Single Cell Analysis

Single Cell Analysis

Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millio...

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HerausgeberIn:
Tseng, Fan-Gang
Sonstige:
Santra, Tuhin Subhra
Tseng, Fan-Gang
Year of Publication:2021
Language:English
Physical Description:1 electronic resource (254 p.)
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spelling Santra, Tuhin Subhra edt
Single Cell Analysis
Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
1 electronic resource (254 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits.
English
Research & information: general bicssc
Biology, life sciences bicssc
single-cell RNA sequencing
cholestatic liver injury
hepatocyte heterogeneity
inflammation
liver tissue repair
single cell mass cytometry
single cell proteomics
non-small cell lung cancer
three-dimensional tissue culture
snRNA-seq
RNA velocity
cluster analysis
cardiomyocytes
seurat
cell heterogeneity
sarcoma
single-cell analysis
total mRNA level
transcriptome size
proteomics
immunofluorescence
immunohistochemistry
protein
genome
biomedical applications
commercialization
protein characterization
conventional approaches
microfluidic technologies
single cell
infectious disease
pathophysiology
therapeutics
diagnostics
microfluidics
single-cell cloning
monoclonal cell lines
single-neuron models
mapping
electrophysiological recording
isolation
therapy
micro/nanofluidic devices
microelectrode array
transfection
artificial intelligence
localized high-risk prostate cancer
circulating tumor cells
three-dimensional (3-D) telomere profiling
laser microdissection
whole-exome genome sequencing
somatic single nucleotide variants
copy number alterations
precision medicine
3-0365-0628-4
3-0365-0629-2
Tseng, Fan-Gang edt
Santra, Tuhin Subhra oth
Tseng, Fan-Gang oth
language English
format eBook
author2 Tseng, Fan-Gang
Santra, Tuhin Subhra
Tseng, Fan-Gang
author_facet Tseng, Fan-Gang
Santra, Tuhin Subhra
Tseng, Fan-Gang
author2_variant t s s ts tss
f g t fgt
author2_role HerausgeberIn
Sonstige
Sonstige
title Single Cell Analysis
spellingShingle Single Cell Analysis
title_full Single Cell Analysis
title_fullStr Single Cell Analysis
title_full_unstemmed Single Cell Analysis
title_auth Single Cell Analysis
title_new Single Cell Analysis
title_sort single cell analysis
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2021
physical 1 electronic resource (254 p.)
isbn 3-0365-0628-4
3-0365-0629-2
illustrated Not Illustrated
work_keys_str_mv AT santratuhinsubhra singlecellanalysis
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status_str n
ids_txt_mv (CKB)5400000000042011
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carrierType_str_mv cr
is_hierarchy_title Single Cell Analysis
author2_original_writing_str_mv noLinkedField
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