Sub-Cluster Identification through Semi-Supervised Optimization of Rare-Cell Silhouettes (SCISSORS) in single-cell RNA-sequencing.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) has enabled the molecular profiling of thousands to millions of cells simultaneously in biologically heterogenous samples. Currently, the common practice in scRNA-seq is to determine cell type labels through unsupervised clustering and the examination of cluster-specific genes. However, even small differences in analysis and parameter choosing can greatly alter clustering results and thus impose great influence on which cell types are identified. Existing methods largely focus on determining the optimal number of robust clusters, which can be problematic for identifying cells of extremely low abundance due to their subtle contributions toward overall patterns of gene expression. RESULTS: Here, we present a carefully designed framework, SCISSORS, which accurately profiles subclusters within broad cluster(s) for the identification of rare cell types in scRNA-seq data. SCISSORS employs silhouette scoring for the estimation of heterogeneity of clusters and reveals rare cells in heterogenous clusters by a multi-step semi-supervised reclustering process. Additionally, SCISSORS provides a method for the identification of marker genes of high specificity to the cell type. SCISSORS is wrapped around the popular Seurat R package and can be easily integrated into existing Seurat pipelines. AVAILABILITY AND IMPLEMENTATION: SCISSORS, including source code and vignettes, are freely available at https://github.com/jr-leary7/SCISSORS.

Isoforms of MUC16 activate oncogenic signaling through EGF receptors to enhance the progression of pancreatic cancer.

Aberrant expression of CA125/MUC16 is associated with pancreatic ductal adenocarcinoma (PDAC) progression and metastasis. However, knowledge of the contribution of MUC16 to pancreatic tumorigenesis is limited. Here, we show that MUC16 expression is associated with disease progression, basal-like and squamous tumor subtypes, increased tumor metastasis, and short-term survival of PDAC patients. MUC16 enhanced tumor malignancy through the activation of AKT and GSK3ß oncogenic signaling pathways. Activation of these oncogenic signaling pathways resulted in part from increased interactions between MUC16 and epidermal growth factor (EGF)-type receptors, which were enhanced for aberrant glycoforms of MUC16. Treatment of PDAC cells with monoclonal antibody (mAb) AR9.6 significantly reduced MUC16-induced oncogenic signaling. mAb AR9.6 binds to a unique conformational epitope on MUC16, which is influenced by O-glycosylation. Additionally, treatment of PDAC tumor-bearing mice with either mAb AR9.6 alone or in combination with gemcitabine significantly reduced tumor growth and metastasis. We conclude that the aberrant expression of MUC16 enhances PDAC progression to an aggressive phenotype by modulating oncogenic signaling through ErbB receptors. Anti-MUC16 mAb AR9.6 blocks oncogenic activities and tumor growth and could be a novel immunotherapeutic agent against MUC16-mediated PDAC tumor malignancy.

Truncated O-Glycan-Bearing MUC16 Enhances Pancreatic Cancer Cells Aggressiveness via α4ß1 Integrin Complexes and FAK Signaling.

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4ß1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and ß1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.

Bioinformatics Approaches for Fetal DNA Fraction Estimation in Noninvasive Prenatal Testing.

The discovery of cell-free fetal DNA molecules in plasma of pregnant women has created a paradigm shift in noninvasive prenatal testing (NIPT). Circulating cell-free DNA in maternal plasma has been increasingly recognized as an important proxy to detect fetal abnormalities in a noninvasive manner. A variety of approaches for NIPT using next-generation sequencing have been developed, which have been rapidly transforming clinical practices nowadays. In such approaches, the fetal DNA fraction is a pivotal parameter governing the overall performance and guaranteeing the proper clinical interpretation of testing results. In this review, we describe the current bioinformatics approaches developed for estimating the fetal DNA fraction and discuss their pros and cons.

Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes.

Cancer-associated fibroblast (CAF) subpopulations in pancreatic ductal adenocarcinoma (PDAC) have been identified using single-cell RNA sequencing (scRNAseq) with divergent characteristics, but their clinical relevance remains unclear. We translate scRNAseq-derived CAF cell-subpopulation-specific marker genes to bulk RNAseq data, and develop a single- sample classifier, DeCAF, for the classification of clinically rest raining and perm issive CAF subtypes. We validate DeCAF in 19 independent bulk transcriptomic datasets across four tumor types (PDAC, mesothelioma, bladder and renal cell carcinoma). DeCAF subtypes have distinct histology features, immune landscapes, and are prognostic and predict response to therapy across cancer types. We demonstrate that DeCAF is clinically replicable and robust for the classification of CAF subtypes in patients for multiple tumor types, providing a better framework for the future development and translation of therapies against permissive CAF subtypes and preservation of restraining CAF subtypes. Significance: We introduce a replicable and robust classifier, DeCAF, that delineates the significance of the role of permissive and restraining CAF subtypes in cancer patients. DeCAF is clinically tractable, prognostic and predictive of treatment response in multiple cancer types and lays the translational groundwork for the preclinical and clinical development of CAF subtype specific therapies.

Bioinformatics Pipeline for Accurate Quantification of Fetal DNA Fraction in Maternal Plasma.

The discovery of circulating cell-free fetal DNA has profoundly transformed the landscape of noninvasive prenatal testing (NIPT) and rapidly found its way into global clinical applications. The fractional concentration of cell-free fetal DNA in plasma DNA of a pregnant woman is an important parameter for understanding and interpreting analytical results of NIPT. Thus, the accurate quantification of fetal DNA fraction is indispensable when NIPT is involved. In this protocol, we describe the bioinformatics workflow to calculate fetal DNA fraction using two programs developed by our group, which provide accurate estimation.

De novo compartment deconvolution and weight estimation of tumor samples using DECODER.

Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. Here, we develop DECODER, an integrated framework which performs de novo deconvolution and single-sample compartment weight estimation. We use DECODER to deconvolve 33 TCGA tumor RNA-seq data sets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

Whole Genome Chromatin IP-Sequencing (ChIP-Seq) in Skeletal Muscle Cells.

Transcriptional control of gene expression in skeletal muscle cell is involved in different processes ranging from muscle formation to regeneration. The identification of an increasing number of transcription factors, co-factors, and histone modifications has been greatly advanced by methods that allow studies of genome-wide chromatin-protein interactions. Chromatin immunoprecipitation with massively parallel DNA sequencing, or ChIP-seq, is a powerful tool for identifying binding sites of TFs/co-factors and histone modifications. The major steps of this technique involve immunoprecipitation of fragmented chromatin, followed by high-throughput sequencing to identify the protein bound regions genome-wide. Here, in this protocol, we will illustrate how the entire ChIP-seq is performed using global H3K27ac profiling in myoblast cells as an example.