A nasal cell atlas reveals heterogeneity of tuft cells and their role in directing olfactory stem cell proliferation.

The olfactory neuroepithelium serves as a sensory organ for odors and forms part of the nasal mucosal barrier. Olfactory sensory neurons are surrounded and supported by epithelial cells. Among them, microvillous cells (MVCs) are strategically positioned at the apical surface, but their specific functions are enigmatic, and their relationship to the other specialized epithelial cells is unclear. Here, we establish that the family of MVCs comprises tuft cells and ionocytes in both mice and humans. Integrating analysis of the respiratory and olfactory epithelia, we define the distinct receptor expression of TRPM5+ tuft-MVCs compared with Gɑ-gustducinhigh respiratory tuft cells and characterize a previously undescribed population of glandular DCLK1+ tuft cells. To establish how allergen sensing by tuft-MVCs might direct olfactory mucosal responses, we used an integrated single-cell transcriptional and protein analysis. Inhalation of Alternaria induced mucosal epithelial effector molecules including Chil4 and a distinct pathway leading to proliferation of the quiescent olfactory horizontal basal stem cell (HBC) pool, both triggered in the absence of olfactory apoptosis. Alternaria- and ATP-elicited HBC proliferation was dependent on TRPM5+ tuft-MVCs, identifying these specialized epithelial cells as regulators of olfactory stem cell responses. Together, our data provide high-resolution characterization of nasal tuft cell heterogeneity and identify a function of TRPM5+ tuft-MVCs in directing the olfactory mucosal response to allergens.

Single extracellular vesicle surface protein-based blood assay identifies potential biomarkers for detection and screening of five cancers.

Extracellular vesicles (EVs) and EV proteins are promising biomarkers for cancer liquid biopsy. Herein, we designed a case-control study involving 100 controls and 100 patients with esophageal, stomach, colorectal, liver, or lung cancer to identify common and type-specific biomarkers of plasma-derived EV surface proteins for the five cancers. EV surface proteins were profiled using a sequencing-based proximity barcoding assay. In this study, five differentially expressed proteins (DEPs) and eight differentially expressed protein combinations (DEPCs) showed promising performance (area under curve, AUC > 0.900) in pan-cancer identification [e.g., TENM2 (AUC = 0.982), CD36 (AUC = 0.974), and CD36-ITGA1 (AUC = 0.971)]. Our classification model could properly discriminate between cancer patients and controls using DEPs (AUC = 0.981) or DEPCs (AUC = 0.965). When distinguishing one cancer from the other four, the accuracy of the classification model using DEPCs (85-92%) was higher than that using DEPs (78-84%). We validated the performance in an additional 14 cancer patients and 14 controls, and achieved an AUC value of 0.786 for DEPs and 0.622 for DEPCs, highlighting the necessity to recruit a larger cohort for further validation. When clustering EVs into subpopulations, we detected cluster-specific proteins highly expressed in immune-related tissues. In the context of colorectal cancer, we identified heterogeneous EV clusters enriched in cancer patients, correlating with tumor initiation and progression. These findings provide epidemiological and molecular evidence for the clinical application of EV proteins in cancer prediction, while also illuminating their functional roles in cancer physiopathology.

Quantification of mutant-allele expression at isoform level in cancer from RNA-seq data.

Even though the role of DNA mutations in cancer is well recognized, current quantification of the RNA expression, performed either at gene or isoform level, typically ignores the mutation status. Standard methods for estimating allele-specific expression (ASE) consider gene-level expression, but the functional impact of a mutation is best assessed at isoform level. Hence our goal is to quantify the mutant-allele expression at isoform level. We have developed and implemented a method, named MAX, for quantifying mutant-allele expression given a list of mutations. For a gene of interest, a mutant reference is constructed by incorporating all possible mutant versions of the wild-type isoforms in the transcriptome annotation. The mutant reference is then used for the RNA-seq reads mapping, which in principle works similarly for any quantification tool. We apply an alternating EM algorithm to the read-count data from the mapping step. In a simulation study, MAX performs well against standard isoform-quantification methods. Also, MAX achieves higher accuracy than conventional gene-based ASE methods such as ASEP. An analysis of a real dataset of acute myeloid leukemia reveals a subgroup of NPM1-mutated patients responding well to a kinase inhibitor. Our findings indicate that quantification of mutant-allele expression at isoform level is feasible and has potential added values for assessing the functional impact of DNA mutations in cancers.

Fusion Gene Detection Using Whole-Exome Sequencing Data in Cancer Patients.

Several fusion genes are directly involved in the initiation and progression of cancers. Numerous bioinformatics tools have been developed to detect fusion events, but they are mainly based on RNA-seq data. The whole-exome sequencing (WES) represents a powerful technology that is widely used for disease-related DNA variant detection. In this study, we build a novel analysis pipeline called Fuseq-WES to detect fusion genes at DNA level based on the WES data. The same method applies also for targeted panel sequencing data. We assess the method to real datasets of acute myeloid leukemia (AML) and prostate cancer patients. The result shows that two of the main AML fusion genes discovered in RNA-seq data, PML-RARA and CBFB-MYH11, are detected in the WES data in 36 and 63% of the available samples, respectively. For the targeted deep-sequencing of prostate cancer patients, detection of the TMPRSS2-ERG fusion, which is the most frequent chimeric alteration in prostate cancer, is 91% concordant with a manually curated procedure based on four other methods. In summary, the overall results indicate that it is challenging to detect fusion genes in WES data with a standard coverage of ∼ 15-30x, where fusion candidates discovered in the RNA-seq data are often not detected in the WES data and vice versa. A subsampling study of the prostate data suggests that a coverage of at least 75x is necessary to achieve high accuracy.

The transcriptome-wide landscape of molecular subtype-specific mRNA expression profiles in acute myeloid leukemia.

Molecular classification of acute myeloid leukemia (AML) aids prognostic stratification and clinical management. Our aim in this study is to identify transcriptome-wide mRNAs that are specific to each of the molecular subtypes of AML. We analyzed RNA-sequencing data of 955 AML samples from three cohorts, including the BeatAML project, the Cancer Genome Atlas, and a cohort of Swedish patients to provide a comprehensive transcriptome-wide view of subtype-specific mRNA expression. We identified 729 subtype-specific mRNAs, discovered in the BeatAML project and validated in the other two cohorts. Using unique proteomics data, we also validated the presence of subtype-specific mRNAs at the protein level, yielding a rich collection of potential protein-based biomarkers for the AML community. To enable the exploration of subtype-specific mRNA expression by the broader scientific community, we provide an interactive resource to the public.

A fast detection of fusion genes from paired-end RNA-seq data.

BACKGROUND: Fusion genes are known to be drivers of many common cancers, so they are potential markers for diagnosis, prognosis or therapy response. The advent of paired-end RNA sequencing enhances our ability to discover fusion genes. While there are available methods, routine analyses of large number of samples are still limited due to high computational demands. RESULTS: We develop FuSeq, a fast and accurate method to discover fusion genes based on quasi-mapping to quickly map the reads, extract initial candidates from split reads and fusion equivalence classes of mapped reads, and finally apply multiple filters and statistical tests to get the final candidates. We apply FuSeq to four validated datasets: breast cancer, melanoma and glioma datasets, and one spike-in dataset. The results reveal high sensitivity and specificity in all datasets, and compare well against other methods such as FusionMap, TRUP, TopHat-Fusion, SOAPfuse and JAFFA. In terms of computational time, FuSeq is two-fold faster than FusionMap and orders of magnitude faster than the other methods. CONCLUSIONS: With this advantage of less computational demands, FuSeq makes it practical to investigate fusion genes in large numbers of samples. FuSeq is implemented in C++ and R, and available at https://github.com/nghiavtr/FuSeq for non-commercial uses.

Accumulation of potential driver genes with genomic alterations predicts survival of high-risk neuroblastoma patients.

BACKGROUND: Neuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge. RESULTS: We integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008). CONCLUSIONS: Integration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy. REVIEWERS: This article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca.