Tag: Single-Cell

MEC by Single-Cell RNA-seq

[PMID: 29225342] [Nature Communications]

Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing

“Here we report the use of single-cell RNA sequencing to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution. Our analysis of 23,184 cells identifies 15 clusters, few of which could be fully characterised by a single marker gene. We argue instead that the epithelial cells—especially in the luminal compartment—should rather be conceptualised as being part of a continuous spectrum of differentiation. Furthermore, our data support the existence of a common luminal progenitor cell giving rise to intermediate, restricted alveolar and hormone-sensing progenitors. This luminal progenitor compartment undergoes transcriptional changes in response to a full pregnancy, lactation and involution.”

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CNA Evolution from Primary to CTCs

[PMID: 28487279] [Genome Research]

Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells

“Here, we studied genomic alterations in single primary tumor cells and circulating tumor cells (CTCs) from the same patient. Single-nucleotide variants (SNVs) in single cells from both samples occurred sporadically, whereas CNAs among primary tumor cells emerged accumulatively rather than abruptly, converging toward the CNA in CTCs. Focal CNAs affecting the MYC gene and the PTEN gene were observed only in a minor portion of primary tumor cells but were present in all CTCs, suggesting a strong selection toward metastasis. Single-cell structural variant (SV) analyses revealed a two-step mechanism, a complex rearrangement followed by gene amplification, for the simultaneous formation of anomalous CNAs in multiple chromosome regions.”

ddClone

[PMID:28249593] [Genome Biology]

ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data

“Next-generation sequencing (NGS) of bulk tumour tissue can identify constituent cell populations in cancers and measure their abundance. This requires computational deconvolution of allelic counts from somatic mutations, which may be incapable of fully resolving the underlying population structure. Single cell sequencing (SCS) is a more direct method, although its replacement of NGS is impeded by technical noise and sampling limitations. We propose ddClone, which analytically integrates NGS and SCS data, leveraging their complementary attributes through joint statistical inference. We show on real and simulated datasets that ddClone produces more accurate results than can be achieved by either method alone.”