Simone Picelli

Full-length RNA-seq from single cells using Smart-seq2

Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1–3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA−) RNA.

Smart-seq2 for sensitive full-length transcriptome profiling in single cells

Single-cell gene expression analyses hold promise for characterizing cellular heterogeneity, but current methods compromise on either the coverage, the sensitivity or the throughput. Here, we introduce Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells. Smart-seq2 transcriptome libraries have improved detection, coverage, bias and accuracy compared to Smart-seq libraries and are generated with off-the-shelf reagents at lower cost.

Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer.

We carried out a genome-wide association study of breast cancer predisposition with replication and refinement studies involving 6,145 cases and 33,016 controls and identified two SNPs (rs4415084 and rs10941679) on 5p12 that confer risk, preferentially for estrogen receptor (ER)-positive tumors (OR = 1.27, P = 2.5 × 10−12 for rs10941679). The nearest gene, MRPS30, was previously implicated in apoptosis, ER-positive tumors and favorable prognosis. A recently reported signal in FGFR2 was also found to associate specifically with ER-positive breast cancer.

Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes

Summary Hormone-secreting cells within pancreatic islets of Langerhans play important roles in metabolic homeostasis and disease. However, their transcriptional characterization is still incomplete. Here, we sequenced the transcriptomes of thousands of human islet cells from healthy and type 2 diabetic donors. We could define specific genetic programs for each individual endocrine and exocrine cell type, even for rare δ, γ, ε, and stellate cells, and revealed subpopulations of α, β, and acinar cells. Intriguingly, δ cells expressed several important receptors, indicating an unrecognized importance of these cells in integrating paracrine and systemic metabolic signals. Genes previously associated with obesity or diabetes were found to correlate with BMI. Finally, comparing healthy and T2D transcriptomes in a cell-type resolved manner uncovered candidates for future functional studies. Altogether, our analyses demonstrate the utility of the generated single-cell gene expression resource.

The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing

Innate lymphoid cells (ILCs) are increasingly appreciated as important participants in homeostasis and inflammation. Substantial plasticity and heterogeneity among ILC populations have been reported. Here we have delineated the heterogeneity of human ILCs through single-cell RNA sequencing of several hundreds of individual tonsil CD127+ ILCs and natural killer (NK) cells. Unbiased transcriptional clustering revealed four distinct populations, corresponding to ILC1 cells, ILC2 cells, ILC3 cells and NK cells, with their respective transcriptomes recapitulating known as well as unknown transcriptional profiles. The single-cell resolution additionally divulged three transcriptionally and functionally diverse subpopulations of ILC3 cells. Our systematic comparison of single-cell transcriptional variation within and between ILC populations provides new insight into ILC biology during homeostasis, with additional implications for dysregulation of the immune system.

Tn5 transposase and tagmentation procedures for massively scaled sequencing projects

Massively parallel DNA sequencing of thousands of samples in a single machine-run is now possible, but the preparation of the individual sequencing libraries is expensive and time-consuming. Tagmentation-based library construction, using the Tn5 transposase, is efficient for generating sequencing libraries but currently relies on undisclosed reagents, which severely limits development of novel applications and the execution of large-scale projects. Here, we present simple and robust procedures for Tn5 transposase production and optimized reaction conditions for tagmentation-based sequencing library construction. We further show how molecular crowding agents both modulate library lengths and enable efficient tagmentation from subpicogram amounts of cDNA. The comparison of single-cell RNA-sequencing libraries generated using produced and commercial Tn5 demonstrated equal performances in terms of gene detection and library characteristics. Finally, because naked Tn5 can be annealed to any oligonucleotide of choice, for example, molecular barcodes in single-cell assays or methylated oligonucleotides for bisulfite sequencing, custom Tn5 production and tagmentation enable innovation in sequencing-based applications.

Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42 103 individuals

Objective Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data. Design Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39 266) and in combination with gender, age and FH (n=11 324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. Results The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. Conclusion Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.

Association studies on 11 published colorectal cancer risk loci

Background:Recently, several genome-wide association studies (GWAS) have independently found numerous loci at which common single-nucleotide polymorphisms (SNPs) modestly influence the risk of developing colorectal cancer. The aim of this study was to test 11 loci, reported to be associated with an increased or decreased risk of colorectal cancer: 8q23.3 (rs16892766), 8q24.21 (rs6983267), 9p24 (rs719725), 10p14 (rs10795668), 11q23.1 (rs3802842), 14q22.2 (rs4444235), 15q13.3 (rs4779584), 16q22.1 (rs9929218), 18q21.1 (rs4939827), 19q13.1 (rs10411210) and 20p12.3 (rs961253), in a Swedish-based cohort.Methods:The cohort was composed of 1786 cases and 1749 controls that were genotyped and analysed statistically. Genotype–phenotype analysis, for all 11 SNPs and sex, age of onset, family history of CRC and tumour location, was performed.Results:Of eleven loci, 5 showed statistically significant odds ratios similar to previously published findings: 8q23.3, 8q24.21, 10p14, 15q13.3 and 18q21.1. The remaining loci 11q23.1, 16q22.1, 19q13.1 and 20p12.3 showed weak trends but somehow similar to what was previously published. The loci 9p24 and 14q22.2 could not be confirmed. We show a higher number of risk alleles in affected individuals compared to controls. Four statistically significant genotype–phenotype associations were found; the G allele of rs6983267 was associated to older age, the G allele of rs1075668 was associated with a younger age and sporadic cases, and the T allele of rs10411210 was associated with younger age.Conclusions:Our study, using a Swedish population, supports most genetic variants published in GWAS. More studies are needed to validate the genotype–phenotype correlations.

Genome-wide linkage scan for colorectal cancer susceptibility genes supports linkage to chromosome 3q.

BackgroundColorectal cancer is one of the most common causes of cancer-related mortality. The disease is clinically and genetically heterogeneous though a strong hereditary component has been identified. However, only a small proportion of the inherited susceptibility can be ascribed to dominant syndromes, such as Hereditary Non-Polyposis Colorectal Cancer (HNPCC) or Familial Adenomatous Polyposis (FAP). In an attempt to identify novel colorectal cancer predisposing genes, we have performed a genome-wide linkage analysis in 30 Swedish non-FAP/non-HNPCC families with a strong family history of colorectal cancer.MethodsStatistical analysis was performed using multipoint parametric and nonparametric linkage.ResultsParametric analysis under the assumption of locus homogeneity excluded any common susceptibility regions harbouring a predisposing gene for colorectal cancer. However, several loci on chromosomes 2q, 3q, 6q, and 7q with suggestive linkage were detected in the parametric analysis under the assumption of locus heterogeneity as well as in the nonparametric analysis. Among these loci, the locus on chromosome 3q21.1-q26.2 was the most consistent finding providing positive results in both parametric and nonparametric analyses Heterogeneity LOD score (HLOD) = 1.90, alpha = 0.45, Non-Parametric LOD score (NPL) = 2.1).ConclusionThe strongest evidence of linkage was seen for the region on chromosome 3. Interestingly, the same region has recently been reported as the most significant finding in a genome-wide analysis performed with SNP arrays; thus our results independently support the finding on chromosome 3q.

TGFBR1(*)6A and Int7G24A variants of transforming growth factor-beta receptor 1 in Swedish familial and sporadic breast cancer.

Two common variants in transforming growth factor-β receptor 1 (TGFBR1), TGFBR1*6A and Int7G24A, A allele, have been shown to act as low-penetrance tumour susceptibility alleles in several common cancers, including breast cancer. We evaluated the TGFBR1 9A/6A and Int7G24A variant frequencies in two breast cancer cohorts; a population-based cohort of breast cancer with defined family history (n=459) and in breast cancer patients from a familial cancer clinic (n=340) and in 856 controls from the Stockholm region. The familial patients from both cohorts were further divided into high- and low-risk familial breast cancer based on pedigree analysis. There was no overall association with either variant and breast cancer risk. The TGFBR1*6A allelic frequency was, however, higher in low-risk familial breast cancer (0.138), compared to controls (0.106; P=0.04). No significant difference was found in the high-risk familial (0.102) or sporadic cases (0.109; P=0.83 and 0.83, respectively). TGFBR1*6A carrier status was further associated with a high-grade sporadic breast cancer (odds ratio: 2.27; 95% confidence interval: 1.01–5.11; P=0.049). These results indicate that the TGFBR1*6A variant may be associated with an increased risk of low-risk familial breast cancer and might be a marker for poorly differentiated breast cancer. The Int7G24A variant was not associated with breast cancer risk or clinical presentation of the disease including prognosis in our material.