Biology subjects

Marchini, M.

Publications and source records attributed to Marchini, M..

5 recordsLinked to original sources

bioRxiv2024

Pre-oviposition development of the brown anole (Anolis sagrei)

The brown anole, Anolis sagrei, has emerged as a representative squamate species during the past several decades. Novel functional tools have been established to manipulate embryogenesis through genome editing or the introduction of small molecule inhibitors, and their effective use requires a thorough understanding of early anole embryogenesis. To enable precise and reproducible staging of anole embryos, we need knowledge of the progression of anole embryogenesis and morphogenesis. While post-oviposition development has been described, the pre-oviposition period remains to be explored. Here, we provide the first staging series of pre-oviposition development for the brown anole. Analysing the follicles and embryos through brightfield imaging, SEM, STEM, histology, and DAPI staining, we define 26 distinct developmental stages. Our dataset reveals that peri-gastrulation morphogenesis up to the initiation of neurulation diverges significantly from chicken, the common representative model of reptile embryogenesis. Furthermore, we followed heart development, neural crest cell migration and central nervous system development through immunofluorescence analyses and provide new comparative insights into the morphogenesis of each of these organ systems over time. Our work establishes the brown anole as a squamate model organism for cross clade evolutionary studies of early embryogenesis.

developmental biology↗

bioRxiv2023

Quantifying the relationship between cell proliferation and morphology during development of the face

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates elegant images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.

developmental biology↗

bioRxiv2021

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N=10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph).

genetics↗

bioRxiv2021

Segmentation of Tissues and Proliferating Cells in Light-Sheet Microscopy Images using Convolutional Neural Networks

Background and ObjectiveA variety of genetic mutations are known to affect cell proliferation and apoptosis during organism development, leading to structural birth defects such as facial clefting. Yet, the mechanisms how these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied in high detail using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points. However, the large number of LSM images captured at cellular resolution precludes manual analysis. Thus, the aim of this work was to develop and evaluate automatic methods to segment tissues and proliferating cells in these images in an accurate and efficient way. MethodsWe developed, trained, and evaluated convolutional neural networks (CNNs) for segmenting tissues, cells, and specifically proliferating cells in LSM datasets. We compared the automatically extracted tissue and cell annotations to corresponding manual segmentations for three specific applications: (i) tissue segmentation (neural ectoderm and mesenchyme) in nuclear-stained LSM images, (ii) cell segmentation in nuclear-stained LSM images, and (iii) segmentation of proliferating cells in Phospho-Histone H3 (PHH3)-stained LSM images. ResultsThe automatic CNN-based tissue segmentation method achieved a macro-average F-score of 0.84 compared to a macro-average F-score of 0.89 comparing corresponding manual segmentations from two observers. The automatic cell segmentation method in nuclear-stained LSM images achieved an F-score of 0.57, while comparing the manual segmentations resulted in an F-score of 0.39. Finally, the automatic segmentation method of proliferating cells in the PHH3-stained LSM datasets achieved an F-score of 0.56 for the automated method, while comparing the manual segmentations resulted in an F-score of 0.45. ConclusionsThe proposed automatic CNN-based framework for tissue and cell segmentation leads to results comparable to the inter-observer agreement, accelerating the LSM image analysis. The trained CNN models can also be applied for shape or morphological analysis of embryos, and more generally in other areas of cell biology.

bioinformatics↗

bioRxiv2018

The genomic and molecular basis of response to selection for longer limbs in mice

Evolutionary studies are often limited by missing data that are critical to understanding the history of selection. Selection experiments, which reproduce rapid evolution under controlled conditions, are excellent tools to study how genomes evolve under strong selection. Here we present a genomic dissection of the Longshanks selection experiment, in which mice were selectively bred over 20 generations for longer tibiae relative to body mass, resulting in 13% longer tibiae in two replicate lines. We synthesized evolutionary theory, genome sequences and molecular genetics to understand the selection response and found that it involved both polygenic adaptation and discrete loci of major effect, with the strongest loci likely to be selected in parallel between replicates. We show that selection may favor de-repression of bone growth through inactivation of two limb enhancers of an inhibitor, Nkx3-2. Our integrative genomic analyses thus show that it is possible to connect individual base-pair changes to the overall selection response.

evolutionary biology↗