ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-06092015-172038


Tipo di tesi
Tesi di laurea magistrale
Autore
CALLARA, ALEJANDRO LUIS
URN
etd-06092015-172038
Titolo
3D reconstruction of single Purkinje Neurons from clarified cerebellar tissue for the study of sexual dimorphism in animal models of autism
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Prof.ssa Ahluwalia, Arti Devi
correlatore Dott.ssa Magliaro, Chiara
Parole chiave
  • segmentation
  • purkinje cells
  • image processing
  • CLARITY
  • cerebellum
  • autism
  • 3D tracing
Data inizio appello
17/07/2015
Consultabilità
Completa
Riassunto
This thesis aims to study sexual dimorphisms in animal models of autism through the investigation of morphological differences of Purkinje Cells (PCs) in the mouse cerebellum. Several studies available in the literature confirm that there is a close relationship between cell morphology and cell function. Thus, imaging neural structures in their native 3D arrangement may help our understanding of neural function. To this end, a new scheme of work is designed to obtain faithful information on the 3D structure of PCs in knock out and wild type mice of both sexes. In particular, the CLARITY2 protocol, which is a method for tissue clarification, is integrated with 3D imaging techniques and methods. The workflow adopted can be divided into two parts. First, in order to reach the best conditions of clarification (i.e. a compromise between signal goodness and signal loss), a rigorous optimization of the CLARITY2 protocol for 1 mm thick brain slices from L7GFP mice is proposed. The need of clarifying tissues derives from the presence of lipids in biological tissues. In fact, these cause significant light scattering and thus represent one of the major limiting factors for imaging purposes (i.e. confocal acquisitions). CLARITY2, which represents a simplified version of the CLARITY method, is a clearing tissue technique based on removing tissue lipids and replacing them with an optically transparent hydrogel. Therefore, clarified tissues are optically transparent but still preserve the 3D arrangement of structures within them. In this thesis, clarified tissue slices from L7GFP mice cerebella are studied at 3, 5 and 7 days of clarification. Slices immersed in PBS are used as controls. Three different analyses are performed on both clarified tissues and control at the same time points. First, macroscopic imaging is performed in order to quantify the bulk slice transparency. Then, single photon confocal microscopy imaging is done: Mean pixel intensity (MPI) and Contrast-to-Noise ratio (CNR) as markers of the amount of clarification are calculated at different depths in the sample. Moreover, the release of GFP from the specimens in the CLARITY solution is quantified in order to monitor the loss of signal from the samples.
A wide range of neural developmental abnormalities have been observed in Austim-Spectrum-Disorder (ASD). The cerebellum is one of the most affected regions in such disorder, both structurally and functionally. Among the different type of cells within the cerebellum, the PCs seem to play an important role in the development of ASD and thus represent an interesting feld of study. In some fields of research animal models constitute the only way for study: this is the case of disease modeling. Concerning ASD, different murine models have been presented to date. One of the most promising of such models is the Engrailed2 (En2) knock-out mouse, which harbors cerebellar abnormalities that are similar to those found in autistic individuals. For these reasons, finding the best day for the clearing treatment, tissue slices of L7GFP WT (wild-type) and L7GFP/En2-/- mutant mice cerebella are clarified. Because of the unbalanced incidence of ASD between male and female individuals (i.e. 4:1 ratio), for each genotype both sexed mice are considered and confocal image stacks of the treated tissues are acquired. In order to isolate single PCs from the datasets obtained, a smart region growing algorithm (SmRG algorithm) based on local features of the intensity value histogram is developed in Matlab environment (The Mathworks, Inc.). To allow an easy use of the algorithm, this is integrated in a Matlab Graphical-User- Interface (GUI). Single purkinje neurons from the four groups of mice are isolated. A morphologic analysis in terms of Surface-Area-to-Volume ratio as a raw index of neuron complexity is done.
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