Time-course transcriptome analysis of human cellular reprogramming from multiple cell types reveals the drastic change occurs between the mid phase and the late phase

BackgroundHuman induced pluripotent stem cells (hiPSCs) have been attempted for clinical application with diverse iPSCs sources derived from various cell types. This proposes that there would be a shared reprogramming route regardless of different starting cell types. However, the insights of reprogramming process are mostly restricted to only fibroblasts of both human and mouse. To understand molecular mechanisms of cellular reprogramming, the investigation of the conserved reprogramming routes from various cell types is needed. Particularly, the maturation, belonging to the mid phase of reprogramming, was reported as the main roadblock of reprogramming from human dermal fibroblasts to hiPSCs. Therefore, we investigated first whether the shared reprogramming routes exists across various human cell types and second whether the maturation is also a major blockage of reprogramming in various cell types.ResultsWe selected 3615 genes with dynamic expressions during reprogramming from five human starting cell types by using time-course microarray dataset. Then, we analyzed transcriptomic variances, which were clustered into 3 distinct transcriptomic phases (early, mid and late phase); and greatest difference lied in the late phase. Moreover, functional annotation of gene clusters classified by gene expression patterns showed the mesenchymal-epithelial transition from day 0 to 3, transient upregulation of epidermis related genes from day 7 to 15, and upregulation of pluripotent genes from day 20, which were partially similar to the reprogramming process of mouse embryonic fibroblasts. We lastly illustrated variations of transcription factor activity at each time point of the reprogramming process and a major differential transition of transcriptome in between day 15 to 20 regardless of cell types. Therefore, the results implied that the maturation would be a major roadblock across multiple cell types in the human reprogramming process.ConclusionsHuman cellular reprogramming process could be traced into three different phases across various cell types. As the late phase exhibited the greatest dissimilarity, the maturation step could be suggested as the common major roadblock during human cellular reprogramming. To understand further molecular mechanisms of the maturation would enhance reprogramming efficiency by overcoming the roadblock during hiPSCs generation

KOnezumi: a web application for automating gene disruption strategies to generate knockout mice

A Summary: Although gene editing using the CRISPR/Cas9 system enables the rapid generation of knockout mice, constructing an optimal gene disruption strategy is still labourious. Here, we propose KOnezumi, a simple and user-friendly web application, for use in automating the design of knockout strategies for multiple genes. Users only need to input gene symbols, and then KOnezumi returns target exons, gRNA candidates to delete the target exons, genotyping PCR primers, nucleotide sequences of the target exons and coding sequences of expected deletion products. KOnezumi enables users to easily and rapidly apply a rational strategy to accelerate the generation of KO mic

BALB/c-Fcgr2b−/−Pdcd1−/− mouse expressing anti-urothelial antibody is a novel model of autoimmune cystitis

We report the impact of anti-urothelial autoantibody (AUAb) on urinary bladder phenotype in BALB/c mice deficient of the FcγRIIb and PD-1. AUAb was present in serum samples from approximately half of the double-knockout (DKO) mice, as detected by immunofluorescence and immunoblots for urothelial proteins including uroplakin IIIa. The AUAb-positive DKO mice showed degeneration of urothelial plaque and umbrella cells, along with infiltration of inflammatory cells in the suburothelial layer. TNFα and IL-1β were upregulated in the bladder and the urine of AUAb-positive DKO mice. Voiding behavior of mice was analyzed by the Voided Stain on Paper method. 10-week-old and older AUAb-positive DKO mice voided significantly less urine per void than did wild type (WT) mice. Furthermore, administration of the AUAb-containing serum to WT mice significantly reduced their urine volume per void. In summary, this report presents a novel comprehensive mouse model of autoimmune cystitis

TRMT2A is a novel cell cycle regulator that suppresses cell proliferation

During the maturation of transfer RNA (tRNA), a variety of chemical modifications can be introduced at specific nucleotide positions post-transcriptionally. 5-Methyluridine (m5U) is one of the most common and conserved modifications from eubacteria to eukaryotes. Although TrmA protein in Escherichia coli and Trm2p protein in Saccharomyces cerevisiae, which are responsible for the 5-methylation of uracil at position 54 (m5U54) on tRNA, are well characterized, the biological function of the U54 methylation responsible enzyme in mammalian species remains largely unexplored. Here, we show that the mammalian tRNA methyltransferase 2 homolog A (TRMT2A) protein harbors an RNA recognition motif in the N-terminus and the conserved uracil-C5-methyltransferase domain of the TrmA family in the C-terminus. TRMT2A predominantly localizes to the nucleus in HeLa cells. TRMT2A-overexpressing cells display decreased cell proliferation and altered DNA content, while TRMT2A-deficient cells exhibit increased growth. Thus, our results reveal the inhibitory role of TRMT2A on cell proliferation and cell cycle control, providing evidence that TRMT2A is a candidate cell cycle regulator in mammals

Versatile whole-organ/body staining and imaging based on electrolyte-gel properties of biological tissues

Whole-organ/body three-dimensional (3D) staining and imaging have been enduring challenges in histology. By dissecting the complex physicochemical environment of the staining system, we developed a highly optimized 3D staining imaging pipeline based on CUBIC. Based on our precise characterization of biological tissues as an electrolyte gel, we experimentally evaluated broad 3D staining conditions by using an artificial tissue-mimicking material. The combination of optimized conditions allows a bottom-up design of a superior 3D staining protocol that can uniformly label whole adult mouse brains, an adult marmoset brain hemisphere, an ~1 cm3 tissue block of a postmortem adult human cerebellum, and an entire infant marmoset body with dozens of antibodies and cell-impermeant nuclear stains. The whole-organ 3D images collected by light-sheet microscopy are used for computational analyses and whole-organ comparison analysis between species. This pipeline, named CUBIC-HistoVIsion, thus offers advanced opportunities for organ- and organism-scale histological analysis of multicellular systems

akikuno/DAJIN2: 0.3.4

<h1> Documentation</h1> <ul> <li>Added <a href="https://github.com/akikuno/DAJIN2/blob/main/docs/TROUBLESHOOTING.md">docs/TROUBLESHOOTING.md</a></li> <li>Added <a href="https://github.com/akikuno/DAJIN2/blob/main/docs/CODE_OF_CONDUCT.md">docs/CODE_OF_CONDUCT.md</a></li> <li>Added <a href="https://github.com/akikuno/DAJIN2/blob/main/docs/CONTRIBUTING.md">docs/CONTRIBUTING.md</a></li> </ul> <h1>✨ New Features</h1> <ul> <li>None</li> </ul> <h1> Maintenance</h1> <h2>Update <code>preprocess.mutation_extractor.py</code></h2> <ul> <li><p><strong><code>count_indels</code></strong>:</p> <ul> <li><strong>Change</strong>: Method of counting indels modified to use only matches as the denominator, instead of matches + indels.</li> <li><strong>Reason</strong>: To specifically focus on the occurrence rate of particular mutations.</li> </ul> </li> <li><p><strong><code>find_dissimilar_indices</code></strong>:</p> <ul> <li><strong>Change</strong>: Mutation detection modified. If the p-value remains < 0.05 after removing the target base sequence, the area is not detected as a mutation, assuming the significance is due to other parts.</li> <li><strong>Implication</strong>: Increases mutation detection accuracy by excluding irrelevant base sequences.</li> </ul> </li> <li><p><strong><code>merge_index_of_consecutive_indel</code></strong>:</p> <ul> <li><strong>Change</strong>: Merged <code>merge_surrounding_index</code> and <code>merge_index_of_consecutive_insertions</code> into a single function.</li> <li><strong>Benefit</strong>: Streamlines the process and enhances efficiency in handling consecutive indels.</li> </ul> </li> <li><p><a href="https://github.com/akikuno/DAJIN2/commit/c58855449ed9d84808628bef5f560a7c152510ce">Commit details</a></p> </li> </ul> <h2>Update <code>consensus.consensus.py</code>:</h2> <ul> <li>Addressed a precision issue in floating-point calculations where N equals 100%, leading to <code>100 != 100.000002</code>. Changed the condition to "having only one key and that key being <code>N</code>". <a href="https://github.com/akikuno/DAJIN2/commit/ec33d36578f24ef691ea0c32bf60b3af60300b07">Commit details</a></li> </ul> <h2>Update <code>mutation_extractor.py</code>:</h2> <ul> <li>Switched to the Wilcoxon signed-rank test due to false negatives in the t-test for data with peak-like shapes. <a href="https://github.com/akikuno/DAJIN2/commit/a01d341a7dd72980c155b388913ac2e7aba38a47">Commit details</a></li> </ul> <h2>Others</h2> <ul> <li>Modified batch processing to run on a single CPU thread per process.<ul> <li><a href="https://github.com/akikuno/DAJIN2/commit/c7e2f05c0e2d1c66e3e6422553f7e1686bf56393">Commit details</a></li> </ul> </li> <li>Added <code>clust_formatter.cache_mutation_loci</code>.<ul> <li><a href="https://github.com/akikuno/DAJIN2/commit/a8d68036080031a8eaf33a6fc0383a6b8e38672d">Commit details</a></li> </ul> </li> <li>Changed <code>mutation_extractor.merge_loci</code> to use union instead of intersection.</li> <li>Added a filter for minor insertion alleles in <code>insertions_to_fasta.py</code>.</li> <li>Moved <code>insertion_to_fasta.save_fasta</code> to <code>utils.io.save_fasta</code>.</li> </ul&gt

akikuno/cstag: 0.6.2

New Features Core Enhancements Enhanced cstag.to_vcf to support multiple cs_tags (list[str]). This update introduces additional headers and content for better data representation: INFO= INFO= INFO= INFO= Introduced cstag.to_sequence for reconstructing subsequences. Supplementary Features Added validate_cs_tag for CS tag validation. Introduced validate_pos for position validation. Implemented normalize_positions within cstag.consensus. Added Vcf and VcfInfo classes to to_vcf. Maintenance Updates Revised normalize_read_lengths to eliminate deque output. Debugged to_html to properly handle =N

akikuno/cstag-cli: 1.0.0

Breaking changes Removed -f/--file and allowed direct file input Enhancements None Maintenance Updates Validated that no errors occur with real BAM files If an error occurred, modified to output the query_name of the read that caused the error Confirmed visualizability in IGV Fixed the bug where the program would hang when input was empty Modified to display a help message using the select module when there is no IO Aligned supported Operating Systems with Pysam POSIX, Unix, MacOS Added COC (Code of Conduct) Added GitHub Issue templates (bug and feature) pysam -> bamnostic (failed) We've decided to continue using Pysam, as Bamnostic was unable to convert SAM to AlignmentFile format Changed the build backend in pyproject.toml from setuptools to poetry As homepage wasn't reflected in grayskull's meta.yaml when using setuptools Translate all Japanese to Englis

DAJIN enables multiplex genotyping to simultaneously validate intended and unintended target genome editing outcomes

Repository of Supplementary Dat

akikuno/DAJIN2: 0.4.0

<h2> Breaking</h2> <ul> <li>Changed the input from a path to a FASTQ file to <strong>a path to a directory</strong>: The output of Guppy is now stored in multiple FASTQ files under the <code>barcodeXX/</code> directory. Previously, it was necessary to combine the FASTQ files in the <code>barcodeXX/</code> directory into one and specify it as an argument. With this revision, it is now possible to directly specify the <code>barcodeXX</code> directory, allowing users to seamlessly proceed to DAJIN2 analysis after Guppy processing. <a href="https://github.com/akikuno/DAJIN2/commit/d35ce6f89278d0361cc2b5b30fecfabbc66aa1c4">Commit Detail</a></li> </ul> <h2> Documentation</h2> <ul> <li>Changed <code>conda config --set channel_priority strict</code> to <code>conda config --set channel_priority flexible</code> for installation process in TROUBLESHOOTING.md. <a href="https://github.com/akikuno/DAJIN2/commit/c95681a8f2b6e725b0b737498981ad767eab842c">Commit Detail</a></li> </ul> <h2> New Features</h2> <ul> <li><p>Apple Silicon (ARM64) supoorts. <a href="https://github.com/akikuno/DAJIN2/commit/435bab6c56cb2172601d4b37488850fe48046f9c">Commit Detail</a></p> </li> <li><p>Changed the definition of the minor allele from a read number of less than or equal to 10 to less than or equal to 5. This is based on the assumption that one sample contains 1000 reads, where 0.5% corresponds to 5 reads. <a href="https://github.com/akikuno/DAJIN2/commit/80a3ddcf7cac3eed2bcc76b88ea534873af4dd90">Commit Detail</a></p> </li> </ul> <h2> Update</h2> <ul> <li><p>Update <code>preprocess.insertion_to_fasta</code> to facilitate the discrimination of Insertion alleles, the Reference for Insertion alleles has been saved in FASTA/HTML directory. <a href="https://github.com/akikuno/DAJIN2/commit/5899543077f0398863b6316d8c3e953b5f125f55">Commit Detail</a></p> </li> <li><p>Update <code>insertions_to_fasta.extract_enriched_insertions</code>: Previously, it calculated the presence ratio of insertion alleles separately for samples and controls, filtering at 0.5%. However, due to a threshold issue, some control insertions were narrowly missing the threshold, resulting in them being incorrectly identified as sample-specific insertions. To rectify this, the algorithm now clusters samples and controls together, excluding clusters where both types are mixed. This modification allows for the extraction of sample-specific insertion alleles. <a href="https://github.com/akikuno/DAJIN2/commit/65030daba7c56a6c3f3f685832084b71c6b2e1c3">Commit Detail</a></p> </li> <li><p>Updated <code>preprocess.insertions_to_fasta.count_insertions</code> of the counting method to treat similar insertions as identical. Previously, the same insertion was erroneously counted as different ones due to sequence errors. <a href="https://github.com/akikuno/DAJIN2/commit/7bc18f486253e876d51a296f64909e1c73114e79">Commit Detail</a></p> </li> <li><p>Updated <code>preprocess.insertions_to_fasta.merge_similar_insertions</code>: Previously, clustering was done using MiniBatchKMeans, but this method had an issue where it excessively clustered when only highly similar insertion sequences existed. Therefore, a strategy similar to <code>extract_enriched_insertions</code> was adopted, changing the algorithm to one that mixes with a uniform distribution of random scores before clustering. <a href="https://github.com/akikuno/DAJIN2/commit/fb7074cab9d9e4e3d293cb5487a3525a5faf06fd">Commit Detail</a></p> </li> <li><p>Added <code>preprocess.insertions_to_fasta.clustering_insertions</code>: Combined the clustering methods used in <code>extract_enriched_insertions</code> and <code>merge_similar_insertions</code> into a common function. <a href="https://github.com/akikuno/DAJIN2/commit/6d7ff79351c5f60320b2269accb0e3bc159fdd5b">Commit Detail</a></p> </li> <li><p>Moved the <code>call_sequence</code> function to the <code>cssplits_handler</code> module. <a href="https://github.com/akikuno/DAJIN2/commit/ef5b0bf41ab33a7e8d06d33fe7fa6c27a443742a">Commit Detail</a></p> </li> </ul> <h2> Bug Fixes</h2> <ul> <li><p>Debug <code>clustering.merge_labels</code> to be able to correctly revert minor labels back to parent labels. <a href="https://github.com/akikuno/DAJIN2/commit/8127a94e042328b87e456d3748ebea66a845ba1a">Commit Detail</a></p> </li> <li><p>Updated <code>utils.input_validator.validate_genome_and_fetch_urls</code> to obtain <code>available_server</code> more explicitly. Previously, it relied on HTTP response codes, but there were instances where the UCSC Genome Browser showed a normal (200) response while internally being in error. Therefore, with this change, a more explicit method is employed by searching for specific keywords present in the normal HTML, to determine if the server is functioning correctly. <a href="https://github.com/akikuno/DAJIN2/commit/24a02591e8a146030012dbf564e4b6cd98d42139">Commit Detail</a></p> </li> <li><p>Added <code>config.reset_logging</code> to reset the logging configuration. Previously, when batch processing multiple experiment IDs (names), a bug existed where the log settings from previous experiments remained, and the log file name was not updated. However, with this change, log files are now created for each experiment ID. <a href="https://github.com/akikuno/DAJIN2/commit/b83669c627710a5e358f934212e961373203ee52">Commit Detail</a></p> </li> <li><p>Debugged <code>core.py</code>: Modified the specification of <code>paths_predefined_fasta</code> to accept input from user-entered ALLELE data. Previously, it accepted fasta files stored in the fasta directory. However, this approach had a bug where fasta files left over from a previously aborted run (which included newly created insertions) were treated as predefined. This resulted in new insertions being incorrectly categorized as predefined. <a href="https://github.com/akikuno/DAJIN2/commit/6dd9247f010eb6168157ae9236a634efcfb84a5f">Commit Detail</a></p> </li> </ul&gt