Biology subjects

Mapa, R.

Publications and source records attributed to Mapa, R..

4 recordsLinked to original sources

bioRxiv2026

The ENIGMA-PD-WML Pipeline: A Containerized, User-Friendly Approach for Accurate, Standardized Segmentation of White Matter Lesions in Multi-Site MRI Data

Understanding vascular contributions to disease is a major unmet need. White matter lesions (WML) are an accepted imaging marker of cerebral small vessel disease, giving insights into its related pathologies. A unified approach for WML analyses in large multi-site data is lacking despite the need for pooling of data to overcome the limitations of often small heterogenous MRI studies which make subtyping and identifying patterns within disease groups difficult. Our ENIGMA-PD-WML pipeline is an open-source containerized pipeline containing all the code and packages required for pre-processing, processing and post-processing of T1-weighted and FLAIR data, outputting accurate and reproducible binary WML maps using a UNet approach. The pipeline provides a standardized image analysis approach for WML and outputs data in both native and MNI space to allow for sharing and pooling of data from multiple sites for large-data analysis. In addition to a reliable standardized approach for WML segmentation, key priorities when developing the pipeline included: usability, i.e., requiring minimal manual input and technical expertise to use, and suitability to run on various MRI scanners and acquisition parameters as is common in multi-site data. This paper describes the pipeline in detail, with rationale for each step, providing transparency and facilitating its usage to overcome reproducibility issues in large-scale WML analyses.

neuroscience↗

bioRxiv2025

CRISPR screening reveals genetic regulators associated with the evolution of eye degeneration

Determining the genetic factors contributing to trait evolution is critical for understanding how and why traits evolve; however, establishing which genes underlie the evolution of complex traits remains challenging. The freshwater fish Astyanax mexicanus, a species that includes blind, cave-dwelling and eyed, surface-dwelling fish, is a powerful model for evolutionary genetics. While genetic mapping studies in this species previously identified genomic regions associated with cave-derived traits, few causative genes and genetic changes have been identified. Here, we develop methods to identify and rapidly functionally assess candidate genes in A. mexicanus, focusing on a defining trait of cave animals, eye loss. Candidate genes were identified based on whether they fell within an eye-related quantitative trait locus, were differentially expressed between surface and cave eyes, and showed evidence of positive selection in cavefish. Single-nucleus RNA-sequencing revealed that these candidate genes were expressed in multiple cell types during development, including those in different tissues of the eye. CRISPR-Cas9-based mutagenesis demonstrated that disruption of nine of these candidate genes in surface fish resulted in altered eye size. Perturbation of one of these genes, fibulin-7 (fbln7), revealed changes in eye size across multiple stages of eye development. Together, this work identified multiple genes associated with the evolution of eye degeneration in A. mexicanus. Further, this study represents a roadmap for rapid identification and functional assessment of candidate genes implicated in the evolution of traits in cavefish that can be applied to other evolutionary genetic models.

genetics↗

bioRxiv2025

Hybrid Solid-Liquid Optics Enable Scalable, High-Resolution, Multi-Immersion Light-Sheet Microscopy

Modern biology increasingly depends on data-driven discovery, requiring scalable and affordable high-content 3D imaging across molecular to organ scales. Although tissue clearing, expansion microscopy, and light-sheet microscopy (LSM) enable subcellular-resolution imaging of intact specimens, their scalability remains fundamentally limited by detection optics: immersion objectives deliver high-resolution, aberration-free imaging but with short working distances, high cost, and multi-immersion incompatibility, while air objectives offer long working distances and portability at lower cost but suffer from severe aberrations and reduced photon collection when imaging immersed samples. We introduce the Hybrid Solid-Liquid Immersion Lens (HySIL) framework, which pairs an off-the-shelf solid optical component with a refractive index-matched liquid to precompensate aberrations and enhance resolution. Building on HySIL, we developed SCOPE and Super-SCOPE, objective-agnostic imaging devices achieving submicron lateral resolution (<0.75 {micro}m) across centimeter-scale samples using inexpensive air objectives with >30 mm working distances. Integration with a low-cost LSM platform yielded a compact, scalable system demonstrated for multi-immersion, multi-color, subcellular-resolution mapping of cleared or expanded mouse, salamander, and cavefish brains, human iPSC-derived organoids, and 3D histopathology of breast tissue. HySIL and SCOPE establish an accessible foundation for scalable, high-resolution volumetric imaging, advancing data-driven biological discovery.

bioengineering↗

bioRxiv2025

Automated profiling of social behaviors to assess the genetic basis of evolution of aggressive behaviors in A. mexicanus

Across the animal kingdom, social behaviors such as aggression are critical for survival and reproductive success. While there is significant variation in social behaviors within and between species, the genetic mechanisms underlying natural variation in social behaviors are poorly understood. A central challenge to investigating the mechanisms contributing to the evolution of social behaviors is that these behaviors are typically complex, making them a challenge to quantify. The Mexican tetra, Astyanax mexicanus, is a powerful model for investigating the evolution of traits, as it is a single species that exists as populations of eyed, river-dwelling surface fish and blind cave-dwelling fish. The blind cavefish have evolved morphological and behavioral differences compared to surface fish, including reduced aggression. Here, we developed and validated an automated machine learning pipeline that integrates pose-estimation and supervised behavioral classification to track and quantify aggression-associated behaviors--striking, following, and circling. Using this pipeline, we established that these behaviors are quantitatively different between surface and cave fish during juvenile stages in A. mexicanus, similar to what was observed previously in adults. Moreover, assessment of these aggressive behaviors in surface-cave F2 hybrid fish revealed that striking and following are strongly positively correlated, while striking and circling are negatively correlated, suggesting that these behaviors evolved through some shared genetic mechanisms. These findings demonstrate the power of automated tracking and behavioral phenotyping in multiple fish in A. mexicanus and establish a foundation for future studies investigating the genetic basis of evolution of social behaviors.

evolutionary biology↗