High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing.

Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data.

MOTIVATION: Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. RESULTS: We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. AVAILABILITY AND IMPLEMENTATION: Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues.

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Preparation and delivery of Calling Cards reagents Support Protocol 1: Next-generation sequencing quantification of barcode distribution within self-reporting transposon plasmid pool and adeno-associated virus genome Basic Protocol 2: Sample collection and RNA purification Support Protocol 2: Library density quantitative PCR Basic Protocol 3: Sequencing library preparation Basic Protocol 4: Library pooling and sequencing Basic Protocol 5: Data analysis.

Calling Cards: a customizable platform to longitudinally record protein-DNA interactions over time in cells and tissues.

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next generation sequencing. Compared to other genomic assays, whose readout provides a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon (SRT) "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile specific transcription factor binding with custom transcription factor (TF)-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Card reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 1-2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. Basic Protocol 1: Preparation and delivery of Calling Cards reagentsBasic Protocol 2: Sample preparationBasic Protocol 3: Sequencing library preparationBasic Protocol 4: Library pooling and sequencingBasic Protocol 5: Data analysis.

Identification of Bone Metastatic and Prognostic Alternative Splicing Signatures in Prostate Adenocarcinoma.

As the most common nonepithelial malignancy, prostate adenocarcinoma (PRAD) is the fifth chief cause of cancer mortality in men. Distant metastasis often occurs in advanced PRAD and most patients are dying from it. However, the mechanism of PRAD progression and metastasis is still unclear. It's widely reported that more than 94% of genes are selectively splicing in humans and many isoforms are particularly related with cancer progression and metastasis. Spliceosome mutations occur in a mutually exclusive manner in breast cancer, and different components of spliceosomes are targets of somatic mutations in different types of breast cancer. Existing evidence strongly supports the key role of alternative splicing in breast cancer biology, and innovative tools are being developed to use splicing events for diagnostic and therapeutic purposes. In order to identify if the PRAD metastasis is associated with alternative splicing events (ASEs), the RNA sequencing data and ASEs data of 500 PRAD patients were retrieved from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. By Lasso regression, five genes were screened to construct the prediction model, with a good reliability by ROC curve. Additionally, results in both univariate and multivariate Cox regression analysis confirmed the well prognosis efficacy of the prediction model (both P < 0.001). Moreover, a potential splicing regulatory network was established and after multiple-database validation, we supposed that the signaling axis of HSPB1 up-regulating the PIP5K1C - 46,721 - AT (P < 0.001) might mediate the tumorigenesis, progression and metastasis of PRAD via the key members of Alzheimer's disease pathway (SRC, EGFR, MAPT, APP and PRKCA) (P < 0.001).

Meta-analysis methods for risk difference: A comparison of different models.

Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: (1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and (2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.

Spatially resolved transcriptomics and the kidney: many opportunities.

Defining changes in gene expression during health and disease is critical for the understanding of human physiology. In recent years, single-cell/nuclei RNA sequencing (sc/snRNAseq) has revolutionized the definition and discovery of cell types and states as well as the interpretation of organ- and cell-type-specific signaling pathways. However, these advances require tissue dissociation to the level of the single cell or single nuclei level. Spatially resolved transcriptomics (SrT) now provides a platform to overcome this barrier in understanding the physiological contexts of gene expression and cellular microenvironment changes in development and disease. Some of these transcriptomic tools allow for high-resolution mapping of hundreds of genes simultaneously in cellular and subcellular compartments. Other tools offer genome depth mapping but at lower resolution. We review advances in SrT, considerations for using SrT in your own research, and applications for kidney biology.

The Identification of Prognostic and Metastatic Alternative Splicing in Skin Cutaneous Melanoma.

Skin cutaneous melanoma (SKCM) is a type of highly invasive cancer originated from melanocytes. It is reported that aberrant alternative splicing (AS) plays an important role in the neoplasia and metastasis of many types of cancer. Therefore, we investigated whether ASEs of pre-RNA have such an influence on the prognosis of SKCM and the related mechanism of ASEs in SKCM. The RNA-seq data and ASEs data for SKCM patients were obtained from the TCGA and TCGASpliceSeq database. The univariate Cox regression revealed 1265 overall survival-related splicing events (OS-SEs). Screened by Lasso regression, 4 OS-SEs were identified and used to construct an effective prediction model (AUC: .904), whose risk score was proved to be an independent prognostic factor. Furthermore, Kruskal-Wallis test and Mann-Whitney-Wilcoxon test showed that an aberrant splicing type of aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) regulated by CDC-like kinase 1 (CLK1) was associated with the metastasis and stage of SKCM. Besides, the overlapped signal pathway for AIMP2 was galactose metabolism identified by the co-expression analysis. External database validation also confirmed that AIMP2, CLK1, and the galactose metabolism were associated with the metastasis and stage of SKCM patients. ChIP-seq and ATAC-seq methods further confirmed the transcription regulation of CLK1, AIMP2, and other key genes, whose cellular expression was detected by Single Cell Sequencing. In conclusion, we proposed that CLK1-regulated AIMP2-78704-ES might play a critical role in the tumorigenesis and metastasis of SKCM via galactose metabolism. Besides, we established an effective model with MTMR14-63114-ES, URI1-48867-ES, BATF2-16724-AP, and MED22-88025-AP to predict the metastasis and prognosis of SKCM patients.

The Construction of Bone Metastasis-Specific Prognostic Model and Co-expressed Network of Alternative Splicing in Breast Cancer.

BACKGROUND: Breast cancer (BRCA) ranks among the top most common female malignancies and was regarded as incurable when combined with bone and distant metastasis. Alternative splicing events (ASEs) together with splicing factors (SFs) were considered responsible for the development and progression of tumors. METHODS: Datasets including RNA sequencing and ASEs of BRCA samples were achieved from TCGA and TCGASpliceSeq databases. Then, a survival model was built including 15 overall-survival-associated splicing events (OS-SEs) by Cox regression and Lasso regression. The co-expressed SFs of each bone-and-distant-metastasis-related OS-SE were discovered by Pearson correlation analysis. Additionally, Gene Set Variation Analysis (GSVA) was performed to identify the downstream mechanisms of the key OS-SEs. Finally, the results were validated in different online platforms. RESULTS: A reliable survival model was established (the area under ROC = 0.856), and CIRBP was found co-expressed with FAM110B (R = 0.320, P < 0.001) associated with the fatty acid metabolism pathway. CONCLUSION: Aberrant SF, CIRBP, regulated a specific ASE, exon skip (ES) of FAM110B, during which the fatty acid metabolism pathway played an essential part in tumorigenesis and prognosis of BRCA.

Author Correction: Proteome-wide identification and functional analysis of ubiquitinated proteins in peach leaves.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Proteome-wide identification and functional analysis of ubiquitinated proteins in peach leaves.

Ubiquitination is a critical post-translational modification machinery that governs a wide range of cellular functions by regulating protein homeostasis. Identification of ubiquitinated proteins and lysine residues can help researchers better understand the physiological roles of ubiquitin modification in different biological systems. In this study, we report the first comprehensive analysis of the peach ubiquitome by liquid chromatography-tandem mass spectrometry-based diglycine remnant affinity proteomics. Our systematic profiling revealed a total of 544 ubiquitination sites on a total of 352 protein substrates. Protein annotation and functional analysis suggested that ubiquitination is involved in modulating a variety of essential cellular and physiological processes in peach, including but not limited to carbon metabolism, histone assembly, translation and vesicular trafficking. Our results could facilitate future studies on how ubiquitination regulates the agricultural traits of different peach cultivars and other crop species.