Predicting Astrocytic Nuclear Morphology with Machine Learning: A Tree Ensemble Classifier Study

Machine learning is usually associated with big data; however, experimental or clinical data are usually limited in size. The aim of this study was to describe how supervised machine learning can be used to classify astrocytes from a small sample into different morphological classes. Our dataset was composed of only 193 cells, with unbalanced morphological classes and missing observations. We combined classification trees and ensemble algorithms (boosting and bagging) with under sampling to classify the nuclear morphology (homogeneous, dotted, wrinkled, forming crumples, and forming micronuclei) of astrocytes stained with anti-LMNB1 antibody. Accuracy, sensitivity (recall), specificity, and F1 score were assessed with bootstrapping, leave one-out (LOOCV) and stratified cross-validation. We found that our algorithm performed at rates above chance in predicting the morphological classes of astrocytes based on the nuclear expression of LMNB1. Boosting algorithms (tree ensemble) yielded better classifications over bagging ones (tree bagger). Moreover leave-one-out and bootstrapping yielded better predictions than the more commonly used k-fold cross-validation. Finally, we could identify four important predictors: the intensity of LMNB1 expression, nuclear area, cellular area, and soma area. Our results show that a tree ensemble can be optimized, in order to classify morphological data from a small sample, even in the presence of highly unbalanced classes and numerous missing data

hESC-derived striatal progenitors grafted into a Huntington’s disease rat model support long-term functional motor recovery by differentiating, self-organizing and connecting into the lesioned striatum

Background: Huntington’s disease (HD) is a motor and cognitive neurodegenerative disorder due to prominent loss of striatal medium spiny neurons (MSNs). Cell replacement using human embryonic stem cells (hESCs) derivatives may offer new therapeutic opportunities to replace degenerated neurons and repair damaged circuits. Methods: With the aim to develop effective cell replacement for HD, we assessed the long-term therapeutic value of hESC-derived striatal progenitors by grafting the cells into the striatum of a preclinical model of HD [i.e., adult immunodeficient rats in which the striatum was lesioned by monolateral injection of quinolinic acid (QA)]. We examined the survival, maturation, self-organization and integration of the graft as well as its impact on lesion-dependent motor alterations up to 6 months post-graft. Moreover, we tested whether exposing a cohort of QA-lesioned animals to environmental enrichment (EE) could improve graft integration and function. Results: Human striatal progenitors survived up to 6 months after transplantation and showed morphological and neurochemical features typical of human MSNs. Donor-derived interneurons were also detected. Grafts wired in both local and long-range striatal circuits, formed domains suggestive of distinct ganglionic eminence territories and displayed emerging striosome features. Moreover, over time grafts improved complex motor performances affected by QA. EE selectively increased cell differentiation into MSN phenotype and promoted host-to-graft connectivity. However, when combined to the graft, the EE paradigm used in this study was insufficient to produce an additive effect on task execution. Conclusions: The data support the long-term therapeutic potential of ESC-derived human striatal progenitor grafts for the replacement of degenerated striatal neurons in HD and suggest that EE can effectively accelerate the maturation and promote the integration of human striatal cells