Analyses of Cell Type-Specific Effects of MicroRNA-298 on Native Protein Expression Via Truncated 3’UTR Hold Translational Promise

Alzheimer’s disease (AD) is marked by neurofibrillary tangles and senile plaques comprising amyloid β (Aβ) peptides. However, specific contributions of different cell types to Aβ deposition remain unknown. Non-coding microRNA (miRNA) play important roles in AD by regulating major proteins involved, like Aβ precursor protein (APP) and β-site APP-cleaving enzyme (BACE1), two key proteins associated with Aβ biogenesis. MiRNAs typically silence protein expression via binding specific sites in 3’- untranslated region (3’UTR) mRNA. MiRNA regulates protein levels in a cell-type specific manner; however, mechanism of miRNA’s variable activities remains unknown. We developed “miRNA-associated native protein expression” (miRnape) assays to determine a natural "UTR limit" for a miRNA’s function in a particular cell type. We report that miR-298 treatment reduced native APP protein levels in an astrocytic but not in a neuronal cell line. From miR-298’s effects on APP-3’UTR activity and native protein levels, we infer that APP 3’-UTR length could explain the differential miR-298’s activity. Such truncated, but natural, 3’-UTR found in a specific cell type provides an opportunity to regulate native protein levels by particular miRNA. Thus, miRNA’s effect tailoring to a specific cell type bypassing another undesired cell type with a truncated 3’-UTR would potentially advance translational research.

Loss of vacuolar type H+-ATPase induces caspase-independent necrosis-like death of hair cells in zebrafish neuromast

The vacuolar type H+-ATPase (V-ATPase) is a ubiquitous membrane-bound, multi-subunit proton pump that regulates pH of cellular compartments. V-ATPase activity is known to modulate several cellular processes, but cell type-specific V-ATPase functions remain poorly understood. Patients with mutations in specific V-ATPase subunits can develop sensorineural deafness, but underlying mechanisms are unclear. Here, we show that V-ATPase mutations disrupt formation of zebrafish neuromasts, which serve as a model system to investigate the underpinnings of hearing loss. Neuromasts consist of support cells surrounding mechanosensory hair cells that function similarly to hair cells in the mammalian inner ear. In V-ATPase mutant zebrafish embryos, neuromasts are small, malformed, and contain pyknotic nuclei that denote dying cells. Using molecular markers and live imaging, we find that loss of V-ATPase induces hair cells, but not neighboring support cells, to undergo caspase-independent necrosis-like cell death. This is the first demonstration that loss of V-ATPase can lead to necrosis-like cell death in a specific cell type in vivo. Mechanistically, loss of V-ATPase reduces mitochondrial membrane potential in hair cells, which has previously been associated with necrotic cell death. Modulating the mitochondrial permeability transition pore, which regulates mitochondrial membrane potential, improves hair cell survival. These results have implications for understanding causes of sensorineural deafness, and more broadly, reveal functions for V-ATPase in regulating mitochondrial function and promoting survival of a specific cell type in vivo.

Mapping the tumor frontline

In cutaneous squamous cell carcinoma, a tumor-specific cell type at the leading edge may drive stromal and immune changes that facilitate progression.

Multi-batch cytometry data integration for optimal immunophenotyping

We describe the integration of multi-batch cytometry datasets (iMUBAC), a flexible, robust, and scalable computational framework for unsupervised cell-type identification across multiple batches of high-dimensional cytometry datasets. After overlaying cells from healthy controls across multiple batches, iMUBAC learns batch-specific cell-type classification boundaries and identifies aberrant immunophenotypes in patient samples. We illustrate unbiased and streamlined immunophenotyping, using both in-house and public mass and flow cytometry datasets.

IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq

A group of genes controlled as a unit, usually by the same repressor or activator gene, is known as a regulon. The ability to identify active regulons within a specific cell type, i.e., cell-type-specific regulons (CTSR), provides an extraordinary opportunity to pinpoint crucial regulators and target genes responsible for complex diseases. However, the identification of CTSRs from single-cell RNA-Seq (scRNA-Seq) data is computationally challenging. We introduce IRIS3, the first-of-its-kind web server for CTSR inference from scRNA-Seq data for human and mouse. IRIS3 is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified CTSRs. CTSR data can be used to reliably characterize and distinguish the corresponding cell type from others and can be combined with other computational or experimental analyses for biomedical studies. CTSRs can, therefore, aid in the discovery of major regulatory mechanisms and allow reliable constructions of global transcriptional regulation networks encoded in a specific cell type. The broader impact of IRIS3 includes, but is not limited to, investigation of complex diseases hierarchies and heterogeneity, causal gene regulatory network construction, and drug development. IRIS3 is freely accessible from https://bmbl.bmi.osumc.edu/iris3/ with no login requirement.

Identification of differentially methylated cell-types in Epigenome-Wide Association Studies

An outstanding challenge of Epigenome-Wide Association Studies (EWAS) performed in complex tissues is the identification of the specific cell-type(s) responsible for the observed differential DNA methylation. Here, we present a novel statistical algorithm, called CellDMC, which is able to identify not only differentially methylated positions, but also the specific cell-type(s) driving the differential methylation. We provide extensive validation of CellDMC on in-silico mixtures of DNA methylation data generated with different technologies, as well as on real mixtures from epigenome-wide-association and cancer epigenome studies. We demonstrate how CellDMC can achieve over 90% sensitivity and specificity in scenarios where current state-of-the-art methods fail to identify differential methylation. By applying CellDMC to a smoking EWAS performed in buccal swabs, we identify differentially methylated positions occurring in the epithelial compartment, which we validate in smoking-related lung cancer. CellDMC may help towards the identification of causal DNA methylation alterations in disease.

Cancer nanomedicine: from PDGF targeted drug delivery

An innovative approach for the distinctively efficient action of smart targeted drug delivery to a specific cell type is obtained through the modification of the surface of nanoparticles.