Whole-Slide Image Analysis of Human Pancreas Samples to Elucidate the Immunopathogenesis of Type 1 Diabetes Using the QuPath Software

Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes

Human islets display a high degree of heterogeneity in terms of size, number, architecture, and endocrine cell-type compositions. An ever-increasing number of immunohistochemistry-stained whole slide images (WSIs) are available through the online pathology database of the Network for Pancreatic Organ donors with Diabetes (nPOD) program at the University of Florida (UF). We aimed to develop an enhanced machine learning-assisted WSI analysis workflow to utilize the nPOD resource for analysis of endocrine cell heterogeneity in the natural history of type 1 diabetes (T1D) in comparison to donors without diabetes. To maximize usability, the user-friendly open-source software QuPath was selected for the main interface. The WSI data were analyzed with two pre-trained machine learning models (i.e., Segment Anything Model (SAM) and QuPath's pixel classifier), using the UF high-performance-computing cluster, HiPerGator. SAM was used to define precise endocrine cell and cell grouping boundaries (with an average quality score of 0.91 per slide), and the artificial neural network-based pixel classifier was applied to segment areas of insulin- or glucagon-stained cytoplasmic regions within each endocrine cell. An additional script was developed to automatically count CD3+ cells inside and within 20 μm of each islet perimeter to quantify the number of islets with inflammation (i.e., CD3+ T-cell infiltration). Proof-of-concept analysis was performed to test the developed workflow in 12 subjects using 24 slides. This open-source machine learning-assisted workflow enables rapid and high throughput determinations of endocrine cells, whether as single cells or within groups, across hundreds of slides. It is expected that the use of this workflow will accelerate our understanding of endocrine cell and islet heterogeneity in the context of T1D endotypes and pathogenesis.

Morphological digital assessment and transcripts of gastric and duodenal visfatin in growing piglets fed with increasing amounts of polyphenols from olive mill waste extract

Visfatin is an adipokine with mediatory effects on inflammation. It is expressed at low levels in the pig stomach, but its role in the gastrointestinal (GI) tract is not well understood. This study explored visfatin expression and localisation in the stomach and duodenum of piglets fed varying levels of polyphenols derived from olive mill waste extract, known for their antioxidant and immunomodulatory properties. Twenty-seven piglets were assigned to three dietary groups: control (commercial feed), low polyphenol (120 ppm), and high polyphenol (240 ppm) groups. After 14 days of feeding, samples from the glandular stomach and duodenum were collected from 13 piglets. Immunohistochemistry (IHC), digital image analysis (DIA) using QuPath software, and double-labelled immunofluorescence were performed to detect visfatin-positive cells and co-localise them with serotonin. Additionally, relative gene expression of visfatin was assessed via RT-qPCR. Visfatin-positive cells were identified in 5 out of 13 piglets, localised mainly in the basal portion of gastric and intestinal glands. The morphology of those cells was consistent with neuroendocrine cells and confirmed by co-localisation of visfatin and serotonin. No significant differences were found in cell positivity or morphology between dietary groups or between tissues. However, visfatin transcript levels increased with the dose of polyphenolic extract. These findings suggest that dietary polyphenols may modulate visfatin gene expression in the GI tract. The study also highlights the value of digital anatomy for enhancing the accuracy and reproducibility of anatomical research. Further studies are needed to elucidate the functional role of visfatin transcript and protein in the porcine GI tract.

Quantitative analysis of islet prohormone convertase 1/3 expression in human pancreas donors with diabetes

AIMS/HYPOTHESIS

Islet prohormone-processing enzymes convert peptide hormone precursors to mature hormones. Defective beta cell prohormone processing and the release of incompletely processed peptide hormones are observed prior to the onset of diabetes, yet molecular mechanisms underlying impaired prohormone processing during the development of diabetes remains largely unknown. Previous studies have shown that prohormone convertase 1/3 (PC1/3) protein and mRNA expression levels are reduced in whole islets from donors with type 1 diabetes, although whether PC1/3-mediated prohormone processing in alpha and beta cells is disrupted in type 1 diabetes remained to be explored. Herein, we aimed to analyse the expression of PC1/3 in islets from non-diabetic donors, autoantibody-positive donors and donors diagnosed with type 1 diabetes or type 2 diabetes.

METHODS

Immunostaining and high-dimensional image analysis were performed on pancreatic sections from a cross-sectional cohort of 54 donors obtained from the Network for Pancreatic Organ Donors with Diabetes (nPOD) repository, to evaluate PC1/3 expression patterns in islet alpha, beta and delta cells at different stages of diabetes.

RESULTS

Alpha and beta cell morphology were altered in donors with type 1 diabetes, including decreased alpha and beta cell size. As expected, the insulin-positive and PC1/3-positive areas in the islets were both reduced, and this was accompanied by a reduced percentage of PC1/3-positive and insulin-positive/PC1/3-positive cells in islets. PC1/3 and insulin co-localisation was also reduced. The glucagon-positive area, as well as the percentage of glucagon-positive and glucagon-positive/PC1/3-positive cells in islets, was increased. PC1/3 and glucagon co-localisation was also increased in donors with type 1 diabetes. The somatostatin-positive cell area and somatostatin staining intensity were elevated in islets from donors with recent-onset type 1 diabetes.

CONCLUSIONS/INTERPRETATION

Our high-resolution histomorphological analysis of human pancreatic islets from donors with and without diabetes has uncovered details of the cellular origin of islet prohormone peptide processing defects. Reduced beta cell PC1/3 and increased alpha cell PC1/3 in islets from donors with type 1 diabetes pinpointed the functional deterioration of beta cells and the concomitant potential increase in PC1/3 usage for prohormone processing in alpha cells during the pathogenesis of type 1 diabetes. Our finding of PC1/3 loss in beta cells may inform the discovery of new prohormone biomarkers as indicators of beta cell dysfunction, and the finding of elevated PC1/3 expression in alpha cells may encourage the design of therapeutic targets via leveraging alpha cell adaptation in diabetes.

AI-Assisted High-Throughput Tissue Microarray Workflow

Immunohistochemical (IHC) studies of formalin-fixed paraffin-embedded (FFPE) samples are a gold standard in oncology for tumor characterization, and the identification of prognostic and predictive markers. However, despite the abundance of archived FFPE samples, their research use is limited due to the labor-intensive nature of IHC on large cohorts. This study aimed to create a high-throughput workflow using modern technologies to facilitate IHC biomarker studies on large patient groups. Semiautomatic constructed tissue microarrays (TMAs) were created for two tumor patient cohorts and IHC stained for seven antibodies (ABs). AB expression in the tumor and surrounding stroma was quantified using the AI-supported image analysis software QuPath. The data were correlated with clinicopathological information using an R-script, all results were automatically compiled into formatted reports. By minimizing labor time to 7.7%-compared to whole-slide studies-the established workflow significantly reduced human and material resource consumption. It successfully correlated AB expression with overall patient survival and additional clinicopathological data, providing publication-ready figures and tables. The AI-assisted high-throughput TMA workflow, validated on two patient cohorts, streamlines modern histopathological research by offering cost and time efficiency compared to traditional whole-slide studies. It maintains research quality and preserves patient tissue while significantly reducing material and human resources, making it ideal for high-throughput research centers and collaborations.

Impaired islet function and normal exocrine enzyme secretion occur with low inter-regional variation in type 1 diabetes

Histopathological heterogeneity in the human pancreas is well documented; however, functional evidence at the tissue level is scarce. Herein, we investigate in situ glucose-stimulated islet and carbachol-stimulated acinar cell secretion across the pancreas head (PH), body (PB), and tail (PT) regions in donors without diabetes (ND; n = 15), positive for one islet autoantibody (1AAb+; n = 7), and with type 1 diabetes (T1D; <14 months duration, n = 5). Insulin, glucagon, pancreatic amylase, lipase, and trypsinogen secretion along with 3D tissue morphometrical features are comparable across regions in ND. In T1D, insulin secretion and beta-cell volume are significantly reduced within all regions, while glucagon and enzymes are unaltered. Beta-cell volume is lower despite normal insulin secretion in 1AAb+, resulting in increased volume-adjusted insulin secretion versus ND. Islet and acinar cell secretion in 1AAb+ are consistent across the PH, PB, and PT. This study supports low inter-regional variation in pancreas slice function and, potentially, increased metabolic demand in 1AAb+.

The CD318/CD6 axis limits type 1 diabetes islet autoantigen-specific human T cell activation

CD6 is a glycoprotein expressed on CD4 and CD8 T cells involved in immunoregulation. CD318 has been identified as a CD6 ligand. The role of CD318 in T cell immunity is restricted as it has only been investigated in a few mice autoimmune models but not in human diseases. CD318 expression was thought to be limited to mesenchymal-epithelial cells and, therefore, contribute to CD6-mediated T cell activation in the CD318-expressing tissue rather than through interaction with antigen-presenting cells. Here, we report CD318 expression in a subpopulation of CD318+ myeloid dendritic (mDC), whereas the other peripheral blood populations were CD318 negative. However, CD318 can be induced by activation: a subset of monocytes treated with LPS and IFNγ and in vitro monocyte derived DCs were CD318+. We also showed that recombinant CD318 inhibited T cell function. Strikingly, CD318+ DCs suppressed the proliferation of autoreactive T cells specific for GAD65, a well-known targeted self-antigen in Type 1 Diabetes (T1D). Our study provides new insight into the role of the CD318/CD6 axis in the immunopathogenesis of inflammation, suggesting a novel immunoregulatory role of CD318 in T cell-mediated autoimmune diseases and identifying a potential novel immune checkpoint inhibitor as a target for intervention in T1D which is an unmet therapeutic need.

Type 1 diabetes in the pancreas: A histological perspective

AIMS

This review aims to introduce research in the pancreas to a broader audience. The pancreas is a heterocrine gland residing deep within our abdominal cavity. It is the home to our islets, which play a pivotal role in regulating metabolic homeostasis. Due to its structure and location, it is an impossible organ to study, in molecular detail, in living humans, and yet, understanding the pancreas is critical if we aim to characterise the immunopathology of type 1 diabetes (T1D) and one day prevent the triggering of the autoimmune attack associated with ß-cell demise.

METHODS

Over a 100 years ago, we began studying pancreatic histology using cadaveric samples and clever adaptations to microscopes. As histologists, some may say nothing much has changed. Nevertheless, our microscopes can now interrogate multiple proteins at molecular resolution. Images of pancreas sections are no longer constrained to a single field of view and can capture a thousands and thousands of cells. AI-image-analysis packages can analyse these massive data sets offering breakthrough findings.

CONCLUSION

This narrative review will provide an overview of pancreatic anatomy, and the importance of research focused on the pancreas in T1D. It will range from histological breakthroughs to briefly discussing the challenges associated with characterising the organ. I shall briefly introduce a selection of the available global biobanks and touch on the distinct pancreatic endotypes that differ immunologically and in ß-cell behaviour. Finally, I will introduce the idea of developing a collaborative tool aimed at developing a cohesive framework for characterising heterogeneity and stratifying endotypes in T1D more readily.

PyCreas: a tool for quantification of localization and distribution of endocrine cell types in the islets of Langerhans

Manifest diabetes, but also conditions of increased insulin resistance such as pregnancy or obesity can lead to islet architecture remodeling. The contributing mechanisms are as poorly understood as the consequences of altered cell arrangement. For the quantification of the different cell types but also the frequency of different cell-cell contacts within the islets, different approaches exist. However, few methods are available to characterize islet cell distribution in a statistically valid manner. Here we describe PyCreas, an open-source tool written in Python that allows semi-automated analysis of islet cell distribution based on images of pancreatic sections stained by immunohistochemistry or immunofluorescence. To ensure that the PyCreas tool is suitable for quantitative analysis of cell distribution in the islets at different metabolic states, we studied the localization and distribution of alpha, beta, and delta cells during gestation and prediabetes. We compared the islet cell distribution of pancreatic islets from metabolically healthy NMRI mice with that of New Zealand obese (NZO) mice, which exhibit impaired glucose tolerance (IGT) both preconceptionally and during gestation, and from C57BL/6 N (B6) mice, which acquire this IGT only during gestation. Since substrain(s) of the NZO mice are known to show a variant in the Abcc8 gene, we additionally examined preconceptional SUR1 knock-out (SUR1-KO) mice. PyCreas provided quantitative evidence that alterations in the Abcc8 gene are associated with an altered distribution pattern of islet cells. Moreover, our data indicate that this cannot be a consequence of prolonged hyperglycemia, as islet architecture is already altered in the prediabetic state. Furthermore, the quantitative analysis suggests that states of transient IGT, such as during common gestational diabetes mellitus (GDM), are not associated with changes in islet architecture as observed during long-term IGT. PyCreas provides the ability to systematically analyze the localization and distribution of islet cells at different stages of metabolic disease to better understand the underlying pathophysiology.

Dose-dependent progression of multiple low-dose streptozotocin-induced diabetes in mice

This study investigated the effects of different multiple low doses of streptozotocin (STZ), namely 35 and 55 mg/kg, on the onset and progression of diabetes in mice. Both doses are commonly used in research, and although both induced a loss of beta cell mass, they had distinct effects on whole glucose tolerance, beta cell function, and gene transcription. Mice treated with 55 mg/kg became rapidly glucose intolerant, whereas those treated with 35 mg/kg had a slower onset and remained glucose tolerant for up to a week before becoming equally glucose intolerant as the 55 mg/kg group. Beta cell mass loss was similar between the two groups, but the 35 mg/kg-treated mice had improved glucose-stimulated insulin secretion in gold-standard hyperglycemic clamp studies. Transcriptomic analysis revealed that the 55 mg/kg dose caused disruptions in nearly five times as many genes as the 35 mg/kg dose in isolated pancreatic islets. Pathways that were downregulated in both doses were more downregulated in the 55 mg/kg-treated mice, whereas pathways that were upregulated in both doses were more upregulated in the 35 mg/kg-treated mice. Moreover, we observed a differential downregulation in the 55 mg/kg-treated islets of beta cell characteristic pathways, such as exocytosis or hormone secretion. On the other hand, apoptosis was differentially upregulated in 35 mg/kg-treated islets, suggesting different transcriptional mechanisms in the onset of STZ-induced damage in the islets. This study demonstrates that the two STZ doses induce distinctly mechanistic progressions for the loss of functional beta cell mass.

The extent and magnitude of islet T cell infiltration as powerful tools to define the progression to type 1 diabetes

AIMS/HYPOTHESIS

Insulitis is not present in all islets, and it is elusive in humans. Although earlier studies focused on islets that fulfilled certain criteria (e.g. ≥15 CD45+ cells or ≥6 CD3+ cells), there is a fundamental lack of understanding of the infiltration dynamics in terms of its magnitude (i.e. how much) and extent (i.e. where). Here, we aimed to perform an in-depth characterisation of T cell infiltration by investigating islets with moderate (1-5 CD3+ cells) and high (≥6 CD3+ cells) infiltration in individuals with and without type 1 diabetes.

METHODS

Pancreatic tissue sections from 15 non-diabetic, eight double autoantibody-positive and ten type 1 diabetic (0-2 years of disease duration) organ donors were obtained from the Network for Pancreatic Organ Donors with Diabetes, and stained for insulin, glucagon, CD3 and CD8 by immunofluorescence. T cell infiltration was quantified in a total of 8661 islets using the software QuPath. The percentage of infiltrated islets and islet T cell density were calculated. To help standardise the analysis of T cell infiltration, we used cell density data to develop a new T cell density threshold capable of differentiating non-diabetic and type 1 diabetic donors.

RESULTS

Our analysis revealed that 17.1% of islets in non-diabetic donors, 33% of islets in autoantibody-positive and 32.5% of islets in type 1 diabetic donors were infiltrated by 1 to 5 CD3+ cells. Islets infiltrated by ≥6 CD3+ cells were rare in non-diabetic donors (0.4%) but could be found in autoantibody-positive (4.5%) and type 1 diabetic donors (8.2%). CD8+ and CD8- populations followed similar patterns. Likewise, T cell density was significantly higher in the islets of autoantibody-positive donors (55.4 CD3+ cells/mm2) and type 1 diabetic donors (74.8 CD3+ cells/mm2) compared with non-diabetic individuals (17.3 CD3+ cells/mm2), which was accompanied by higher exocrine T cell density in type 1 diabetic individuals. Furthermore, we showed that the analysis of a minimum of 30 islets and the use of a reference mean value for T cell density of 30 CD3+ cells/mm2 (the 30-30 rule) can differentiate between non-diabetic and type 1 diabetic donors with high specificity and sensitivity. In addition, it can classify autoantibody-positive individuals as non-diabetic or type 1 diabetic-like.

CONCLUSIONS/INTERPRETATION

Our data indicates that the proportion of infiltrated islets and T cell density change dramatically during the course of type 1 diabetes, and these changes can be already observed in double autoantibody-positive individuals. This suggests that, as disease progresses, T cell infiltration extends throughout the pancreas, reaching the islets and exocrine compartment. While it predominantly targets insulin-containing islets, large accumulations of cells are rare. Our study fulfils the need to further understand T cell infiltration, not only after diagnosis but also in individuals with diabetes-related autoantibodies. Furthermore, the development and application of new analytical tools based on T cell infiltration, like the 30-30 rule, will allow us to correlate islet infiltration with demographic and clinical variables with the aim of identifying individuals at the very early stages of the disease.

Dose-dependent progression of multiple low dose streptozotocin-induced diabetes in mice

This study investigated the effects of different multiple low doses of streptozotocin (STZ), namely 35 and 55 mg/kg, on the onset and progression of diabetes in mice. Both doses are commonly used in research, and while both induced a loss of beta cell mass, they had distinct effects on whole glucose tolerance, beta cell function and gene transcription. Mice treated with 55 mg/kg became rapidly glucose intolerant, whereas those treated with 35 mg/kg had a slower onset and remained glucose tolerant for up to a week before becoming equally glucose intolerant as the 55 mg/kg group. Beta cell mass loss was similar between the two groups, but the 35 mg/kg-treated mice had improved glucose-stimulated insulin secretion in gold-standard hyperglycemic clamp studies. Transcriptomic analysis revealed that the 55 mg/kg dose caused disruptions in nearly five times as many genes as the 35 mg/kg dose in isolated pancreatic islets. Pathways that were downregulated in both doses were more downregulated in the 55 mg/kg-treated mice, while pathways that were upregulated in both doses were more upregulated in the 35 mg/kg treated mice. Moreover, we observed a differential downregulation in the 55 mg/kg-treated islets of beta cell characteristic pathways, such as exocytosis or hormone secretion. On the other hand, apoptosis was differentially upregulated in 35 mg/kg-treated islets, suggesting different transcriptional mechanisms in the onset of STZ-induced damage in the islets. This study demonstrates that the two STZ doses induce distinctly mechanistic progressions for the loss of functional beta cell mass.

microRNA 146a ameliorates retinal damage in experimental autoimmune uveitis

Introduction

Uveitis and related intraocular inflammations are a major cause of blindness due to retinal damage caused by degeneration and loss of the photoreceptor cells. In mouse experimental autoimmune uveitis (EAU) previously we have shown mitochondrial oxidative stress with marked upregulation of αA crystallin in the inner segments of the photoreceptors. Furthermore, αA crystallin treatment prevented photoreceptor mitochondrial oxidative stress by suppressing innate and adaptive immunity in EAU.

Methods

Since these immune processes are modulated by microRNAs, in this study we investigated (a) modulation of microRNAs during development of EAU by αA crystallin administration and (b) microRNA therapeutic intervention.

Results

Few microRNAs were significantly upregulated in EAU mice with intravenous injection of αA crystallin and among these, computational bioinformatic analysis revealed that the upregulated microRNA 146a targets the innate and adaptive immune responses. In EAU, intravenous as well as intravitreal administration of this microRNA prevented inflammatory cell infiltration in uvea and retina and preserved photoreceptor cells.

Discussion

This protective function suggests that microRNA146a can be a novel therapeutic agent in preventing retinal damage in uveitis.

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Fatty infiltration in pancreas leading to steatosis is a major risk factor in pancreas transplantation. Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival, its endocrine function and pancreatic steatosis and can cause non-alcoholic fatty pancreas disease (NAFPD). The current automated tools (E.g. Adiposoft) available for fat analysis are suited for white fat tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. The currently, available pancreas segmentation tool focuses on endocrine islet segmentation based on cell nuclei detection for diagnosis of pancreatic cancer. In the current study, we present a fat quantifying tool, Fatquant, which identifies fat cells in heterogeneous H and E tissue sections with reference to diameter of fat cell. Using histological images from a public database, we observed an intersection over union of 0.797 to 0.962 and 0.675 to 0.937 for manual versus Fatquant analysis of pancreas and liver, respectively.

Multi-omics profiling and digital image analysis reveal the potential prognostic and immunotherapeutic properties of CD93 in stomach adenocarcinoma

Background

Recent evidence highlights the fact that immunotherapy has significantly improved patient outcomes. CD93, as a type I transmembrane glycoprotein, was correlated with tumor-associated angiogenesis; however, how CD93 correlates with immunotherapy in stomach adenocarcinoma (STAD) remains unclear.

Methods

TCGA, GTEx, GEO, TIMER2.0, HPA, TISIDB, TCIA, cBioPortal, LinkedOmics, and ImmuCellAI public databases were used to elucidate CD93 in STAD. Visualization and statistical analysis of data were performed by R (Version 4.1.3), GraphPad (Version 8.0.1), and QuPath (Version 0.3.2).

Results

CD93 was highly expressed in STAD compared with adjacent normal tissues. The overexpression of CD93 was significantly correlated with a poor prognosis in STAD. There was a negative correlation between CD93 expression levels with CD93 mutation and methylation in STAD. Our results revealed that CD93 expression was positively associated with most immunosuppressive genes (including PD-1, PD-L1, CTLA-4, and LAG3), immunostimulatory genes, HLA, chemokine, and chemokine receptor proteins in STAD. Furthermore, in STAD, CD93 was noticeably associated with the abundance of multiple immune cell infiltration levels. Functional HALLMARK and KEGG term enhancement analysis of CD93 through Gene Set Enrichment Analysis was correlated with the process of the angiogenesis pathway. Subsequently, digital image analysis results by QuPath revealed that the properties of CD93⁺ cells were statistically significant in different regions of stomach cancer and normal stomach tissue. Finally, we utilized external databases, including GEO, TISIDB, ImmuCellAI, and TCIA, to validate that CD93 plays a key role in the immunotherapy of STAD.

Conclusion

Our study reveals that CD93 is a potential oncogene and is an indicative biomarker of a worse prognosis and exerts its immunomodulatory properties and potential possibilities for immunotherapy in STAD.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Changes of macrophage and CD4+ T cell in inflammatory response in type 1 diabetic mice

Immune cells play an important role in the development of inflammation in type 1 diabetes mellitus, so we want to explore the changes of CD4+ T cells and macrophages in vivo, which can provide an experimental basis for immunotherapy based on CD4+ T cells and macrophages. The intraperitoneal injection of streptozocin was used to induce a type 1 diabetes mellitus mouse model; the blood glucose, body weight, and the expression of inflammatory factors in the kidney were measured. Immunohistochemistry was applied to determine and analyze the infiltration of CD4+ T cells and macrophages in the spleen, pancreas, and kidney. The subtypes of macrophages in the kidney and CD4+ T cells in the spleen were analyzed by flow cytometry. Our study suggests that CD4+ T cells and macrophages increase, while the inflammatory immune response system is activated in the development of T1DM. CD4+ T cells positively correlated with macrophages in the pancreas and kidney of T1DM. CD4+ T cells turn to pro-inflammatory subtypes in the spleen of T1DM, while macrophages turn to pro-inflammatory subtypes in the kidney of T1DM. Therefore, regulation of CD4+ T cells and macrophages may be a potential target for T1DM and kidney complications.

Simultaneous and Spatially-Resolved Analysis of T-Lymphocytes, Macrophages and PD-L1 Immune Checkpoint in Rare Cancers

Penile, vulvar and anal neoplasms show an incidence lower than 0.5% of the population per year and therefore can be considered as rare cancers but with a dramatic impact on quality of life and survival. This work describes the experience of a Chilean cancer center using multiplexed immunofluorescence to study a case series of four penile cancers, two anal cancers and one vulvar cancer and simultaneous detection of CD8, CD68, PD-L1, Cytokeratin and Ki-67 in FFPE samples. Fluorescent image analyses were performed using open sources for automated tissue segmentation and cell phenotyping. Our results showed an objective and reliable counting of objects with a single or combined labeling or within a specific tissue compartment. The variability was below 10%, and the correlation between analytical events was 0.92-0.97. Critical cell phenotypes, such as TILs, PD-L1+ or proliferative tumor cells were detected in a supervised and unsupervised manner with a limit of detection of less than 1% of relative abundance. Finally, the observed diversity and abundance of the different cell phenotypes within the tumor microenvironment for the three studied tumor types confirmed that our methodology is useful and robust to be applicable for many other solid tumors.

Developing image analysis methods for digital pathology

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.