Do women with endometriosis have a different endometrial gene expression profile at the time of embryo implantation than women without endometriosis? The endometrial gene expression profile of women with endometriosis differs from that of women without endometriosis at the mid-secretory phase, although the differences are small. About 50% of women with endometriosis suffer infertility. Several molecular studies have suggested impaired endometrial receptivity in women with endometriosis, while others have detected no dysregulation of endometrial receptivity. Nevertheless, the previous endometrial transcriptome studies comparing women with and without endometriosis have been performed in small sample size with limited statistical power. We set out to systematically search and compile data of endometrial gene expression signatures at the receptive phase in women with endometriosis versus control women. Based on the obtained data, we conducted a meta-analysis of differentially expressed genes in order to raise the power of the analysis for identifying the molecular profiles of receptive phase endometria in endometriosis. A systematic literature search was conducted up to February 2022 following PRISMA criteria and included PubMed, Cochrane and Web of Science databases. For the systematic search, the term ‘endometriosis’ was paired with the terms ‘transcriptomics’, ‘transcriptome’, ‘gene expression’, ‘RNA-seq’, ‘sequencing’ and ‘array’, by using the Boolean operator ‘AND’ to connect them. Articles written in English were screened and interrogated for data extraction. A meta-analysis was performed on the selected studies to extract the differentially expressed genes described at the mid-secretory phase in women with endometriosis versus women without endometriosis in natural cycles, using the robust rank aggregation method. In total, transcriptome data of 125 women (78 patients and 47 controls) were meta-analysed, with a special focus on endometrial receptivity-specific genes based on commercial endometrial receptivity tests. In total, 8 studies were eligible for the quantitative meta-analysis, gathering transcriptome data from the mid-secretory phase endometria of 125 women. A total of 7779 differentially expressed transcripts between the study groups were retrieved (3496 up-regulated and 4283 down-regulated) and were meta-analysed. After stringent multiple correction, there was no differential expression of any single molecule in the endometrium of women with endometriosis versus controls, while enrichment analysis detected that the pathways of chemotaxis and locomotion are dysregulated in endometriosis. Further analysis of endometrial receptivity-specific genes highlighted dysregulation of C4BPA, MAOA and PAEP and enrichment of immune and defence pathways in women with endometriosis. Most of the studies included into the meta-analysis were relatively small and had different study designs, which might have contributed to a bias. The current meta-analysis supports the hypothesis that endometrial receptivity is altered in women with endometriosis, although the changes are small. The molecules and pathways identified could serve as future biomarkers and therapeutical targets in detecting and treating endometriosis-associated infertility. The authors declare no competing interests. This work was supported by the Spanish Ministry of Education, Culture and Sport [grant FPU15/01193] and the Margarita Salas program for the Requalification of the Spanish University system [grant UJAR01MS]; Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER): grants RYC-2016-21199 and ENDORE SAF2017-87526-R; Programa Operativo FEDER Andalucía (B-CTS-500-UGR18; A-CTS-614-UGR20); the Junta de Andalucía [BIO-302; and PAIDI P20_00158]; the University of Jaén [PAIUJA-EI_CTS02_2017]; the University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES), and by the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades and European Regional Development Fund (ERDF), ref. SOMM17/6107/UGR; the Estonian Research Council (grant PRG1076); Horizon 2020 innovation (ERIN, grant no. EU952516) of the European Commission and Enterprise Estonia (grant EU48695). The systematic review was registered at PROSPERO (identifier: CRD42020122054).

Which genes regulate receptivity in the epithelial and stromal cellular compartments of the human endometrium, and which molecules are interacting in the implantation process between the blastocyst and the endometrial cells? A set of receptivity-specific genes in the endometrial epithelial and stromal cells was identified, and the role of galectins (LGALS1 and LGALS3), integrin β1 (ITGB1), basigin (BSG) and osteopontin (SPP1) in embryo–endometrium dialogue among many other protein–protein interactions were highlighted. The molecular dialogue taking place between the human embryo and the endometrium is poorly understood due to ethical and technical reasons, leaving human embryo implantation mostly uncharted. Paired pre-receptive and receptive phase endometrial tissue samples from 16 healthy women were used for RNA sequencing. Trophectoderm RNA sequences were from blastocysts. Cell-type-specific RNA-seq analysis of freshly isolated endometrial epithelial and stromal cells using fluorescence-activated cell sorting (FACS) from 16 paired pre-receptive and receptive tissue samples was performed. Endometrial transcriptome data were further combined in silico with trophectodermal gene expression data from 466 single cells originating from 17 blastocysts to characterize the first steps of embryo implantation. We constructed a protein–protein interaction network between endometrial epithelial and embryonal trophectodermal cells, and between endometrial stromal and trophectodermal cells, thereby focusing on the very first phases of embryo implantation, and highlighting the molecules likely to be involved in the embryo apposition, attachment and invasion. In total, 499 epithelial and 581 stromal genes were up-regulated in the receptive phase endometria when compared to pre-receptive samples. The constructed protein–protein interactions identified a complex network of 558 prioritized protein–protein interactions between trophectodermal, epithelial and stromal cells, which were grouped into clusters based on the function of the involved molecules. The role of galectins (LGALS1 and LGALS3), integrin β1 (ITGB1), basigin (BSG) and osteopontin (SPP1) in the embryo implantation process were highlighted. RNA-seq data are available at www.ncbi.nlm.nih.gov/geo under accession number GSE97929. Providing a static snap-shot of a dynamic process and the nature of prediction analysis is limited to the known interactions available in databases. Furthermore, the cell sorting technique used separated enriched epithelial cells and stromal cells but did not separate luminal from glandular epithelium. Also, the use of biopsies taken from non-pregnant women and using spare IVF embryos (due to ethical considerations) might miss some of the critical interactions characteristic of natural conception only. The findings of our study provide new insights into the molecular embryo–endometrium interplay in the first steps of implantation process in humans. Knowledge about the endometrial cell-type-specific molecules that coordinate successful implantation is vital for understanding human reproduction and the underlying causes of implantation failure and infertility. Our study results provide a useful resource for future reproductive research, allowing the exploration of unknown mechanisms of implantation. We envision that those studies will help to improve the understanding of the complex embryo implantation process, and hopefully generate new prognostic and diagnostic biomarkers and therapeutic approaches to target both infertility and fertility, in the form of new contraceptives. This research was funded by the Estonian Research Council (grant PRG1076); Horizon 2020 innovation grant (ERIN, grant no. EU952516); Enterprise Estonia (grant EU48695); the EU-FP7 Marie Curie Industry-Academia Partnerships and Pathways (IAPP, grant SARM, EU324509); Spanish Ministry of Economy, Industry and Competitiveness (MINECO) and European Regional Development Fund (FEDER) (grants RYC-2016-21199, ENDORE SAF2017-87526-R, and Endo-Map PID2021-127280OB-100); Programa Operativo FEDER Andalucía (B-CTS-500-UGR18; A-CTS-614-UGR20), Junta de Andalucía (PAIDI P20_00158); Margarita Salas program for the Requalification of the Spanish University system (UJAR01MS); the Knut and Alice Wallenberg Foundation (KAW 2015.0096); Swedish Research Council (2012-2844); and Sigrid Jusélius Foundation; Academy of Finland. A.S.-L. is funded by the Spanish Ministry of Science, Innovation and Universities (PRE2018-085440). K.G.-D. has received consulting fees and/or honoraria from RemovAid AS, Norway Bayer, MSD, Gedeon Richter, Mithra, Exeltis, MedinCell, Natural cycles, Exelgyn, Vifor, Organon, Campus Pharma and HRA-Pharma and NIH support to the institution; D.B. is an employee of IGENOMIX. The rest of the authors declare no conflict of interest.

STUDY QUESTION: Does the analysis of endometrial microbes provide the same information when using DNA or RNA sequencing-based techniques? SUMMARY ANSWER: DNA vs. RNA-based microbial analysis techniques demonstrated significant microbial compositional differences and lack of transcriptionally active Lactobacilli in the endometrium. WHAT IS KNOWN ALREADY: Our understanding of the endometrial microbiome is primarily based on DNA-based 16S rRNA gene profiling, but DNA detection does not imply the presence of living microbes. While this method is cost-effective and widely used, it has notable limitations, including the underestimation of microbial diversity, abundance, and functionality, as well as limited species-level resolution. While the microbiome reflects DNA-based characterisation, the microbiota more precisely captures metabolically active communities. In this context, meta-transcriptomic analysis, an RNA-based approach, addresses these shortcomings by capturing functional transcripts that are actively expressed in living microbes. STUDY DESIGN, SIZE, DURATION: This cross-sectional study consisted of forty-nine reproductive-aged women (27–42 years old) who were receiving ART. By simultaneously analysing the microbial composition and gene expression within female reproductive tract samples, we sought to provide a more comprehensive understanding of the microbiota and functional potential of these samples. PARTICIPANTS/MATERIALS, SETTING, METHODS: Vaginal swabs, endometrial brushing, and endometrial biopsy samples were collected from 49 participants during the mid-secretory phase of their menstrual cycle, 6–9 days after the luteinizing hormone surge for parallel 16S rRNA gene sequencing and meta-transcriptome analyses. For DNA-based analysis, 16S rRNA gene V3-V4 regions were sequenced. For RNA-based total RNA was extracted followed by ribosomal RNA depletion. Strand-specific total RNA sequencing libraries were prepared and sequenced. Taxonomy was assigned by using Kraken2 (v2.2.1), Bracken (v2.7). MAIN RESULTS AND THE ROLE OF CHANCE: Our findings suggest that in low-microbial-biomass environments such as the endometrium, the correlation between 16S rRNA gene sequencing and meta-transcriptomics is relatively weak. This highlights the limitations of microbial analysis of low-microbial-biomass samples. Alternatively, microbial functions and genome activity may be tissue-specific and dependent on the host tissue environment. Moreover, RNA-based analysis provides higher resolution in detecting certain pathogens, even within the endometrium. LARGE SCALE DATA: The data presented in the study are deposited in the NCBI SRA Database, accession number PRJNA1247240. LIMITATIONS, REASONS FOR CAUTION: High levels of host RNA and the low abundance of microbial reads in the endometrium complicate microbial identification. Our findings indicate that RNA-seq enables precise profiling of the vaginal microbiome and, in cases of dysbiosis, reveals higher pathogen activity than DNA-based approaches. However, the limited sample size restricts the generalization of these conclusions. WIDER IMPLICATIONS OF THE FINDINGS: Contrary to the general belief of the dominance of Lactobacillus in the human endometrium, our study suggests that the endometrial microenvironment may be harbouring DNA fragments and/or cells of lactobacilli originating from the lower reproductive tract. Our study results indicate a need to re-consider/re-analyse the endometrial microbiome in health and disease. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the projects Endo-Map PID2021-127280OB-I00, ROSY CNS2022-135999 and ENDORE SAF2017-87526-R funded by MICIU/AEI/10.13039/501100011033 and by FEDER, EU. This work was also supported by the Estonian Research Council grants (PSG1082 and PRG1076), Swedish Research Council grant no. 2024-02530 and Novo Nordisk Foundation grant no. NNF24OC0092384. Additionally, A.S.L and I.P.P acknowledge Becas Fundación Ramón Areces para Estudios Postdoctorales—Convocatorias XXXV and XXXVI, para Ampliación de Estudios en el Extranjero en Ciencias de la Vida y de la Materia. A.S. is supported by Horizon Europe (NESTOR, grant no. 101120075) and the Ministry of Education and Research Centres of Excellence grant TK214 name of CoE. All the authors declare no conflict of interest.

Uterine pathologies pose a challenge to women’s health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

Fractal geometry offers a mathematical framework to quantify the complexity of brain structure and function. The fractal dimension (FD) captures self-similarity and irregularity across spatial and temporal scales, surpassing the limits of traditional Euclidean metrics. In neuroscience, FD serves as a key descriptor of the brain’s hierarchical organization—from dendritic arborization and cortical folding to neural dynamics measured by diverse neuroimaging techniques. This review summarizes theoretical foundations and methodological advances in FD estimation, including the box-counting approach for imaging, and Higuchi’s and Katz’s algorithms for electrophysiological data, addressing reliability and reproducibility issues. In addition, we illustrate how fractal analysis characterizes brain complexity in health and disease. Clinical applications include detecting white matter alterations in multiple sclerosis, atypical maturation in intrauterine growth restriction, reduced cortical complexity in Alzheimer’s disease, and altered neuroimaging patterns in schizophrenia. Emerging evidence highlights FD’s potential for distinguishing consciousness states and quantifying neural integration and differentiation. Bridging mathematics, physics, and neuroscience, fractal analysis provides a quantitative lens on the brain’s multiscale organization and pathological deviations. FD thus stands as both a theoretical descriptor and a translational biomarker whose standardization could advance precision diagnostics and understanding of neural dynamics.

Neurological disorders such as Autism Spectrum Disorder (ASD), Schizophrenia (SCH), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) affect millions of people worldwide, yet their molecular mechanisms remain poorly understood. This study describes the application of the Comparative Analysis of Shapley values (CASh) to transcriptomic data from nine datasets associated with these complex disorders, demonstrating its effectiveness in identifying differentially expressed genes (DEGs). CASh, which combines Game Theory with Bootstrap resampling, offers a robust alternative to traditional statistical methods by assessing the contribution of each gene in the broader context of the complete dataset. Unlike conventional approaches, CASh is highly effective at detecting subtle but meaningful molecular patterns that are often missed. These findings highlight the potential of CASh to enhance the precision of transcriptomic analysis, providing a deeper understanding of the molecular mechanisms underlying these disorders and establishing a solid basis to improve diagnostic techniques and developing more targeted therapeutic interventions.