Biology subjects

Martinez, A. S.

Publications and source records attributed to Martinez, A. S..

6 recordsLinked to original sources

bioRxiv2025

Single-cell-scale spatial transcriptome of the developing and adult mouse ovary

Mammalian ovary development is essential for female fertility, involving the complex spatial patterning of diverse cell types to establish the finite reserve of ovarian follicles. To uncover molecular mechanisms driving regionalization of the ovary while maintaining its native cellular architecture, we used Visium HD spatial transcriptomics. Our study captures all ovarian cell types at eight timepoints, generating a near single cell resolution library of spatial gene expression across development. Our analysis identified a previously uncharacterized "medullary core" domain that emerges by E16.5. Pre-granulosa cells (PGs) within this core express genes typically restricted to growing follicles in the postnatal ovary (Nr5a2, Slc18a2, Ephx2) suggesting core PGs are poised for activation while still in the fetal environment. This study provides a comprehensive architectural and molecular blueprint of the ovary, representing a fundamental resource for the investigation of regulatory mechanisms driving spatial patterning of the ovary and opens new avenues to explore the spatial determinants of female fertility and reproductive longevity. TeaserVisium HD provides a comprehensive blueprint of the mouse ovary, revealing a medullary core of activation-poised pregranulosa cells.

developmental biology↗

bioRxiv2025

Outer membrane vesicles from Bacteroides fragilis contain coding and non-coding small RNA species that modulate inflammatory signaling in intestinal epithelial cells.

Alterations to the community structure and function of the microbiome are associated with changes to host physiology, including immune responses. However, the contribution of microbe-derived RNAs carried by outer membrane vesicles (OMVs) to host immune responses remain unclear. This study investigated the role of OMVs and OMV-associated small RNA (sRNA) species from pathogenic and commensal Bacteroides fragilis (ETBF and NTBF respectively) in eliciting different immune responses from intestinal epithelial cells. To distinguish the differences in the sRNA profiles of the two strains and their OMVs, RNA-seq, qRT-PCR, and northern blotting were conducted to identify enrichment of discrete sRNA species in OMVs, which were also differentially expressed between the two strains. Specifically, both coding and non-coding RNAs were enriched in OMVs from NTBF and ETBF, with BF9343_RS22680 and BF9343_RS17870 being significantly enriched in ETBF OMVs compared to NTBF. To understand the effects of OMVs on pattern recognition receptors, reporter cells of Toll-like receptor (TLR) activation were treated with OMVs, demonstrating activation of TLRs 2, 3, and 7. Treatment of Caco-2 and HT29-MTX cells with OMVs demonstrated increased expression of IL-8. Surprisingly, we discovered that degradation of RNase-accessible RNAs within ETBF OMVs, but not NTBF OMVs, resulted in vesicles with enhanced capacity to stimulate IL-8 expression, indicating that these extravesicular RNAs exert an immunosuppressive effect. This suggests a dual role for OMV-associated RNAs in modulating host immune responses, with implications for both bacterial pathogenesis and therapeutic applications. Graphical Abstract

molecular biology↗

bioRxiv2024

OoCount: A machine-learning based approach to mouse ovarian follicle counting and classification

The number and distribution of ovarian follicles in each growth stage provides a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional (3D) imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. However, because of the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of deep-learning algorithms has allowed for the rapid development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more user-friendly and accessible to non-specialists. We used these tools to create OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a deep-learning convolutional neural network (CNN) based approach. We developed a fast tissue-clearing and spinning disk confocal-based imaging protocol to obtain 3D images of whole mount perinatal and adult mouse ovaries. Fluorescently labeled oocytes from 3D images of ovaries were manually annotated in Napari to develop a machine learning training dataset. This dataset was used to retrain StarDist using a CNN within DL4MicEverywhere to automatically label all oocytes in the ovary. In a second phase, we utilize Accelerated Pixel and Object Classification, a Napari plugin, to classify labeled oocytes and sort them into growth stages. Here, we provide an end-to-end protocol for producing high-quality 3D images of the perinatal and adult mouse ovary, obtaining follicle counts and staging. We also demonstrate how to customize OoCount to fit images produced in any lab. Using OoCount, we can obtain accurate counts of oocytes in each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/593993v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@8d7257org.highwire.dtl.DTLVardef@4dbe73org.highwire.dtl.DTLVardef@1283aaorg.highwire.dtl.DTLVardef@1822baf_HPS_FORMAT_FIGEXP M_FIG C_FIG Summary sentenceThis protocol introduces OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a machine learning-based approach.

developmental biology↗

bioRxiv2019

Incipient resistance to an effective pesticide results from genetic adaptation and the canalization of gene expression

The resistance of bacteria, disease vectors, and pest species to chemical controls has vast ecological, economic, and societal costs. In most cases, resistance is only detected after spreading throughout an entire population. Detecting resistance in its incipient stages, by comparison, provides time to implement preventative strategies. Incipient resistance can be detected by coupling standard toxicology assays with large-scale gene expression experiments. We apply this approach to a system where an invasive parasite, sea lamprey (Petromyzon marinus), has been treated with the highly-effective pesticide 3-trifluoromethyl-4-nitrophenol (TFM) for 60 years. Toxicological experiments revealed that lamprey from treated populations did not have higher survival to TFM exposure than lamprey from their native range, demonstrating that full-fledged resistance has not yet evolved. In contrast, we find hundreds of genes differentially expressed in response to TFM in the population with the longest history of exposure, many of which relate to TFMs primary mode of action, the uncoupling of oxidative phosphorylation and subsequent depletion of ATP. Three genes critical to oxidative phosphorylation, ATP5PB, PLCB1, and NDUFA9, were nearly fixed for alternative alleles in comparisons of SNPs between native and treated populations (FST > 5 SD from the mean). ATP5PB encodes subunit b of ATP synthase and an additional subunit, ATP5F1B, was canalized for high expression in treated populations, but remained plastic in response to TFM treatment in individuals from the native range. These combined genomic and transcriptomic results demonstrate that an adaptive, genetic response to TFM is driving incipient resistance in a damaging pest species.

genetics↗

bioRxiv2017

Biological communities as interacting compartments: thermodynamic properties and diversity indices

Diversity indices provide simple and powerful metrics for assessing biological communities. Based on entropy measures, the approach considers statistical and thermodynamic inferences to deduce ecological patterns. However, concerns exist regarding the accuracy of diversity indices. Because relative quantities depend on the sorting of organisms (e.g., guilds and species) and their interactions, field observations carry inherent imprecision, thus leading to misinterpretation. Here, we present a framework that is able to appropriately achieve the thermodynamic properties in ecological systems and ensure the inference power. We demonstrate that effective abundances rather than raw abundances provide a trustful estimator of probabilities, which is evaluated through massive tests. We use empirical and synthetic data to show the advantages and reliability of this new framework under a broad range of conditions. The tests demonstrate that the replication principle is always optimized by the new estimator. Compared to other methods, this approach is simpler and reduces the importance of schemes used for sorting organisms. We highlight the robustness and the valor of effective abundances for ecological contexts: i) to assess and monitor the biodiversity, ii) to define the best sorting of organisms according to maximum entropy principles, and iii) to link local to regional diversity (-, {beta}-, and{gamma} -diversity).

ecology↗

bioRxiv2017

Empirical evidence reveals the phase diagram of vegetation patterns in Mediterranean drylands

Drylands are ecosystems with limited water resources, often subjected to desertification. Conservation and restoration efforts towards these ecosystems depend on the interplay between ecological functioning and spatial patterns formed by local vegetation. Despite recent advances on the subject, an adequate description of phase transitions between the various vegetated phases remains an open issue. Here, we gather vegetation data of drylands from Southern Spain using satellite images. Our findings support three vegetated phases, separated by two distinct phase transitions, including a continuous phase transition, with new relations between scaling exponents of ecological variables. The phase diagram is obtained without a priori assumption about underlying ecological dynamics. We apply our analysis to a different dryland system in the Western United States and verify a compatible critical behavior, in agreement with the universality hypothesis.

ecology↗