Many biological experiments are performed on groups of cells, under the assumption that all cells of a particular “type” are identical. However, recent evidence from studies of single cells reveals that this assumption is incorrect. Individual cells within the same population may differ dramatically, and these differences can have important consequences for the health and function of the entire population.
Single Cell Analysis Program Data Portal Launched!
Single cell transcriptomic protocols and data generated by the Single Cell Analysis Program-Transcriptome (SCAP-T) project can be accessed via the data portal or dbGaP study page. These resources include phenotypic information and next generation sequencing data of the whole transcriptome for nearly 700 single cells from the human brain and heart.
Phase 2 of the Follow that Cell Challenge is underway and is only open to Phase 1 finalists. During Phase 2, Reduction to Practice, finalists will generate proof-of-concept data related to their Phase 1 entries. Submissions are due March 30, 2017.
Phase 2 Winner(s) Announced: July 31, 2017
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NEW! Resolving dense gene expression profiling using corrFISH
Messenger RNAs (mRNAs) are key biological intermediates in translating the genetic code and therefore can be used to measure gene expression. Single cell biology to follow gene expression in individual cells, in their native environment, is important for the advancement of several fields of biomedical research. Although single molecule fluorescence in situ hybridization (smFISH) methods allow for the detection of a large number of mRNAs directly in cells or tissues at a single cell level, challenges can arise in resolving the barcodes due to the density of the spots and high background. To address this challenge, Single Cell Analysis researcher Dr. Long Cai, Assistant Professor at the California Institute of Technology, developed the correlation FISH (corrFISH) method. With corrFISH, the Cai group was able to quantify gene expression and spatial location in single cultured cells and mouse thymus sections. Read the full story here.
NEW! Single cell analysis reveals neuronal subtypes of the human brain
Although neuronal diversity is fundamental to human brain function, it is still not known how many different types of neurons there are in the brain. This lack of knowledge has impeded our understanding of how the brain functions in health and human disease. Toward understanding brain neuron diversity, Single Cell Analysis Program researcher Dr. Kun Zhang and collaborators examined the gene expression profiles of individual neurons, isolated from post mortem human brain tissue, using single cell RNA sequencing. The authors report that their robust and scalable method lays the “groundwork for high-throughput global human brain transcriptome mapping”. Read the full story here. More news: Single-Cell RNA Sequencing Reveals Neuronal Diversity.
Keeping Genomic Elements in the Picture
The detection of genomic interactions in single living cells remains a major challenge for research scientists. To address this challenge, Single Cell researcher Dr. Bo Huang and collaborators have further developed CRISPR-Cas9 imaging technology to label genomic elements for microscopy detection. Read the full story here.
Single-Cell Analysis: Powerful Drops in the Bucket
Single Cell researcher Marc Kirschner was featured on the NIH Director's Blog discussing new single cell analysis technology called inDrop. inDrop is capable of analyzing very small tissue samples while capturing a greater percentage of cells than other technology. Kirschner and colleagues used inDrop to analyze thousands of differentiated and embryonic stem cells from mice. More news: Harvard Groups Tap Microfluidics for Single-Cell RNA-Seq Methods