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Crohn’s disease is a lifelong disease characterized by chronic inflammation of the gastrointestinal tract. Defining the cellular and transcriptional composition of the mucosa at different stages of disease progression is needed for personalized therapy in Crohn’s.

Ileal biopsies were obtained from (1) control subjects (n = 6), (2) treatment-naïve patients (n = 7), and (3) established (n = 14) Crohn’s patients along with remission (n = 3) and refractory (n = 11) treatment groups. The biopsies processed using 10x Genomics single cell 5' yielded 139 906 cells. Gene expression count matrices of all samples were analyzed by reciprocal principal component integration, followed by clustering analysis. Manual annotations of the clusters were performed using canonical gene markers. Cell type proportions, differential expression analysis, and gene ontology enrichment were carried out for each cell type.

We identified 3 cellular compartments with 9 epithelial, 1 stromal, and 5 immune cell subtypes. We observed differences in the cellular composition between control, treatment-naïve, and established groups, with the significant changes in the epithelial subtypes of the treatment-naïve patients, including microfold, tuft, goblet, enterocyte,s and BEST4+ cells. Surprisingly, fewer changes in the composition of the immune compartment were observed; however, gene expression in the epithelial and immune compartment was different between Crohn’s phenotypes, indicating changes in cellular activity.

Our study identified cellular and transcriptional signatures associated with treatment-naïve Crohn’s disease that collectively point to dysfunction of the intestinal barrier with an increase in inflammatory cellular activity. Our analysis also highlights the heterogeneity among patients within the same disease phenotype, shining a new light on personalized treatment responses and strategies.

 

Large-scale scRNA-seq studies typically generate data in batches, which often induce nontrivial batch effects that need to be corrected. Given the global efforts for building cell atlases and the increasing number of annotated scRNA-seq datasets accumulated, we propose a supervised strategy for scRNA-seq data integration called SIDA (SupervisedIntegration usingDomainAdaptation), which uses the cell type annotations to guide the integration of diverse batches. The supervised strategy is based on domain adaptation that was initially proposed in the computer vision field. We demonstrate that SIDA is able to generate comprehensive reference datasets that lead to improved accuracy in automated cell type mapping analyses.

 

The effect of DNA methylation on the regulation of gene expression has been extensively discussed in the literature. However, the potential association between DNA methylation and alternative splicing is not understood well. In this study, we integrated multiple omics data types from The Cancer Genome Atlas (TCGA) and systematically examined the relationship between DNA methylation and alternative splicing. Using the methylation data and exon expression data, we identified many CpG sites significantly associated with exon expression in various types of cancers. We further observed that the direction and strength of significant CpG-exon correlation tended to be consistent across different cancer contexts, indicating that some CpG-exon correlation patterns reflect fundamental biological mechanisms that transcend tissue- and cancer- types. We also discovered that CpG sites correlated with exon expressions were more likely to be associated with patient survival outcomes compared to CpG sites that did not correlate with exon expressions. Furthermore, we found that CpG sites were more strongly correlated with exon expression than expression of isoforms harboring the corresponding exons. This observation suggests that a major effect of CpG methylation on alternative splicing may be related to the inclusion or exclusion of exons, which subsequently impacts the relative usage of various isoforms. Overall, our study revealed correlation patterns between DNA methylation and alternative splicing, which provides new insights into the role of methylation in the transcriptional process.

 

One important characteristic of single-cell RNA sequencing (scRNA-seq) data is its high sparsity, where the gene-cell count data matrix contains high proportion of zeros. The sparsity has motivated widespread discussions on dropouts and missing data, as well as imputation algorithms of scRNA-seq analysis. Here, we aim to investigate whether there exist genes that are more prone to be under-detected in scRNA-seq, and if yes, what commonalities those genes may share. From public data sources, we gathered paired bulk RNA-seq and scRNA-seq data from 53 human samples, which were generated in diverse biological contexts. We derived pseudo-bulk gene expression by averaging the scRNA-seq data across cells. Comparisons of the paired bulk and pseudo-bulk gene expression profiles revealed that there indeed exists a collection of genes that are frequently under-detected in scRNA-seq compared to bulk RNA-seq. This result was robust to randomization when unpaired bulk and pseudo-bulk gene expression profiles were compared. We performed motif search to the last 350 bp of the identified genes, and observed an enrichment of poly(T) motif. The poly(T) motif toward the tails of those genes may be able to form hairpin structures with the poly(A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during scRNA-seq library preparation, which is a mechanistic conjecture of why certain genes may be more prone to be under-detected in scRNA-seq. 

Single-cell RNA sequencing (scRNA-seq) data often contain doublets, where a doublet manifests as 1 cell barcode that corresponds to combined gene expression of two or more cells. Existence of doublets can lead to spurious biological interpretations. Here, we present s ingle- c ell MO del-driven D oublet D etection ( scMODD ), a model-driven algorithm to detect doublets in scRNA-seq data. ScMODD achieved similar performance compared to existing doublet detection algorithms which are primarily data-driven, showing the promise of model-driven approach for doublet detection. When implementing scMODD in simulated and real scRNA-seq data, we tested both the negative binomial (NB) model and the zero-inflated negative binomial (ZINB) model to serve as the underlying statistical model for scRNA-seq count data, and observed that incorporating zero inflation did not improve detection performance, suggesting that consideration of zero inflation is not necessary in the context of doublet detection in scRNA-seq. 

Abstract Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA’s ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine. 

When analyzing scRNA-seq data with clustering algorithms, annotating the clusters with cell types is an essential step toward biological interpretation of the data. Annotations can be performed manually using known cell type marker genes. Annotations can also be automated using knowledge-driven or data-driven machine learning algorithms. Majority of cell type annotation algorithms are designed to predict cell types for individual cells in a new dataset. Since biological interpretation of scRNA-seq data is often made on cell clusters rather than individual cells, several algorithms have been developed to annotate cell clusters. In this study, we compared five cell type annotation algorithms, Azimuth, SingleR, Garnett, scCATCH, and SCSA, which cover the spectrum of knowledge-driven and data-driven approaches to annotate either individual cells or cell clusters. We applied these five algorithms to two scRNA-seq datasets of peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy controls, and evaluated their annotation performance. From this comparison, we observed that methods for annotating individual cells outperformed methods for annotation cell clusters. We applied the cell-based annotation algorithm Azimuth to the two scRNA-seq datasets to examine the immune response during COVID-19 infection. Both datasets presented significant depletion of plasmacytoid dendritic cells (pDCs), where differential expression in this cell type and pathway analysis revealed strong activation of type I interferon signaling pathway in response to the infection.