The historical trajectory is remarkable. In 1974, women accounted for 11 of the 87 doctorates recorded in Portugal, roughly 12.6 percent, and represented approximately 18 percent of all doctorates awarded during the 1970s. Women were therefore already present, although under-represented, among doctorate holders before the transition to democracy. What changed after 1974 was a marked increase of women in doctoral research, driven by state investment in education, European integration, and laws promoting gender equity. The Fundação para a Ciência e a Tecnologia, established in 1997, has played a key role by funding fellowships and grants that gave women independent research paths. By the early 2000s, women accounted for around half of all PhDs awarded in Portugal, and today they lead research groups all over the country, from the north to the Azores and Madeira islands.

Funding data reinforce this picture of strong participation. In FCT’s 2023 and 2021 IC&DT project competitions, women led the majority of nationally funded projects, at 58.1 percent and 55.2 percent of principal investigators respectively. In the 2023 PT2030 IC&DT call, whose results were published in 2025, women accounted for 54 percent of principal investigators among submitted applications and 58.1 percent among funded projects. This is consistent with She Figures 2024, which reports that women’s overall research-funding success rate in Portugal was 1.4 percentage points higher than men’s in 2022. Between 56 and 59 percent of FCT doctoral, postdoctoral, and research fellowships were awarded to women as of 2019. The bottleneck lies further along the pipeline: only 24 percent of Portuguese full professors are women, against 48 percent of assistant-level academic staff, and women hold just 20 percent of rectorships and university presidencies nationally.

To illustrate the depth of women-led science, the authors curated a set of studies from 2014 to 2026 where the first or last author is a woman, spanning four thematic areas. The first is the microbial ecology of traditional fermented foods. Portugal has a rich variety of PDO cheeses like Serra da Estrela, Azeitão, and Nisa, and dry-cured sausages like Paio do Alentejo and Salpicão, each with its own unique microbial community of lactic acid bacteria, coagulase negative staphylococci, yeasts, and moulds. At the Faculdade de Medicina Veterinária da Universidade de Lisboa, Teresa Semedo-Lemsaddek’s group has used conventional microbiology along with omics tools, including comparative genomics and metagenomics, to characterize enterococci and other lactic acid bacteria from Portuguese cheeses, fermented meats, and poultry production systems. Enterococcus species play a dual and often contradictory role: they contribute to cheese ripening, yet some strains carry virulence and antimicrobial resistance genes. The group characterized strains isolated from Azeitão and Nisa PDO cheeses, capturing this duality in detail, and has also examined bacterial persisters, dormant cells capable of surviving exposure to antimicrobials and sanitizers, an emerging concern for food safety.

At the Universidade Católica Portuguesa in Porto, Paula Teixeira, Full Professor in Food Microbiology and leader of the CBQF Food and Nutrition group, has built a long-running food microbiology program focused on pathogens. Her research covers the ecology, stress resistance, pathogenesis, and control of Listeria monocytogenes in food production environments, including the characterization of a cheese-related listeriosis outbreak in Portugal. The group has also studied probiotic lactobacilli from traditional Portuguese fermented foods. In the Azores, Célia Silva’s team at the Universidade dos Açores has investigated exopolysaccharide-producing lactic acid bacteria strains from local cheeses as functional starters to improve texture and water retention in low-fat cheeses, while reviewing the broader biotechnological potential and food safety relevance of these microorganisms.

The second thematic area is starter culture development and fermentation technology. At the Universidade de Évora, Marta Laranjo’s research showed that autochthonous starter cultures, strains isolated from the local meat matrix, work better than commercial ones, preserving the authentic sensory profile while improving microbiological safety. For meat sausages like Paio do Alentejo and Salpicão, the team optimized inoculation protocols that reduce biogenic amine accumulation and help beneficial microbes outcompete unwanted ones, work performed in close collaboration with Maria João Fraqueza at FMV-ULisboa, whose expertise in meat science underpinned the definition of starter culture protocols. A recent European network project has focused on mapping fermented foods across Europe and the associated fermentation technologies, as well as the health benefits these foods provide. Work on Madeira Island includes the characterization of indigenous non-Saccharomyces yeast fermentation and its impact on the chemical and phenolic composition of Madeira wine.

The third area is biofilm biology and pathogen persistence in food environments. Biofilms are one of the biggest problems in food safety because pathogens embedded in them resist sanitation and antimicrobials and can contaminate food. At the Instituto Nacional de Investigação Agrária e Veterinária, in collaboration with LEPABE at the Universidade do Porto and CEB at the Universidade do Minho, Carina Almeida built a research program at the intersection of food safety, biofilm microbiology, and molecular diagnostics, using peptide nucleic acid fluorescence in situ hybridization and related nucleic acid mimic technologies to rapidly detect foodborne pathogens in biofilm environments. At LEPABE, Luciana Gomes has worked on engineered surfaces that prevent microbial adhesion, with recent work showing that biomimetic surfaces can significantly reduce Escherichia coli attachment and retention on food contact materials. Research at CBQF on Listeria monocytogenes has documented links between antimicrobial resistance patterns and stress tolerance in strains from food processing environments, directly supporting risk assessment.

The fourth area is antimicrobial resistance surveillance under a One Health framework. The World Health Organization considers antimicrobial resistance one of the biggest threats to global public health, and the food chain is a major reservoir and transmission route for resistant bacteria and their resistance genes. At the Universidade de Trás-os-Montes e Alto Douro, Maria José Saavedra, founder and scientific coordinator of the Antimicrobials, Biocides & Biofilms Laboratory, has built an internationally recognized program studying resistance mechanisms, with particular emphasis on beta-lactam resistance and carbapenemase production, horizontal gene transfer, biofilm-associated resistance, and the molecular epidemiology of multidrug-resistant bacteria across human, veterinary, food, and environmental compartments. Datasets cover Aeromonas species, Enterobacteriaceae, Campylobacter, Enterococcus, Staphylococcus, and carbapenemase-producing Klebsiella pneumoniae from Portuguese hospitals, where genome-wide longitudinal analyses traced the predominance of KPC-2 and OXA-48 lineages in northern Portugal. A decade-long study on Campylobacter in humans and animals showed a persistent and growing prevalence of fluoroquinolone resistance, and the group has extended surveillance to rivers, freshwater mussels, and marine gastropods as reservoirs and bioindicators of resistant bacteria.

One striking methodological feature of this women-led research is how well classical microbiology is combined with advanced molecular and omics tools. Culture-based methods remain important for isolating strains and assessing safety, but they are now backed by whole genome sequencing, metagenomics, resistome profiling, and comparative genomics. Sequencing whole genomes of food isolates lets researchers identify species, count resistance and virulence genes, find mobile genetic elements, and see how strains relate to clinical and environmental ones, information directly useful for regulators and industry. Portuguese women-led groups at FMV-ULisboa, CBQF-UCP, UTAD, and INIAV were among the early adopters of whole genome sequencing in Portugal. Portugal also holds the second-highest share of women patent inventors in Europe, at 27 percent against a European average of 13.2 percent, a genuinely positive indicator that nonetheless coexists with the leadership gap and raises the question of whether patenting activity by Portuguese women microbiologists translates into leadership of commercialization or remains, like authorship, a form of participation that stops short of institutional power.

The authors close with a concrete research and policy agenda rather than broad calls for inclusion. They propose gender-disaggregated bibliometric and patent analyses specific to industrial microbiology, tracking first and last authorship, patent inventorship, and principal-investigator status over the same 2014 to 2026 period; longitudinal tracking of funding and promotion outcomes by gender within this subfield to identify where the main bottleneck occurs, whether in promotion criteria, evaluation panels, or access to leadership opportunities; systematic evaluation of whether existing initiatives such as Portugal’s Scientific Employment Stimulus and the European GENDERACTION Plus project have measurably improved retention and career progression; and qualitative research on mentoring, professional networks, and the composition of evaluation panels extended to microbiology-specific research groups and industry consortia. The argument is that closing the gender gap in industrial microbiology is not simply a matter of social justice, it is a structural requirement to improve scientific and economic competitiveness, particularly as the field faces managing antimicrobial resistance across global food systems, making fermentation processes more sustainable, and preserving traditional food heritage.

Subject of Research: Women in Portuguese industrial microbiology: a fifty-year perspective on progress and persistent barriers

Article Title: Women in Portuguese industrial microbiology: a fifty-year perspective on progress and persistent barriers

Article References: Semedo-Lemsaddek, T., Laranjo, M., & Saavedra, M. J. (2026). Women in Portuguese industrial microbiology: a fifty-year perspective on progress and persistent barriers. Frontiers in Industrial Microbiology, 4, Article 1939832. https://doi.org/10.3389/finmi.2026.1939832

Image Credits: AI Generated

DOI: 10.3389/finmi.2026.1939832

Keywords: Women, Portuguese, industrial, microbiology, fifty-year, perspective, progress, persistent, barriers, scientific research

The power of image-based profiling lies in its scalability and its agnosticism. Rather than measuring a predefined molecular target, profiles capture the phenotypic consequences of perturbations, allowing researchers to group compounds by mechanism of action, detect off-target effects, and identify subtle morphological changes that molecular assays might overlook. This has made the approach central to phenotypic drug discovery, functional genomics, and disease modeling. In functional genomics, image-based profiles quantify the phenotypic impact of CRISPR knockouts or RNAi perturbations, helping map gene function and pathway relationships at single-cell resolution. In disease modeling, morphological features derived from patient cells can distinguish disease subtypes, identify biomarkers, and screen for phenotype-correcting compounds. The landmark 2017 review by Caicedo and colleagues established the foundational standards for the field, outlining critical steps such as illumination correction, cell segmentation, feature quantification, feature processing, and profile generation. Since then, the field has advanced rapidly, driven by progress in experimental protocols, complementary profiling modalities, and rapidly advancing computational tools.

The experimental side of the workflow begins with careful design. Negative controls such as untreated or vehicle-treated samples establish baselines for normalization, while biological replicates capture natural variability and improve statistical power. Plate layout constrains replicate number; in a standard 96-well plate using the inner 60 wells, six replicates per treatment with flanking positive controls is common. The authors recommend including as many replicates as feasible, typically a minimum of four for treatments and at least nine for controls. Maintaining consistent seeding density and media conditions is equally critical, as inconsistent conditions can introduce artifacts like overcrowding or edge effects. Higher-density formats such as 1536-well plates minimize data loss from discarding edge wells affected by spatial biases. The Cell Painting assay, which stains multiple subcellular compartments across five to six channels, has become the predominant fluorescence-based approach, providing a rich and unbiased representation of cellular morphology. Label-free techniques including brightfield and phase-contrast imaging are increasingly explored for their non-invasive nature and compatibility with live-cell experiments.

Once images are acquired, the computational core of the pipeline takes over. Whole-image quality control removes out-of-focus images or those containing large smudges and debris that would decrease segmentation quality and introduce noise. Illumination correction follows, learning and correcting patterns of uneven background lighting and signal variations from optical artifacts so that downstream measurements reflect biological differences rather than technical noise. Cell segmentation, the delineation of individual cells from the background, has witnessed a decisive shift toward deep learning. Classical approaches implemented in tools like CellProfiler and Ilastik use intensity thresholding and watershed algorithms, but foundational architectures like U-Net catalyzed a transition to convolutional neural networks that achieve superior segmentation accuracy. This paved the way for tools such as StarDist and Cellpose that offer models capable of identifying cell borders with minimal parameter tuning. More recently, transformer-based foundation models like CellSAM and CellViT leverage these architectures to further improve performance and generalizability across diverse imaging conditions.

Feature extraction represents the most consequential transformation in the field. Classical workflows extract hundreds to thousands of hand-crafted features from segmented objects, quantifying aspects like size, shape, texture, intensity, and spatial relationships. These features provide a mathematically well-defined representation of cell morphology, and their transparency supports hypothesis-driven research. However, the feature set might miss particular phenotypic changes and can be prone to redundancy and noise. Deep learning methods learn relevant morphological representations directly from raw pixel data, producing embeddings that are typically less redundant and frequently outperform classical features in downstream tasks. The review highlights striking examples of both directions: the count of cells or mitochondria outperformed convolutional neural networks to predict assay outcomes, and mechanism of action prediction using K-nearest neighbors on CellProfiler features achieved an F1 score of 0.41, comparable to DeepProfiler’s 0.42. Yet self-supervised DINO frameworks increased mean average precision by 29 percent when classifying chemical perturbations compared to hand-crafted features, and zero-shot image-to-image mechanism of action classification using contrastive learning achieved 61.3 percent in top-10 accuracy compared to 24.5 percent with CellProfiler.

Two major neural network architectural families dominate deep learning-based feature extraction. Convolutional neural networks process images through stacked convolutional filters that perform localized transformations to extract features hierarchically, learning increasingly descriptive representations while maintaining relatively low computational demands. Vision transformers, adapted from natural language processing, partition an input image into non-overlapping patches that are flattened and linearly embedded, then processed in parallel through self-attention blocks that capture global relationships among all image patches at once. The scaling properties of transformers have made the architecture especially useful with large datasets. Training strategies have evolved in parallel. Supervised learning requires ground truth labels that are frequently unavailable in profiling experiments. Weakly supervised learning uses easily obtained annotations like perturbation labels, which are biologically meaningful but do not directly reveal phenotypic effects, as auxiliary tasks. Self-supervised learning relies entirely on unannotated image data, defining objective functions derived from the data itself, such as reconstructing masked patches or distinguishing between augmented views of the same image.

Post-processing deep learning features requires careful attention to order of operations. Applying a sphering transform to deep learning features followed by robust z-scoring against plate-level statistics yields the most effective post-processing strategy for self-supervised features, and performing z-scoring before sphering was found to substantially degrade performance in most cases. Feature selection, which is essential for classical features due to their high correlation, might be unnecessary for deep learning embeddings since only weak correlations are generally observed. Batch effect correction remains a persistent challenge regardless of feature type. A recent study evaluated ten batch correction techniques from single-cell RNA sequencing on image-based profiling data and concluded that none could adequately remove batch effects in the most complex cases involving multiple microscope types and a large number of compounds, underscoring the ongoing need for new methods.

The field is now moving toward foundation models, large models trained on massive and diverse datasets that can be adapted to solve a wide range of tasks. Early work repurposed natural-image convolutional networks by pseudo-RGB channel mapping, while more recent efforts have trained self-supervised vision transformers on millions of microscopy images. One masked autoencoder-based strategy scaled vision transformer models to approximately 1.9 billion parameters and datasets with roughly 16 million images, demonstrating that performance improves at that scale. Channel adaptation remains a significant hurdle, as most models have been trained using the five-channel Cell Painting assay while imaging panels vary with experimental goals. Several strategies have emerged, including models that process one channel at a time, channel-agnostic masked autoencoders that simultaneously process all channels adaptively, and novel transformer architectures incorporating channel-aware attention mechanisms.

Emerging frontiers are expanding the dimensions of profiling beyond static two-dimensional snapshots. Temporal imaging captures dynamic cell processes that static methods cannot, circumventing the survivorship bias of fixed time point profiles. Live-cell painting using minimally toxic dyes enables time-lapse imaging, and advances in spinning disk confocal and light sheet microscopy make acquisition of large temporal datasets possible. The third spatial dimension remains underexplored but holds promise for revealing more nuanced cellular signatures, particularly for complex cell shapes like neurons or macrophages with extended protrusions that may appear disconnected in a two-dimensional slice. Virtual staining, which predicts fluorescence signals from label-free modalities such as brightfield imaging using machine learning, offers lower cost and reduced phototoxicity. However, virtual staining has not yet been used in any published studies for image-based profiling applications, and its generalizability across unseen cell types and phenotypes remains highly variable and dependent on the biological context of training data.

Single-cell image-based profiling offers an effective, affordable, high-content alternative to single-cell RNA sequencing. While population-level aggregation creates robust representations that minimize technical noise, averaging leads to loss of information about cell heterogeneity and the diversity of cell states. Methods that capture heterogeneity, subpopulations, and cell state transitions have been applied to identify drug toxicity, cell types, cell cycle stages, and cell health alterations in rare subpopulations. Emerging evidence suggests that single-cell analysis may improve mechanism of action prediction compared to population-level analysis. Optical pooled screening has emerged as a transformative technology, enabling high-throughput single-cell resolution mapping of genotype-to-phenotype relationships at unprecedented scale by integrating high-content imaging with in situ genotyping methods. The review identifies six core challenges for the next decade: developing end-to-end workflows, fortifying methodological infrastructure for temporal, three-dimensional, and virtual staining modalities, bridging profiles to mechanisms through interpretable features, establishing rigorous quality control standards, building systematic benchmarking frameworks, and reaping the rewards of artificial intelligence and foundation models while accounting for their high computational costs. The authors emphasize that these challenges form a cohesive foundation for the next generation of image-based profiling, and that realizing the field’s promise depends on collective resolve to confront and overcome the obstacles that remain.

Subject of Research: Image-based profiling methods and computational challenges in high-content microscopy

Article Title: Progress and new challenges in image-based profiling

Article References: Serrano, E., Peters, J., Wagner, J., Graham, R. E., Chen, Z., Feng, B. Y., Miranda, G., Kalinin, A. A., Vulliard, L., Tomkinson, J., Mattson, C., Lippincott, M. J., Kang, Z., Sitani, D., Bunten, D., Seal, S., Carragher, N. O., Carpenter, A. E., Singh, S., … Way, G. P. (2026). Progress and new challenges in image-based profiling. Molecular Systems Biology, 22(5), 624-658. https://doi.org/10.1038/s44320-026-00197-7

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00197-7

Keywords: image-based profiling, Cell Painting, deep learning, high-content screening, cell segmentation, foundation models, Progress, challenges, image-based, profiling, scientific research