Single-cell multimodal profiling reveals cellular epigenetic heterogeneity.

Sample heterogeneity often masks DNA methylation signatures in subpopulations of cells. Here, we present a method to genotype single cells while simultaneously interrogating gene expression and DNA methylation at multiple loci. We used this targeted multimodal approach, implemented on an automated, high-throughput microfluidic platform, to assess primary lung adenocarcinomas and human fibroblasts undergoing reprogramming by profiling epigenetic variation among cell types identified through genotyping and transcriptional analysis.

Single-cell analysis of endometriosis reveals a coordinated transcriptional programme driving immunotolerance and angiogenesis across eutopic and ectopic tissues.

Endometriosis is characterized by the growth of endometrial-like tissue outside the uterus. It affects many women during their reproductive age, causing years of pelvic pain and potential infertility. Its pathophysiology remains largely unknown, which limits early diagnosis and treatment. We characterized peritoneal and ovarian lesions at single-cell transcriptome resolution and compared them to matched eutopic endometrium, unaffected endometrium and organoids derived from these tissues, generating data on over 122,000 cells across 14 individuals. We spatially localized many of the cell types using imaging mass cytometry. We identify a perivascular mural cell specific to the peritoneal lesions, with dual roles in angiogenesis promotion and immune cell trafficking. We define an immunotolerant peritoneal niche, fundamental differences in eutopic endometrium and between lesion microenvironments and an unreported progenitor-like epithelial cell subpopulation. Altogether, this study provides a holistic view of the endometriosis microenvironment that represents a comprehensive cell atlas of the disease in individuals undergoing hormonal treatment, providing essential information for future therapeutics and diagnostics.

Characterization of Mucosal Dysbiosis of Early Colonic Neoplasia.

Aberrant crypt foci (ACF) are the earliest morphologically identifiable lesions in the colon that can be detected by high-definition chromoendoscopy with contrast dye spray. Although frequently associated with synchronous adenomas, their role in colorectal tumor development, particularly in the proximal colon, is still not clear. The goal of this study was to evaluate the profile of colon-adherent bacteria associated with proximal ACF and to investigate their relationship to the presence and subtype of synchronous polyps present throughout the colon. Forty-five subjects undergoing a screening or surveillance colonoscopy were included in this retrospective study. Bacterial cells adherent to the epithelia of ACF and normal mucosal biopsies were visualized by in situ hybridization within confocal tissue sections. ACF showed significantly greater heterogeneity in their bacterial microbiome profiles compared with normal mucosa. One of the bacterial community structures we characterized was strongly correlated with the presence of synchronous polyps. Finally, using DNA mass spectrometry to evaluate a panel of colorectal cancer hotspot mutations present in the ACF, we found that three APC gene mutations were positively associated with the presence of Instestinibacter sp., whereas KRAS mutations were positively correlated with Ruminococcus gnavus. This result indicates a potential relationship between specific colon-associated bacterial species and somatically acquired CRC-related mutations. Overall, our findings suggest that perturbations to the normal adherent mucosal flora may constitute a risk factor for early neoplasia, demonstrating the potential impact of mucosal dysbiosis on the tissue microenvironment and behavior of ACF that may facilitate their progression towards more advanced forms of neoplasia.

Author Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.

In the version of the article published, the author list is not accurate. Igor Cima and Min-Han Tan should have been authors, appearing after Mark Wong in the author list, while Paul Jongjoon Choi should not have been listed as an author. Igor Cima and Min-Han Tan both have the affiliation Institute of Bioengineering and Nanotechnology, Singapore, Singapore, and their contributions should have been noted in the Author Contributions section as "I.C. preprocessed Primary Cell Atlas data with inputs from M.-H.T." The following description of the contribution of Paul Jongjoon Choi should not have appeared: "P.J.C. supported the smFISH experiments." In the 'RCA: global panel' section of the Online Methods, the following sentence should have appeared as the second sentence, "An expression atlas of human primary cells (the Primary Cell Atlas) was preprocessed similarly to in ref. 55," with new reference 55 (Cima, I. et al. Tumor-derived circulating endothelial cell clusters in colorectal cancer. Science Transl. Med. 8, 345ra89, 2016).

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.

Intratumoral heterogeneity is a major obstacle to cancer treatment and a significant confounding factor in bulk-tumor profiling. We performed an unbiased analysis of transcriptional heterogeneity in colorectal tumors and their microenvironments using single-cell RNA-seq from 11 primary colorectal tumors and matched normal mucosa. To robustly cluster single-cell transcriptomes, we developed reference component analysis (RCA), an algorithm that substantially improves clustering accuracy. Using RCA, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs). Additionally, epithelial-mesenchymal transition (EMT)-related genes were found to be upregulated only in the CAF subpopulation of tumor samples. Notably, colorectal tumors previously assigned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with divergent survival probability by using single-cell signatures, thus underscoring the prognostic value of our approach. Overall, our results demonstrate that unbiased single-cell RNA-seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.