Effective microtissue RNA extraction coupled with Smart-seq2 for reproducible and robust spatial transcriptome analysis.

Spatial transcriptomics is useful for understanding the molecular organization of a tissue and providing insights into cellular function in a morphological context. In order to obtain reproducible results in spatial transcriptomics, we have to maintain tissue morphology and RNA molecule stability during the image acquisition and biomolecule collection processes. Here, we developed a tissue processing method for robust and reproducible RNA-seq from tissue microdissection samples. In this method, we suppressed RNA degradation in fresh-frozen tissue specimens by dehydration fixation and effectively collected a small amount of RNA molecules from microdissection samples by magnetic beads. We demonstrated the spatial transcriptome analysis of the mouse liver and brain in serial microdissection samples (100 µm in a diameter and 10 µm in thickness) produced by a microdissection punching system. Using our method, we could prevent RNA degradation at room temperature and effectively produce a sequencing library with Smart-seq2. This resulted in reproducible sequence read mapping in exon regions and the detection of more than 2000 genes compared to non-fixed samples in the RNA-seq analysis. Our method would be applied to various transcriptome analyses, providing the information for region specific gene expression in tissue specimens.

Single-cell genomics of uncultured bacteria reveals dietary fiber responders in the mouse gut microbiota.

BACKGROUND: The gut microbiota can have dramatic effects on host metabolism; however, current genomic strategies for uncultured bacteria have several limitations that hinder their ability to identify responders to metabolic changes in the microbiota. In this study, we describe a novel single-cell genomic sequencing technique that can identify metabolic responders at the species level without the need for reference genomes, and apply this method to identify bacterial responders to an inulin-based diet in the mouse gut microbiota. RESULTS: Inulin-feeding changed the mouse fecal microbiome composition to increase Bacteroides spp., resulting in the production of abundant succinate in the mouse intestine. Using our massively parallel single-cell genome sequencing technique, named SAG-gel platform, we obtained 346 single-amplified genomes (SAGs) from mouse gut microbes before and after dietary inulin supplementation. After quality control, the SAGs were classified as 267 bacteria, spanning 2 phyla, 4 classes, 7 orders, and 14 families, and 31 different strains of SAGs were graded as high- and medium-quality draft genomes. From these, we have successfully obtained the genomes of the dominant inulin-responders, Bacteroides spp., and identified their polysaccharide utilization loci and their specific metabolic pathways for succinate production. CONCLUSIONS: Our single-cell genomics approach generated a massive amount of SAGs, enabling a functional analysis of uncultured bacteria in the intestinal microbiome. This enabled us to estimate metabolic lineages involved in the bacterial fermentation of dietary fiber and metabolic outcomes such as short-chain fatty acid production in the intestinal environment based on the fibers ingested. The technique allows the in-depth isolation and characterization of uncultured bacteria with specific functions in the microbiota and could be exploited to improve human and animal health. Video abstract.

Site-specific gene expression analysis using an automated tissue micro-dissection punching system.

Site-specific gene expression analyses are important for understanding tissue functions. Despite rapid developments in DNA-related technologies, the site-specific analysis of whole genome expression for a tissue remains challenging. Thus, a new tool is required for capturing multiple tissue micro-dissections or single cells while retaining the positional information. Here, we describe the development of such a system, which can pick up micro-dissections by punching a tissue repeatedly in a very short period, e.g., 5 s/sampling cycle. A photo of the punched tissue provides information on the dissected positions, allowing site-specific gene expression analysis. We demonstrate the site-specific analysis of a frozen tissue slice of mouse brain by analyzing many micro-dissections produced from the tissue at a 300-µm pitch. The site-specific analysis provided new insights into the gene expression profiles in a tissue and on tissue functions. The analysis of site-specific whole genome expression may therefore, open new avenues in life science.

Homozygous deletions of UGT2B17 modifies effects of smoking on TP53-mutations and relapse of head and neck carcinoma.

BACKGROUND: Smoking induces oncogenic TP53-mutations in head and neck squamous cell carcinomas (HNSCCs). Disruptive mutations of TP53-gene and expression of p16 protein [p16 (+)] in tumor tissue associate with worse and better prognosis, respectively. UDP-glucuronosyltransferase 2 family, polypeptide B17 (UGT2B17) detoxifies smoking-related metabolites. Differences among ethnic groups in UGT2B17 are extremely high. Homozygous deletions of UGT2B17 gene (UGT2B17-deletion) are a common copy number variant (CNV) among Japanese, but not a common CNV among Africans and Europeans. Thus, we examined Japanese patients with HNSCC to explore if UGT2B17-deletion and/or p16 (+) modify effects of smoking on TP53-mutations and affect relapse. METHODS: We conducted a posthoc analysis of a prospective cohort. Polymerase chain reaction, immunohistochemistry, and direct sequencing were used to determine UGT2B17-deletion, p16 (+), and detailed TP53-mutations, respectively. RESULTS: UGT2B17-deletion was observed in 80% of this study population. For this 80%, TP53-mutations were significantly more common among smokers than non-smokers (P = 0.0016), but this difference between smokers and nonsmokers was not significant for the 20% with UGT2B17. In patients with UGT2B17-deletion and p16 (+), simultaneously, TP53-mutations were much more common among smokers than among non-smokers (81% versus 17%; P = 0.0050). Patients with both UGT2B17-deletion and disruptive TP53-mutations had higher relapse rates than other patients (hazard ratio, 2.22; 95% confidence interval, 1.30 to 3.80, P = 0.004) in a stepwise method. CONCLUSIONS: These results suggest that UGT2B17-deletion interacting with p16 (+) may modify effects of smoking on TP53-mutations and may further interact with the disruptive TP53-mutations to raise relapse rates among Japanese patients with HNSCC.

Distinct effects of alcohol consumption and smoking on genetic alterations in head and neck carcinoma.

BACKGROUND: Tobacco and alcohol consumption are risk factors for head and neck squamous cell carcinoma (HNSCC). Recently, whole-exome sequencing clarified that smoking increased TP53 and other mutations in HNSCC; however, the effects of alcohol consumption on these genetic alterations remain unknown. We explored the association between alcohol consumption and somatic copy-number alterations (SCNAs) across the whole genome in human papillomavirus (HPV)-negative HNSCCs, and compared with the effects of smoking on genetic alterations. METHODS: SCNA and TP53 mutations in tumor samples were examined by high-resolution comparative genomic hybridization microarray 180K and by direct sequencing, respectively, and statistically analyzed for associations with alcohol consumption and smoking during the 20 years preceding diagnosis of HNSCC. Probes with a corrected p-value (=q-value) less than 0.05 and fold change greater than 1.2 or less than -1.2 were considered statistically significant. RESULTS: A total of 248 patients with HNSCC were enrolled. In the HPV-negative patients (n=221), heavy alcohol consumption was significantly associated with SCNAs of oncogenes/oncosuppressors that were previously reported to occur frequently in HNSCCs: CDKN2A (q=0.005), FHIT (q=0.005), 11q13 region including CCND1, FADD and CTTN (q=0.005), ERBB2 (HER2) (q=0.009), 3q25-qter including CCNL1, TP63, DCUN1D1 and PIK3CA (q=0.014), and CSMD1 (q=0.019). But, TP53 mutations were not affected. In contrast, smoking was associated with increased risk of TP53 mutations, but did not induce any significant SCNAs of oncogenes/oncosuppressors. CONCLUSION: These results suggest that both alcohol consumption and smoking had distinct effects on genetic alterations in HNSCCs. Heavy alcohol consumption may trigger previously known and unknown SCNAs, but may not induce TP53 mutation. In contrast, smoking may induce TP53 mutation, but may not trigger any SCNAs.