Cell Pluripotency Levels Associated with Imprinted Genes in Human

Predicting multipotency of human adult stem cells derived from various donors through deep learning

Adult stem cell-based therapeutic approaches have great potential in regenerative medicine because of their immunoregulatory properties and multidifferentiation capacity. Nevertheless, the outcomes of stem cell‑based therapies to date have shown inconsistent efficacy owing to donor variation, thwarting the expectation of clinical effects. However, such donor dependency has been elucidated by biological consequences that current research could not predict. Here, we introduce cellular morphology-based prediction to determine the multipotency rate of human nasal turbinate stem cells (hNTSCs), aiming to predict the differentiation rate of keratocyte progenitors. We characterized the overall genes and morphologies of hNTSCs from five donors and compared stemness-related properties, including multipotency and specific lineages, using mRNA sequencing. It was demonstrated that transformation factors affecting the principal components were highly related to cell morphology. We then performed a convolutional neural network-based analysis, which enabled us to assess the multipotency level of each cell group based on their morphologies with 85.98% accuracy. Surprisingly, the trend in expression levels after ex vivo differentiation matched well with the deep learning prediction. These results suggest that AI‑assisted cellular behavioral prediction can be utilized to perform quantitative, non-invasive, single-cell, and multimarker characterizations of live stem cells for improved quality control in clinical cell therapies.

New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques

BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study. An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years. This dataset consisted of 125 features of 2070 high-risk breast cancer patients. All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm. The performance of the algorithm was identified by studying the machine learning model with the test data. k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective. The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT. BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented. That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice.

Modeling the autistic cell: iPSCs recapitulate developmental principles of syndromic and nonsyndromic ASD

The opportunity to model autism spectrum disorders (ASD) through generation of patient-derived induced pluripotent stem cells (iPSCs) is currently an emerging topic. Wide-scale research of altered brain circuits in syndromic ASD, including Rett Syndrome, Fragile X Syndrome, Angelman's Syndrome and sporadic Schizophrenia, was made possible through animal models. However, possibly due to species differences, and to the possible contribution of epigenetics in the pathophysiology of these diseases, animal models fail to recapitulate many aspects of ASD. With the advent of iPSCs technology, 3D cultures of patient-derived cells are being used to study complex neuronal phenotypes, including both syndromic and nonsyndromic ASD. Here, we review recent advances in using iPSCs to study various aspects of the ASD neuropathology, with emphasis on the efforts to create in vitro model systems for syndromic and nonsyndromic ASD. We summarize the main cellular activity phenotypes and aberrant genetic interaction networks that were found in iPSC-derived neurons of syndromic and nonsyndromic autistic patients.