scholarly journals Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Edwin Roger Parra
Keyword(s):  
Image Analysis ◽  
Spatial Distribution ◽  
Spatial Analysis ◽  
Tumor Microenvironment ◽  
Individual Cell ◽  
Clinical Information ◽  
Cell Populations ◽  
Cellular Distribution ◽  
Main Challenge ◽  
Cell Phenotypes

Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.

scholarly journals 668 A toolkit for the quantitative analysis of the spatial distribution of cells of the tumor immune microenvironment

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A705-A705
Author(s):  
Anna Trigos ◽  
Tianpei Yang ◽  
Yuzhou Feng ◽  
Volkan Ozcoban ◽  
Maria Doyle ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Analysis ◽  
Spatial Distribution ◽  
Data Analysis ◽  
Spatial Data ◽  
R Package ◽  
Immune Microenvironment ◽  
Spatial Image ◽  
Tumor Immune Microenvironment ◽  
Cell Phenotypes

BackgroundSpatial technologies that query the location of cells in tissues such as multiplex immunohistochemistry and spatial transcriptomics are gaining popularity and are likely to become commonplace. The resulting data often includes the X, Y coordinates of millions of cells, cell phenotypes and marker or gene expression levels. In cancer, the spatial location of lymphocytes has been linked to prognosis and response to immunotherapy. While these advances have been exciting for the field, the methods currently being used are still coarse, making us severely underpowered in our ability to extract quantifiable information. Appropriate quantitative tools are desperately needed to refine and uncover novel biologically and clinically meaningful insights from the spatial distribution of cells of the tumor immune microenvironment.MethodsWe compiled over 60 prostate cancer and melanoma FFPE tumor sections and performed Opal multiplex immunohistochemistry for a diversity of T-cell and other immune markers, including CD3, CD4, CD8, FOXP3 and PDL1, as well as a prostate cancer (AMACR) or melanoma (SOX10) marker and DAPI. Following spectral imaging on the Vectra Polaris, we performed cell and tissue segmentation and phenotyping with the inForm or HALO image analysis software. The detected X, Y coordinates of cells and marker intensities were used for subsequent method development.ResultsWe developed SPIAT (Spatial Image Analysis of Tissues)1, an R package with a suite of data processing, quality control, visualization, data handling and data analysis tools for spatial data. SPIAT includes our novel algorithms for the identification of cell clusters, tumor margins and cell gradients, the calculation of neighborhood proportions and algorithms for the prediction of cell phenotypes. By interfacing with packages used in ecology, geographic data analysis and spatial statistics, we have begun to robustly address fundamental questions in the analysis of cell spatial data, such as metrics to measure mixing between cell types, the identification of tumor borders and statistical approaches to compare samples.ConclusionsSPIAT is compatible with multiplex immunohistochemistry, spatial transcriptomics and data generated from other spatial platforms, and continues to be actively developed. We expect SPIAT to become a user-friendly and speedy go-to package for the spatial analysis of cells in tissues, as well as promote the use of quantitative metrics in the spatial analysis of the tumor immune microenvironment.ReferencesTianpei Yang, Volkan Ozcoban, Anu Pasam, Nikolce Kocovski, Angela Pizzolla, Yu-Kuan Huang, Greg Bass, Simon P. Keam, Paul J. Neeson, Shahneen K. Sandhu, David L. Goode, Anna S. Trigos. SPIAT: An R package for the Spatial Image Analysis of Cells in Tissues. BioRxiv doi: https://doi.org/10.1101/2020.05.28.122614

scholarly journals Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Danielle J. Fassler ◽  
Shahira Abousamra ◽  
Rajarsi Gupta ◽  
Chao Chen ◽  
Maozheng Zhao ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Spatial Analysis ◽  
Tumor Microenvironment ◽  
Nearest Neighbor ◽  
Digital Pathology ◽  
Ensemble Methods ◽  
Ductal Adenocarcinoma ◽  
Learning Methods ◽  
Color Deconvolution

Abstract Background Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of uniquely colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel. Methods Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and (3) ensemble methods that employ both ColorAE and U-Net, collectively referred to as ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor). Results We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect six different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net in ensemble methods outperform ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). Summary We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also utilized the ColorAE:U-Net ensemble method to analyze 3 mIHC WSIs with nearest neighbor spatial analysis. We demonstrate a proof of concept that these methods can be employed to quantitatively describe the spatial distribution of immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.

scholarly journals Identification of distinct immune landscapes using an automated nine-color multiplex immunofluorescence staining panel and image analysis in paraffin tumor tissues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Edwin R. Parra ◽  
Jie Zhai ◽  
Auriole Tamegnon ◽  
Nicolas Zhou ◽  
Renganayaki Krishna Pandurengan ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Image Analysis ◽  
Spatial Distribution ◽  
Improve Patient Outcome ◽  
Cell Phenotype ◽  
Tumor Tissues ◽  
Translational Studies ◽  
Cell Phenotypes ◽  
Immune Profiling ◽  
Improve Patient

AbstractImmune profiling is becoming a vital tool for identifying predictive and prognostic markers for translational studies. The study of the tumor microenvironment (TME) in paraffin tumor tissues such as malignant pleural mesothelioma (MPM) could yield insights to actionable targets to improve patient outcome. Here, we optimized and tested a new immune-profiling method to characterize immune cell phenotypes in paraffin tissues and explore the co-localization and spatial distribution between the immune cells within the TME and the stromal or tumor compartments. Tonsil tissues and tissue microarray (TMA) were used to optimize an automated nine-color multiplex immunofluorescence (mIF) panel to study the TME using eight antibodies: PD-L1, PD-1, CD3, CD8, Foxp3, CD68, KI67, and pancytokeratin. To explore the potential role of the cells into the TME with this mIF panel we applied this panel in twelve MPM cases to assess the multiple cell phenotypes obtained from the image analysis and well as their spatial distribution in this cohort. We successful optimized and applied an automated nine-color mIF panel to explore a small set of MPM cases. Image analysis showed a high degree of cell phenotype diversity with immunosuppression patterns in the TME of the MPM cases. Mapping the geographic cell phenotype distribution in the TME, we were able to identify two distinct, complex immune landscapes characterized by specific patterns of cellular distribution as well as cell phenotype interactions with malignant cells. Successful we showed the optimization and reproducibility of our mIF panel and their incorporation for comprehensive TME immune profiling into translational studies that could refine our ability to correlate immunologic phenotypes with specific patterns of cells distribution and distance analysis. Overall, this will improve our ability to understand the behavior of cells within the TME and predict new treatment strategies to improve patient outcome.

scholarly journals Spatial Analysis (Measurements at Heights of 10 m and 20 m above Ground Level) of the Concentrations of Particulate Matter (PM10, PM2.5, and PM1.0) and Gaseous Pollutants (H2S) on the University Campus: A Case Study

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 62
Author(s):  
Robert Cichowicz ◽  
Maciej Dobrzański
Keyword(s):  
Spatial Distribution ◽  
Hydrogen Sulfide ◽  
Particulate Matter ◽  
Air Quality ◽  
Spatial Analysis ◽  
Ground Level ◽  
Leeward Side ◽  
High Concentration ◽  
Above Ground Level ◽  
High Concentrations

Spatial analysis of the distribution of particulate matter PM10, PM2.5, PM1.0, and hydrogen sulfide (H2S) gas pollution was performed in the area around a university library building. The reasons for the subject matter were reports related to the perceptible odor characteristic of hydrogen sulfide and a general poor assessment of air quality by employees and students. Due to the area of analysis, it was decided to perform measurements at two heights, 10 m and 20 m above ground level, using measuring equipment attached to a DJI Matrice 600 unmanned aerial vehicle (UAV). The aim of the measurements was air quality assessment and investigate the convergence of the theory of air flow around the building with the spatial distribution of air pollutants. Considerable differences of up to 63% were observed in the concentrations of pollutants measured around the building, especially between opposite sides, depending on the direction of the wind. To explain these differences, the theory of aerodynamics was applied to visualize the probable airflow in the direction of the wind. A strong convergence was observed between the aerodynamic model and the spatial distribution of pollutants. This was evidenced by the high concentrations of dust in the areas of strong turbulence at the edges of the building and on the leeward side. The accumulation of pollutants was also clearly noticeable in these locations. A high concentration of H2S was recorded around the library building on the side of the car park. On the other hand, the air turbulence around the building dispersed the gas pollution, causing the concentration of H2S to drop on the leeward side. It was confirmed that in some analyzed areas the permissible concentration of H2S was exceeded.

scholarly journals 49 In situ phenotypic analysis of T cells in the tumor microenvironment of a pre-clinical model of non-small cell lung cancer (NSCLC) by tissue sectioning and whole-mount immunofluorescence imaging

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A52-A52
Author(s):  
Elen Torres ◽  
Stefani Spranger
Keyword(s):  
T Cells ◽  
Spatial Distribution ◽  
T Cell ◽  
Spatial Location ◽  
Cell Infiltration ◽  
Cell Populations ◽  
Tumor Bearing ◽  
T Cell Infiltration ◽  
Volume Imaging ◽  
Whole Mount

BackgroundUnderstanding the interactions between tumor and immune cells is critical for improving current immunotherapies. Pre-clinical and clinical evidence has shown that failed T cell infiltration into lung cancer lesions might be associated with low responsiveness towards checkpoint blockade.1 For this reason, it is necessary to characterize not only the phenotype of T cells in tumor-bearing lungs but also their spatial location in the tumor microenvironment (TME). Multiplex immunofluorescence staining allows the simultaneous use of several cell markers to study the state and the spatial location of cell populations in the tissue of interest. Although this technique is usually applied to thin tissue sections (5 to 12 µm), the analysis of large tissue volumes may provide a better understanding of the spatial distribution of cells in relation to the TME. Here, we analyzed the number and spatial distribution of cytotoxic T cells and other immune cells in the TME of tumor-bearing lungs, using both 12 µm sections and whole-mount preparations imaged by confocal microscopy.MethodsLung tumors were induced in C57BL/6 mice by tail vein injection of a cancer cell line derived from KrasG12D/+ and Tp53-/- mice. Lung tissue with a diverse degree of T cell infiltration was collected after 21 days post tumor induction. Tissue was fixed in 4% PFA, followed by snap-frozen for sectioning. Whole-mount preparations were processed according to Weizhe Li et al. (2019) 2 for tissue clearing and multiplex volume imaging. T cells were labeled with CD8 and FOXP3 antibodies to identify cytotoxic or regulatory T cells, respectively. Tumor cells were labeled with a pan-Keratin antibody. Images were acquired using a Leica SP8 confocal microscope. FIJI3 and IMARIS were used for image processing.ResultsWe identified both cytotoxic and regulatory T cell populations in the TME using thin sections and whole-mount. However, using whole-mount after tissue clearing allowed us to better evaluate the spatial distribution of the T cell populations in relation to the tumor structure. Furthermore, tissue clearance facilitates the imaging of larger volumes using multiplex immunofluorescence.ConclusionsAnalysis of large lung tissue volumes provides a better understanding of the location of immune cell populations in relation to the TME and allows to study heterogeneous immune infiltration on a per-lesion base. This valuable information will improve the characterization of the TME and the definition of cancer-immune phenotypes in NSCLC.ReferencesTeng MW, et al., Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res 2015;75(11): p. 2139–45.Li W, Germain RN, and Gerner MY. High-dimensional cell-level analysis of tissues with Ce3D multiplex volume imaging. Nat Protoc 2019;14(6): p. 1708–1733.Schindelin J, et al, Fiji: an open-source platform for biological-image analysis. Nat Methods 2012;9(7): p. 676–82.

scholarly journals Spatial Distribution of Breast Cancer in Sudan 2010-2016

2019 ◽  
Author(s):  
Marwa Maweya Abdelbagi Elbasheer ◽  
Ayah Galal Abdelrahman Alkhidir ◽  
Siham Mohammed Awad Mohammed ◽  
Areej Abuelgasim Hassan Abbas ◽  
Aisha Osman Mohamed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Spatial Distribution ◽  
Spatial Analysis ◽  
High Risk ◽  
Geographical Information ◽  
Risk Level ◽  
Histopathological Diagnosis ◽  
Cross Sectional ◽  
Distribution Maps ◽  
Risk Areas

AbstractBackgroundBreast cancer is the most prevalent cancer among females worldwide including Sudan. The aim of this study was to determine the spatial distribution of breast cancer in Sudan.Materials and methodsA facility based cross-sectional study was implemented in eighteen histopathology laboratories distributed in the three localities of Khartoum State on a sample of 4630 Breast Cancer cases diagnosed during the period 2010-2016. A master database was developed through Epi Info™ 7.1.5.2 for computerizing the data collected: the facility name, type (public or private), and its geo- location (latitude and longitude). Personal data on patients were extracted from their respective medical records (name, age, marital status, ethnic group, State, locality, administrative unit, permanent address and phone number, histopathology diagnosis). The data was summarized through SPSS to generate frequency tables for estimating prevalence and the geographical information system (ArcGIS 10.3) was used to generate the epidemiological distribution maps. ArcGIS 10.3 spatial analysis features were used to develop risk maps based on the kriging method.ResultsBreast cancer prevalence was 3.9 cases per 100,000 female populations. Of the 4423 cases of breast cancer, invasive breast carcinoma of no special type (NST) was the most frequent (79.5%, 3517/4423) histopathological diagnosis. The spatial analysis indicated as high risk areas for breast cancer in Sudan the States of Nile River, Northern, Red Sea, White Nile, Northern and Southern Kordofan.ConclusionsThe attempt to develop a predictive map of breast cancer in Sudan revealed three levels of risk areas (risk, intermediate and high risk areas); regardless the risk level, appropriate preventive and curative health interventions with full support from decision makers are urgently needed.

scholarly journals Units of chromosome replication and packing

1983 ◽  
Vol 64 (1) ◽  
pp. 179-193
Author(s):  
A.M. Mullinger ◽  
R.T. Johnson
Keyword(s):  
Spatial Distribution ◽  
Light Microscope ◽  
Individual Cell ◽  
S Phase ◽  
Scanning Electron Micrographs ◽  
Metaphase Chromosomes ◽  
Impregnation Technique ◽  
Osmium Impregnation ◽  
The Relationship ◽  
Scanning Electron

Fusion between mitotic and S-phase cells induces the formation of prematurely condensed chromosomes (PCC) in the interphase partner. Viewed in the light microscope, S-phase PCC derived from the Indian muntjac appear to be fragmented and heterogeneous. In scanning electron micrographs prepared by an osmium impregnation technique, which avoids the need to sputter-coat the specimen, the S-phase fragments derived from an individual cell are resolved into about 1000 fibre aggregates, together with more dispersed fibres. Aggregates are roughly spherical and vary in diameter between about 0.25 and 1.6 micron. The spatial distribution of the aggregates shows some order: chains of single aggregates and, less commonly, duplicated chains occur. Regions of the PCC where the fibres are more dispersed are considered to be likely candidates for sites of replication at the time of fusion. The relationship between the condensed aggregate structure of the S-phase PCC and replication clusters is discussed, and also the assembly of aggregates to form metaphase chromosomes.

scholarly journals Ultrasonic image analysis and image-guided interventions

2011 ◽  
Vol 1 (4) ◽  
pp. 673-685 ◽  
Author(s):  
J. Alison Noble ◽  
Nassir Navab ◽  
H. Becher
Keyword(s):  
Image Analysis ◽  
Imaging Modality ◽  
Medical Image Analysis ◽  
Three Dimensional ◽  
Clinical Information ◽  
Image Guided ◽  
Software Algorithms ◽  
Positron Emission ◽  
Imaging Research ◽  
Magnetic Resonance Imaging Mri

The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

scholarly journals Spatial distribution and determinant factors of anaemia among women of reproductive age in Ethiopia: a multilevel and spatial analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. e027276 ◽  
Author(s):  
Kelemu Tilahun Kibret ◽  
Catherine Chojenta ◽  
Ellie D’Arcy ◽  
Deborah Loxton
Keyword(s):  
Spatial Distribution ◽  
Spatial Analysis ◽  
Breast Feeding ◽  
Reproductive Age ◽  
Hiv Positive ◽  
Women Of Reproductive Age ◽  
Factors Associated ◽  
Determinant Factors ◽  
Depth Analysis ◽  
North Eastern

ObjectiveThe aim of this study was to assess the spatial distribution and determinant factors of anaemia among reproductive age women in Ethiopia.MethodsAn in-depth analysis of the 2016 Ethiopian Demographic and Health Survey data was undertaken. Getis-Ord Gi* statistics were used to identify the hot and cold spot areas for anaemia among women of reproductive age. A multilevel logistic regression model was used to identify independent predictors of anaemia among women of reproductive age.ResultsOlder age (adjusted OR [AOR]=0.75; 95% CI 0.64 to 0.96), no education (AOR=1.37; 95% CI 1.102 to 1.72), lowest wealth quantile (AOR=1.29; 95% CI 1.014 to 1.60), currently pregnant (AOR=1.28; 95% CI 1.10 to 1.51, currently breast feeding (AOR=1.09; 95% CI 1.025 to 1.28), high gravidity (AOR=1.39; 95% CI 1.13 to 1.69) and HIV positive (AOR=2.11; 95% CI 1.59 to 2.79) are individual factors associated with the occurrence of anaemia. Likewise, living in a rural area (AOR=1.29; 95% CI 1.02 to 1.63) and availability of unimproved latrine facilities (AOR=1.18; 95% CI 1.01 to 1.39) are community-level factors associated with higher odds of anaemia. The spatial analysis indicated that statistically high hotspots of anaemia were observed in the eastern (Somali, Dire Dawa and Harari regions) and north-eastern (Afar) parts of the country.ConclusionThe prevalence rate of anaemia among women of reproductive age varied across the country. Significant hotspots/high prevalence of anaemia was observed in the eastern and north-eastern parts of Ethiopia. Anaemia prevention strategies need to be targeted on rural residents, women with limited to no education, women who are breast feeding, areas with poor latrine facilities and women who are HIV positive.

Sign in / Sign up

Export Citation Format

Share Document