Contribution of resident macrophages to the fetal liver hematopoietic stem cell niche

Tissue-resident macrophages are a heterogeneous population of phagocytes found in almost all tissues of the body. Recent studies indicate that most resident macrophages originate from yolk sac progenitors and are maintained by local proliferation independent of hematopoietic stem cells (HSC). It is known that HSC can quickly respond to organismal demands. During embryogenesis, the fetal liver supports the HSC with a unique microenvironment called the “HSC niche” that helps their expansion and maintenance. One of the most important aspects of a niche is cell-cell interactions that can happen either directly or through signaling. In the fetal liver, hepatic macrophages are known to participate in the maturation of erythroblasts. However, whether resident macrophages can also contribute to the fetal liver HSC niche remains enigmatic. Furthermore, the macrophages heterogeneity across different organs is essential for tissue-specific niches and cellular interactions during tissue development as well as tissue homeostasis. However, their heterogeneity within one organ, such as the fetal liver, especially during development, has not been addressed in detail. In this dissertation, using single-cell RNA-sequencing, it was shown that hepatic macrophages are heterogeneous and have different subpopulations that can have different functions. This heterogeneity was further confirmed through multi-color flow-cytometry and unsupervised clustering of cells using computational methods. Investigating the ontogeny of the identified clusters using three fate-mapping models revealed that all macrophage clusters originated from the yolk sac and are not HSC derived. Further, immunofluorescent-labeled sections from the fetal liver could show that while many of the macrophages serve as a platform for erythroblasts maturation in the fetal liver, yet some of them also interact with HSC. To test the functionality of hepatic macrophages in HSC, conditional mouse models were developed that lead to the depletion of macrophages in the fetal liver. The results indicated that hepatic macrophages are responsible for erythroblast maturation, as enucleation of red blood cells is not efficient when macrophages are lacking. Further, it was shown that macrophages are part of the HSC niche since stem cells have a differentiation bias towards the myeloid linage in knock-out embryos. These results were further confirmed by performing a bulk RNA- sequencing using HSC from embryos of mouse models. All in all, the findings of this dissertation support the hypothesis that hepatic macrophages play a crucial role in the development and maintenance of fetal liver hematopoiesis and provide evidences for their heterogeneity within the fetal liver

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations

<i>Decision Tree</i> generated model showing separation of olive populations in the 16-targeted (16-t) experiment by different alleles.

<p>In this model, DCA-178 was selected as the main classifying attribute.</p

Examples of indigenous Iranian olive.

<p>A) Torang <i>cuspidata</i> specimen, Kerman; B) Mavi local ecotype, Khuzestan; C) Gardineko local ecotype, Ilam; D) Pirzeytun local ecotype, Fars; adapted from Hosseini-Mazinani <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143465#pone.0143465.ref036" target="_blank">36</a>].</p

Microsatellite allele lengths, loci and the total alleles.

<p>Alleles private to the Iranian accessions are highlighted in bold.</p><p>Microsatellite allele lengths, loci and the total alleles.</p

Prediction rate (accuracy) details of each decision tree with 10-fold cross validation for each of the populations in the 4-targeted (4-t) experiment, i.e. reference cultivars, Mediterranean varieties, cuspidata specimens, and local ecotypes.

<p>Prediction rows indicate how records (olive accessions) were predicted by the model. True columns indicate how many records were predicted correctly.</p><p>Prediction rate (accuracy) details of each decision tree with 10-fold cross validation for each of the populations in the 4-targeted (4-t) experiment, i.e. reference cultivars, Mediterranean varieties, cuspidata specimens, and local ecotypes.</p

Map of Iran with the main provinces where olive accessions had been sampled.

<p>Blue) local ecotypes; green) <i>cuspidata</i> specimens.</p