Deep Learning Method for Estimating Germ-layer Regions of Early Differentiated Human Induced Pluripotent Stem Cells on Micropattern Using Bright-field Microscopy Image.

Live cell staining is expensive and may bring potential safety issues in downstream clinical applications, bright-field images rather than staining images should be more suitable to reveal time-series changes of differentiating hiPSCs (human induced pluripotent stem cells) and three-germ layers differentiated from the hiPSCs. This study proposed a deep learning method for estimating immunofluorescence regions on a bright-field microscopy images. The networks trained by multiple types of fluorescence images can estimate the types of fluorescence images from a bright-field image.

Yuragi biomarker concept for evaluating human induced pluripotent stem cells using heterogeneity-based Raman finger-printing.

Considering the fundamental mechanism causing singularity phenomena, we performed the following abduction: Assuming that a multicellular system is driven by spontaneous fluctuation of each cell and dynamic interaction of the cells, state transition of the system would be experimentally predictable from cellular heterogeneity. This study evaluates the abductive hypothesis by analyzing cellular heterogeneity to distinguish pre-state of state transition of differentiating cells with Raman spectroscopy and human induced pluripotent stem cells (hiPSCs) technique. Herein, we investigated the time development of cellular heterogeneity in Raman spectra during cardiomyogenesis of six hiPSC lines and tested two types of analyses for heterogeneity.

Deep learning for quantifying spatial patterning and formation process of early differentiated human-induced pluripotent stem cells with micropattern images.

Micropatterning is reliable method for quantifying pluripotency of human-induced pluripotent stem cells (hiPSCs) that differentiate to form a spatial pattern of sorted, ordered and nonoverlapped three germ layers on the micropattern. In this study, we propose a deep learning method to quantify spatial patterning of the germ layers in the early differentiation stage of hiPSCs using micropattern images. We propose decoding and encoding U-net structures learning labelled Hoechst (DNA-stained) hiPSC regions with corresponding Hoechst and bright-field micropattern images to segment hiPSCs on Hoechst or bright-field images.

The 36th International Mammalian Genome Conference: A scientific gathering under the cherry blossoms in Tsukuba.

The 36th International Mammalian Genome Conference (IMGC) was held in a hybrid format at the Tsukuba International Congress Center in Tsukuba, Ibaraki, Japan, for 4 days from March 28 to 31, 2023. This international conference on functional genomics of mouse, human, and other mammalian species attracted 246 participants in total, of which 129 were from outside Japan, including Europe, the United States and Asia, and 117 participants were from Japan. The conference included three technical workshops, keynote lectures by domestic researchers, commemorative lectures for the conference awards, 57 oral presentations, and 97 poster presentations.

Quantitative Analysis of Differentiation Activity for Mouse Embryonic Stem Cells by Deep Learning for Cell Center Detection using Three-Dimensional Confocal Fluorescence Microscopy Images.

Accurate single cell segmentation provides means to monitor the behavior of single cell within a population of cells. Time-lapse fluorescence images are used to reveal heterogeneous nature of single mouse embryonic stem cell (ESC) colony and monitor fluctuations of the cell states. Automatic quantification of speed and status shifts of the ESCs depends on accurate single cell segmentation that is used to calculate the 3D center of every cell and track this cell for the quantification.

Quantitative Analyses for Early Tempo-spatial Patterning of Differentiated Human Induced Pluripotent Stem Cells on Micropatterns using Time-lapse Bright-field Microscopy Images.

Three germ layer formation on micropatterns are extremely useful for quantitative analysis of hiPSC (human induced pluripotent stem cells) pluripotency. Spatial patterns of stem cells differentiated on the micropatterns will be formed from about 24 hours after differentiation induction and usually quantitated near 48 hours. To delineate the germ layer formation process, temporal changes in spatial patterning of germ layers should be analyzed by noninvasive microscopy.

Development of two mouse strains conditionally expressing bright luciferases with distinct emission spectra as new tools for in vivo imaging.

In vivo bioluminescence imaging (BLI) has been an invaluable noninvasive method to visualize molecular and cellular behaviors in laboratory animals. Bioluminescent reporter mice harboring luciferases for general use have been limited to a classical luciferase, Luc2, from Photinus pyralis, and have been extremely powerful for various in vivo studies. However, applicability of reporter mice for in vivo BLI could be further accelerated by increasing light intensity through the use of other luciferases and/or by improving the biodistribution of their substrates in the animal body.

Single-molecule tracking of Nanog and Oct4 in living mouse embryonic stem cells uncovers a feedback mechanism of pluripotency maintenance.

Nanog and Oct4 are core transcription factors that form part of a gene regulatory network to regulate hundreds of target genes for pluripotency maintenance in mouse embryonic stem cells (ESCs). To understand their function in the pluripotency maintenance, we visualised and quantified the dynamics of single molecules of Nanog and Oct4 in a mouse ESCs during pluripotency loss. Interestingly, Nanog interacted longer with its target loci upon reduced expression or at the onset of differentiation, suggesting a feedback mechanism to maintain the pluripotent state.

Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods.

Background And Objective: Human induced pluripotent stem cells (hiPSCs) represent an ideal source for patient specific cell-based regenerative medicine; however, efficiency of hiPSC formation from reprogramming cells is low. We use several deep-learning results from time-lapse brightfield microscopy images during culture, to early detect the cells potentially reprogramming into hiPSCs and predict the colony morphology of these cells for improving efficiency of culturing a new hiPSC line.

Methods: Sets of time-lapse bright-field images are taken to track reprogramming process of CD34+ cells biologically identified as just beginning reprogramming.

U-net structures for segmentation of single mouse embryonic stem cells using three-dimensional confocal microscopy images.

Cell segmentation at a single cell resolution is required to provide insights for basic biology and application study. However, there are issues of low signal-to-noise ratio, weak fluorescence response, and insufficient resolution along the image stacking direction in 3D confocal images (volume). It has been difficult to segment out single cells from close or contacted cells in a cell volume using image processing methods or together with geometric processing methods.

Prediction of Human Induced Pluripotent Stem Cell Formation Based on Deep Learning Analyses Using Time-lapse Brightfield Microscopy Images.

We use deep learning methods to predict human induced pluripotent stem cell (hiPSC) formation using time-lapse brightfield microscopy images taken from a cell identified as the beginning of entered into the reprogramming process. A U-net is used to segment cells and a CNN is used to classify the segmented cells into eight types of cells during the reprogramming and hiPSC formation based on cellular morphology on the microscopy images. The numbers of respective types of cells in cell clusters before the hiPSC formation stage are used to predict if hiPSC regions can be well formed lately.

Universal Surface Biotinylation: a simple, versatile and cost-effective sample multiplexing method for single-cell RNA-seq analysis.

Recent advances in single-cell analysis technology have made it possible to analyse tens of thousands of cells at a time. In addition, sample multiplexing techniques, which allow the analysis of several types of samples in a single run, are very useful for reducing experimental costs and improving experimental accuracy. However, a problem with this technique is that antigens and antibodies for universal labelling of various cell types may not be fully available.

Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images.

We present a cell tracking method for time-lapse confocal microscopy (3D) images that uses dynamic hierarchical data structures to assist cell and colony segmentation and tracking. During the segmentation, the cell and colony numbers and their geometric data are recorded for each 3D image set. In tracking, the colony correspondences between neighboring frames of time-lapse 3D images are first computed using the recorded colony centers.

Epigenetic effects of insecticides on early differentiation of mouse embryonic stem cells.

Increasing evidence indicates that many insecticides produce significant epigenetic changes during embryogenesis, leading to developmental toxicities. However, the effects of insecticides on DNA methylation status during early development have not been well studied. We developed a novel nuclear phenotypic approach using mouse embryonic stem cells harboring enhanced green fluorescent protein fused with methyl CpG-binding protein to evaluate global DNA methylation changes via high-content imaging analysis.

Prediction for Morphology and States of Stem Cell Colonies using a LSTM Network with Progressive Training Microscopy Images.

We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memory dependent LSTM networks may not predict accurately.

House mouse Mus musculus dispersal in East Eurasia inferred from 98 newly determined complete mitochondrial genome sequences.

The Eurasian house mouse Mus musculus is useful for tracing prehistorical human movement related to the spread of farming. We determined whole mitochondrial DNA (mtDNA) sequences (ca. 16,000 bp) of 98 wild-derived individuals of two subspecies, M.

Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images using LSTM based RNN.

We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to predict growth and transition of reprogramming processes of CD34+ human cord blood cells into hiPS cells. The prediction can be visualized by output time-series probability images.

Xist/Tsix expression dynamics during mouse peri-implantation development revealed by whole-mount 3D RNA-FISH.

During peri-implantation development in mice, X chromosome inactivation (XCI) status changes dynamically. Here, we examined the expression of Xist and its antisense partner, Tsix, via whole-mount 3D RNA-FISH using strand-specific probes and evaluated XCI status. The results indicate that Xist expression disappears completely by embryonic day (E) 4.

WNT Inhibition and Increased FGF Signaling Promotes Derivation of Less Heterogeneous Primed Human Embryonic Stem Cells, Compatible with Differentiation.

Human embryonic stem cells (hESCs) hold great value for future clinical applications. However, standard culture conditions maintain hESCs in a primed state, which bears heterogeneity in pluripotency and a tendency for spontaneous differentiation. To counter these drawbacks, primed hESCs have been converted to a naive state, but this has restricted the efficiency of existing directed differentiation protocols.

Publisher Correction: Female mice lacking Ftx lncRNA exhibit impaired X-chromosome inactivation and a microphthalmia-like phenotype.

In the original HTML version of this Article, the affiliation details for Hirosuke Shiura, Hidetoshi Hasuwa and Takashi Kohda were incorrect, as detailed in the associated Publisher Correction. These errors have been corrected in both the HTML version of the Article.

Female mice lacking Ftx lncRNA exhibit impaired X-chromosome inactivation and a microphthalmia-like phenotype.

X-chromosome inactivation (XCI) is an essential epigenetic process in female mammalian development. Although cell-based studies suggest the potential importance of the Ftx long non-protein-coding RNA (lncRNA) in XCI, its physiological roles in vivo remain unclear. Here we show that targeted deletion of X-linked mouse Ftx lncRNA causes eye abnormalities resembling human microphthalmia in a subset of females but rarely in males.

RNA-FISH and Immunofluorescence of Mouse Preimplantation and Postimplantation Embryos.

There are two modes of X chromosome inactivation (XCI) in the mouse. One mode is imprinted XCI: it is initiated at around the four-cell stage in favor of the paternal X chromosome, and is maintained in the extraembryonic tissues. The other mode is random XCI, which takes place in the epiblast lineage at the periimplantation stage.