, with a total of 87 assay endpoints resulting in more than 100,000 data points. Within the context of the BioMAP database, ToxCast compounds could be classified based on their ability to cause overt cytotoxicity in primary human cell types or according to toxicity mechanism class derived from comparisons to activity profiles of BioMAP reference compounds. ToxCast chemicals with similarity to inducers of mitochondrial dysfunction, cAMP elevators, inhibitors of tubulin function, inducers of endoplasmic reticulum stress, or NFκB pathway inhibitors were identified based on this BioMAP analysis. This data set is being combined with additional ToxCast data sets for development of predictive toxicity models at the EPA. ( Journal of Biomolecular Screening 2009:1054-1066)

Label-free RF biosensors for human cell dielectric spectroscopy

2009 ◽  
Vol 1 (6) ◽  
pp. 497-504 ◽  
Author(s):  
Claire Dalmay ◽  
Arnaud Pothier ◽  
Mathilde Cheray ◽  
Fabrice Lalloue ◽  
Marie-Odile Jauberteau ◽  
...  
Keyword(s):  
Human Cell ◽  
Cell Types ◽  
Label Free ◽  
Microwave Frequencies ◽  
Non Invasive ◽  
System Cell ◽  
Electromagnetic Characteristics ◽  
Biosensor Chip ◽  
Rf Signal

This paper presents an original biosensor chip allowing determination of intrinsic relative permittivity of biological cells at microwave frequencies. This sensor permits non-invasive cell identification and discrimination using an RF signal to probe intracellular medium of biological samples. Indeed, these sensors use an RF planar resonator that allows detection capabilities on less than 10 cells, thanks to the microscopic size of its sensitive area. Especially, measurements between 15 and 35 GHz show the ability label-free biosensors to differentiate two human cell types using their own electromagnetic characteristics. The real part of permittivity of cells changes from 20 to 48 for the nervous system cell types studied. The proposed biodetection method is detailed and we show how the accuracy and the repeatability of measurements have been improved to reach reproducible measurements.

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

Interactions between Neisseria meningitidis and human cells that promote colonisation and disease

2004 ◽  
Vol 6 (14) ◽  
pp. 1-14 ◽  
Author(s):  
Anne Corbett ◽  
Rachel Exley ◽  
Sandrine Bourdoulous ◽  
Christoph M. Tang
Keyword(s):  
Cerebrospinal Fluid ◽  
Bacterial Meningitis ◽  
Neisseria Meningitidis ◽  
Human Cell ◽  
Molecular Basis ◽  
Cell Types ◽  
Cellular Level ◽  
Human Cells ◽  
Healthy Individuals ◽  
Host Pathogen

Neisseria meningitidis is the leading cause of bacterial meningitis, a potentially fatal condition that particularly affects children. Multiple steps are involved during the pathogenesis of infection, including the colonisation of healthy individuals and invasion of the bacterium into the cerebrospinal fluid. The bacterium is capable of adhering to, and entering into, a range of human cell types, which facilitates its ability to cause disease. This article summarises the molecular basis of host–pathogen interactions at the cellular level during meningococcal carriage and disease.

scholarly journals Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrick S. Stumpf ◽  
Xin Du ◽  
Haruka Imanishi ◽  
Yuya Kunisaki ◽  
Yuichiro Semba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transfer Learning ◽  
Biomedical Research ◽  
Human Cell ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

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