Yamauchi, K. | bioRxiv Science

bioRxiv2021

Inhibition of N-myristoyltransferase Promotes Naive Pluripotency in Mouse and Human Pluripotent Stem Cells

Naive and primed states are distinct states of pluripotency during early embryonic development that can be captured and converted to each other in vitro. To elucidate the regulatory mechanism of pluripotency, we performed a recessive genetic screen of homozygous mutant mouse embryonic stem cells (mESCs) and found that suppression of N-myristoyltransferase (Nmt) promotes naive pluripotency. Disruption of Nmt1 in mESCs conferred resistance to differentiation. Suppression of Nmt in mouse epiblast stem cells (mEpiSCs) promoted the conversion from the primed to the naive state. This effect was independent of Src, which is a major substrate of Nmt and is known to promote differentiation of mESCs. Suppression of Nmt in naive-state human induced pluripotent stem cells (hiPSCs) increased the expression of the naive-state marker. These results indicate that Nmt is a novel target for the regulation of naive pluripotency conserved between mice and humans.

developmental biology↗

bioRxiv2021

Multi-Scale LM/EM Neuronal Imaging from Brain to Synapse with a Tissue Clearing Method, ScaleSF

The mammalian brain is organized over sizes that span several orders of magnitude, from synapses to the entire brain. Thus, a technique to visualize neural circuits across multiple spatial scales (multi-scale neuronal imaging) is vital for deciphering brain-wide connectivity. Here, we developed this technique by coupling successive light microscope/electron microscope (LM/EM) imaging with an ultrastructurally-preserved tissue clearing method, ScaleSF. Our multi-scale neuronal imaging incorporates 1) brain-wide macroscopic observation, 2) mesoscopic circuit mapping, 3) microscopic subcellular imaging, and 4) EM imaging of nanoscopic structures, allowing seamless integration of structural information from the brain to synapses. We applied the technique to three neural circuits of two different species, mouse striatofugal, mouse callosal, and marmoset corticostriatal projection systems, and succeeded in the simultaneous interrogation of their circuit structure and synaptic connectivity in a targeted way. Our multi-scale neuronal imaging will significantly advance the understanding of brain-wide connectivity by expanding the scales of objects.

neuroscience↗

bioRxiv2020

Validation and tuning of in situ transcriptomics image processing workflows with crowdsourced annotations

Recent advancements in in situ methods, such as multiplexed in situ RNA hybridization and in situ RNA sequencing, have deepened our understanding of the way biological processes are spatially organized in tissues. Automated image processing and spot-calling algorithms for analyzing in situ transcriptomics images have many parameters which need to be tuned for optimal detection. Having ground truth datasets (images where there is very high confidence on the accuracy of the detected spots) is essential for evaluating these algorithms and tuning their parameters. We present a first-in-kind open-source toolkit and framework for in situ transcriptomics image analysis that incorporates crowdsourced annotations, alongside expert annotations, as a source of ground truth for the analysis of in situ transcriptomics images. The kit includes tools for preparing images for crowdsourcing annotation to optimize crowdsourced workers ability to annotate these images reliably, performing quality control (QC) on worker annotations, extracting candidate parameters for spot-calling algorithms from sample images, tuning parameters for spot-calling algorithms, and evaluating spot-calling algorithms and worker performance. These tools are wrapped in a modular pipeline with a flexible structure that allows users to take advantage of crowdsourced annotations from any source of their choice. We tested the pipeline using real and synthetic in situ transcriptomics images and annotations from the Amazon Mechanical Turk system obtained via Quanti.us. Using real images from in situ experiments and simulated images produced by one of the tools in the kit, we studied worker sensitivity to spot characteristics and established rules for annotation QC. We explored and demonstrated the use of ground truth generated in this way for validating spot-calling algorithms and tuning their parameters, and confirmed that consensus crowdsourced annotations are a viable substitute for expert-generated ground truth for these purposes. Data AvailabilityThe iPython notebooks, input images, and In Situ Transcriptomics Annotation (INSTA) pipeline software are available from https://github.com/czbiohub/instapipeline. The SpotImage software is available from https://github.com/czbiohub/spotimage. The files and iPython notebooks used to generate the figures for this paper are available from https://github.com/czbiohub/instapaper.

bioengineering↗