Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships, but require efficient methods for cell capture and mRNA sequencing1–4. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths5, the limitations of shallow sequencing have not been directly investigated. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In developing cortex we identify diverse cell types including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

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Somatic mosaicism occurs throughout normal development and contributes to numerous disease etiologies, including tumorigenesis and neurological disorders. Intratumor genetic heterogeneity is inherent to many cancers, creating challenges for effective treatments. Unfortunately, analysis of bulk DNA masks subclonal phylogenetic architectures created by the acquisition and distribution of somatic mutations amongst cells. As a result, single-cell genetic analysis is becoming recognized as vital for accurately characterizing cancers. Despite this, methods for single-cell genetics are lacking. Here we present an automated microfluidic workflow enabling efficient cell capture, lysis, and whole genome amplification (WGA). We find that ~90% of the genome is accessible in single cells with improved uniformity relative to current single-cell WGA methods. Allelic dropout (ADO) rates were limited to 13.75% and variant false discovery rates (SNV FDR) were 4.11x10-6, on average. Application to ER-/PR-/HER2+ breast cancer cells and matched normal controls identified novel mutations that arose in a subpopulation of cells and effectively resolved the segregation of known cancer-related mutations with single-cell resolution. Finally, we demonstrate effective cell classification using mutation profiles with 10X average exome coverage depth per cell. Our data demonstrate an efficient automated microfluidic platform for single-cell WGA that enables the resolution of somatic mutation patterns in single cells.

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BackgroundSeveral observational studies suggest that coffee consumption may be associated with an increased risk of gastric cancer, but the results are inconsistent. We conducted a meta-analysis to evaluate the relationship of coffee consumption with gastric cancer risk and quantify the dose–response relationship between them.MethodsRelevant prospective studies were identified by a search of PubMed, Embase, and Web of Science to May 2015 and by reviewing the references of retrieved articles. Two independent reviewers extracted data and performed the quality assessment. A random-effects model was used to calculate the pooled risk estimates and 95 % confidence intervals (CI). The heterogeneity was assessed using the I2 statistic. Publication bias was assessed by using funnel plot, the Begg test and the Egger test.ResultsThirteen prospective cohort studies with 20 independent reports involving 3,368 patients with gastric cancer and 1,372,811 participants during a follow-up period ranging from 4.3–8 years were included. Compared with the lowest consumption level of coffee, the pooled relative risk (RR) was 1.13 (95 % CI: 0.94–1.35). The dose–response analysis indicated that, the RR of gastric cancer was 1.03 (95 % CI; 0.95–1.11) for per 3 cups/day of coffee consumption. Any nonlinear association of gastric cancer risk with coffee consumption was not found (P for nonlinearity = 0.68). Subgroup analyses indicated that the pooled RR for participants from the United States comparing the highest with the lowest coffee consumption was 1.36 (95 % CI, 1.06–1.75, I2 = 0 %). In addition, people with higher coffee consumption was associated with 25 % higher risk of gastric cancer in equal to or less than 10 years follow-up group (RR = 1.25; 95 % CI, 1.01–1.55, I2 = 0 %). Visual inspection of a funnel plot and the Begg’s and the Egger’s tests did not indicate evidence of publication bias.ConclusionsThis meta-analysis does not support the hypothesis that coffee consumption is associated with the risk of gastric cancer. The increased risk of gastric cancer for participants from the United States and equal to or less than 10 years follow-up group associated with coffee consumption warrant further studies.

We have investigated the correlation between proteins and mRNAs in single cells employing an integrated workflow for dual-analyte co-detection. This is achieved by combining the oligo extension reaction (OER), which converts protein levels to DNA levels, with reverse transcription for mRNA detection. Unsupervised gene expression profiling analysis, including principal component analysis and hierarchical clustering, revealed different aspects of the protein-mRNA relationship. Violin plot analysis showed that some genes exhibited similar distribution patterns for proteins and mRNAs. We also demonstrate that cells can be separated into subpopulations based on their protein-mRNA expression profiles, and that different subpopulations have distinct correlation coefficient values. Our results demonstrated that integrated investigations of mRNA and protein levels in single cells allows comprehensive analysis not attainable at bulk levels.

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