Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

Identification of Potential Key Biomarkers of Atrial Fibrillation and Their Correlation with Immune Infiltration in Atrial Tissue.

Objective: To identify potential key biomarkers and characterize immune infiltration in atrial tissue of patients with atrial fibrillation (AF) through bioinformatics analysis. Methods: Differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional and pathway enrichment analyses were undertaken using GO and KEGG. The LASSO logistic regression and BORUTA algorithm were employed to screen for potential novel key markers of AF from all DEGs. Gene set variation analysis was also performed. Single-sample gene set enrichment analysis was employed to quantify the infiltration levels for each immune cell type, and the correlation between hub genes and infiltrating immune cells was analyzed. Results: A total of 52 DEGs were identified, including of 26 downregulated DEGs and 26 upregulated DEGs. DEGs were primarily enriched in the Major Histocompatibility Complex class II protein complex, glucose homeostasis, protein tetramerization, regulation of synapse organization, cytokine activity, heart morphogenesis, and blood circulation. Three downregulated genes and three upregulated genes were screened by LASSO logistic regression and the BORUTA algorithm. Finally, immune infiltration analysis indicated that the atrial tissue of AF patients contained significant infiltration of APC_co_inhibition, Mast_cell, neutrophils, pDCs, T_cell_costimulation, and Th1_cells compared with paired sinus rhythm (SR) atrial tissue, and the three downregulated genes were negatively correlated with the six kinds of immune cells mentioned above. Conclusion: The hub genes identified in this study and the differences in immune infiltration of atrial tissue observed between AF and SR tissue might help to characterize the occurrence and progression of AF.

Deep Phenotypic Analysis of Blood and Lymphoid T and NK Cells From HIV+ Controllers and ART-Suppressed Individuals.

T and natural killer (NK) cells are effector cells with key roles in anti-HIV immunity, including in lymphoid tissues, the major site of HIV persistence. However, little is known about the features of these effector cells from people living with HIV (PLWH), particularly from those who initiated antiretroviral therapy (ART) during acute infection. Our study design was to use 42-parameter CyTOF to conduct deep phenotyping of paired blood- and lymph node (LN)-derived T and NK cells from three groups of HIV+ aviremic individuals: elite controllers (N = 5), and ART-suppressed individuals who had started therapy during chronic (N = 6) vs. acute infection (N = 8), the latter of which is associated with better outcomes. We found that acute-treated individuals are enriched for specific subsets of T and NK cells, including blood-derived CD56-CD16+ NK cells previously associated with HIV control, and LN-derived CD4+ T follicular helper cells with heightened expansion potential. An in-depth comparison of the features of the cells from blood vs. LNs of individuals from our cohort revealed that T cells from blood were more activated than those from LNs. By contrast, LNs were enriched for follicle-homing CXCR5+ CD8+ T cells, which expressed increased levels of inhibitory receptors and markers of survival and proliferation as compared to their CXCR5- counterparts. In addition, a subset of memory-like CD56brightTCF1+ NK cells was enriched in LNs relative to blood. These results together suggest unique T and NK cell features in acute-treated individuals, and highlight the importance of examining effector cells not only in blood but also the lymphoid tissue compartment, where the reservoir mostly persists, and where these cells take on distinct phenotypic features.

Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

Peripheral immune cells and perinatal brain injury: a double-edged sword?

Perinatal brain injury is the leading cause of neurological mortality and morbidity in childhood ranging from motor and cognitive impairment to behavioural and neuropsychiatric disorders. Various noxious stimuli, including perinatal inflammation, chronic and acute hypoxia, hyperoxia, stress and drug exposure contribute to the pathogenesis. Among a variety of pathological phenomena, the unique developing immune system plays an important role in the understanding of mechanisms of injury to the immature brain. Neuroinflammation following a perinatal insult largely contributes to evolution of damage to resident brain cells, but may also be beneficial for repair activities. The present review will focus on the role of peripheral immune cells and discuss processes involved in neuroinflammation under two frequent perinatal conditions, systemic infection/inflammation associated with encephalopathy of prematurity (EoP) and hypoxia/ischaemia in the context of neonatal encephalopathy (NE) and stroke at term. Different immune cell subsets in perinatal brain injury including their infiltration routes will be reviewed and critical aspects such as sex differences and maturational stage will be discussed. Interactions with existing regenerative therapies such as stem cells and also potentials to develop novel immunomodulatory targets are considered. IMPACT: Comprehensive summary of current knowledge on the role of different immune cell subsets in perinatal brain injury including discussion of critical aspects to be considered for development of immunomodulatory therapies.

The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy.

BACKGROUND: Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, their potential of shaping the tumor immune microenvironment (TIME) has been related to the immunotherapy of human cancers. However, the landscape of viral expressions and immune status in human cancers remains incompletely understood. METHODS: We developed a next-generation sequencing (NGS)-based pipeline to detect viral sequences from the whole transcriptome and used machine learning algorithms to classify different TIME subtypes. RESULTS: We revealed a pan-cancer landscape of viral expressions in human cancers where 9 types of viruses were detected in 744 tumors of 25 cancer types. Viral infections showed different tissue tendencies and expression levels. Multi-omics analyses further revealed their distinct impacts on genomic, transcriptomic and immune responses. Epstein-Barr virus (EBV)-infected stomach adenocarcinoma (STAD) and Human Papillomavirus (HPV)-infected head and neck squamous cell carcinoma (HNSC) showed decreased genomic variations, significantly altered gene expressions, and effectively triggered anti-viral immune responses. We identified three TIME subtypes, in which the "Immune-Stimulation" subtype might be the promising candidate for immunotherapy. EBV-infected STAD and HPV-infected HNSC showed a higher frequency of the "Immune-Stimulation" subtype. Finally, we constructed the eVIIS pipeline to simultaneously evaluate viral infection and immune status in external datasets. CONCLUSIONS: Viral infections are prevalent in human cancers and have distinct influences on hosts. EBV and HPV infections combined with the TIME subtype could be promising biomarkers of immunotherapy in STAD and HNSC, respectively. The eVIIS pipeline could be a practical tool to facilitate clinical practice and relevant studies.

A Polylobar Nucleus Identifying and Extracting Method for Leukocyte Counting.

Accurate counting of leukocytes is an important method for diagnosing human blood diseases. Because most nuclei of neutrophils and eosinophils are polylobar, it is easily confused with the unilobar nuclei in nucleus segmentation. Therefore, it is very essential to accurately identify and determine the polylobar leukocytes. In this paper, a polylobar nucleus identification and extracting method is proposed. Firstly, by using the Otsu threshold and area threshold method, the nuclei of leukocytes are accurately segmented. According to the morphological characteristics of polylobar leukocytes, the edges of the mitotic polylobar leukocytes are detected, and the numbers of polylobar leukocytes are determined according to the minimal distance rule. Therefore, the accurate counting of leukocytes can be realized. From the experimental results, we can see that using the Otsu method and the area threshold to segment the polylobar nuclear leukocytes, the segmentation ratio of the leukocyte nucleus reached 98.3%. After using the morphological features, the polylobar nuclear leukocytes can be accurately counted. The experimental results have verified the feasibility and practicability of the proposed method.

Enrichment of leukocytes in peripheral blood using 3D printed tubes.

Leukocytes have an essential role in patient clinical trajectories and progression. Traditional methods of leukocyte enrichment have many significant limitations for current applications. It is demonstrated a novel 3D printing leukocyte sorting accumulator that combines with centrifugation to ensure label-free initial leukocyte enrichment based on cell density and size. The internal structure of leukocyte sorting accumulator (revealed here in a new design, leukocyte sorting accumulator-3, upgraded from earlier models), optimizes localization of the buffy coat fraction and the length of the period allocated for a second centrifugation step to deliver a higher recovery of buffy coats than earlier models. Established methodological parameters were evaluated for reliability by calculating leukocyte recovery rates and erythrocyte depletion rates by both pushing and pulling methods of cell displacement. Results indicate that leukocyte sorting accumulator-3 achieves a mean leukocytes recovery fraction of 96.2 ± 2.38% by the pushing method of layer displacement. By the pulling method, the leukocyte sorting accumulator-3 yield a mean leukocytes recovery fraction of 94.4 ± 0.8%. New procedures for preliminary enrichment of leukocytes from peripheral blood that avoid cellular damage, as well as avert metabolic and phase cycle intervention, are required as the first step in many modern clinical and basic research assays.

Identification of SARS-CoV-2-specific immune alterations in acutely ill patients.

Dysregulated immune profiles have been described in symptomatic patients infected with SARS-CoV-2. Whether the reported immune alterations are specific to SARS-CoV-2 infection or also triggered by other acute illnesses remains unclear. We performed flow cytometry analysis on fresh peripheral blood from a consecutive cohort of (a) patients hospitalized with acute SARS-CoV-2 infection, (b) patients of comparable age and sex hospitalized for another acute disease (SARS-CoV-2 negative), and (c) healthy controls. Using both data-driven and hypothesis-driven analyses, we found several dysregulations in immune cell subsets (e.g., decreased proportion of T cells) that were similarly associated with acute SARS-CoV-2 infection and non-COVID-19-related acute illnesses. In contrast, we identified specific differences in myeloid and lymphocyte subsets that were associated with SARS-CoV-2 status (e.g., elevated proportion of ICAM-1+ mature/activated neutrophils, ALCAM+ monocytes, and CD38+CD8+ T cells). A subset of SARS-CoV-2-specific immune alterations correlated with disease severity, disease outcome at 30 days, and mortality. Our data provide an understanding of the immune dysregulation specifically associated with SARS-CoV-2 infection among acute care hospitalized patients. Our study lays the foundation for the development of specific biomarkers to stratify SARS-CoV-2-positive patients at risk of unfavorable outcomes and to uncover candidate molecules to investigate from a therapeutic perspective.

Interest of the cellular population data analysis as an aid in the early diagnosis of SARS-CoV-2 infection.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) is characterized by a high contagiousness requiring isolation measures. At this time, diagnosis is based on the positivity of specific RT-PCR and/or chest computed tomography scan, which are time-consuming and may delay diagnosis. Complete blood count (CBC) can potentially contribute to the diagnosis of COVID-19. We studied whether the analysis of cellular population data (CPD), provided as part of CBC-Diff analysis by the DxH 800 analyzers (Beckman Coulter), can help to identify SARS-CoV-2 infection. METHODS: Cellular population data of the different leukocyte subpopulations were analyzed in 137 controls, 322 patients with proven COVID-19 (COVID+), and 285 patients for whom investigations were negative for SARS-CoV-2 infection (COVID-). When CPD of COVID+ were different from controls and COVID- patients, we used receiver operating characteristic analysis to test the discriminating capacity of the individual parameters. Using a random forest classifier, we developed the algorithm based on the combination of 4 monocyte CPD to discriminate COVID+ from COVID- patients. This algorithm was tested prospectively in a series of 222 patients referred to the emergency unit. RESULTS: Among the 222 patients, 86 were diagnosed as COVID-19 and 60.5% were correctly identified using the discriminating protocol. Among the 136 COVID- patients, 10.3% were misclassified (specificity 89.7%, sensitivity 60.5%). False negatives were observed mainly in patients with a low inflammatory state whereas false positives were mainly seen in patients with sepsis. CONCLUSION: Consideration of CPD could constitute a first step and potentially aid in the early diagnosis of COVID-19.

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM.

In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time-consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best-selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

Leukocyte populations in peripheral blood of dromedary camels with clinical endometritis.

Endometritis represents the main cause of reproductive failure in dromedary camels. In dromedary camels, associations between endometritis-causing pathogen-species, disease severity, and systemic changes in the immune system have not been evaluated. In the current study, there was use of flow cytometry and immunofluorescence of membrane proteins for the evaluation of leukocyte subsets and the cellular phenotype in blood of camels with clinical endometritis and evaluations of associations with disease severity and endometritis-causing pathogens. Animals with endometritis had markedly larger numbers of total leukocytes and neutrophils. Although total lymphocyte and monocyte counts did not differ between camels with and without clinical endometritis, there were lesser numbers of total and effector CD4-positive T cells in camels with endometritis. Among monocytes, number of camel inflammatory monocytes (Mo-II) was markedly greater, whereas Mo-III numbers were less in the blood of camels with clinical endometritis. Number of inflammatory monocytes was also indicative of endometritis severity grade. Among camels with clinical endometritis, E. coli- and S. aureus-infected animals had similar endometritis grades and comparable phenotype and composition patterns of leukocytes. Neutrophils and monocytes of camels with clinical endometritis had fewer cell adhesion molecules (i.e., CD11a and CD18). Collectively, the results from the current study allowed for identification of associations between endometritis severity grade and larger numbers of inflammatory monocytes. The results also indicate there is no association between endometritis pathogen-species and changes in phenotype or composition of blood leukocytes.

Peripheral immunophenotypes in children with multisystem inflammatory syndrome associated with SARS-CoV-2 infection.

Recent reports highlight a new clinical syndrome in children related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)1-multisystem inflammatory syndrome in children (MIS-C)-which comprises multiorgan dysfunction and systemic inflammation2-13. We performed peripheral leukocyte phenotyping in 25 children with MIS-C, in the acute (n = 23; worst illness within 72 h of admission), resolution (n = 14; clinical improvement) and convalescent (n = 10; first outpatient visit) phases of the illness and used samples from seven age-matched healthy controls for comparisons. Among the MIS-C cohort, 17 (68%) children were SARS-CoV-2 seropositive, suggesting previous SARS-CoV-2 infections14,15, and these children had more severe disease. In the acute phase of MIS-C, we observed high levels of interleukin-1ß (IL-1ß), IL-6, IL-8, IL-10, IL-17, interferon-γ and differential T and B cell subset lymphopenia. High CD64 expression on neutrophils and monocytes, and high HLA-DR expression on γδ and CD4+CCR7+ T cells in the acute phase, suggested that these immune cell populations were activated. Antigen-presenting cells had low HLA-DR and CD86 expression, potentially indicative of impaired antigen presentation. These features normalized over the resolution and convalescence phases. Overall, MIS-C presents as an immunopathogenic illness1 and appears distinct from Kawasaki disease.

Leukocyte segmentation in peripheral blood images using a novel edge strength cue-based location detection method.

The classification of leukocytes in peripheral blood images is an important milestone to be achieved because it can greatly assist pathologists to diagnose diseases such as leukemia, anemia, and other blood disorders. To a certain extent, a good segmentation method for identifying leukocytes from their background is the first step to the efficient functioning of the leukocytes classification system. However, the morphological structure of leukocytes, poor contrast, and the variations in their shape and size lead to the degradation of the segmentation accuracy. In this paper, we propose a new leukocyte segmentation framework that first locates and then segments leukocytes from peripheral blood images. Here, the locations of the leukocytes are first identified using a novel edge strength cue (ESc), and later, the Grabcut model is deployed to obtain the segmentation of the leukocytes. The novelty lies in the way the location of the leukocytes is detected, and this improves the leukocyte segmentation accuracy. The experimental evaluation is performed on ALL-IDB1, Cellavision, and LISC datasets for leukocyte segmentation based on the detection of the ESc location. Experimental results are evaluated using precision, recall, and F-score measures. The proposed method outperforms the state-of-the-art techniques. Additionally, the computation time of the proposed method is analyzed and presented in the study. Graphical Abstract Leukocytes Location Detection and Segmentation.

Classification of white blood cells using capsule networks.

BACKGROUND: While the number and structural features of white blood cells (WBC) can provide important information about the health status of human beings, the ratio of sub-types of these cells and the deformations that can be observed serve as a good indicator in the diagnosis process of some diseases. Hence, correct identification and classification of the WBC types is of great importance. In addition, the fact that the diagnostic process that is carried out manually is slow, and the success is directly proportional to the expert's skills makes this problem an excellent field of application for computer-aided diagnostic systems. Unfortunately, both the ethical reasons and the cost of image acquisition process is one of the biggest obstacles to the fact that researchers working with medical images are able to collect enough data to produce a stable model. For that reasons, researchers who want to perform a successful analysis with small data sets using classical machine learning methods need to undergo their data a long and error-prone pre-process, while those using deep learning methods need to increase the data size using augmentation techniques. As a result, there is a need for a model that does not need pre-processing and can perform a successful classification in small data sets. METHODS: WBCs were classified under five categories using a small data set via capsule networks, a new deep learning method. We improved the model using many techniques and compared the results with the most known deep learning methods. RESULTS: Both the above-mentioned problems were overcame and higher success rates were obtained compared to other deep learning models. While, convolutional neural networks (CNN) and transfer learning (TL) models suffered from over-fitting, capsule networks learned well training data and achieved a high accuracy on test data (96.86%). CONCLUSION: In this study, we briefly discussed the abilities of capsule networks in a case study. We showed that capsule networks are a quite successful alternative for deep learning and medical data analysis when the sample size is limited.

White blood cells detection and classification based on regional convolutional neural networks.

White blood cells (WBC) are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. There are five types of WBC. These are called Lymphocytes, Monocytes, Eosinophils, Basophils and Neutrophils. The number of WBC types and the total number of WBCs provide important information about our health status. Diseases such as leukemia, AIDS, autoimmune diseases, immune deficiencies, blood diseases can be diagnosed based on the number of WBCs. In this study, a computer-aided automated system that can easily identify and locate WBC types in blood images has been proposed. Current blood test devices usually detect WBCs with traditional image processing methods such as preprocessing, segmentation, feature extraction, feature selection and classification. Deep learning methodology is superior to traditional image processing methods in literature. In addition, traditional methods require the appearance of the whole object to be able to recognize objects. Contrary to traditional methods, convolutional neural networks (CNN), a deep learning architecture, can extract features from a part of an object and perform object recognition. In this case, a CNN-based system shows a higher performance in recognizing partially visible cells for reasons such as overlap or only partial visibility of the image. Therefore, it has been the motivation of this study to increase the performance of existing blood test devices with deep learning method. Blood cells have been identified and classified by Regional Based Convolutional Neural Networks. Designed architectures have been trained and tested by combining BCCD data set and LISC data set. Regional Convolutional Neural Networks (R - CNN) has been used as a methodology. In this way, different cell types within the same image have been classified simultaneously with a detector. While training CNN which is the basis of R - CNN architecture; AlexNet, VGG16, GoogLeNet, ResNet50 architectures have been tested with full learning and transfer learning. At the end of the study, the system has showed 100% success in determining WBC cells. ResNet50, one of the CNN architectures, has showed the best performance with transfer learning. Cell types of Lymphocyte were determined with 99.52% accuracy rate, Monocyte with 98.40% accuracy rate, Basophil with 98.48% accuracy rate, Eosinophil with 96.16% accuracy rate and Neutrophil with 95.04% accuracy rate.

CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.

Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address tissue heterogeneity have drawbacks. Experimental techniques, such as fluorescence-activated cell sorting, and single cell RNA sequencing are expensive. Computational approaches that use expression data from heterogeneous samples are promising, but most of the current methods estimate either cell-type proportions or cell-type-specific expression profiles by requiring the other as input. Although such partial deconvolution methods have been successfully applied to tumor samples, the additional input required may be unavailable. We introduce a novel complete deconvolution method, CDSeq, that uses only RNA-Seq data from bulk tissue samples to simultaneously estimate both cell-type proportions and cell-type-specific expression profiles. Using several synthetic and real experimental datasets with known cell-type composition and cell-type-specific expression profiles, we compared CDSeq's complete deconvolution performance with seven other established deconvolution methods. Complete deconvolution using CDSeq represents a substantial technical advance over partial deconvolution approaches and will be useful for studying cell mixtures in tissue samples. CDSeq is available at GitHub repository (MATLAB and Octave code): https://github.com/kkang7/CDSeq.

Tumor-Infiltrating Leukocyte Composition and Prognostic Power in Hepatitis B- and Hepatitis C-Related Hepatocellular Carcinomas.

BACKGROUND: Tumor-infiltrating leukocytes (TILs) are immune cells surrounding tumor cells, and several studies have shown that TILs are potential survival predictors in different cancers. However, few studies have dissected the differences between hepatitis B- and hepatitis C-related hepatocellular carcinoma (HBV-HCC and HCV-HCC). Therefore, we aimed to determine whether the abundance and composition of TILs are potential predictors for survival outcomes in HCC and which TILs are the most significant predictors. METHODS: Two bioinformatics algorithms, ESTIMATE and CIBERSORT, were utilized to analyze the gene expression profiles from 6 datasets, from which the abundance of corresponding TILs was inferred. The ESTIMATE algorithm examined the overall abundance of TILs, whereas the CIBERSORT algorithm reported the relative abundance of 22 different TILs. Both HBV-HCC and HCV-HCC were analyzed. RESULTS: The results indicated that the total abundance of TILs was higher in non-tumor tissue regardless of the HCC type. Alternatively, the specific TILs associated with overall survival (OS) and recurrence-free survival (RFS) varied between subtypes. For example, in HBV-HCC, plasma cells (hazard ratio [HR] = 1.05; 95% CI 1.00-1.10; p = 0.034) and activated dendritic cells (HR = 1.08; 95% CI 1.01-1.17; p = 0.03) were significantly associated with OS, whereas in HCV-HCC, monocytes (HR = 1.21) were significantly associated with OS. Furthermore, for RFS, CD8+ T cells (HR = 0.98) and M0 macrophages (HR = 1.02) were potential biomarkers in HBV-HCC, whereas neutrophils (HR = 1.01) were an independent predictor in HCV-HCC. Lastly, in both HBV-HCC and HCV-HCC, CD8+ T cells (HR = 0.97) and activated dendritic cells (HR = 1.09) had a significant association with OS, while γ delta T cells (HR = 1.04), monocytes (HR = 1.05), M0 macrophages (HR = 1.04), M1 macrophages (HR = 1.02), and activated dendritic cells (HR = 1.15) were highly associated with RFS. Conclusions: These findings demonstrated that TILs are potential survival predictors in HCC and different kinds of TILs are observed according to the virus type. Therefore, further investigations are warranted to elucidate the role of TILs in HCC, which may improve immunotherapy outcomes.

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology.

High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types.

Multiplexed imaging of immune cells in staged multiple sclerosis lesions by mass cytometry.

Multiple sclerosis (MS) is characterized by demyelinated and inflammatory lesions in the brain and spinal cord that are highly variable in terms of cellular content. Here, we used imaging mass cytometry (IMC) to enable the simultaneous imaging of 15+ proteins within staged MS lesions. To test the potential for IMC to discriminate between different types of lesions, we selected a case with severe rebound MS disease activity after natalizumab cessation. With post-acquisition analysis pipelines we were able to: (1) Discriminate demyelinating macrophages from the resident microglial pool; (2) Determine which types of lymphocytes reside closest to blood vessels; (3) Identify multiple subsets of T and B cells, and (4) Ascertain dynamics of T cell phenotypes vis-à-vis lesion type and location. We propose that IMC will enable a comprehensive analysis of single-cell phenotypes, their functional states and cell-cell interactions in relation to lesion morphometry and demyelinating activity in MS patients.