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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

17.5K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Related Experiment Video

Updated: Apr 19, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations
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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis.

Assieh Saadatpour, Guoji Guo, Stuart H Orkin

    Genome Biology
    |December 18, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Single-cell analysis reveals significant heterogeneity in acute myeloid leukemia cells, identifying distinct subtypes with different functional properties. This deepens our understanding of leukemia's complex cellular landscape.

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    Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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    Area of Science:

    • Hematology
    • Cancer Biology
    • Genomics

    Background:

    • Cancer therapy faces challenges due to tumor cell heterogeneity.
    • Single-cell gene expression profiling offers new ways to study this heterogeneity.

    Purpose of the Study:

    • To characterize heterogeneity within leukemic cells.
    • To dissect the intra-cancer cellular hierarchy.

    Main Methods:

    • Single-cell analysis of MLL-AF9 driven mouse model of acute myeloid leukemia.
    • Fluorescence-activated cell sorting and multiplex quantitative PCR for gene expression.
    • Computational tools for data analysis and network analysis.

    Main Results:

    • Identified striking heterogeneity within leukemic cells.
    • Discovered two distinct leukemic cell subtypes, resembling normal hematopoietic progenitors and macrophage/dendritic cells.
    • Observed differences in proliferation rates and clonal phenotypes between subtypes.
    • Found similarities and organizational differences in co-expression networks compared to normal cells.

    Conclusions:

    • Single-cell analysis revealed previously uncharacterized heterogeneity in leukemic cells.
    • Provided new molecular insights into acute myeloid leukemia.
    • Highlighted distinct cellular subtypes within leukemia.