Convolutional Blur Attention Network for Cell Nuclei Segmentation.

Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task. In this work, we present a new deep learning-based method for cell nucleus segmentation. The proposed convolutional blur attention (CBA) network consists of downsampling and upsampling procedures. A blur attention module and a blur pooling operation are used to retain the feature salience and avoid noise generation in the downsampling procedure. A pyramid blur pooling (PBP) module is proposed to capture the multi-scale information in the upsampling procedure. The superiority of the proposed method has been compared with a few prior segmentation models, namely U-Net, ENet, SegNet, LinkNet, and Mask RCNN on the 2018 Data Science Bowl (DSB) challenge dataset and the multi-organ nucleus segmentation (MoNuSeg) at MICCAI 2018. The Dice similarity coefficient and some evaluation matrices, such as F1 score, recall, precision, and average Jaccard index (AJI) were used to evaluate the segmentation efficiency of these models. Overall, the proposal method in this paper has the best performance, the AJI indicator on the DSB dataset and MoNuSeg is 0.8429, 0.7985, respectively.

Automatic three-dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images.

Time-lapse confocal fluorescence microscopy images from mouse embryonic stem cells (ESCs) carrying reporter genes, histone H2B-mCherry and Mvh-Venus, have been used to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. Accurate cell nucleus segmentation is required to analyse the ESC dynamics and differentiation at a single cell resolution. Several methods used concavities on nucleus contours to segment overlapping cell nuclei. Our proposed method evaluates not only the concavities but also the size and shape of every 2D nucleus region to determine if any of the strait, extrusion, convexity and large diameter criteria is satisfied to segment overlapping nuclei inside the region. We then use a 3D segmentation method to reconstruct simple, convex, and reasonably sized 3D nuclei along the image stacking direction using the radius and centre of every segmented region in respective microscopy images. To avoid false concavities on nucleus boundaries, fluorescence images of the H2B-mCherry reporter are used for localisation of cell nuclei and Venus fluorescence images are used for determining the cell colony ranges. We use a series of image preprocessing procedures to remove noise outside and inside cell colonies, and in respective nuclei, and to smooth nucleus boundaries based on the colony ranges.  We propose dynamic data structures to record every segmented nucleus region and solid in sets (volumes) of 3D confocal images. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei on microscopy images and that the segmentation method effectively segment out every nucleus with a reasonable size and shape. All 3D nuclei in a set (volume) of confocal microscopy images can be accessed by the dynamic data structures for 3D reconstruction. The 3D nuclei in time-lapse confocal microscopy images can be tracked to calculate cell movement and proliferation in consecutive volumes for understanding the dynamics of the differentiation characteristics about ESCs. LAY DESCRIPTION: Embryonic stem cells (ESCs) are considered as an ideal source for basic cell biology study and producing medically useful cells in vitro. This study uses time-lapse confocal fluorescence microscopy images from mouse ESCs carrying reporter gene to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. To automate analyses of ESC differentiation behaviours, accurate cell nucleus segmentation to distinguish respective cells are required. A series of image preprocessing procedures are implemented to remove noise in live-cell fluorescence images but yield overlapping cell nuclei. A segmentation method that evaluates boundary concavities and the size and shape of every nucleus is then used to determine if any of the strait, extrusion, convexity, large and local minimum diameter criteria satisfied to segment overlapping nuclei. We propose a dynamic data structure to record every newly segmented nucleus. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei and that the segmentation method effectively detects overlapping nuclei. All segmented nuclei in confocal images can be accessed using the dynamic data structures to be visualised and manipulated for quantitative analyses of the ESC differentiation behaviours. The manipulation can be tracking of segmented 3D cell nuclei in time-lapse images to calculate their dynamics of differentiation characteristics.

Microscopy cell nuclei segmentation with enhanced U-Net.

BACKGROUND: Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. RESULTS: An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. CONCLUSIONS: Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

Profound Nanoscale Structural and Biomechanical Changes in DNA Helix upon Treatment with Anthracycline Drugs.

In our study, we describe the outcomes of the intercalation of different anthracycline antibiotics in double-stranded DNA at the nanoscale and single molecule level. Atomic force microscopy analysis revealed that intercalation results in significant elongation and thinning of dsDNA molecules. Additionally, using optical tweezers, we have shown that intercalation decreases the stiffness of DNA molecules, that results in greater susceptibility of dsDNA to break. Using DNA molecules with different GC/AT ratios, we checked whether anthracycline antibiotics show preference for GC-rich or AT-rich DNA fragments. We found that elongation, decrease in height and decrease in stiffness of dsDNA molecules was highest in GC-rich dsDNA, suggesting the preference of anthracycline antibiotics for GC pairs and GC-rich regions of DNA. This is important because such regions of genomes are enriched in DNA regulatory elements. By using three different anthracycline antibiotics, namely doxorubicin (DOX), epirubicin (EPI) and daunorubicin (DAU), we could compare their detrimental effects on DNA. Despite their analogical structure, anthracyclines differ in their effects on DNA molecules and GC-rich region preference. DOX had the strongest overall effect on the DNA topology, causing the largest elongation and decrease in height. On the other hand, EPI has the lowest preference for GC-rich dsDNA. Moreover, we demonstrated that the nanoscale perturbations in dsDNA topology are reflected by changes in the microscale properties of the cell, as even short exposition to doxorubicin resulted in an increase in nuclei stiffness, which can be due to aberration of the chromatin organization, upon intercalation of doxorubicin molecules.

ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling.

BACKGROUND: The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open. RESULTS: In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions. CONCLUSIONS: The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.

Tracking the Oxygen Status in the Cell Nucleus with a Hoechst-Tagged Phosphorescent Ruthenium Complex.

Molecular oxygen in living cells is distributed and consumed inhomogeneously, depending on the activity of each organelle. Therefore, tractable methods that can be used to monitor the oxygen status in each organelle are needed to understand cellular function. Here we report the design of a new oxygen-sensing probe for use in the cell nucleus. We prepared "Ru-Hoechsts", each consisting of a phosphorescent ruthenium complex linked to a Hoechst 33258 moiety, and characterized their properties as oxygen sensors. The Hoechst unit shows strong DNA-binding properties in the nucleus, and the ruthenium complex shows oxygen-dependent phosphorescence. Thus, Ru-Hoechsts accumulated in the cell nucleus and showed oxygen-dependent signals that could be monitored. Of the Ru-Hoechsts prepared in this study, Ru-Hoechst b, in which the ruthenium complex and the Hoechst unit were linked through a hexyl chain, showed the most suitable properties for monitoring the oxygen status. Ru-Hoechsts are probes with high potential for visualizing oxygen fluctuations in the nucleus.

Characterization of an antigenic serine protease in the Trichinella spiralis adult.

Serine proteases have been identified as important molecules that are involved in many parasitic infections, and these molecules have also been suggested to play important roles in Trichinella spiralis infections. In the present study, the antigenic serine protease gene Ts-ADSp-7, which was screened from a cDNA library of Trichinella spiralis Adults at 3 days post-infection (p.i.), was cloned and expressed in Escherichia coli. The encoded protein, Ts-ADSp-7, revealed a potential trypsin-like serine protease domain but lacked substrate banding site at position 227 and protease activity. Transcription could be detected in the Adult and muscle larval stage but not in the newborn larval stage, where no fluorescent signal was detected. Western blot analysis revealed that the 3 days p.i. Adults and muscle larvae could secrete Ts-ADSp-7. Interestingly, strong fluorescent signal of Ts-ADSp-7 could be detected in the nucleoli of the enlarged muscle cell nuclei from 12 to 16 days p.i. and in the ß-stichosomes of the muscle larvae from 16 to 35 days p.i.. The coagulation assay indicated that Ts-ADSp-7 could inhibit intrinsic coagulation pathway. Regarding the putatively important function of the serine protease in the helminth infection to hosts, a total of 81 serine proteases were found in the parasite and mainly comprised eight subfamilies. These subfamilies exhibited high similarity to transmembrane serine protease, coagulation factor XI, lipocalin, guanylin, ceropin, kallikrein, and plasminogen. Moreover, stage specificity was detected in several subfamilies. In summary, the putatively inactive serine protease-like protein Ts-ADSp-7 could inhibit blood coagulation, and the protein is located in the enlarged nuclei of nurse cells during capsule formation. Furthermore, members of the serine protease family in the parasite might be important molecules in the parasite-host interaction.

Design and biological characterization of novel cell-penetrating peptides preferentially targeting cell nuclei and subnuclear regions.

Within this study, we report about the design and biological characterization of novel cell-penetrating peptides (CPPs) with selective suborganelle-targeting properties. The nuclear localization sequence N50, as well as the nucleoli-targeting sequence NrTP, respectively, were fused to a shortened version of the cell-penetrating peptide sC18. We examined cellular uptake, subcellular fate and cytotoxicity of these novel peptides, N50-sC18* and NrTP-sC18*, and found that they are nontoxic up to a concentration of 50 or 100 µM depending on the cell lines used. Moreover, detailed cellular uptake studies revealed that both peptides enter cells via energy-independent uptake, although endocytotic processes cannot completely excluded. However, initial drug delivery studies demonstrated the high versatility of these new peptides as efficient transport vectors targeting specifically nuclei and nucleoli. In future, they could be further explored as parts of newly created peptide-drug conjugates.

Quantification of cell density in rat Achilles tendon: development and application of a new method.

Increased tendon cell nuclei density (TCND) has been proposed to induce tendon mechanical adaptations. However, it is unknown whether TCND is increased in tendon tissue after mechanical loading and whether such an increase can be quantified in a reliable manner. The aim of this study was to develop a reliable method for quantification of TCND and to investigate potential changes in TCND in rat Achilles tendons in response to 12 weeks of running. Eight adult male Sprague-Dawley rats ran (RUN) on a treadmill with 10° incline, 1 h/day, 5 days/wk (17-20 m/min) for 12 weeks (which improved tendon mechanical properties) and were compared with 11 control rats (SED). Tissue-Tek-embedded cryosections (10 µm) from the mid region of the Achilles tendon were cut longitudinally on a cryostat. Sections were stained with alcian blue and picrosirius red. One blinded investigator counted the number of tendon cell nuclei 2-3 times in three separate regions of the mid longitudinal tendon sections with fields of 390 µm × 280 µm. Unpaired t tests were used for the statistical analysis (mean ± SE). Typical Error % for replicate counts was 5.5 and 14 % coefficient of variation for the three regions. There was no difference in TCND between running rats versus control rats (nuclei per image (≈105 µm2): RUN, 152 ± 9; SED, 146 ± 8, p = 0.642). This new method provided reproducible quantification of TCND. There was no difference in TCND despite improvements in tendon mechanics, which suggests that cell number is not a major cause for altered tendon mechanical properties with loading.

Single nucleus versus single-cell gel electrophoresis: kinetics of DNA track formation.

Single-cell gel electrophoresis, or the comet assay, is usually performed with nucleoids prepared after a lysis of either whole cells (more often) or isolated cell nuclei (rarely). Electrophoretic properties of the second type of nucleoids have never been investigated carefully. We measured the kinetics of the DNA exit from nuclei-derived nucleoids in comparison with cell-derived nucleoids. The results show that general organization of the nuclei-derived nucleoids is not changed very much in comparison with nucleoids commonly obtained from whole cells. At the same time, in contrast to the cell-derived nucleoids, for which the exit is stepwise and cooperative, the DNA exit from the nuclei-derived nucleoids can be described by a simple monomolecular kinetics. This difference is probably due to agarose penetration into nuclei (but not into cells) before polymerization of the agarose gel. We suggest that single-nucleus gel electrophoresis may be a way for the comet assay standardization.

Evolutionarily advanced ant farmers rear polyploid fungal crops.

Innovative evolutionary developments are often related to gene or genome duplications. The crop fungi of attine fungus-growing ants are suspected to have enhanced genetic variation reminiscent of polyploidy, but this has never been quantified with cytological data and genetic markers. We estimated the number of nuclei per fungal cell for 42 symbionts reared by 14 species of Panamanian fungus-growing ants. This showed that domesticated symbionts of higher attine ants are polykaryotic with 7-17 nuclei per cell, whereas nonspecialized crops of lower attines are dikaryotic similar to most free-living basidiomycete fungi. We then investigated how putative higher genetic diversity is distributed across polykaryotic mycelia, using microsatellite loci and evaluating models assuming that all nuclei are either heterogeneously haploid or homogeneously polyploid. Genetic variation in the polykaryotic symbionts of the basal higher attine genera Trachymyrmex and Sericomyrmex was only slightly enhanced, but the evolutionarily derived crop fungi of Atta and Acromyrmex leaf-cutting ants had much higher genetic variation. Our opposite ploidy models indicated that the symbionts of Trachymyrmex and Sericomyrmex are likely to be lowly and facultatively polyploid (just over two haplotypes on average), whereas Atta and Acromyrmex symbionts are highly and obligatorily polyploid (ca. 5-7 haplotypes on average). This stepwise transition appears analogous to ploidy variation in plants and fungi domesticated by humans and in fungi domesticated by termites and plants, where gene or genome duplications were typically associated with selection for higher productivity, but allopolyploid chimerism was incompatible with sexual reproduction.

Clustered nuclei splitting via curvature information and gray-scale distance transform.

Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.

Impact of the accuracy of automatic segmentation of cell nuclei clusters on classification of thyroid follicular lesions.

Automatic segmentation of cell nuclei clusters is a key building block in systems for quantitative analysis of microscopy cell images. For that reason, it has received a great attention over the last decade, and diverse automatic approaches to segment clustered nuclei with varying levels of performance under different test conditions have been proposed in literature. To the best of our knowledge, however, so far there is no comparative study on the methods. This study is a first attempt to fill this research gap. More precisely, the purpose of this study is to present an objective performance comparison of existing state-of-the-art segmentation methods. Particularly, the impact of their accuracy on classification of thyroid follicular lesions is also investigated "quantitatively" under the same experimental condition, to evaluate the applicability of the methods. Thirteen different segmentation approaches are compared in terms of not only errors in nuclei segmentation and delineation, but also their impact on the performance of system to classify thyroid follicular lesions using different metrics (e.g., diagnostic accuracy, sensitivity, specificity, etc.). Extensive experiments have been conducted on a total of 204 digitized thyroid biopsy specimens. Our study demonstrates that significant diagnostic errors can be avoided using more advanced segmentation approaches. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate automatic segmentation technique adopted for building automated systems for specifically classifying follicular thyroid lesions.

Lightweight semantic segmentation network for tumor cell nuclei and skin lesion.

In the field of medical image segmentation, achieving fast and accurate semantic segmentation of tumor cell nuclei and skin lesions is of significant importance. However, the considerable variations in skin lesion forms and cell types pose challenges to attaining high network accuracy and robustness. Additionally, as network depth increases, the growing parameter size and computational complexity make practical implementation difficult. To address these issues, this paper proposes MD-UNet, a fast cell nucleus segmentation network that integrates Tokenized Multi-Layer Perceptron modules, attention mechanisms, and Inception structures. Firstly, tokenized MLP modules are employed to label and project convolutional features, reducing computational complexity. Secondly, the paper introduces Depthwise Attention blocks and Multi-layer Feature Extraction modules. The Depthwise Attention blocks eliminate irrelevant and noisy responses from coarse-scale extracted information, serving as alternatives to skip connections in the UNet architecture. The Multi-layer Feature Extraction modules capture a wider range of high-level and low-level semantic features during decoding and facilitate feature fusion. The proposed MD-UNet approach is evaluated on two datasets: the International Skin Imaging Collaboration (ISIC2018) dataset and the PanNuke dataset. The experimental results demonstrate that MD-UNet achieves the best performance on both datasets.

Water, starch, and nuclear behavior in ray parenchyma during heartwood formation of Catalpa bungei 'Jinsi'.

Catalpa bungei 'Jinsi', a cultivar of C. bungei C. A. Mey., is valued for its heartwood with good overall mechanical properties, naturally durable and golden-yellow color. Little is known about heartwood formation in C. bungei 'Jinsi' trees. The behavior of starch, water, and nuclei was studied in the xylem tissue of C. bungei 'Jinsi' concerning aging in ray parenchyma cells. Blocks containing heartwood, golden zone, transition zone, and sapwood were collected from the stems of six C. bungei 'Jinsi' trees. The moisture content of the blocks was measured by oven drying. Changes in starch and nuclei in ray parenchyma were investigated in radial profiles from sapwood to heartwood blocks using microscopy and various staining techniques. The nuclear size and starch content gradually decreased to heartwood. While the horizontal distribution of moisture content of C. bungei 'Jinsi' was very varied, with the heartwood and golden zone being lower than sapwood but slightly higher than the transition zone. Starch grains were rare, but nuclei were still present in some ray parenchyma cells in the heartwood and golden zone. The nuclei showed irregular shape and elongation before disintegration. These results suggest that the most apparent change occurs in the transition zone, the critical location involved in forming C. bungei 'Jinsi' heartwood. Water and starch appear to be actively engaged in heartwood formation. The loss of function of ray parenchyma cells results from heartwood formation.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry.

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

Structural changes in laser-induced retinopathy examined by adaptive optics optical coherence tomography.

Purpose: We report a case of laser-induced retinopathy that posed diagnostic challenges with conventional spectral domain optical coherence tomography (SD-OCT), but was successfully diagnosed using adaptive optics-optical coherence tomography (AO-OCT). Observations: A 27-year-old man with a history of occupational laser device use presented with central scotoma and visual disturbances in the right eye. Conventional SD-OCT only revealed decreased reflectivity in parts of the foveal ellipsoidal zone band. However, other multimodal observations indicated damage to the retinal pigment epithelium (RPE) and choriocapillaris. Additionally, a well-defined circular, dark lesion, approximately 80 µm in diameter, was identified in the outer retina. AO-OCT demonstrated the absence of the RPE and Bruch's membrane, accompanied by the loss of inner and outer segments of cone photoreceptors and dropout of cone cell nuclei, with Müller cells remaining unaffected. Conclusions and Importance: This case of laser-induced retinopathy advances our understanding of the pathophysiological effect of laser exposure on the retina, suggesting a higher incidence of laser-induced retinopathy than previously diagnosed. It also serves as a crucial reminder for laser users to exercise caution and highlights the necessity for ophthalmologists to carefully observe and examine such cases.

Cervical cell nuclei segmentation based on GC-UNet.

Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.

A Method for Detection of Somatic LINE-1 Insertions at the Single-Cell Level from Postmortem Human Brain.

Long Interspersed Element-1 (LINE-1, L1) is a retrotransposon that has the ability to amplify its copy in the genome autonomously. L1Hs is a human-specific active subtype of L1 reported to amplify its copy in neural progenitor cells causing genomic mosaicism. This chapter describes a new method named NECO-seq (Novel Elements Concentrated-sequence) to identify the genomic locus of L1Hs insertions at the single-cell level. This protocol contains the steps of (1) preparation of neuronal cell nuclei from a postmortem human brain, (2) whole genome amplification from single neural nuclei (snWGA), (3) single nucleotide polymorphisms (SNPs) genotyping for quality control of snWGA products, (4) library preparation for next-generation sequencing to enrich the genomic locus of L1Hs insertions, and (5) bioinformatic analysis to detect novel somatic L1Hs insertions. This method can detect approximately 97% of L1Hs originally existing in reference human genome and approximately 10-20 newly inserted L1Hs copies in a neuronal cell of a postmortem human brain.