The two defining features of mouse embryonic stem (ES) cells are self-renewal and naive pluripotency, the ability to give rise to all cell lineages in the adult body. In addition to being a unique and interesting cell type, pluripotent ES cells have demonstrated their potential for continued advancements in biomedical science. Currently, there is an improved understanding in the chemical signals and the gene regulatory network responsible for the maintenance of ES cells in the naive pluripotent state. However, less is understood about how ES cells exit pluripotency. My main aim is to study the dynamics and the factors affecting the irreversible exit from pluripotency. Expression of the reporter Rex1-GFPd2, which is inactivated upon exit from naive pluripotency, was analyzed by quantitative long-term single-cell imaging over many generations. This technique allowed chemical, physical, and genealogical information to be recorded during the transition to exit. Culture conditions that provided homogeneous populations were used in all assays and these data were validated against bulk-culture data where appropriate. Changes in real-time cell behavior were seen in cell-cell contact, motility, and cell-cycle duration. Undifferentiated ES cells form tightly joined colonies, with cells that exhibit low motility and a constant cell-cycle duration. Exit is associated with increasing cell motility, decreased cell-cell contact, and an acceleration in cell proliferation. The onset of exit is associated with a sudden and irreversible inactivation of the Rex1-GFPd2 reporter. This inactivation is asynchronous, as it occurs at different times and in different generations during ES cell differentiation. However, examination of daughter cells generated from the same mother revealed a high level of synchronicity. Further investigation revealed that high levels of correlation in cell-cycle duration and Rex1-GFPd2 expression exist between differentiating sister and cousin cells, providing strong evidence that cell potency is inherited symmetrically in cell divisions during exit $\textit{in vitro}$. How cells change fate is a fundamental question in developmental biology. Knowing the cellular dynamics during the transition out of naive pluripotency is important for harnessing the potential of ES cells and understanding how cell fate decisions are made during embryonic development. The quantification of the timing of exit from naive pluripotency coupled with identifiable changes in cellular behaviors, such as motility, cell size, and cell-cycle duration, enhances the understanding of how cell fate changes are regulated during directed differentiation.

Cardiac tissue engineering promises to create therapeutic tissue replacements for repair of diseased native myocardium. The main goals of this thesis were four-fold: 1) to evaluate cardiac tissues engineered using multiple cell types including endothelial cells (EC), fibroblasts (FB), and cardiomyocytes (CM); 2) to spatiotemporally track cells in organoids and optimize their seeding percentages for improved function; 3) to enhance vascular cord formation through sequential versus simultaneous seeding of ECs and FBs; and 4) to perform mechanistic studies to elucidate the role of soluble factors in cell-cell communication. Microscale templates fabricated from photocrosslinkable poly(ethylene glycol) diacrylate (PEG-DA) were used for all studies for rapid screening. When ECs and FBs were precultured for two days prior to seeding enriched CMs, cells self-assembled into three-dimensional, beating organoids, compared to simultaneously tricultured EC/ FB / CM which formed non-contractile clusters. Fluorescent dyes were used to label and track each cell type for up to 4 days, demonstrating an even distribution of cells within precultured organoids versus EC clustering in simultaneous triculture. When ECs were seeded first, followed by FBs 24 hours later and CMs 48 hours later, vascular-like cords formed that persisted with time in a seeding density-dependent manner. Vascular endothelial growth factor (VEGF) signaling was quantified, showing higher endogenous VEGF secretion rates in sequential preculture (16.6 ng/mL/hr) compared to undetectable VEGF secretion in simultaneous triculture. Blocking of endogenous VEGF signaling through addition of VEGF antibody / VEGFR2 inhibitor resulted in a significant decrease in mRNA and protein expression of the key cardiac gap junctional marker connexin-43. These findings provide a foundation for future work into the mechanisms governing functional cardiac tissue engineering performance and may aid in the development of novel therapies for heart failure based on growth factor signaling and engineering of vascularized, clinically relevant cardiac tissue patches.

Cardiac tissue engineering promises to create therapeutic tissue replacements for repair of diseased native myocardium. The main goals of this thesis were four-fold: 1) to evaluate cardiac tissues engineered using multiple cell types including endothelial cells (EC), fibroblasts (FB), and cardiomyocytes (CM); 2) to spatiotemporally track cells in organoids and optimize their seeding percentages for improved function; 3) to enhance vascular cord formation through sequential versus simultaneous seeding of ECs and FBs; and 4) to perform mechanistic studies to elucidate the role of soluble factors in cell-cell communication. Microscale templates fabricated from photocrosslinkable poly(ethylene glycol) diacrylate (PEG-DA) were used for all studies for rapid screening. When ECs and FBs were precultured for two days prior to seeding enriched CMs, cells self-assembled into three-dimensional, beating organoids, compared to simultaneously tricultured EC/ FB / CM which formed non-contractile clusters. Fluorescent dyes were used to label and track each cell type for up to 4 days, demonstrating an even distribution of cells within precultured organoids versus EC clustering in simultaneous triculture. When ECs were seeded first, followed by FBs 24 hours later and CMs 48 hours later, vascular-like cords formed that persisted with time in a seeding density-dependent manner. Vascular endothelial growth factor (VEGF) signaling was quantified, showing higher endogenous VEGF secretion rates in sequential preculture (16.6 ng/mL/hr) compared to undetectable VEGF secretion in simultaneous triculture. Blocking of endogenous VEGF signaling through addition of VEGF antibody / VEGFR2 inhibitor resulted in a significant decrease in mRNA and protein expression of the key cardiac gap junctional marker connexin-43. These findings provide a foundation for future work into the mechanisms governing functional cardiac tissue engineering performance and may aid in the development of novel therapies for heart failure based on growth factor signaling and engineering of vascularized, clinically relevant cardiac tissue patches.

Today many cell biological techniques study large cell populations where an average estimate of individual cells’ behavior is observed. On the other hand, single cell analysis is required for studying functional heterogeneities between cells within populations. This thesis presents work that combines the use of microfluidic devices, optical microscopy and automated image analysis to design various cell biological assays with single cell resolution including cell proliferation, clonal expansion, cell migration, cell-cell interaction and cell viability tracking. In fact, automated high throughput single cell techniques enable new studies in cell biology which are not possible with conventional techniques. In order to automatically track dynamic behavior of single cells, we developed a microwell based device as well as a droplet microfluidic platform. These high throughput microfluidic assays allow automated time-lapse imaging of encapsulated single cells in micro droplets or confined cells inside microwells. Algorithms for automatic quantification of cells in individual microwells and micro droplets are developed and used for the analysis of cell viability and clonal expansion. The automatic counting protocols include several image analysis steps, e.g. segmentation, feature extraction and classification. The automatic quantification results were evaluated by comparing with manual counting and revealed a high success rate. In combination these automatic cell counting protocols and our microfluidic platforms can provide statistical information to better understand behavior of cells at the individual level under various conditions or treatments in vitro exemplified by the analysis of function and regulation of immune cells. Thus, together these tools can be used for developing new cellular imaging assays with resolution at the single cell level. To automatically characterize transient migration behavior of natural killer (NK) cells compartmentalized in microwells, we developed a method for single cell tracking. Time-lapse imaging showed that the NK cells often exhibited periods of high motility, interrupted with periods of slow migration or complete arrest. These transient migration arrest periods (TMAPs) often overlapped with periods of conjugations between NK cells and target cells. Such conjugation periods sometimes led to cell-mediated killing of target cells. Analysis of cytotoxic response of NK cells revealed that a small sub-class of NK cells called serial killers was able to kill several target cells. In order to determine a starting time point for cell-cell interaction, a novel technique based on ultrasound was developed to aggregate NK and target cells into the center of the microwells. Therefore, these assays can be used to automatically and rapidly assess functional and migration behavior of cells to detect differences between health and disease or the influence of drugs. The work presented in this thesis gives good examples of how microfluidic devices combined with automated imaging and image analysis can be helpful to address cell biological questions where single cell resolution is necessary. / <p>QC 20130927</p>

This dissertation deals with customised image analysis tools in cancer research. In the field of biomedical sciences, mathematical imaging has become crucial in order to account for advancements in technical equipment and data storage by sound mathematical methods that can process and analyse imaging data in an automated way. This thesis contributes to the development of such mathematically sound imaging models in four ways: (i) automated cell segmentation and tracking. In cancer drug development, time-lapse light microscopy experiments are conducted for performance validation. The aim is to monitor behaviour of cells in cultures that have previously been treated with chemotherapy drugs, since atypical duration and outcome of mitosis, the process of cell division, can be an indicator of successfully working drugs. As an imaging modality we focus on phase contrast microscopy, hence avoiding phototoxicity and influence on cell behaviour. As a drawback, the common halo- and shade-off effect impede image analysis. We present a novel workflow uniting both automated mitotic cell detection with the Hough transform and subsequent cell tracking by a tailor-made level-set method in order to obtain statistics on length of mitosis and cell fates. The proposed image analysis pipeline is deployed in a MATLAB software package called MitosisAnalyser. For the detection of mitotic cells we use the circular Hough transform. This concept is investigated further in the framework of image regularisation in the general context of imaging inverse problems, in which circular objects should be enhanced, (ii) exploiting sparsity of first-order derivatives in combination with the linear circular Hough transform operation. Furthermore, (iii) we present a new unified higher-order derivative-type regularisation functional enforcing sparsity of a vector field related to an image to be reconstructed using curl, divergence and shear operators. The model is able to interpolate between well-known regularisers such as total generalised variation and infimal convolution total variation. Finally, (iv) we demonstrate how we can learn sparsity promoting parametrised regularisers via quotient minimisation, which can be motivated by generalised Eigenproblems. Learning approaches have recently become very popular in the field of inverse problems. However, the majority aims at fitting models to favourable training data, whereas we incorporate knowledge about both fit and misfit data. We present results resembling behaviour of well-established derivative-based sparse regularisers, introduce novel families of non-derivative-based regularisers and extend this framework to classification problems.

The goal of this thesis is the development of a tracking algorithm for populations of unmarked cancer cells that migrate in 3D in vitro gels. The tracking algorithm is intended to be a tool for analysing the motility of large population (i.e. hundreds) of cells in the context of the anti-migratory drug development and more specifically drug screening. In oncology, cancer cell migration plays pivotal roles in the spread of cancer cells from a primary tumor site to neighboring and secondary sites, i.e. the processes of tissue invasion and metastasis. Preventing such processes represents an important therapeutic approach to cancer treatment. Providing tools able to test potential anti-migratory drugs thus constitutes currently a real need in oncology therapy. The goal of drug screening in this context aims to rapidly and efficiently test the anti-migratory effects of many experimental conditions on cancer cell populations.<p>The focus in this thesis lies in two specific aspects that are important in anti-migratory drug screening: tracking cells inside an in vitro 3D environment and doing so using unmarked cells. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Par leur stabilité, leur petite taille et leur nature monomérique, les domaines variables des immunoglobulines à chaînes lourdes, ou Nanobodies (Nb), sont incontournables en diagnostic et recherche médicale. Pourtant, leur utilisation en agro-biotechnologies demeure confidentielle.Dans l'idée de les utiliser pour étudier et combattre le Grapevine fanleaf virus (GFLV), responsable de la maladie du court-noué très préjudiciable à l'économie viticole mondiale, j'ai produit une collection de Nb spécifiques du GFLV.Fusionné à une protéine fluorescente et exprimé en plante de façon stable, un de ces Nb (alors appelé Chromobody, Cb) a conféré une haute résistance au GFLV inoculé mécaniquement ou transmis par nématodes.Le potentiel du Cb comme biocapteur a été validé par le suivi in vivo d’un isolat contournant la résistance mais toujours reconnu par le Cb. La structure du complexe Nb/GFLV a été résolue à 2,8 Å et révèle la zone occupée par le Nb à la surface de la capside.Ces résultats ouvrent des perspectives innovantes pour la compréhension du cycle infectieux d'un phytovirus et l'élaboration de nouvelles stratégies de lutte antivirale. / Due to their small size, high stability and strict monomeric nature, Nanobodies (Nbs) deriving from camelids heavy chain only antibodies have proven very valuable as diagnostic and therapeutic tools. However their use in agro biotechnology remains limited.In order to apply Nbs to the study and the control of grapevine fanleaf degeneration, I produced acollection of Nbs against Grapevine fanleaf virus (GFLV), the causal agent of this devastating disease worldwide.When fused to a fluorescent protein and stably expressed in plants, one of these Nbs (calledChromobody, Cb) conferred high resistance to GFLV, whether inoculated mechanically or by vector-mediated transmission.The identification of an isolate overcoming the resistance but still bound by the Cb allowed real-time tracking of the infection showing the high potential of Cbs as biosensors.The cryoEM structure of the Nb/GFLV complex was obtained at 2,8 Å and provides a clear picture of the footprint of the Nb on the surface of the GFLV capsid.These results pave the way for the innovative use of Nbs to unravel viral life cycle and to counter viral diseases.

Abstract Biology and medicine rely heavily on images to understand how the body functions, for diagnosing diseases and to test the effects of treatments. In recent decades, microscopy has experienced rapid improvements, enabling imaging of fixed and living cells at higher resolutions and frame rates, and deeper inside the biological samples. This has led to rapid growth in the image data. Automated methods are needed to quantitatively analyze these huge datasets and find statistically valid patterns. Cell segmentation and tracking is critical for automated analysis, yet it is a challenging problem due to large variations in cell shapes and appearances caused by various factors, including cell type, sample preparation and imaging setup. This thesis proposes novel methods for segmentation and tracking of cells, which rely on machine learning based approaches to improve the performance, generalization and reusability of automated methods. Cell proposals are used to efficiently exploit spatial and temporal context for resolving detection ambiguities in high-cell-density regions, caused by weak boundaries and deformable shapes of cells. This thesis presents two cell proposal methods: the first method uses multiple blob-like filter banks for detecting candidates for round cells, while the second method, Cell Proposal Network (CPN), uses convolutional neural networks to learn the cell shapes and appearances, and can propose candidates for cells in a wide variety of microscopy images. CPN first regresses cell candidate bounding boxes and their scores, then, it segments the regions inside the top ranked boxes to obtain cell candidate masks. CPN can be used as a general cell detector, as is demonstrated by training a single model to segment images from histology, fluorescence and phase-contrast microscopy. This work poses segmentation and tracking as proposal selection problems, which are solved optimally using integer linear programming or approximately using iterative shortest cost path search and non-maximum suppression. Additionally, this thesis presents a method which utilizes graph-cuts and an off-the-shelf edge detector to accurately segment highly deformable cells. The main contribution of this thesis is a cell tracking method which uses CPN to propose cell candidates, represents alternative tracking hypotheses using a graphical model, and selects the globally optimal sub-graph providing cell tracks. It achieves state-of-the-art tracking performance on multiple public benchmark datasets from both phase-contrast and fluorescence microscopy containing cells of various shapes and appearances. / Tiivistelmä Biologia ja lääketiede nojaavat vahvasti kuvatietoon solujen ja kehon toimintojen ymmärtämiseksi sairauksien diagnostiikassa ja hoitojen vaikutusten seuraamisessa. Viime vuosikymmeninä mikroskopiassa on tapahtunut nopeaa teknistä kehitystä, mikä on mahdollistanut elävien solujen kuvantamisen tarkemmin, nopeammin sekä syvemmältä automatisoidusti useasta näytteestä. Tämä taas on johtanut kuvadatan nopeaan kasvuun ja suurempaan määrään biologisia kysymyksiä, joihin voidaan vastata. Kuvadatan räjähdysmäisen kasvun vuoksi kaikkia tuloksia ei voida enää tulkita pelkästään ihmistyövoimaa käyttämällä, mikä on johtanut tarpeeseen kehittää automaattisia menetelmiä analysoimaan kvantitatiivisesti suuria datajoukkoja ja löytämään tilastollisesti kelvollisia malleja. Solujen erottaminen niiden ympäristöstä ja toisista soluista (segmentointi) ja solujen seuranta ovat kriittisiä alkuvaiheen osia onnistuneessa automaattisessa analyysissä. Automaattisten menetelmien kehittämisessä solusegmentointi on kuitenkin osoittautunut hyvin haastavaksi ongelmaksi solujen muodon ja ulkonäön suurten muutosten vuoksi solutyypistä, näytteen valmistelusta ja kuvantamisjärjestelmästä johtuen. Tämä väitöskirja esittää uusia menetelmiä solujen segmentointiin ja seurantaan keskittyen koneoppimiseen perustuviin lähestymistapoihin, jotka parantavat automaattisten menetelmien suorituskykyä ja uudelleenkäytettävyyttä. Spatiaalista ja ajallista kontekstia tehokkaasti hyödyntäviä soluehdotelmia käytetään ratkaisemaan solujen heikosti erottuvista reunoista ja joustavista muodoista johtuvaa solujen muodon monitulkintaisuutta erityisesti silloin kun tutkittava solutiheys on suuri. Tämä väitöskirja esittää kaksi menetelmää soluehdotelmille; ensimmäinen menetelmä käyttää useita läikkätyyppisiä suodatinpankkeja ilmaisemaan kandidaatteja pyöreänmuotoisille soluille, kun taas toinen menetelmä nimeltään soluehdotelmaverkko (Cell Proposal Network, CPN) käyttää konvoluutionaalisia neuroverkkoja oppiakseen tunnistamaan solut niiden muodon sekä ulkonäön perusteella erityyppisissä mikroskooppikuvissa. CPN regressoi ensin solukandidaatteja ympäröivät suorakaiteet ja niiden pistemäärän, jonka jälkeen se segmentoi alueet parhaiten sijoittuneiden suorakaiteiden joukosta tuottaen solukandidaattimaskit. CPN:ää voidaan mahdollisesti käyttää yleisenä soluilmaisimena erityyppisilla kuvantamistekniikoilla tuotetuissa kuvissa mukaan lukien histologisen valo-, fluoresenssi- ja vaihekontrastimikroskooppian. Väitöskirja esittää solujen segmentoinnin ja seurannan soluehdotelmien valintaongelmina, mitkä ratkaistaan joko optimaalisesti käyttämällä kokonaislukuoptimointia tai likimääräisesti käyttämällä iteratiivista lyhimmän kustannuspolun hakua sekä ei-maksimien vaimennusta. Tämä väitöskirja esittää myös verkon leikkaukseen (graph cut) perustuvan menetelmän, mikä hyödyntää valmiiksi saatavilla olevaa reunanilmaisinta segmentoimaan tarkasti muotoaan voimakkaasti muuttavia soluja. Väitöskirjatutkimuksen keskeinen tulos on uusi solujen seurantamenetelmä, mikä käyttää CPN:ää solukandidaattien ehdottamiseen, esittää vaihtoehtoiset seurantahypoteesit verkkomallia hyödyntämällä, ja valitsee globaalisti optimaalisen aliverkon solujen kulkemille reitille. Verrattuna useisiin muihin julkisesti saatavilla oleviin kuva-analyysiohjelmistoihin tässä väitöskirjassa kehitetyt menetelmät olivat suorituskyvyltään parhaita vaihekontrasti- ja fluoresenssimikroskopialla tuotettujen kuva-aineistojen analyyseissa, joissa solujen ulkomuoto oli hyvin vaihteleva.

Abstract Desmoglein 3 (Dsg3) is an adhesion receptor in desmosomes, but relatively little is known about its role in cancer. In this study, the function of Dsg3 was investigated in oral squamous cell carcinoma (SCC) cell lines in vitro using locally established human leiomyoma tumor microenvironment (TME) matrices. Since Dsg3 has been identified as a key regulator in cell adhesion, we hypothesized that it may play a role in oral SCC cells adhesion and motility. Thus, one aim of the study was to explore this hypothesis by both gain and loss of function methods in four human buccal mucosa SCC SqCC/Y1 cell lines: transduction of vector control (Ct), full-length (FL) or two different C-terminally truncated Dsg3 mutants (&#916;238 and &#916;560). Live cell imaging was performed for 2D migration and 3D sandwich, alongside other assays. In 3D sandwich, we tested the effects of the monoclonal antibody, AK23, targeting the extracellular domain of Dsg3 in SqCC/Y1 cells. Our results showed that loss of Dsg3 disrupted cell adhesion and protein expression. In 2D assays, FL and Dsg3 mutants migrated faster with higher accumulated distances than Ct. In contrast with 2D, mutants showed accelerated invasion over the Ct in 3D models. The AK23 antibody inhibited only the invasion of FL cells. The TME in vivo consists of cellular and matrix elements playing a leading role in carcinoma progression. To study carcinoma cells invasion in vitro, mouse Matrigel® and rat type 1 collagen are the most commonly used matrices in 3D models. Since they are non-human in origin, they do not perfectly mimic human TME. To address this, we have developed a solid organotypic myoma disc model derived from human uterus leiomyoma tumor. Here, we introduce a novel Myogel, prepared from leiomyoma similar to Matrigel®. We validated Myogel for cell-TME interactions in 3D models, using SqCC/Y1 and HSC-3 cell lines. Compared with Matrigel® and type I collagen, oral SCC cell lines invaded more efficiently in Myogel containing matrices. This study describes promising 3D models using human TME mimicking Myogel which is suitable to analyze oral SCC cells both in carcinoma monocultures and in co-cultures, such as with TME fibroblasts. We also introduce a possible novel therapeutic target against Dsg3 to suppress cancer cell invasion. / Tiivistelmä Desmogleiini 3 (Dsg3) on desmosomien adheesioreseptori, jonka merkityksestä syövässä tiedetään vähän. Koska Dsg3 on tärkeä epiteelisolujen välisissä liitoksissa, oletimme sillä olevan vaikutusta myös suun karsinoomasolujen tarttumisessa ja niiden liikkuvuudessa. Testasimme hypoteesiamme muuttamalla Dsg3:n toimintaa ihmisen posken karsinoomasolulinjassa SqCC/Y1, josta oli aiemmin valmistettu neljä erilaista muunnosta: tyhjän vektorin sisältävä kontrollisolulinja (Ct), kokopitkää Dsg3 tuottava solulinja (FL), sekä kaksi Dsg3 C-päästä lyhennettyä mutanttisolulinjaa (&#916;238 ja &#916;560). Immunofluoresenssi-menetelmää käyttäen analysoimme solulinjoissamme solujen välisiä liitoksia. Lisäksi mittasimme solujen liikkeitä 2D-migraatio- ja 3D-sandwich-kokeissa. Testasimme myös Dsg3:n solunulkoista osaa tunnistavan monoklonaalisen vasta-aineen (AK23) vaikutusta solujen invaasioon. Osoitimme, että Dsg3:n rakenteen muuttaminen ja toiminnan estyminen häiritsi solujen tarttumista. 2D-kokeissa sekä FL että mutanttilinjat (&#916;238 ja &#916;560) migroivat kontrollisoluja nopeammin ja pidemmälle, mutta 3D-kokeissa vain mutanttilinjat invasoituivat kontrollisoluja tehokkaammin. AK23-vasta-aine esti vain FL-solujen invaasiota. Syöpäsolujen 3D-invaasiota mittaavissa kokeissa käytetään yleensä hiiren kasvaimesta valmistettua kaupallista Matrigeeliä® tai rotan kudoksista eristettyä tyypin I kollageenia. Tutkimusryhmämme on jo aiemmin kehittänyt organotyyppisen myoomamallin, jossa valmistamme myoomakudosnapit ihmisen kohdun leiomyoomakasvaimista. Tässä työssä valmistimme leiomyoomasta Myogeelia, vertasimme sitä Matrigeeliin®, sekä tutkimme tarkemmin Myogeeli-valmisteen soveltuvuutta 3D-tutkimuksiin. Totesimme, että kielen (HSC-3) ja posken (SqCC/Y1) karsinoomasolut invasoituivat tehokkaimmin Myogeeli-pitoisissa matrikseissa kuin Matrigeeliä® tai kollageeniä sisältävissä kasvatusalustoissa. Tutkimustulostemme perusteella Myogeeli-pohjaiset 3D-mallit soveltuvat hyvin sekä syöpäsolulinjojen invaasiotutkimuksiin että yhteisviljelmiin, joissa syöpäsoluja viljellään yhdessä syöpäkasvaimen ympärillä olevien solujen, kuten fibroblastien, kanssa.

The dissertation deals with the image processing in digital holographic microscopy and X-ray computed tomography. The focus of the work lies in the proposal of data processing techniques to meet the needs of the biological experiments. Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The phase images are affected by the phase aberrations that make the analysis particularly difficult. Here, we present a novel algorithm for dynamical processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. High resolution X-ray computed tomography is increasingly used technique for the study of the small rodent bones micro-structure. In this part of the work, the trabecular and cortical bone morphology is assessed in the distal half of rat femur. We developed new method for mapping the cortical position and dimensions from a central longitudinal axis with one degree angular resolution. This method was used to examine differences between experimental groups. The bone position in tomographic slices is aligned before the mapping using the propound standardization procedure. The activity of remodelling process of the long bone is studied on the system of cortical canals.