Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images.

Automatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep learning-based approach that couples remarkably precise nuclear segmentation with quantitation of fluorescent labeling intensity within segmented nuclei, and then apply it to the analysis of cell cycle dependent protein concentration in mouse tissues using 2D fluorescent still images. First, several existing deep learning-based methods were evaluated to accurately segment nuclei using different imaging modalities with a small training dataset. Next, we developed a deep learning-based approach to identify and measure fluorescent labels within segmented nuclei, and created an ImageJ plugin to allow for efficient manual correction of nuclear segmentation and label identification. Lastly, using fluorescence intensity as a readout for protein concentration, a three-step global estimation method was applied to the characterization of the cell cycle dependent expression of E2F proteins in the developing mouse intestine.

Fragile X-related protein family: a double-edged sword in neurodevelopmental disorders and cancer.

The fragile X-related (FXR) family proteins FMRP, FXR1, and FXR2 are RNA binding proteins that play a critical role in RNA metabolism, neuronal plasticity, and muscle development. These proteins share significant homology in their protein domains, which are functionally and structurally similar to each other. FXR family members are known to play an essential role in causing fragile X mental retardation syndrome (FXS), the most common genetic form of autism spectrum disorder. Recent advances in our understanding of this family of proteins have occurred in tandem with discoveries of great importance to neurological disorders and cancer biology via the identification of their novel RNA and protein targets. Herein, we review the FXR family of proteins as they pertain to FXS, other mental illnesses, and cancer. We emphasize recent findings and analyses that suggest contrasting functions of this protein family in FXS and tumorigenesis based on their expression patterns in human tissues. Finally, we discuss current gaps in our knowledge regarding the FXR protein family and their role in FXS and cancer and suggest future studies to facilitate bench to bedside translation of the findings.

Integrated metagenomic and metatranscriptomic analyses of microbial communities in the meso- and bathypelagic realm of north pacific ocean.

Although emerging evidence indicates that deep-sea water contains an untapped reservoir of high metabolic and genetic diversity, this realm has not been studied well compared with surface sea water. The study provided the first integrated meta-genomic and -transcriptomic analysis of the microbial communities in deep-sea water of North Pacific Ocean. DNA/RNA amplifications and simultaneous metagenomic and metatranscriptomic analyses were employed to discover information concerning deep-sea microbial communities from four different deep-sea sites ranging from the mesopelagic to pelagic ocean. Within the prokaryotic community, bacteria is absolutely dominant (~90%) over archaea in both metagenomic and metatranscriptomic data pools. The emergence of archaeal phyla Crenarchaeota, Euryarchaeota, Thaumarchaeota, bacterial phyla Actinobacteria, Firmicutes, sub-phyla Betaproteobacteria, Deltaproteobacteria, and Gammaproteobacteria, and the decrease of bacterial phyla Bacteroidetes and Alphaproteobacteria are the main composition changes of prokaryotic communities in the deep-sea water, when compared with the reference Global Ocean Sampling Expedition (GOS) surface water. Photosynthetic Cyanobacteria exist in all four metagenomic libraries and two metatranscriptomic libraries. In Eukaryota community, decreased abundance of fungi and algae in deep sea was observed. RNA/DNA ratio was employed as an index to show metabolic activity strength of microbes in deep sea. Functional analysis indicated that deep-sea microbes are leading a defensive lifestyle.

Heterozygous mutations of the kinesin KIF21A in congenital fibrosis of the extraocular muscles type 1 (CFEOM1).

Congenital fibrosis of the extraocular muscles type 1 (CFEOM1; OMIM #135700) is an autosomal dominant strabismus disorder associated with defects of the oculomotor nerve. We show that individuals with CFEOM1 harbor heterozygous missense mutations in a kinesin motor protein encoded by KIF21A. We identified six different mutations in 44 of 45 probands. The primary mutational hotspots are in the stalk domain, highlighting an important new role for KIF21A and its stalk in the formation of the oculomotor axis.

Targeting protein tyrosine phosphatases for CDK6-induced immunotherapy resistance.

Elucidating the mechanisms of resistance to immunotherapy and developing strategies to improve its efficacy are challenging goals. Bioinformatics analysis demonstrates that high CDK6 expression in melanoma is associated with poor progression-free survival of patients receiving single-agent immunotherapy. Depletion of CDK6 or cyclin D3 (but not of CDK4, cyclin D1, or D2) in cells of the tumor microenvironment inhibits tumor growth. CDK6 depletion reshapes the tumor immune microenvironment, and the host anti-tumor effect depends on cyclin D3/CDK6-expressing CD8+ and CD4+ T cells. This occurs by CDK6 phosphorylating and increasing the activities of PTP1B and T cell protein tyrosine phosphatase (TCPTP), which, in turn, decreases tyrosine phosphorylation of CD3ζ, reducing the signal transduction for T cell activation. Administration of a PTP1B and TCPTP inhibitor prove more efficacious than using a CDK6 degrader in enhancing T cell-mediated immunotherapy. Targeting protein tyrosine phosphatases (PTPs) might be an effective strategy for cancer patients who resist immunotherapy treatment.

Team principles for successful interdisciplinary research teams.

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about $680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity.

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

A new highly selective fluorescent K+ sensor.

We describe the synthesis, properties, and application of a new fluorescent potassium chemosensor, KS2, for K(+) sensing and imaging in live cells. By virtue of a strong electron-withdrawing group, 2-dicyanomethylene-3-cyano-4,5,5-trimethyl-2,5-dihydrofuran (TCF), with a triazacryptand ligand, the new sensor can respond to K(+) up to 1.6 M. This is the first highly selective intracellular sensor suitable for sensing K(+) over a broad and high concentration range. Confocal fluorescence microscopy has established the utility of KS2 for live-cell K(+) detection. The application of KS2 combined with other sensors will be of great benefit for investigating cellular metabolism, detecting and diagnosing diseases including cancer, and monitoring responses to therapy.

Quantitative single-cell gene expression measurements of multiple genes in response to hypoxia treatment.

Cell-to-cell heterogeneity in gene transcription plays a central role in a variety of vital cell processes. To quantify gene expression heterogeneity patterns among cells and to determine their biological significance, methods to measure gene expression levels at the single-cell level are highly needed. We report an experimental technique based on the DNA-intercalating fluorescent dye SYBR green for quantitative expression level analysis of up to ten selected genes in single mammalian cells. The method features a two-step procedure consisting of a step to isolate RNA from a single mammalian cell, synthesize cDNA from it, and a qPCR step. We applied the method to cell populations exposed to hypoxia, quantifying expression levels of seven different genes spanning a wide dynamic range of expression in randomly picked single cells. In the experiment, 72 single Barrett's esophageal epithelial (CP-A) cells, 36 grown under normal physiological conditions (controls) and 36 exposed to hypoxia for 30 min, were randomly collected and used for measuring the expression levels of 28S rRNA, PRKAA1, GAPDH, Angptl4, MT3, PTGES, and VEGFA genes. The results demonstrate that the method is sensitive enough to measure alterations in gene expression at the single-cell level, clearly showing heterogeneity within a cell population. We present technical details of the method development and implementation, and experimental results obtained by use of the procedure. We expect the advantages of this technique will facilitate further developments and advances in the field of single-cell gene expression profiling on a nanotechnological scale, and eventually as a tool for future point-of-care medical applications.

Platinum (II) Porphyrin-Containing Thermoresponsive Poly(N-isopropylacrylamide) Copolymer as Fluorescence Dual Oxygen and Temperature Sensor.

A random copolymer, poly(NIPAAm-co-PtPorphyrin), consisting of N-isopropylacrylamide (NIPAAm) and platinum (II) porphyrin units, behaves as an optical dual sensor for oxygen and temperature. The dual sensor is designed by incorporating an oxygen-sensitive platinum (II) porphyrin (M1) into a temperature-sensitive polymer (PNIPAAm). The polymer exhibited low critical solution temperature (LCST) property at 31.5 °C. This LCST affected the polymer's aggregation status, which in turn affected the nanostructures, fluorescence intensities, and responses to dissolved oxygen. This enables the polymer to functionalize as a dual temperature and dissolved oxygen sensor. Oxygen response of the platinum (II) porphyrin probes in the polymer followed a two-site Stern-Volmer model, indicating the nonuniform distribution of the probes. The copolymer was used to preliminarily monitor the oxygen consumption of Escherichia coli (E. coli) bacteria. The results indicate a potential application of the polymer in biological fields.

Nuclear PFKP promotes CXCR4-dependent infiltration by T cell acute lymphoblastic leukemia.

PFKP (phosphofructokinase, platelet), the major isoform of PFK1 expressed in T cell acute lymphoblastic leukemia (T-ALL), is predominantly expressed in the cytoplasm to carry out its glycolytic function. Our study showed that PFKP is a nucleocytoplasmic shuttling protein with functional nuclear export and nuclear localization sequences (NLSs). Cyclin D3/CDK6 facilitated PFKP nuclear translocation by dimerization and by exposing the NLS of PFKP to induce the interaction between PFKP and importin 9. Nuclear PFKP stimulated the expression of C-X-C chemokine receptor type 4 (CXCR4), a chemokine receptor regulating leukemia homing/infiltration, to promote T-ALL cell invasion, which depended on the activity of c-Myc. In vivo experiments showed that nuclear PFKP promoted leukemia homing/infiltration into the bone marrow, spleen, and liver, which could be blocked with CXCR4 antagonists. Immunohistochemical staining of tissues from a clinically well-annotated cohort of T cell lymphoma/leukemia patients showed nuclear PFKP localization in invasive cancers, but not in nonmalignant T lymph node or reactive hyperplasia. The presence of nuclear PFKP in these specimens correlated with poor survival in patients with T cell malignancy, suggesting the potential utility of nuclear PFKP as a diagnostic marker.

Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

MOTIVATION: Gene expression profiling technologies can generally produce mRNA abundance data for all genes in a genome. A dearth of proteomic data persists because identification range and sensitivity of proteomic measurements lag behind those of transcriptomic measurements. Using partial proteomic data, it is likely that integrative transcriptomic and proteomic analysis may introduce significant bias. Developing methodologies to accurately estimate missing proteomic data will allow better integration of transcriptomic and proteomic datasets and provide deeper insight into metabolic mechanisms underlying complex biological systems. RESULTS: In this study, we present a non-linear data-driven model to predict abundance for undetected proteins using two independent datasets of cognate transcriptomic and proteomic data collected from Desulfovibrio vulgaris. We use stochastic gradient boosted trees (GBT) to uncover possible non-linear relationships between transcriptomic and proteomic data, and to predict protein abundance for the proteins not experimentally detected based on relevant predictors such as mRNA abundance, cellular role, molecular weight, sequence length, protein length, guanine-cytosine (GC) content and triple codon counts. Initially, we constructed a GBT model using all possible variables to assess their relative importance and characterize the behavior of the predictive model. A strong plateau effect in the regions of high mRNA values and sparse data occurred in this model. Hence, we removed genes in those areas based on thresholds estimated from the partial dependency plots where this behavior was captured. At this stage, only the strongest predictors of protein abundance were retained to reduce the complexity of the GBT model. After removing genes in the plateau region, mRNA abundance, main cellular functional categories and few triple codon counts emerged as the top-ranked predictors of protein abundance. We then created a new tuned GBT model using the five most significant predictors. The construction of our non-linear model consists of a set of serial regression trees models with implicit strength in variable selection. The model provides variable relative importance measures using as a criterion mean square error. The results showed that coefficients of determination for our nonlinear models ranged from 0.393 to 0.582 in both datasets, providing better results than linear regression used in the past. We evaluated the validity of this non-linear model using biological information of operons, regulons and pathways, and the results demonstrated that the coefficients of variation of estimated protein abundance values within operons, regulons or pathways are indeed smaller than those for random groups of proteins. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An improved one-tube RT-PCR protocol for analyzing single-cell gene expression in individual mammalian cells.

It is well known that gene expression is regulated at the level of individual cells, and more evidence is now emerging that heterogeneity of cell regulation is orders of magnitude greater than previously thought. In order to detect meaningful variations in transcription levels, it is necessary to measure gene expression at single cell levels rather than in bulk cells, where individual differences or heterogeneity could be lost. In this work, we report an improved reverse-transcriptase polymerase chain reaction (RT-PCR) protocol which allows the direct measurement of gene expression in one tube (5-25 microl of total PCR mixture) at the single mammalian cell level. The protocol employs a new cell lysis buffer, and involves no RNA isolation or nested PCR steps, significantly reducing the possibility of contamination and errors. We successfully applied this protocol in qRT-PCR and linear-after-the-exponential (LATE)-PCR to analyze selected genes of various expression levels from three cell lines. Although further characterization of RNA stability is important, the preliminary results showed that gene expression heterogeneity could be common among members of genetically identical cell populations. The protocol illustrated can be utilized for a wide array of applications without much modification, such as cancer cell analysis and preimplantation genetic diagnostics. In addition, the protocol is based on intercalator-based (SYBR Green PCR) chemistry, which is less expensive and suitable for high-throughput platforms.

Dually Fluorescent Sensing of pH and Dissolved Oxygen Using a Membrane Made from Polymerizable Sensing Monomers.

Using a thermal polymerization approach and polymerizable pH and oxygen sensing monomers with green and red emission spectra, respectively, new pH, oxygen, and their dual sensing membranes were prepared using poly(2-hydroxyethyl methacrylate)-co-poly(acrylamide) as a matrix. The sensors were grafted on acrylate-modified quartz glass and characterized under different pH values, oxygen concentrations, ion strengths, temperatures and cell culture media. The pH and oxygen sensors were excited using the same excitation wavelength and exhibited well-separated emission spectra. The pH-sensing films showed good response over the pH range 5.5 to 8.5, corresponding to pK(a) values in the biologically-relevant range between 6.9 and 7.1. The oxygen-sensing films exhibited linear Stern-Volmer quenching responses to dissolved oxygen. As the sensing membranes were prepared using thermally initiated polymerization of sensing moiety-containing monomers, no leaching of the sensors from the membranes to buffers or medium was observed. This advantageous characteristic accounts in part for the sensors' biocompatibility without apparent toxicity to HeLa cells after 40 hours incubation. The dual-sensing membrane was used to measure pH and dissolved oxygen simultaneously. The measured results correlated with the set-point values.

Influence of Matrices on Oxygen Sensing of Three Sensing Films with Chemically Conjugated Platinum Porphyrin Probes and Preliminary Application for Monitoring of Oxygen Consumption of Escherichia coli (E. coli).

Oxygen sensing films were synthesized by a chemical conjugation of functional platinum porphyrin probes in silica gel, polystyrene (PS), and poly(2-hydroxyethyl methacrylate) (PHEMA) matrices. Responses of the sensing films to gaseous oxygen and dissolved oxygen were studied and the influence of the matrices on the sensing behaviors was investigated. Silica gel films had the highest fluorescence intensity ratio from deoxygenated to oxygenated environments and the fastest response time to oxygen. PHEMA films had no response to gaseous oxygen, but had greater sensitivity and a faster response time for dissolved oxygen than those of PS films. The influence of matrices on oxygen response, sensitivity and response time was discussed. The influence is most likely attributed to the oxygen diffusion abilities of the matrices. Since the probes were chemically immobilized in the matrices, no leaching of the probes was observed from the sensing films when applied in aqueous environment. One sensing film made from the PHEMA matrix was used to preliminarily monitor the oxygen consumption of Escherichia coli (E. coli) bacteria. E. coli cell density and antibiotics ampicillin concentration dependent oxygen consumption was observed, indicating the potential application of the oxygen sensing film for biological application.

Mitochondrial protein import is regulated by p17/PERMIT to mediate lipid metabolism and cellular stress.

How lipid metabolism is regulated at the outer mitochondrial membrane (OMM) for transducing stress signaling remains largely unknown. We show here that this process is controlled by trafficking of ceramide synthase 1 (CerS1) from the endoplasmic reticulum (ER) to the OMM by a previously uncharacterized p17, which is now renamed protein that mediates ER-mitochondria trafficking (PERMIT). Data revealed that p17/PERMIT associates with newly translated CerS1 on the ER surface to mediate its trafficking to the OMM. Cellular stress induces Drp1 nitrosylation/activation, releasing p17/PERMIT to retrieve CerS1 for its OMM trafficking, resulting in mitochondrial ceramide generation, mitophagy and cell death. In vivo, CRISPR-Cas9-dependent genetic ablation of p17/PERMIT prevents acute stress-mediated CerS1 trafficking to OMM, attenuating mitophagy in p17/PERMIT-/- mice, compared to controls, in various metabolically active tissues, including brain, muscle, and pancreas. Thus, these data have implications in diseases associated with accumulation of damaged mitochondria such as cancer and/or neurodegeneration.

Optical computed tomography for spatially isotropic four-dimensional imaging of live single cells.

Quantitative three-dimensional (3D) computed tomography (CT) imaging of living single cells enables orientation-independent morphometric analysis of the intricacies of cellular physiology. Since its invention, x-ray CT has become indispensable in the clinic for diagnostic and prognostic purposes due to its quantitative absorption-based imaging in true 3D that allows objects of interest to be viewed and measured from any orientation. However, x-ray CT has not been useful at the level of single cells because there is insufficient contrast to form an image. Recently, optical CT has been developed successfully for fixed cells, but this technology called Cell-CT is incompatible with live-cell imaging due to the use of stains, such as hematoxylin, that are not compatible with cell viability. We present a novel development of optical CT for quantitative, multispectral functional 4D (three spatial + one spectral dimension) imaging of living single cells. The method applied to immune system cells offers truly isotropic 3D spatial resolution and enables time-resolved imaging studies of cells suspended in aqueous medium. Using live-cell optical CT, we found a heterogeneous response to mitochondrial fission inhibition in mouse macrophages and differential basal remodeling of small (0.1 to 1 fl) and large (1 to 20 fl) nuclear and mitochondrial structures on a 20- to 30-s time scale in human myelogenous leukemia cells. Because of its robust 3D measurement capabilities, live-cell optical CT represents a powerful new tool in the biomedical research field.

A platform for high-throughput bioenergy production phenotype characterization in single cells.

Driven by an increasing number of studies demonstrating its relevance to a broad variety of disease states, the bioenergy production phenotype has been widely characterized at the bulk sample level. Its cell-to-cell variability, a key player associated with cancer cell survival and recurrence, however, remains poorly understood due to ensemble averaging of the current approaches. We present a technology platform for performing oxygen consumption and extracellular acidification measurements of several hundreds to 1,000 individual cells per assay, while offering simultaneous analysis of cellular communication effects on the energy production phenotype. The platform comprises two major components: a tandem optical sensor for combined oxygen and pH detection, and a microwell device for isolation and analysis of single and few cells in hermetically sealed sub-nanoliter chambers. Our approach revealed subpopulations of cells with aberrant energy production profiles and enables determination of cellular response variability to electron transfer chain inhibitors and ion uncouplers.

Vorinostat differentially alters 3D nuclear structure of cancer and non-cancerous esophageal cells.

The histone deacetylase (HDAC) inhibitor vorinostat has received significant attention in recent years as an 'epigenetic' drug used to treat solid tumors. However, its mechanisms of action are not entirely understood, particularly with regard to its interaction with the aberrations in 3D nuclear structure that accompany neoplastic progression. We investigated the impact of vorinostat on human esophageal epithelial cell lines derived from normal, metaplastic (pre-cancerous), and malignant tissue. Using a combination of novel optical computed tomography (CT)-based quantitative 3D absorption microscopy and conventional confocal fluorescence microscopy, we show that subjecting malignant cells to vorinostat preferentially alters their 3D nuclear architecture relative to non-cancerous cells. Optical CT (cell CT) imaging of fixed single cells showed that drug-treated cancer cells exhibit significant alterations in nuclear morphometry. Confocal microscopy revealed that vorinostat caused changes in the distribution of H3K9ac-marked euchromatin and H3K9me3-marked constitutive heterochromatin. Additionally, 3D immuno-FISH showed that drug-induced expression of the DNA repair gene MGMT was accompanied by spatial relocation toward the center of the nucleus in the nuclei of metaplastic but not in non-neoplastic cells. Our data suggest that vorinostat's differential modulation of 3D nuclear architecture in normal and abnormal cells could play a functional role in its anti-cancer action.

A simple derivation and analysis of a helical cone beam tomographic algorithm for long object imaging via a novel definition of region of interest.

We derive and analyse a simple algorithm first proposed by Kudo et al (2001 Proc. 2001 Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine (Pacific Grove, CA) pp 7-10) for long object imaging from truncated helical cone beam data via a novel definition of region of interest (ROI). Our approach is based on the theory of short object imaging by Kudo et al (1998 Phys. Med. Biol. 43 2885-909). One of the key findings in their work is that filtering of the truncated projection can be divided into two parts: one, finite in the axial direction, results from ramp filtering the data within the Tam window. The other, infinite in the z direction, results from unbounded filtering of ray sums over PI lines only. We show that for an ROI defined by PI lines emanating from the initial and final source positions on a helical segment, the boundary data which would otherwise contaminate the reconstruction of the ROI can be completely excluded. This novel definition of the ROI leads to a simple algorithm for long object imaging. The overscan of the algorithm is analytically calculated and it is the same as that of the zero boundary method. The reconstructed ROI can be divided into two regions: one is minimally contaminated by the portion outside the ROI, while the other is reconstructed free of contamination. We validate the algorithm with a 3D Shepp-Logan phantom and a disc phantom.