Single-cell isolation techniques such as flow sorting 5, 6, optical tweezers 7, embedment in bulk gels 8, and microfluidics 9 are capable of processing hundreds of cells for sequencing however, this represents only a small fraction of most samples. For these reasons, limitations in experimental throughput have hindered the effectiveness of single-cell studies, biasing results towards the most abundant cells.
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A primary challenge in studying genomes at the single-cell level is the low quantities of DNA available, as well as the need to analyze thousands of cells to sample the full diversity of genotypes. In bacterial infections, pathogenicity islands present in a small fraction of genomes can be horizontally transferred and lead to the proliferation of antibiotic-resistant bacteria 3, 4. For example, gene copy number variations among tumor cells are linked to the evolution and spread of cancer 1, 2. This heterogeneity is apparent in biological systems and has broad implications for human health and disease. An understanding of cellular biology at the genome level can explain the observed phenotypic diversity within heterogeneous cell populations. The genome serves as a blueprint of cellular identity and function, containing the entirety of an organism's coding potential. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing >50,000 single cells to be sequenced per run. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems.
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