Multiple Tissue Sections Research Articles

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics.

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets.

Comprehensive spatial distribution profiling of bioactive emodin in mouse organs using mass spectrometry imaging.

Emodin is an important anthraquinone compound with good anti-inflammatory activity in Chinese traditional medicine rhubarb. Detailed spatial distribution information in bio-tissues plays an important role in revealing the pharmacodynamics, toxicology and chemical mechanism of emodin. Herein, the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-TOF-MSI) analytical method was established to obtain information on the spatial and temporal changes of emodin in multiple mouse tissue sections (heart, liver, spleen, lung, kidney, and brain) after intraperitoneal injection of emodin in mice. The measurements were accomplished in the negative ion mode in the range of m/z 250-285Da with a spatial resolution on 40µm. It was found that emodin was predominantly distributed in the arteriolar vascular region of the heart, the capsule region of the spleen, and the cortex of the kidney. Moreover, the MALDI-TOF-MSI result implied that emodin might be distributed in the brain. These more detailed spatial distribution information provides the significant reference for investigating the action mechanism of emodin, which cannot be obtained from conventional LC-MS analysis. The distribution trend of emodin in the results of MALDI-TOF-MSI analysis agreed with the ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) results well, demonstrating the complementarity and reliability of the established MALDI-TOF-MSI method. Our work provided a label-free molecular imaging method to investigate the precise spatial distribution of emodin in various organs, which prove great potential in studying the effective substances and mechanism of rhubarb.

Optics-free Spatial Genomics for Mapping Mouse Brain Aging.

Spatial transcriptomics has revolutionized our understanding of cellular network dynamics in aging and disease by enabling the mapping of molecular and cellular organization across various anatomical locations. Despite these advances, current methods face significant challenges in throughput and cost, limiting their utility for comprehensive studies. To address these limitations, we introduce IRISeq (Imaging Reconstruction using Indexed Sequencing), a optics-free spatial transcriptomics platform that eliminates the need for predefined capture arrays or extensive imaging, allowing for the rapid and cost-effective processing of multiple tissue sections simultaneously. Its capacity to reconstruct images based solely on sequencing local DNA interactions allows for profiling of tissues without size constraints and across varied resolutions. Applying IRISeq, we examined gene expression and cellular dynamics in thirty brain regions of both adult and aged mice, uncovering region-specific changes in gene expression associated with aging. Further cell type-centric analysis further identified age-related cell subtypes and intricate changes in cell interactions that are distinct to certain spatial niches, emphasizing the unique aspects of aging in different brain regions. The affordability and simplicity of IRISeq position it as a versatile tool for mapping region-specific gene expression and cellular interactions across various biological systems.

A case of diabetic mastopathy: An unusual breast lesion mimicking cancer in type 1 diabetes mellitus

Diabetic mastopathy is a rare fibroinflammatory disorder seen among middle aged and premenopausal age group. It is also called lymphocytic mastopathy or sclerosing lymphocytic mastitis. This is seen associated with type 1 diabetes mellitus and other autoimmune disorders specially related to endocrine disorders like thyroid. The definite diagnosis of diabetic mastopathy is truly based on biopsy and histopathological examination.A 33 year old female came to hospital with complaints of swelling in right breast since one month and pain since one week. She was a known case of type 1 diabetes mellitus since 10 years and on regular medications. Radiological Impression was possibilities of mastitis with abscess formation more likely to be considered. But infiltrating malignant neoplasm with areas of necrosis is also a differential diagnosis. Our histopathology laboratory, we received incision biopsy from right breast and microscopic examination from multiple tissue sections showed breast parenchyma with extensive fibrosis and periductal inflammatory infiltrate predominantly lymphocytes, plasma cells and a few neutrophils. Some of the ducts show epithelial proliferation forming bridges and micropapillae. Histopathological diagnosis was Lymphocytic Mastitis with features of Atypical Ductal Hyperplasia.: The diagnosis of Diabetic Mastopathy is based on clinical details, radiological evaluation and biopsy correlation. Sometimes it’s difficult to differentiate from carcinoma breast, so histopathological examination is indicated for a definitive diagnosis and management.

Abstract 1539: A comprehensive analytical validation framework for antibodies used in the CellScape multiplex immunofluorescence assay

Abstract Background: Multiplex immunofluorescence (mIF) is a powerful tool that enables deep phenotyping of cells within the tumor microenvironment (TME). Careful optimization of multiplex antibody panels for specificity, sensitivity and reproducibility is crucial to achieving robust data and is a precondition for assay repeatability and standardization. We describe our validation procedure for VistaPlex™ multiplex assay panels. Methods: Experiments were performed using the CellScape™ platform for precise spatial multiplexing. The platform enables automated cyclic mIF for readily available fluorophore-conjugated antibodies compatible with fresh frozen or FFPE tissues. The use of high dynamic range imaging further facilitates quantitative phenotyping, while excellent imaging resolution provides single-cell discrimination with high accuracy. To design a ready-to-use panel for immune cell phenotyping, individual antibodies were first screened for specificity and then optimized for labeling sensitivity. Signal-to-noise, inter-assay reproducibility, and intra-assay precision were evaluated across serial sections in a multi-stage testing matrix to validate each antibody in isolation and assembled into a panel. Resulting from this effort, we introduce a 15-plex ready-to-use VistaPlex immune profiling panel for human FFPE tissues. Results: Image analysis of signal-to-noise ratios demonstrated robust performance of multiplexed antibodies across multiple tissue sections and tissue types. Measurements of both signal and quantitative immune phenotyping across technical replicates showed a high degree of intra-assay precision and inter-assay reproducibility. The 15-plex panel was validated on human tonsil, lung, breast, and colon tissues. Conclusions: The CellScape platform enables simultaneous detection of multiple protein biomarkers on a single tissue section for deep immune cell profiling in the TME. Rigorously validated VistaPlex panels further streamline this spatial biology workflow and will aid in the generation of consistent and reliable results for end users. Citation Format: Charles E. Jackson, Arne Christians, Jannik Boog, Matthew H. Ingalls, Xenia Meshik, Adam Northcutt, Kayla E. Cashion, J Spencer Schwarz, Oliver Braubach. A comprehensive analytical validation framework for antibodies used in the CellScape multiplex immunofluorescence assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1539.

Abstract 6169: High throughput in-situ spatial sequencing of proteins and RNA in FFPE tissue

Abstract We have developed an high throughput high resolution in situ multi-omic platform to enable studies in the rapidly evolving landscape of oncology and immunology research. Despite the potential of existing tools, the speed and throughput of current methodologies pose significant limitations. Here, we introduce a novel spatial sequencing platform employing a rapid 4-color SBS chemistry with sub-micron resolution imaging and an ultra-high throughput capacity. The platform simultaneously profiles RNA transcripts and proteins within formalin-fixed paraffin-embedded (FFPE) tissues. With up to 40 cm^2 of tissue area imaging within a 24-hour timeframe, the platform can power extensive, multi-omic studies in cancer research. To evaluate the performance of the platform, we prepared 5 um serial sections from healthy and tumor kidney FFPE block and used a novel tool to precisely position multiple tissue sections on a slide. Following probe binding and amplification, we used SBS readout to profile 105 transcripts and 6 proteins (via DNA-conjugated antibodies). Images were analyzed using a custom pipeline for feature detection and decoding, and cell segmentation using Cellpose. Results showed strong correlation to single-cell transcription profiles and cell type signatures defined by scRNA-Seq. Additionally, we see good agreement between single cell transcript and protein profiles. Existing spatial biology platforms are known to struggle with degraded and challenging clinical sample types (e.g. decalcified bone marrow), due to RNA fragmentation. We successfully profiled immune cells in FFPE-embedded bone marrow biopsies, measuring 143 transcript targets and 8 proteins. We believe this represents a significant step forward for in-situ analysis of bone marrow samples, and could be valuable for studying the pathology and response to treatment in blood cancers such as AML and CML. In-situ sequencing of antibody genes and T-cell receptors provides a deeper view of cellular immune response. As a step towards this goal, we demonstrated in-situ sequencing of antibody heavy and light chains within a B cell line, resulting in accurate readout of 28 bases within the CDR3 regions. In conclusion, we present an SBS-based approach for in-situ detection of targeted gene transcripts and proteins at an unprecedented throughput, and its application to the analysis of human kidney and bone marrow FFPE tissue samples. The capability to deeply profile tissues at high throughput could enable larger translational studies, and assembly of 3D maps of gene and protein expression at sub-cellular resolution. Citation Format: Michael Lawson, Yuji Ishitsuka, Andrew Pawlowski, Zhenmin Hong, Jake Koh, Kenneth Gouin, Nathan Ing, Richard Que, Yeoan Youn, Sara Ding, Tony Facchini, Ryan Costello, Katelyn Nelson, Jamie Stover, Howon Lee, Sarthak Duggal, Taylor Plaziak, Kaitlin Cameron, Mimi Abdu, Daan Witters, Martin Fabani, Eli Glezer. High throughput in-situ spatial sequencing of proteins and RNA in FFPE tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6169.

SpatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics.

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

Splenic stromal sarcomas in dogs: Outcome and clinicopathological prognostic factors in 32 cases.

Due to the low frequency and the changes in diagnostic techniques and terminology during the last few years, only little clinical information is available on splenic stromal sarcoma (SSS). This multi-institutional study aimed at gathering clinical cases of SSS in dogs and investigates their clinical behaviour, as well as analyse possible clinicopathological prognostic factors, including the use of adjuvant therapy. Dogs with a histologically confirmed SSS that underwent splenectomy were retrospectively included. To be included in the study, either FFPE tissue blocks or multiple tissue sections had to be available for histopathologic and immunohistochemical revision. Clinical and pathological variables, along with adjuvant therapy data, were collected. Cumulative incidence of metastatic disease was analysed through univariate and bivariate analyses. The impact of adjuvant chemotherapy on metastasis incidence and survival was assessed, considering an estimated propensity score. A total of 32 dogs were included. Among them, 22 developed metastases with an incidence of 37.5%, 59.38%, and 65.94% at 6, 12, and 24 months, respectively. Univariate analysis identified mitotic count, total scoring, and necrosis as prognostic factors. In bivariate analysis, mitotic count remained prognostic. The administration of adjuvant chemotherapy did not have an impact on metastasis incidence or survival time. The study found that dogs with SSSs are at high risk of metastasis, although a small subgroup may experience longer survival after splenectomy. Mitotic count was the only variable having a reliable prognostic impact. Adjuvant chemotherapy did not appear to decrease the incidence of metastasis or prolong survival in these dogs.

HAVOC: Small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks.

Intratumoral heterogeneity can wreak havoc on current precision medicine strategies because of challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. To address this gap, we developed a deep learning pipeline that leverages histomorphologic fingerprints of tissue to create "Histomic Atlases of Variation Of Cancers" (HAVOC). Using a number of objective molecular readouts, we demonstrate that HAVOC can define regional cancer boundaries with distinct biology. Using larger tumor specimens, we show that HAVOC can map biodiversity even across multiple tissue sections. By guiding profiling of 19 partitions across six high-grade gliomas, HAVOC revealed that distinct differentiation states can often coexist and be regionally distributed within these tumors. Last, to highlight generalizability, we benchmark HAVOC on additional tumor types. Together, we establish HAVOC as a versatile tool to generate small-scale maps of tissue heterogeneity and guide regional deployment of molecular resources to relevant biodiverse niches.

An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography

BackgroundAs an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure.ResultsWe propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow.ConclusionsOur workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.

Three-dimensional molecular cartography of human cerebral organoids revealed by double-barcoded spatial transcriptomics

Spatially resolved transcriptomics is revolutionizing our understanding of complex tissues, but scaling these approaches to multiple tissue sections and three-dimensional tissue reconstruction remains challenging and cost prohibitive. In this work, we present a low-cost strategy for manufacturing molecularly double-barcoded DNA arrays, enabling large-scale spatially resolved transcriptomics studies. We applied this technique to spatially resolve gene expression in several human brain organoids, including the reconstruction of a three-dimensional view from multiple consecutive sections, revealing gene expression heterogeneity throughout the tissue.

ISC.MEB: an R package for multi-sample spatial clustering analysis of spatial transcriptomics data.

Emerging spatially resolved transcriptomics (SRT) technologies are powerful in measuring gene expression profiles while retaining tissue spatial localization information and typically provide data from multiple tissue sections. We have previously developed the tool SC.MEB-an empirical Bayes approach for SRT data analysis using a hidden Markov random field. Here, we introduce an extension to SC.MEB, denoted as integrated spatial clustering with hidden Markov random field using empirical Bayes (iSC.MEB) that permits the users to simultaneously estimate the batch effect and perform spatial clustering for low-dimensional representations of multiple SRT datasets. We demonstrate that iSC.MEB can provide accurate cell/domain detection results using two SRT datasets. iSC.MEB is implemented in an open-source R package, and source code is freely available at https://github.com/XiaoZhangryy/iSC.MEB. Documentation and vignettes are provided on our package website (https://xiaozhangryy.github.io/iSC.MEB/index.html). Supplementary data are available at Bioinformatics Advances online.

BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

An efficient CNN based algorithm for detecting melanoma cancer regions in H&E-stained images.

Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.

Detection of malignant melanoma in H&E-stained images using deep learning techniques

Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin (H&E) stained histopathological images. In this technique, the nuclei in the image are first segmented using a Convolutional Neural Network (CNN). The segmented nuclei are then used to generate melanoma region masks. Experimental results with a small melanoma dataset show that the proposed method can potentially segment the nuclei with more than 94 % accuracy and segment the melanoma regions with a Dice coefficient of around 85 %. The proposed technique also has a small execution time making it suitable for clinical diagnosis with a fast turnaround time.

Locally Transmitted Trypanosoma cruzi in a Domestic Llama (Lama glama) in a Rural Area of Greater New Orleans, Louisiana, USA.

Trypanosoma cruzi-associated megaesophagus was diagnosed in a domestic Louisiana-born llama with no significant travel history. The llama resided in the same rural area of greater New Orleans, Louisiana, where the first human autochthonous case of Chagas disease was identified in the state. Venous blood from the llama tested positive for T. cruzi kinetoplastid DNA by conventional PCR. The cardiac evaluation was unremarkable, while thoracic radiographs revealed generalized megaesophagus. The llama received supportive care, but was ultimately humanely euthanized. The esophagus was severely distended throughout its length on necropsy, and histologic evaluation showed no microscopic changes in esophageal tissue and minimal to mild lymphoplasmacytic inflammation in cardiac tissue. T. cruzi DNA was detected by conventional PCR in the esophagus, small intestine, and blood despite no protozoan organisms being observed in multiple tissue sections examined. This report contributes to the growing body of evidence of local transmission of T. cruzi in the southern United States, and Chagas disease should be considered a differential diagnosis when evaluating llamas and other large animal species for esophageal dysfunction. There is little research describing megaesophagus or Chagas disease in llamas, and this report aims to increase awareness about this zoonotic disease that is becoming more frequently reported in the southern United States.