Whole-slide image analysis outperforms micrograph acquisition for adipocyte size quantification

Perilipin-1 immunostaining improves semi-automated digital quantitation of bone marrow adipocytes in histological bone sections

Bone marrow adipocytes (BMAds) are not just passive fillers inside the bone marrow compartment but respond to various metabolic changes. Assessment of those responses requires evaluation of the number of BMAds and their morphology for which laborious and error-prone manual histological analysis remains the most widely used method. Here, we report an alternative image analysis strategy to semi-automatically quantitate and analyse the morphology of BMAds in histological bone sections. Decalcified, formalin-fixed paraffin-embedded histological sections of long bones of Sprague-Dawley rats were stained with either haematoxylin and eosin (HE) or by immunofluorescent staining for adipocyte-specific protein perilipin-1 (PLIN1). ImageJ-based commands were constructed to detect BMAds sized 200 µm2 or larger from standardized 1 mm2 analysis regions by either classifying the background colour (HE) or the positive and circular PLIN1 fluorescent signal. Semi-automated quantitation strongly correlated with independent, single-blinded manual counts regardless of the staining method (HE-based: r=0.85, p<0.001; PLIN1 based: r=0.95, p<0.001). The detection error was higher in HE-stained sections than in PLIN1-stained sections (14% versus 5%, respectively; p<0.001), which was due to false-positive detections of unstained adipocyte-like circular structures. In our dataset, the total adiposity area from standardised ROIs in PLIN-1-stained sections correlated with that in whole-bone sections (r=0.60, p=0.02).

A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations

Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.

A Straightforward Method for Adipocyte Size and Count Analysis Using Open-source Software QuPath

Changes in adipose tissue morphology, depicted by cell morphology alterations such as enlargement of fat cells, always accompany over-weight and obesity. The variables related to cell size have been shown to associate with low-grade inflammation of adipose tissue and common obesity-related comorbidities including metabolic syndrome and type 2 diabetes. Quantifying fat cell morphology from images of histological specimens can be tedious. Here, we present a straightforward method for the task using the free open-source software QuPath with its inbuilt tools only. Measurements of human adipose tissue samples with the described protocol showed an excellent correlation with those obtained with ImageJ software with Adipocyte Tools plugin combined with manual correction of misdetections. Intraclass correlation between the two methods was at good to excellent level. The method described here can be applied to considerably large tissue areas, even whole-slide analysis.

AdipoQ - a simple, open-source software to quantify adipocyte morphology and function in tissues and in vitro

The different adipose tissues can be distinguished according to their function. For example, white adipose tissue (WAT) stores energy in form of lipids, whereas brown adipose tissue (BAT) dissipates energy in the form of heat. These functional differences are represented in the respective adipocyte morphology: whereas white adipocytes contain large, unilocular lipid droplets, brown adipocytes contain smaller, multilocular lipid droplets. However, an automated, image-analysis pipeline to comprehensively analyze adipocytes in vitro in cell culture as well as ex vivo in tissue sections is missing. We here present AdipoQ, an open-source software implemented as ImageJ plugins that allows to analyze adipocytes in tissue sections and in vitro after histological and/or immunofluorescent labelling. AdipoQ is compatible with different imaging modalities and staining methods, allows batch processing of large datasets and simple post-hoc analysis, provides a broad band of parameters, and allows combining multiple fluorescent read-outs. Thereby, AdipoQ is of immediate use not only for basic research but also for clinical diagnosis.

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.

Semi-automated digital quantification of cellular infiltrates for in vivo evaluation of transplanted islets of Langerhans encapsulated with bioactive materials

BACKGROUND

In the field of xenotransplantation, digital image analysis (DIA) is an asset to quantify heterogeneous cell infiltrates around transplanted encapsulated islets.

MATERIALS AND METHODS

RGD-alginate was used to produce empty capsules or to encapsulate neonatal porcine islets (NPI) with different combinations of human pancreatic extracellular matrix (hpECM), porcine mesenchymal stem cells (pMSC) and a chitosan anti-fouling coating. Capsules were transplanted subcutaneously in rats for one month and then processed for immunohistochemistry. Immunostainings for macrophages (CD68) and lymphocytes (CD3) were quantified by DIA in two concentric regions of interest (ROI) around the capsules. DIA replicability and reproducibility were assessed by two blind operators. Repeatability was evaluated by processing the same biopsies at different time points. DIA was also compared with quantification by point counting (PC).

RESULTS

Methodology validation: different sizes of ROIs were highly correlated. Intraclass correlation coefficients confirmed replicability and reproducibility. Repeatability showed a very strong correlation with CD3 stains and moderate/strong for CD68 stains. Group comparisons for CD68 IHC at each time point proved internal consistency. Point counting and DIA were strongly correlated with both CD3 and CD68. Capsule biocompatibility: Macrophage infiltration was higher around capsules containing biomaterials than around empty and RGD-alginate-NPI capsules. Lymphocytic infiltration was comparable among groups containing cells and higher than in empty capsules.

CONCLUSION

We validated a semi-automated quantification methodology to assess cellular infiltrates and successfully applied it to investigate graft biocompatibility, showing that neonatal porcine islets encapsulated in alginate alone triggered less infiltration than capsules containing islets and bioactive materials.

Quality Considerations When Using Tissue Samples for Biomarker Studies in Cancer Research

Tissue obtained from biobanks is frequently employed in biomarker studies. Biomarkers define objective, measurable characteristics of biological and biomedical procedures and have been used as indicators of clinical outcome. This article outlines some of the steps scientists should consider when embarking on biomarker research in cancer research using samples from biobanks and the importance and challenges of linking clinical data to biological samples.

A distribution-centered approach for analyzing human adipocyte size estimates and their association with obesity-related traits and mitochondrial function

OBJECTIVE

Cell diameter, area, and volume are established quantitative measures of adipocyte size. However, these different adipocyte sizing parameters have not yet been directly compared regarding their distributions. Therefore, the study aimed to investigate how these adipocyte size measures differ in their distribution and assessed their correlation with anthropometry and laboratory chemistry. In addition, we were interested to investigate the relationship between fat cell size and adipocyte mitochondrial respiratory chain capacity.

METHODS

Subcutaneous and visceral histology-based adipocyte size estimates from 188 individuals were analyzed by applying a panel of parameters to describe the underlying cell population. Histology-based adipocyte diameter distributions were compared with adipocyte diameter distributions from collagenase digestion. Associations of mean adipocyte size with body mass index (BMI), glucose, HbA_1C, blood lipids as well as mature adipocyte mitochondrial respiration were investigated.

RESULTS

All adipocyte area estimates derived from adipose tissue histology were not normally distributed, but rather characterized by positive skewness. The shape of the size distribution depends on the adipocyte sizing parameter and on the method used to determine adipocyte size. Despite different distribution shapes histology-derived adipocyte area, diameter, volume, and surface area consistently showed positive correlations with BMI. Furthermore, associations between adipocyte sizing parameters and glucose, HbA_1C, or HDL specifically in the visceral adipose depot were revealed. Increasing subcutaneous adipocyte diameter was negatively correlated with adipocyte mitochondrial respiration.

CONCLUSIONS

Despite different underlying size distributions, the correlation with obesity-related traits was consistent across adipocyte sizing parameters. Decreased mitochondrial respiratory capacity with increasing subcutaneous adipocyte diameter could display a novel link between adipocyte hypertrophy and adipose tissue function.

Combination Usage of AdipoCount and Image-Pro Plus/ImageJ Software for Quantification of Adipocyte Sizes

In recent decades, the prevalence of obesity has been rising. One of the major characteristics of obesity is fat accumulation, including hyperplasia (increase in number) and hypertrophy (increase in size). After histological staining, it is critical to accurately measure the number and size of adipocytes for assessing the severity of obesity in a timely fashion. Manual measurement is accurate but time-consuming. Although commercially available adipocyte counting tools, including AdipoCount, Image-Pro Plus, and ImageJ were helpful, limitations still exist in accuracy and time consuming. In the present study, we introduced the protocol of combined usage of these tools and illustrated the process with histological staining slides from adipose tissues of lean and obese mice. We found that the adipocyte sizes quantified by the tool combination were comparable as manual measurement, whereas the combined methods were more efficient. Besides, the recognition effect of monochrome segmentation image is better than that of color segmentation image. Overall, we developed a combination method to measure adipocyte sizes accurately and efficiently, which may be helpful for experimental process in laboratory and also for clinic diagnosis.