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. The event was a great success. Topics included the establishment and analysis of disease models using genetically engineered or spontaneous mutant mice, systems genetic analysis using mouse strains such as wild-derived mice and recombinant inbred mouse strains, infectious diseases, immunology, and epigenetics. In addition, as a joint program, a two-day RIKEN Symposium was held, and active discussions continued over the four-day period. Also, there was a trainee symposium, in which young researchers were encouraged to participate, and excellent papers were selected as oral presentations in the main session.

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. This study proposed a series of image processing methods combined with a U-net automatic segmentation to segment differentiated hiPSCs captured by bright-field microscopy. High segmentation accuracy (83.3%) for the test bright-field images compared with their concurrent Hoechst images (85%) was achieved. Tempo-spatial patterning and formation process of germ layers on the micropatterns can be visualized and quantified by segmenting time-lapse bright-field microscopy images using our method.

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. This study proposes a new 3D U-net to accurately detect center of each single cell in 3D confocal images. The dimension of input 3D images to the U-net is flexible so that multiple center detections from different image directions can be implemented simultaneously to improve the center detection accuracy. This study showed that our method can improve accuracy for cell center detection and thus the quantification for ESC speeds and status shifts.

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. Here we created two Cre-dependent reporter mice incorporating luciferases oFluc derived from Pyrocoeli matsumurai and Akaluc, both of which had been reported previously to be brighter than Luc2 when using appropriate substrates; we then tested their bioluminescence in neural tissues and other organs in living mice. When expressed throughout the body, both luciferases emitted an intense yellow (oFluc) or far-red (Akaluc) light easily visible to the naked eye. oFluc and Akaluc were similarly bright in the pancreas for in vivo BLI; however, Akaluc was superior to oFluc for brain imaging, because its substrate, AkaLumine-HCl, was distributed to the brain more efficiently than the oFluc substrate, D-luciferin. We also demonstrated that the lights produced by oFluc and Akaluc were sufficiently spectrally distinct from each other for dual-color imaging in a single living mouse. Taken together, these novel bioluminescent reporter mice are an ideal source of cells with bright bioluminescence and may facilitate in vivo BLI of various tissues/organs for preclinical and biomedical research in combination with a wide variety of Cre-driver mice.

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. The expression level and interaction time of Nanog and Oct4 correlate with their fluctuation and interaction frequency, respectively, which in turn depend on the ESC differentiation status. The DNA viscoelasticity near the Oct4 target locus remained flexible during differentiation, supporting its role either in chromatin opening or a preferred binding to uncondensed chromatin regions. Based on these results, we propose a new negative feedback mechanism for pluripotency maintenance via the DNA condensation state-dependent interplay of Nanog and Oct4.

iGEM as a human iPS cell-based global epigenetic modulation detection assay provides throughput characterization of chemicals affecting DNA methylation.

Chemical-induced dysregulation of DNA methylation during the fetal period is known to contribute to developmental disorders or increase the risk of certain diseases later in life. In this study, we developed an iGEM (iPS cell-based global epigenetic modulation) detection assay using human induced pluripotent stem (hiPS) cells that express a fluorescently labeled methyl-CpG-binding domain (MBD), which enables a high-throughput screening of epigenetic teratogens/mutagens. 135 chemicals with known cardiotoxicity and carcinogenicity were categorized according to the MBD signal intensity, which reflects the degree of nuclear spatial distribution/concentration of DNA methylation. Further biological characterization through machine-learning analysis that integrated genome-wide DNA methylation, gene expression profiling, and knowledge-based pathway analysis revealed that chemicals with hyperactive MBD signals strongly associated their effects on DNA methylation and expression of genes involved in cell cycle and development. These results demonstrated that our MBD-based integrated analytical system is a powerful framework for detecting epigenetic compounds and providing mechanism insights of pharmaceutical development for sustainable human health.

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. Prior the experiment, 9 classes of templates with distinct cell features clipped from microscopy images at various reprogramming stages are used to train a CNN model. The CNN is then used to classify a microscopy image as probability images of these classes. Probability images of some class are used to train a densely connected convolutional network for extracting regions of this class on a microscopy image. A U-net is trained to segment cells on the time-lapse images in early reprogramming stage during culture. The segmented cells are classified by the extracted regions to count various types of cells appearing in the early reprogramming stage for predicting the identified cells potentially forming hiPSCs. The probability images of hiPSC classes are also used to train a spatiotemporal RNN for predicting the future hiPSC colony morphology of the potential cells. RESULTS: Experimental results show the prediction (before 7 days after of beginning of the reprogramming) achieved 0.8 accuracy, and 66% of the identified cells under different culture conditions, predicted as forming, finally formed hiPSCs. The predicted hiPSC images and extracted colonies on the images show the prediction for future 1.5 days achieved high accuracy of hiPSC colony areas and image similarity. CONCLUSIONS: Our study proposes a method using several deep learning models to efficiently select the reprogramming cells possibly forming hiPSCs and to predict the shapes of growing hiPSC colonies. The proposed method is expected to improve the efficiency when establishing a new hiPSC line culture.

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. Experimental results show good prediction by the criteria using the numbers of different cells in the clusters. Time-series images with respective types of classified cells can be used to visualize and quantitatively analyze the growth and transition among dispersed cells not in cell clusters, various types of cells in the clusters before the hiPSC formation stage and hiPSC cells.

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. Recently, 3D deep learning methods have been used to avoid tedious parameter settings in the image and geometric processing, but still not easy to segment out close or contacted single cells. This paper proposes a 2D U-net to segment cell regions in high accuracy and computing performance. Better 3D cell images and single cell segmentation for close or contacted cells are achieved by combining a 3D U-net to detect the centers of single cells in the volume.

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. To overcome this issue, we developed a universal labelling technique, Universal Surface Biotinylation (USB), which does not depend on specific cell surface proteins. By introducing biotin into the amine group of any cell surface protein, we have obtained good labelling results in all the cell types we have tested. Combining with DNA-tagged streptavidin, it is possible to label each cell sample with specific DNA 'hashtag'. Compared with the conventional cell hashing method, the USB procedure seemed to have no discernible adverse effect on the acquisition of the transcriptome in each cell, according to the model experiments using differentiating mouse embryonic stem cells. This method can be theoretically used for any type of cells, including cells to which the conventional cell hashing method has not been applied successfully.

Human iPS cell-derived cartilaginous tissue spatially and functionally replaces nucleus pulposus.

The loss of nucleus pulposus (NP) precedes the intervertebral disk (IVD) degeneration that causes back pain. Here, we demonstrate that the implantation of human iPS cell-derived cartilaginous tissue (hiPS-Cart) restores this loss by replacing lost NP spatially and functionally. NP cells consist of notochordal NP cells and chondrocyte-like NP cells. Single cell RNA sequencing (scRNA-seq) analysis revealed that cells in hiPS-Cart corresponded to chondrocyte-like NP cells but not to notochordal NP cells. The implantation of hiPS-Cart into a nuclectomized space of IVD in nude rats prevented the degeneration of the IVD and preserved its mechanical properties. hiPS-Cart survived and occupied the nuclectomized space for at least six months after implantation, indicating spatial and functional replacement of lost NP by hiPS-Cart. Further scRNA-seq analysis revealed that hiPS-Cart cells changed their profile after implantation, differentiating into two lineages that are metabolically distinct from each other. However, post-implanted hiPS-Cart cells corresponded to chondrocyte-like NP cells only and did not develop into notochordal NP cells, suggesting that chondrocyte-like NP cells are nearly sufficient for NP function. The data collectively indicate that hiPS-Cart is a candidate implant for regenerating NP spatially and functionally and preventing IVD degeneration.

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. Then, cell correspondences in the correspondent colonies are computed using the recorded cell centers. The examples show the proposed cell tracking method can achieve high tracking accuracy for time-lapse 3D images of undifferentiated but self-renewing mouse embryonic stem (mES) cells where the number and mobility of ES cells in a cell colony may change suddenly by a colony merging or splitting, and cell proliferation or death. The geometric data in the hierarchical data structures also help the visualization and quantitation of the cell shapes and mobility.

High Resolution U-Net for Quantitatively Analyzing Early Spatial Patterning of Human Induced Pluripotent Stem Cells on Micropatterns.

Human induced pluripotent stem cells (hiPSCs) can differentiate into three germ layer cells, i.e. ectoderm, mesoderm and endoderm, on micropatterned chips in highly synchronous and reproducible manners. The cells are confined within the chip, expanding two-dimensionally as almost in the form of monolayer, thus to be ideal for serving quantitative analysis of their pluripotency. We present a new U-Net (MP-UNet) structure for cell segmentation of early spatial patterning of hiPSCs on micropattern chips using Hoechst fluorescence images. In this structure, the encoding/decoding layers can be dynamically adjusted to extract sufficient image features and be flexible to image sizes. Dice and weight loss functions are designed to identify slight difference in low signal-to-noise ratio, high boundary-to-area ratio and compacted cell images. Several sizes of Hoechst images were tested to show MP-UNet can achieve high accuracy in cell regions and number counting for various sizes of micropattern chips, thus to be excellent quantitative tool for early spatial patterning of hiPSCs.

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. Exposure to imidacloprid, carbaryl, and o,p'-DDT increased the fluorescent intensity of granules in the nuclei, indicating global DNA methylating effects. However, DNA methylation profiling in cell-cycle-related genes, such as Cdkn2a, Dapk1, Cdh1, Mlh1, Timp3, and Rarb, decreased in imidacloprid treatments, suggesting the potential influence of DNA methylation patterns on cell differentiation. We developed a rapid method for evaluating global DNA methylation and used this approach to show that insecticides pose risks of developmental toxicity through DNA methylation.

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. m. musculus (MUS) and M. m. castaneus (CAS). We revealed directional dispersals reaching as far as the Japanese Archipelago from their homelands. Our phylogenetic analysis indicated that the eastward movement of MUS was characterised by five step-wise regional extension events: (1) broad spatial expansion into eastern Europe and the western part of western China, (2) dispersal to the eastern part of western China, (3) dispersal to northern China, (4) dispersal to the Korean Peninsula and (5) colonisation and expansion in the Japanese Archipelago. These events were estimated to have occurred during the last 2000-18,000 years. The dispersal of CAS was characterised by three events: initial divergences (ca. 7000-9000 years ago) of haplogroups in northernmost China and the eastern coast of India, followed by two population expansion events that likely originated from the Yangtze River basin to broad areas of South and Southeast Asia, including Sri Lanka, Bangladesh and Indonesia (ca. 4000-6000 years ago) and to Yunnan, southern China and the Japanese Archipelago (ca. 2000-3500). This study provides a solid framework for the spatiotemporal movement of the human-associated organisms in Holocene Eastern Eurasia using whole mtDNA sequences, reliable evolutionary rates and accurate branching patterns. The information obtained here contributes to the analysis of a variety of animals and plants associated with prehistoric human migration.

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. The P-LSTM network incorporates the images newly generated from cell imaging progressively into LSTM training to emphasize the LSTM short-term memory and thus improve the prediction accuracy. The new images are input into a buffer to be selected for batch training. For real-time processing, parallel computation is introduced to implement concurrent training and prediction on partitioned images.Two types of stem cell images were used to show effectiveness of the P-LSTM network. One is for tracking of ES cell colonies. The actual and predicted ES cell images possess similar colony areas and the same transitions of colony states (moving, merging or morphology changing), although the predicted colony mergers may delay in several time-steps. The other is for prediction of iPS cell reprogramming from the CD34+ human cord blood cells. The actual and predicted iPS cell images possess high similarity evaluated by the PSNR and SSIM similarity evaluation metrics, indicating the reprogramming iPS cell colony features and morphology can be accurately predicted.

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) 4.5 without Tsix activation in the ICM and that Xist re-expression occurs at E4.75 in some cells, suggesting that random XCI is already initiated in these cells. Intriguingly, epiblast cells exhibiting biallelic Xist expression were observed frequently (~15%) at E5.25 and E5.5. Immunostaining analysis of epigenetic modifications suggests that global change in epigenomic status occurs concomitantly with the transition from imprinted to random XCI. However, global upregulation of H3K27me3 modifications initiated earlier than other modifications, occurring specifically in ICM during progression of Xist erasure. Although both Xist expression and imprinted XCI are thought to be stable in the primitive endoderm/visceral endoderm and trophectoderm/extraembryonic ectoderm lineages, transient loss of Xist clouds was noted only in a subset of extraembryonic ectodermal cells, suggesting distinct features of Xist regulation among the three different embryonic tissue layers. These results will serve as a basis for future functional studies of XCI regulation in vivo.

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. In mouse, WNT inhibition by inhibitor of WNT production-2, together with a higher dose of fibroblast growth factor 2 (12 ng/mL) in DMEM/F12 basal medium (DhiFI), markedly improved derivation and maintenance of primed mouse epiblast stem cells. In this study, we show that DhiFI conditions similarly improved primed hESC traits, such as conferring a primed transcriptional signature with high levels of pluripotency markers and reduced levels of differentiation markers. When triggered to differentiate to neuronal and cardiac lineages, DhiFI hESCs and isogenic primed hESCs progressed similarly. Moreover, DhiFI conditions supported the derivation of hESC lines from a post-inner cell mass intermediate (PICMI). DhiFI-derived hESCs showed less spontaneous differentiation and expressed significantly lower levels of lineage-specific markers, compared to primed-derived lines from the same PICMI. Overall, DhiFI hESCs retained advantages of both primed and naive pluripotency and may ultimately represent a more favorable starting point for differentiation toward clinically desired cell types.

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. The growth and transition are thus analyzed quantitatively by region areas of distinct cells emerged during the iPS formation processes. The experimental results show that our LSTM network is a potentially powerful tool to predict the cells at the distinct phases of the reprogramming to hiPS cells. This method should be extremely useful not only for basic biology of iPS cells but also detection of the reprogramming cells that will become genuine hiPS cells even at early stages of hiPS formation. Such predictive power should greatly reduce cost, labor and time required for establishment of the genuine hiPS cells, thereby accelerating the practical use of hiPS cells in regenerative medicine.

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.