Eduardo Conde-Sousa

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin–eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.

Microsatellites (or short-tandem repeats (STRs)) are widely used in anthropology and evolutionary studies. Their extensive polymorphism and rapid evolution make them the ideal genetic marker for dating events, such as the age of a gene or a population. This usage requires the estimation of mutation rates, which are usually estimated by counting the observed Mendelian incompatibilities in one-generation familial configurations (typically parent(s)–child duos or trios). Underestimations are inevitable when using this approach, due to the occurrence of mutational events that do not lead to incompatibilities with the parental genotypes (‘hidden’ or ‘covert’ mutations). It is known that the likelihood that one mutation event leads to a Mendelian incompatibility depends on the mode of genetic transmission considered, the type of familial configuration (duos or trios) considered, and the genotype(s) of the progenitor(s). In this work, we show how the magnitude of the underestimation of autosomal microsatellite mutation rates varies with the populations’ allele frequency distribution spectrum. The Mendelian incompatibilities approach (MIA) was applied to simulated parent(s)/offspring duos and trios in different populational scenarios. The results showed that the magnitude and type of biases depend on the population allele frequency distribution, whatever the type of familial data considered, and are greater when duos, instead of trios, are used to obtain the estimates. The implications for molecular anthropology are discussed and a simple framework is presented to correct the naïf estimates, along with an informatics tool for the correction of incompatibility rates obtained through the MIA.

Background and Objectives: Breast cancer is the most common malignancy in women and is responsible for more than half a million deaths each year. Early and accurate diagnosis is therefore of utmost importance. Human expertise is required to diagnose and correctly classify breast cancer and determine appropriate therapy, which depends on evaluating the expression of various biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including specialized techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. In this work, we present the HEROHE challenge, a parallel event of the 16th European Congress on Digital Pathology, which aims to automate the assessment of HER2 status based only on hematoxylin and eosin stained tissue samples from invasive breast cancer. Methods: The organizational protocol is presented, from sample preparation to timeline and evaluation metric. The best-performing models are presented by detailing the network architectures and key parameters. Results: Models for assessing HER2 status were presented by 21 teams worldwide. Their results in the main dataset and in the subset of cases classified as equivocal by the immunohistochemistry test were evaluated. Methods were compared and approaches, core methodologies, and software choices were contrasted. Conclusions: Promising results were obtained, especially for the subset of equivocal cases. The performance of each model was evaluated according to multiple evaluation metrics highlighting that the chosen metric may be misleading and, in some extreme scenarios, may distort the perception of the adequacy of the model to solve the problem.

Parkinson’s disease’s etiology is unknown, although evidence suggests the involvement of oxidative modifications of intracellular components in disease pathobiology. Despite the known involvement of the extracellular matrix in physiology and disease, the influence of oxidative stress on the matrix has been neglected. The chemical modifications that might accumulate in matrix components due to their long half-live and the low amount of extracellular antioxidants could also contribute to the disease and explain ineffective cellular therapies. The enriched striatal extracellular matrix from a mouse model of Parkinson’s disease was characterized by Raman spectroscopy. We found a matrix fingerprint of increased oxalate content and oxidative modifications. To uncover the effects of these changes on brain cells, we morphologically characterized the primary microglia used to repopulate this matrix and further quantified the effects on cellular mechanical stress by an intracellular fluorescence resonance energy transfer (FRET)-mechanosensor using the U-2 OS cell line. Our data suggest changes in microglia survival and morphology, and a decrease in cytoskeletal tension in response to the modified matrix from both hemispheres of 6-hydroxydopamine (6-OHDA)-lesioned animals. Collectively, these data suggest that the extracellular matrix is modified, and underscore the need for its thorough investigation, which may reveal new ways to improve therapies or may even reveal new therapies.

Forensic genetics is a fast-growing field that frequently requires DNA-based taxonomy, namely, when evidence are parts of specimens, often highly processed in food, potions, or ointments. Reference DNA-sequences libraries, such as BOLD or GenBank, are imperative tools for taxonomic assignment, particularly when morphology is inadequate for classification. The auditing and curation of these datasets require reliable mechanisms, preferably with automated data preprocessing. Software tools were developed to grade these datasets considering as primary criterion the number of records, which is not compliant with forensic standards, where the priority is validation from independent sources. Moreover, 4SpecID is an efficient and freely available software tool developed to audit and annotate reference libraries, specifically designed for forensic applications. Its intuitive user-friendly interface virtually accesses any database and includes specific data mining functions tuned for the widespread BOLD repositories. The built tool was evaluated in laptop MacBook and a dual-Xeon server with a large BOLD dataset (Culicidae, 36,115 records), and the best execution time to grade the dataset on the laptop was 0.28 s. Datasets of Bovidae and Felidae families were used to evaluate the quality of the tool and the relevance of independent sources validation.

Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin–Eosin slides, which partly mimics the pathologist’s behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.

The study of mitogenomes allows the unraveling of some paths of yeast evolution that are often not exposed when analyzing the nuclear genome. Although both nuclear and mitochondrial genomes are known to determine phenotypic diversity and fitness, no concordance has yet established between the two, mainly regarding strains’ technological uses and/or geographical distribution. In the current work, we proposed a new method to align and analyze yeast mitogenomes, overcoming current difficulties that make it impossible to obtain comparable mitogenomes for a large number of isolates. To this end, 12,016 mitogenomes were considered, and we developed a novel approach consisting of the design of a reference sequence intended to be comparable between all mitogenomes. Subsequently, the population structure of 6646 Saccharomyces cerevisiae mitogenomes was assessed. Results revealed the existence of particular clusters associated with the technological use of the strains, in particular regarding clinical isolates, laboratory strains, and yeasts used for wine-associated activities. As far as we know, this is the first time that a positive concordance between nuclear and mitogenomes has been reported for S. cerevisiae, in terms of strains’ technological applications. The results obtained highlighted the importance of including the mtDNA genome in evolutionary analysis, in order to clarify the origin and history of yeast species.