Single-cell Profiling
Single-cell Profiling - Related Videos
10:20
Single-cell Profiling of Developing and Mature Retinal Neurons
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Cited by 13 •
2012
A method for the isolation of single retinal cells and subsequent amplification of their cDNAs is described. Single-cell transcriptomics reveals the degree of cellular heterogeneity present in a tissue and uncovers new marker genes for rare cell populations. The accompanying protocol can be adjusted to suit many different cell types.
08:23
Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes
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Cited by 13 •
2011
Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.
10:50
Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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Cited by 7 •
2017
We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.
07:31
Profiling Voltage-gated Potassium Channel mRNA Expression in Nigral Neurons using Single-cell RT-PCR Techniques
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Cited by 2 •
2011
Neurons are first characterized electrophysiologically. Then the cytoplasm from the recorded neuron is aspirated and subjected to reverse transcription-PCR analysis to detect the expression of mRNAs for neurotransmitter synthesis enzymes, ion channels, and receptors.
12:30
Glycan Profiling of Plant Cell Wall Polymers using Microarrays
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Cited by 26 •
2012
A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.
FAQs
Cell-specific barcodes preserve the connection between a molecular or functional measurement and the cell that produced it. After cells are isolated or partitioned, labeling assigns each measurement an identity that sequencing or imaging can retain. Computational analysis can then compare measurements across cells rather than combining them into one aggregate signal, enabling distinct cell states or populations to be recognized.
Bulk measurements summarize many cells in a shared signal, which can conceal differences among them. Single-cell profiling retains those differences, allowing analysis to distinguish cellular states and populations within the same sample. This matters when a bioengineered tissue, organoid, or therapy contains cells responding differently to their surroundings, because the distribution of responses may be more informative than an average.
Sequencing and imaging provide different routes for linking labeled measurements to individual cells. Sequencing records information from tagged molecular material, whereas imaging associates labeled features with their cellular sources. The choice depends on whether the experiment emphasizes nucleic acids, proteins, or functional features and on how cells are labeled and partitioned. Both approaches support cell-resolved comparisons.
A workflow begins by isolating or partitioning cells, followed by labeling nucleic acids or proteins with cell-specific barcodes. Sequencing or imaging then records the labeled measurements, and computational analysis organizes results by source cell. Researchers compare those cell-level measurements to identify populations and states relevant to the biological or engineered system under study.
Applications extend across engineered tissues, organoids, biomaterials, and cell-based therapies. In each setting, cell-resolved measurements can show how constituent cells respond to their local environment. That information helps connect the design of a material or construct with the cellular states it produces, making the method useful for studying complex engineered systems rather than only isolated cell populations.
Profiles can reveal cellular states, distinct populations, and differences in how cells respond to their surroundings. Those findings can guide design and optimization while supporting quality control. In regenerative and diagnostic systems, the resulting cell-level evidence can help evaluate performance and inform refinement of tissues, organoids, biomaterials, or cell-based therapies.