Machine learning-assisted imaging analysis of a human epiblast model

2021 ◽  
Author(s):  
Agnes M Resto Irizarry ◽  
Sajedeh Nasr Esfahani ◽  
Yi Zheng ◽  
Robin Zhexuan Yan ◽  
Patrick Kinnunen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Cell Tracking ◽  
Complex Structure ◽  
Cyst Formation ◽  
System Level ◽  
Imaging Data ◽  
Cell Level ◽  
Human Blastocyst ◽  
Cell Cell

Abstract The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell–cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from a human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell–cell and cell–environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.

scholarly journals Machine learning-assisted neurotoxicity prediction in human midbrain organoids

2019 ◽  
Author(s):  
Anna S. Monzel ◽  
Kathrin Hemmer ◽  
Tony Kaoma Mukendi ◽  
Philippe Lucarelli ◽  
Isabel Rosety ◽  
...  
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Neurodegenerative Diseases ◽  
Cellular Response ◽  
Environmental Changes ◽  
Complex Structure ◽  
Cellular Tissue ◽  
Imaging Data ◽  
Induced Pluripotent

AbstractA major challenge in the field of neurodegenerative diseases is the poor translation of pre-clinical models to clinical applications. The human brain is an immensely complex structure, which makes it difficult to recapitulate its development, function and disorders. In the recent years, brain organoids derived from human induced pluripotent stem cells have risen as novel tools to study neurodegenerative diseases such as Parkinson’s disease (PD). PD is a multifactorial disorder, with aging, genetics and environmental factors as key etiological elements. The majority of the PD cases are idiopathic and proposed to result from a complex interaction between genetic predisposition and environmental exposure. Consequently, the identification of potentially disease causing environmental factors is of critical importance. Organoids, as complex multi-cellular tissue proxies, are an ideal tool to study cellular response to environmental changes. However, with increasing complexity of the system, usage of quantitative tools becomes challenging. This led us to develop an automated high-content image analysis pipeline for image-based cell profiling in the organoid system. Here, we introduce a midbrain organoid system that recapitulates features of neurotoxin-induced PD, representing a platform for machine-learning-assisted prediction of neurotoxicity in high-content imaging data. This model is a valuable tool for advanced in vitro PD modeling and for the screening of putative neurotoxic compounds.

scholarly journals In vivo tracking of 14C thymidine labeled mesenchymal stem cells using ultra-sensitive accelerator mass spectrometry

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Min-Seok Oh ◽  
Seul-Gi Lee ◽  
Gwan-Ho Lee ◽  
C-Yoon Kim ◽  
Eun-Young Kim ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Single Cell ◽  
Accelerator Mass Spectrometry ◽  
Cell Tracking ◽  
Liquid Scintillator ◽  
Single Cell Level ◽  
Cell Level

AbstractDespite the tremendous advancements made in cell tracking, in vivo imaging and volumetric analysis, it remains difficult to accurately quantify the number of infused cells following stem cell therapy, especially at the single cell level, mainly due to the sensitivity of cells. In this study, we demonstrate the utility of both liquid scintillator counter (LSC) and accelerator mass spectrometry (AMS) in investigating the distribution and quantification of radioisotope labeled adipocyte derived mesenchymal stem cells (AD-MSCs) at the single cell level after intravenous (IV) transplantation. We first show the incorporation of 14C-thymidine (5 nCi/ml, 24.2 ng/ml) into AD-MSCs without affecting key biological characteristics. These cells were then utilized to track and quantify the distribution of AD-MSCs delivered through the tail vein by AMS, revealing the number of AD-MSCs existing within different organs per mg and per organ at different time points. Notably, the results show that this highly sensitive approach can quantify one cell per mg which effectively means that AD-MSCs can be detected in various tissues at the single cell level. While the significance of these cells is yet to be elucidated, we show that it is possible to accurately depict the pattern of distribution and quantify AD-MSCs in living tissue. This approach can serve to incrementally build profiles of biodistribution for stem cells such as MSCs which is essential for both research and therapeutic purposes.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

scholarly journals Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Soil Systems ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 41
Author(s):  
Tulsi P. Kharel ◽  
Amanda J. Ashworth ◽  
Phillip R. Owens ◽  
Dirk Philipp ◽  
Andrew L. Thomas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Tree ◽  
Plant Production ◽  
System Level ◽  
Learning Approaches ◽  
Nutrient Distribution ◽  
Wetness Index ◽  
Variable Approach ◽  
Animal Grazing

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

scholarly journals Construction of a mammalian embryo model from stem cells organized by a morphogen signalling centre

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peng-Fei Xu ◽  
Ricardo Moraes Borges ◽  
Jonathan Fillatre ◽  
Maraysa de Oliveira-Melo ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cell Tracking ◽  
Cardiac Tissue ◽  
Embryonic Stem ◽  
Mammalian Embryo ◽  
Wide Range ◽  
Vitro Model ◽  
Neurula Stage

AbstractGenerating properly differentiated embryonic structures in vitro from pluripotent stem cells remains a challenge. Here we show that instruction of aggregates of mouse embryonic stem cells with an experimentally engineered morphogen signalling centre, that functions as an organizer, results in the development of embryo-like entities (embryoids). In situ hybridization, immunolabelling, cell tracking and transcriptomic analyses show that these embryoids form the three germ layers through a gastrulation process and that they exhibit a wide range of developmental structures, highly similar to neurula-stage mouse embryos. Embryoids are organized around an axial chordamesoderm, with a dorsal neural plate that displays histological properties similar to the murine embryo neuroepithelium and that folds into a neural tube patterned antero-posteriorly from the posterior midbrain to the tip of the tail. Lateral to the chordamesoderm, embryoids display somitic and intermediate mesoderm, with beating cardiac tissue anteriorly and formation of a vasculature network. Ventrally, embryoids differentiate a primitive gut tube, which is patterned both antero-posteriorly and dorso-ventrally. Altogether, embryoids provide an in vitro model of mammalian embryo that displays extensive development of germ layer derivatives and that promises to be a powerful tool for in vitro studies and disease modelling.

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

scholarly journals Bone marrow mesenchymal stem cells promote prostate cancer cell stemness via cell–cell contact to activate the Jagged1/Notch1 pathway

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ji-wen Cheng ◽  
Li-xia Duan ◽  
Yang Yu ◽  
Pu Wang ◽  
Jia-le Feng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Western Blot ◽  
Cell Contact ◽  
Activity Levels ◽  
Tumor Resistance ◽  
Cell Cell

Abstract Background Mesenchymal stem cells (MSCs) play a crucial role in cancer development and tumor resistance to therapy in prostate cancer, but the influence of MSCs on the stemness potential of PCa cells by cell–cell contact remains unclear. In this study, we investigated the effect of direct contact of PCa cells with MSCs on the stemness of PCa and its mechanisms. Methods First, the flow cytometry, colony formation, and sphere formation were performed to determine the stemness of PCaMSCs, and the expression of stemness-related molecules (Sox2, Oct4, and Nanog) was investigated by western blot analysis. Then, we used western blot and qPCR to determine the activity levels of two candidate pathways and their downstream stemness-associated pathway. Finally, we verified the role of the significantly changed pathway by assessing the key factors in this pathway via in vitro and in vivo experiments. Results We established that MSCs promoted the stemness of PCa cells by cell–cell contact. We here established that the enhanced stemness of PCaMSCs was independent of the CCL5/CCR5 pathway. We also found that PCaMSCs up-regulated the expression of Notch signaling-related genes, and inhibition of Jagged1-Notch1 signaling in PCaMSCs cells significantly inhibited MSCs-induced stemness and tumorigenesis in vitro and in vivo. Conclusions Our results reveal a novel interaction between MSCs and PCa cells in promoting tumorigenesis through activation of the Jagged1/Notch1 pathway, providing a new therapeutic target for the treatment of PCa.

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