Dynamic imaging of nascent RNA reveals general principles of transcription dynamics and stochastic splice site selection.

The activities of RNA polymerase and the spliceosome are responsible for the heterogeneity in the abundance and isoform composition of mRNA in human cells. However, the dynamics of these megadalton enzymatic complexes working in concert on endogenous genes have not been described. Here, we establish a quasi-genome-scale platform for observing synthesis and processing kinetics of single nascent RNA molecules in real time. We find that all observed genes show transcriptional bursting. We also observe large kinetic variation in intron removal for single introns in single cells, which is inconsistent with deterministic splice site selection. Transcriptome-wide footprinting of the U2AF complex, nascent RNA profiling, long-read sequencing, and lariat sequencing further reveal widespread stochastic recursive splicing within introns. We propose and validate a unified theoretical model to explain the general features of transcription and pervasive stochastic splice site selection.

A Deep Learning Pipeline for Nucleus Segmentation.

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size, and preprocessing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pretrained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets. Published [2020]. This article is a U.S. Government work and is in the public domain in the USA.

Epigenetic Regulation of DNA Repair Pathway Choice by MacroH2A1 Splice Variants Ensures Genome Stability.

The inactive X chromosome (Xi) is inherently susceptible to genomic aberrations. Replication stress (RS) has been proposed as an underlying cause, but the mechanisms that protect from Xi instability remain unknown. Here, we show that macroH2A1.2, an RS-protective histone variant enriched on the Xi, is required for Xi integrity and female survival. Mechanistically, macroH2A1.2 counteracts its structurally distinct and equally Xi-enriched alternative splice variant, macroH2A1.1. Comparative proteomics identified a role for macroH2A1.1 in alternative end joining (alt-EJ), which accounts for Xi anaphase defects in the absence of macroH2A1.2. Genomic instability was rescued by simultaneous depletion of macroH2A1.1 or alt-EJ factors, and mice deficient for both macroH2A1 variants harbor no overt female defects. Notably, macroH2A1 splice variant imbalance affected alt-EJ capacity also in tumor cells. Together, these findings identify macroH2A1 splicing as a modulator of genome maintenance that ensures Xi integrity and may, more broadly, predict DNA repair outcome in malignant cells.

Membrane interactions of the globular domain and the hypervariable region of KRAS4b define its unique diffusion behavior.

The RAS proteins are GTP-dependent switches that regulate signaling pathways and are frequently mutated in cancer. RAS proteins concentrate in the plasma membrane via lipid-tethers and hypervariable region side-chain interactions in distinct nano-domains. However, little is known about RAS membrane dynamics and the details of RAS activation of downstream signaling. Here, we characterize RAS in live human and mouse cells using single-molecule-tracking methods and estimate RAS mobility parameters. KRAS4b exhibits confined mobility with three diffusive states distinct from the other RAS isoforms (KRAS4a, NRAS, and HRAS); and although most of the amino acid differences between RAS isoforms lie within the hypervariable region, the additional confinement of KRAS4b is largely determined by the protein's globular domain. To understand the altered mobility of an oncogenic KRAS4b, we used complementary experimental and molecular dynamics simulation approaches to reveal a detailed mechanism.

Transcriptional Bursting and Co-bursting Regulation by Steroid Hormone Release Pattern and Transcription Factor Mobility.

Genes are transcribed in a discontinuous pattern referred to as RNA bursting, but the mechanisms regulating this process are unclear. Although many physiological signals, including glucocorticoid hormones, are pulsatile, the effects of transient stimulation on bursting are unknown. Here we characterize RNA synthesis from single-copy glucocorticoid receptor (GR)-regulated transcription sites (TSs) under pulsed (ultradian) and constant hormone stimulation. In contrast to constant stimulation, pulsed stimulation induces restricted bursting centered around the hormonal pulse. Moreover, we demonstrate that transcription factor (TF) nuclear mobility determines burst duration, whereas its bound fraction determines burst frequency. Using 3D tracking of TSs, we directly correlate TF binding and RNA synthesis at a specific promoter. Finally, we uncover a striking co-bursting pattern between TSs located at proximal and distal positions in the nucleus. Together, our data reveal a dynamic interplay between TF mobility and RNA bursting that is responsive to stimuli strength, type, modality, and duration.

Effects of human sex chromosome dosage on spatial chromosome organization.

Sex chromosome aneuploidies (SCAs) are common genetic syndromes characterized by the presence of an aberrant number of X and Y chromosomes due to meiotic defects. These conditions impact the structure and function of diverse tissues, but the proximal effects of SCAs on genome organization are unknown. Here, to determine the consequences of SCAs on global genome organization, we have analyzed multiple architectural features of chromosome organization in a comprehensive set of primary cells from SCA patients with various ratios of X and Y chromosomes by use of imaging-based high-throughput chromosome territory mapping (HiCTMap). We find that X chromosome supernumeracy does not affect the size, volume, or nuclear position of the Y chromosome or an autosomal chromosome. In contrast, the active X chromosome undergoes architectural changes as a function of increasing X copy number as measured by a decrease in size and an increase in circularity, which is indicative of chromatin compaction. In Y chromosome supernumeracy, Y chromosome size is reduced suggesting higher chromatin condensation. The radial positioning of chromosomes is unaffected in SCA karyotypes. Taken together, these observations document changes in genome architecture in response to alterations in sex chromosome numbers and point to trans-effects of dosage compensation on chromosome organization.

HiCTMap: Detection and analysis of chromosome territory structure and position by high-throughput imaging.

The spatial organization of chromosomes in the nuclear space is an extensively studied field that relies on measurements of structural features and 3D positions of chromosomes with high precision and robustness. However, no tools are currently available to image and analyze chromosome territories in a high-throughput format. Here, we have developed High-throughput Chromosome Territory Mapping (HiCTMap), a method for the robust and rapid analysis of 2D and 3D chromosome territory positioning in mammalian cells. HiCTMap is a high-throughput imaging-based chromosome detection method which enables routine analysis of chromosome structure and nuclear position. Using an optimized FISH staining protocol in a 384-well plate format in conjunction with a bespoke automated image analysis workflow, HiCTMap faithfully detects chromosome territories and their position in 2D and 3D in a large population of cells per experimental condition. We apply this novel technique to visualize chromosomes 18, X, and Y in male and female primary human skin fibroblasts, and show accurate detection of the correct number of chromosomes in the respective genotypes. Given the ability to visualize and quantitatively analyze large numbers of nuclei, we use HiCTMap to measure chromosome territory area and volume with high precision and determine the radial position of chromosome territories using either centroid or equidistant-shell analysis. The HiCTMap protocol is also compatible with RNA FISH as demonstrated by simultaneous labeling of X chromosomes and Xist RNA in female cells. We suggest HiCTMap will be a useful tool for routine precision mapping of chromosome territories in a wide range of cell types and tissues.

SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches.

DNA fluorescence in situ hybridization (FISH) is the technique of choice to map the position of genomic loci in three-dimensional (3D) space at the single allele level in the cell nucleus. High-throughput DNA FISH methods have recently been developed using complex libraries of fluorescently labeled synthetic oligonucleotides and automated fluorescence microscopy, enabling large-scale interrogation of genomic organization. Although the FISH signals generated by high-throughput methods can, in principle, be analyzed by traditional spot-detection algorithms, these approaches require user intervention to optimize each interrogated genomic locus, making analysis of tens or hundreds of genomic loci in a single experiment prohibitive. We report here the design and testing of two separate machine learning-based workflows for FISH signal detection in a high-throughput format. The two methods rely on random forest (RF) classification or convolutional neural networks (CNNs), respectively. Both workflows detect DNA FISH signals with high accuracy in three separate fluorescence microscopy channels for tens of independent genomic loci, without the need for manual parameter value setting on a per locus basis. In particular, the CNN workflow, which we named SpotLearn, is highly efficient and accurate in the detection of DNA FISH signals with low signal-to-noise ratio (SNR). We suggest that SpotLearn will be useful to accurately and robustly detect diverse DNA FISH signals in a high-throughput fashion, enabling the visualization and positioning of hundreds of genomic loci in a single experiment.

Dissection of specific binding of HIV-1 Gag to the 'packaging signal' in viral RNA.

Selective packaging of HIV-1 genomic RNA (gRNA) requires the presence of a cis-acting RNA element called the 'packaging signal' (Ψ). However, the mechanism by which Ψ promotes selective packaging of the gRNA is not well understood. We used fluorescence correlation spectroscopy and quenching data to monitor the binding of recombinant HIV-1 Gag protein to Cy5-tagged 190-base RNAs. At physiological ionic strength, Gag binds with very similar, nanomolar affinities to both Ψ-containing and control RNAs. We challenged these interactions by adding excess competing tRNA; introducing mutations in Gag; or raising the ionic strength. These modifications all revealed high specificity for Ψ. This specificity is evidently obscured in physiological salt by non-specific, predominantly electrostatic interactions. This nonspecific activity was attenuated by mutations in the MA, CA, and NC domains, including CA mutations disrupting Gag-Gag interaction. We propose that gRNA is selectively packaged because binding to Ψ nucleates virion assembly with particular efficiency.

TCR Microclusters pre-exist and contain molecules necessary for TCR signal transduction.

TCR-dependent signaling events have been observed to occur in TCR microclusters. We found that some TCR microclusters are present in unstimulated murine T cells, indicating that the mechanisms leading to microcluster formation do not require ligand binding. These pre-existing microclusters increase in absolute number following engagement by low-potency ligands. This increase is accompanied by an increase in cell spreading, with the result that the density of TCR microclusters on the surface of the T cell is not a strong function of ligand potency. In characterizing their composition, we observed a constant number of TCRs in a microcluster, constitutive exclusion of the phosphatase CD45, and preassociation with the signaling adapters linker for activation of T cells and growth factor receptor-bound protein 2. The existence of TCR microclusters prior to ligand binding in a state that is conducive for the initiation of downstream signaling could explain, in part, the rapid kinetics with which TCR signal transduction occurs.

Ceramide transfer protein deficiency compromises organelle function and leads to senescence in primary cells.

Ceramide transfer protein (CERT) transfers ceramide from the endoplasmic reticulum (ER) to the Golgi complex. Its deficiency in mouse leads to embryonic death at E11.5. CERT deficient embryos die from cardiac failure due to defective organogenesis, but not due to ceramide induced apoptotic or necrotic cell death. In the current study we examined the effect of CERT deficiency in a primary cell line, namely, mouse embryonic fibroblasts (MEFs). We show that in MEFs, unlike in mutant embryos, lack of CERT does not lead to increased ceramide but causes an accumulation of hexosylceramides. Nevertheless, the defects due to defective sphingolipid metabolism that ensue, when ceramide fails to be trafficked from ER to the Golgi complex, compromise the viability of the cell. Therefore, MEFs display an incipient ER stress. While we observe that ceramide trafficking from ER to the Golgi complex is compromised, the forward transport of VSVG-GFP protein is unhindered from ER to Golgi complex to the plasma membrane. However, retrograde trafficking of the plasma membrane-associated cholera toxin B to the Golgi complex is reduced. The dysregulated sphingolipid metabolism also leads to increased mitochondrial hexosylceramide. The mitochondrial functions are also compromised in mutant MEFs since they have reduced ATP levels, have increased reactive oxygen species, and show increased glutathione reductase activity. Live-cell imaging shows that the mutant mitochondria exhibit reduced fission and fusion events. The mitochondrial dysfunction leads to an increased mitophagy in the CERT mutant MEFs. The compromised organelle function compromise cell viability and results in premature senescence of these MEFs.

Quantitative analysis of F-actin redistribution in astrocytoma cells treated with candidate pharmaceuticals.

Actin fibers (F-actin) control the shape and internal organization of cells, and generate force. It has been long appreciated that these functions are tightly coupled, and in some cases drive cell behavior and cell fate. The distribution and dynamics of F-actin is different in cancer versus normal cells and in response to small molecules, including actin-targeting natural products and anticancer drugs. Therefore, quantifying actin structural changes from high resolution fluorescence micrographs is necessary for further understanding actin cytoskeleton dynamics and phenotypic consequences of drug interactions on cells. We applied an artificial neural network algorithm, which used image intensity and anisotropy measurements, to quantitatively classify F-actin subcellular features into actin along the edges of cells, actin at the protrusions of cells, internal fibers and punctate signals. The algorithm measured significant increase in F-actin at cell edges with concomitant decrease in internal punctate actin in astrocytoma cells lacking functional neurofibromin and p53 when treated with three structurally-distinct anticancer small molecules: OSW1, Schweinfurthin A (SA) and a synthetic marine compound 23'-dehydroxycephalostatin 1. Distinctly different changes were measured in cells treated with the actin inhibitor cytochalasin B. These measurements support published reports that SA acts on F-actin in NF1(-/-) neurofibromin deficient cancer cells through changes in Rho signaling. Quantitative pattern analysis of cells has wide applications for understanding mechanisms of small molecules, because many anti-cancer drugs directly or indirectly target cytoskeletal proteins. Furthermore, quantitative information about the actin cytoskeleton may make it possible to further understand cell fate decisions using mathematically testable models.

Segmentation and quantitative analysis of individual cells in developmental tissues.

Image analysis is vital for extracting quantitative information from biological images and is used extensively, including investigations in developmental biology. The technique commences with the segmentation (delineation) of objects of interest from 2D images or 3D image stacks and is usually followed by the measurement and classification of the segmented objects. This chapter focuses on the segmentation task and here we explain the use of ImageJ, MIPAV (Medical Image Processing, Analysis, and Visualization), and VisSeg, three freely available software packages for this purpose. ImageJ and MIPAV are extremely versatile and can be used in diverse applications. VisSeg is a specialized tool for performing highly accurate and reliable 2D and 3D segmentation of objects such as cells and cell nuclei in images and stacks.

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning.

BACKGROUND: Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH), followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task. RESULTS: Logistic regression was applied to features extracted from candidate segmented nuclei, including nuclear shape, texture, context, and gene copy number, in order to rank objects according to the likelihood of being an accurately segmented nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 and FRA2 genes were labeled with FISH probes. Three trained reviewers independently classified nuclei into three classes of segmentation accuracy. In man vs. machine studies, the automated method outperformed the inter-observer agreement between reviewers, as measured by area under the receiver operating characteristic (ROC) curve. Robustness of gene position measurements to boundary inaccuracies was demonstrated by comparing 1086 manually and automatically segmented nuclei. Pearson correlation coefficients between the gene position measurements were above 0.9 (p < 0.05). A preliminary experiment was conducted to validate the ranked retrieval in a test to detect cancer. Independent manual measurement of gene positions agreed with automatic results in 21 out of 26 statistical comparisons against a pooled normal (benign) gene position distribution. CONCLUSIONS: Accurate segmentation is necessary to automate quantitative image analysis for applications such as gene repositioning. However, due to heterogeneity within images and across different applications, no segmentation algorithm provides a satisfactory solution. Automated assessment of segmentations by ranked retrieval is capable of reducing or even eliminating the need to select segmented objects by hand and represents a significant improvement over binary classification. The method can be extended to other high-throughput applications requiring accurate detection of cells or nuclei across a range of biomedical applications.

Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach.

Supervised learning framework for screening nuclei in tissue sections.

Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.

Human T-cell leukemia virus type 1 p8 protein increases cellular conduits and virus transmission.

The human T-cell leukemia virus type 1 (HTLV-1) is the cause of adult T-cell leukemia/lymphoma as well as tropical spastic paraparesis/HTLV-1-associated myelopathy. HTLV-1 is transmitted to T cells through the virological synapse and by extracellular viral assemblies. Here, we uncovered an additional mechanism of virus transmission that is regulated by the HTLV-1-encoded p8 protein. We found that the p8 protein, known to anergize T cells, is also able to increase T-cell contact through lymphocyte function-associated antigen-1 clustering. In addition, p8 augments the number and length of cellular conduits among T cells and is transferred to neighboring T cells through these conduits. p8, by establishing a T-cell network, enhances the envelope-dependent transmission of HTLV-1. Thus, the ability of p8 to simultaneously anergize and cluster T cells, together with its induction of cellular conduits, secures virus propagation while avoiding the host's immune surveillance. This work identifies p8 as a viral target for the development of therapeutic strategies that may limit the expansion of infected cells in HTLV-1 carriers and decrease HTLV-1-associated morbidity.

Disease-specific gene repositioning in breast cancer.

Genomes are nonrandomly organized within the three-dimensional space of the cell nucleus. Here, we have identified several genes whose nuclear positions are altered in human invasive breast cancer compared with normal breast tissue. The changes in positioning are gene specific and are not a reflection of genomic instability within the cancer tissue. Repositioning events are specific to cancer and do not generally occur in noncancerous breast disease. Moreover, we show that the spatial positions of genes are highly consistent between individuals. Our data indicate that cancer cells have disease-specific gene distributions. These interphase gene positioning patterns may be used to identify cancer tissues.

Quantifying the astrocytoma cell response to candidate pharmaceutical from F-ACTIN image analysis.

The distribution, directionality and motility of the actin fibers control cell shape, affect cell function and are different in cancer versus normal cells. Quantification of actin structural changes is important for further understanding differences between cell types and for elucidation of the effects and dynamics of drug interactions. We have developed an image analysis framework for quantifying F-actin organization patterns in confocal microscope images in response to different candidate pharmaceutical treatments. The main problem solved was to determine which quantitative features to compute from the images that both capture the visually-observed F-actin patterns and correlate with predicted biological outcomes. The resultant numerical features were effective to quantitatively profile the changes in the spatial distribution of F-actin and facilitate the comparison of different pharmaceuticals. The validation for the segmentation was done through visual inspection and correlation to expected biological outcomes. This is the first study quantifying different structural formations of the same protein in intact cells. Preliminary results show uniquely significant increases in cortical F-actin to stress fiber ratio for increasing doses of OSW-1 and Schweinfurthin A(SA) and a less marked increase for cephalostatin 1 derivative (ceph). This increase was not observed for the actin inhibitors: cytochalasin B (cytoB) and Y-27632 (Y). Ongoing studies are further validating the algorithms, elucidating the underlying molecular pathways and will utilize the algorithms for understanding the kinetics of the F-actin changes. Since many anti-cancer drugs target the cytoskeleton, we believe that the quantitative image analysis method reported here will have broad applications to understanding the mechanisms of action of candidate pharmaceuticals.

Automatic nuclei segmentation and spatial FISH analysis for cancer detection.

Spatial analysis of gene localization using fluorescent in-situ hybridization (FISH) labeling is potentially a new method for early cancer detection. Current methodology relies heavily upon accurate segmentation of cell nuclei and FISH signals in tissue sections. While automatic FISH signal detection is a relatively simpler task, accurate nuclei segmentation is still a manual process which is fairly time consuming and subjective. Hence to use the methodology as a clinical application, it is necessary to automate all the steps involved in the process of spatial FISH signal analysis using fast, robust and accurate image processing techniques. In this work, we describe an intelligent framework for analyzing the FISH signals by coupling hybrid nuclei segmentation algorithm with pattern recognition algorithms to automatically identify well segmented nuclei. Automatic spatial statistical analysis of the FISH spots was carried out on the output from the image processing and pattern recognition unit. Results are encouraging and show that the method could evolve into a full fledged clinical application for cancer detection.