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Single cell ChIP-Seq (chromatin immunoprecipitation followed by sequencing) is a method for mapping histone modifications, transcription factors, and other protein-DNA interactions in a single cell genome-wide.

A limitation of chromatin mapping technologies is that they require large amounts of input material and yield 'average' profiles insensitive to cellular heterogeneity. This is a significant shortcoming, given that cell-to-cell variability is inherent to most tissues and cell populations.

Single cell ChIP-Seq (scChIP-seq)

To study the intra-tumor heterogeneity of chromatin states, scientists developed a high-throughput scChIP-seq approach that combines droplet microfluidics with single cell DNA barcoding technologies. This scChIP-seq approach enables the segmentation of cell populations solely based on their chromatin landscape and the identification of key chromatin features of each subpopulation.

Overview of the microfluidic scChIP-seq workflow.Fig.1 Overview of the microfluidic scChIP-seq workflow. (Grosselin 2019)

scChIP-seq Workflow

The single cell ChIP-seq procedure is divided into 3 workflows including a droplet-microfluidic workflow, a Chip-seq workflow, and an analytical workflow. Fig.2 The single cell ChIP-seq procedure is divided into 3 workflows including a droplet-microfluidic workflow, a Chip-seq workflow, and an analytical workflow. (Rotem, 2015)

Published Data

Paper Title Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state
Journal Nature Biotechnology
IF 54.908
Published 2019
Abstract A flexible way to research functional genomic components and their control is through chromatin profiling. However, the ensemble profiles produced by the present techniques are not sensitive to cell-to-cell variance. Here, authors integrate sequencing, DNA barcoding, and microfluidics to gather chromatin data at the single-cell level. By testing tens of thousands of individual cells, they show how useful the technology is by using the information to create high-quality chromatin state maps for each cell type from a mixture of fibroblasts, hematopoietic progenitors, and embryonic stem cells (ES cells). Each cell only contains a small amount of data, roughly 1,000 distinct reads. Nevertheless, by analyzing a large number of ES cells, they distinguish a variety of subpopulations based on variations in the chromatin signatures of pluripotency and differentiation priming. By comparing these results to orthogonal single-cell gene expression data, they confirm these findings. Single-cell analysis using new techniques shows the epigenetic heterogeneity of transcriptional analysis.
Results They determined the number of reads that overlapped each signature for each individual ES cell (or mouse embryonic fibroblasts, MEFs), resulting in a matrix of 5,405 single cells with 91 signatures. The signature matrix's aggregative hierarchical clustering identified several notable cell populations with correlated chromatin landscapes. All MEFs were separated from ES cells, which were dispersed throughout various clusters, by the major division. They created multi-dimensional scaling (MDS) plots from the signature matrix to show the relationship between cells. The MEFs' tighter distribution is indicative of H3K4me2 landscapes that are more consistent. In contrast, the MDS plot shows that the individual ES cells cover a much larger area, dividing into three loose groups. The observation that such lineage-committed cells adopt a relatively constrained chromatin state may explain the tighter distribution among specific MEFs. In contrast, the accessible and malleable state of the chromatin.

A spectrum of ES cell sub-populations with variable chromatin signatures for pluripotency and priming.Fig.3 A spectrum of ES cell sub-populations with variable chromatin signatures for pluripotency and priming. (Rotem, 2015)

At Creative Biolabs, we offer scChIP-seq for a better interpretation of the intra-tumor heterogeneity of chromatin states. If you have any requirements, please feel free to contact us for further communication about your project.

References

  1. Groesslin, K.; et al. High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nature Genetics. 2019, 51: 1060-1066.
  2. Rotem, A.; et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nature Biotechnology. 2015, 33: 1165-1172.
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