Cell Reports Methods最新文献

Optical chromatin tracing experiments directly capture the three-dimensional folding of thousands of individual alleles, highlighting the need for a tool that enables fast, interactive, and analytical browsing of such data. Here, we introduce optical looping interactive viewing engine (OLIVE), the first web-based application designed for high-throughput ball-and-stick chromatin tracing data studies that functions similarly to genome browsers. OLIVE allows users, regardless of computational expertise, to input their own data for automated reconstruction of chromatin fibers at individual alleles or to browse and analyze annotated publicly available datasets. Using OLIVE's functionalities, users can interact with three-dimensional presentation of traced alleles and query them based on spatial features, including pairwise distances and perimeters between their segments. Finally, OLIVE calculates and presents several polymer physics metrics of each allele, providing quantitative summaries for hypothesis-driven studies. OLIVE is an open-source project accessible at https://faryabilab.github.io/chromatin-traces-vis/.

We establish a click reaction-based workflow (tissue clearing coupled with click-chemistry in 3D, C⁴-3D) to visualize 5-ethynyl-2'-deoxyuridine (EdU) in whole mouse brain tissue cleared by CUBIC, iDisco+, and PACT. C⁴-3D was compatible with immunostaining, nuclear staining, and a fluorescent reporter mouse. Machine learning-based identification of EdU-positive nuclear coordinates followed by normalization for the Allen Brain Atlas revealed that proliferating neuronal progenitors were enriched in the subventricular zones (SVZs) and in their migration pathways to the olfactory bulbs and were decreased with aging. C⁴-3D for EdU was also applied to mouse models of cerebral infarction, glioblastoma multiforme, and metastatic brain tumor, as well as to kidney, liver, lung, and embryo in normal mouse. C⁴-3D will enable the exploration of cellular proliferation profiles in 3D. Especially, timed pulse-chase of EdU in normal development, disease progression, and tissue repair coupled with immunostaining will disclose the spatiotemporal generation, migration, and differentiation of newly synthesized cells.

Non-peptide ligands (NPLs), including lipids, amino acids, carbohydrates, and non-peptide neurotransmitters and hormones, play a critical role in ligand-receptor-mediated cell-cell communication, driving diverse physiological and pathological processes. To facilitate the study of NPL-dependent intercellular interactions, we introduce MetaLigand, a tool designed to infer NPL availability and NPL-receptor interactions using transcriptomic data. MetaLigand compiles data for 233 NPLs, including their biosynthetic enzymes, transporter genes, and receptor genes, through a combination of automated pipelines and manual curation from comprehensive databases. The tool integrates both de novo and salvage synthesis pathways, incorporating multiple biosynthetic steps and transport mechanisms. Comparisons with existing tools demonstrate MetaLigand's ability to account for complex biogenesis pathways and model NPL availability across diverse tissues and cell types. Furthermore, analysis of single-nucleus RNA sequencing (RNA-seq) datasets from age-related macular degeneration samples revealed that distinct retinal cell types exhibit unique NPL profiles and participate in specific NPL-mediated pathological cell-cell interactions.

Human reads are a key contaminant in microbial metagenomics and enrichment-based studies, requiring removal for computational efficiency, biological analysis, and privacy protection. Various in silico methods exist, but their effectiveness depends on the parameters and reference genomes used. Here, we assess different methods, including the impact of the updated telomere-to-telomere (T2T)-CHM13 human genome versus GRCh38. Using a synthetic dataset of viral and human reads, we evaluated performance metrics for multiple approaches. We found that the usage of high-sensitivity configuration of Bowtie2 with the T2T-CHM13 reference assembly significantly improves human read removal with minimal loss of specificity, albeit at higher computational cost compared to other methods investigated. Applying this approach to a publicly available microbiome dataset, we effectively removed sex-determining SNPs with little impact on microbial assembly. Our results suggest that our high-sensitivity Bowtie2 approach with the T2T-CHM13 is the best method tested to minimize identifiability risks from residual human reads.

Chromosome conformation capture (3C) assays such as HiChIP are widely used to study interactions between cis-regulatory and structural elements. However, robust methods for detecting condition-specific loops remain limited. We introduce DiffHiChIP, the first comprehensive framework to call differential loops from HiChIP and similar 3C protocols. DiffHiChIP supports DESeq2 and edgeR using either a complete contact map or a subset of contacts for background estimation, incorporates edgeR with generalized linear model (GLM) using either quasi-likelihood F test or likelihood ratio test, and implements independent hypothesis weighting (IHW) as well as a distance stratification technique for modeling distance decay of contacts in estimating statistical significance. Our results on five datasets suggest that edgeR GLM-based models with IHW correction reliably capture differential interactions, including long-range interactions, that are supported by published Hi-C data and reference studies. As HiChIP data become increasingly used for modeling chromatin regulation, DiffHiChIP promises to have a broad impact and utility.

DNA double-strand breaks (DSBs) are among the most genotoxic lesions. Investigating the cellular dynamics of repair factors during DSB repair requires methodologies that preserve both spatial and temporal information. Here, we describe a method for tracking repair progression over time at any desired genomic locus by combining DSB induction on the seconds timescale (very fast CRISPR) and genomic labeling using local genome denaturation (genome oligopaint via local denaturation fluorescence in situ hybridization [GOLDFISH]). Through protocol optimization to retain repair signatures such as γH2AX, p53-binding protein 1 (53BP1), and BRCA1, we show that the kinetics of DSB foci formation at nonrepetitive endogenous loci can be measured with minutes time resolution.

Investigating microglial phagocytosis is essential for understanding the mechanisms underlying brain health and disease. Dysregulation of phagocytosis is implicated in various neurological disorders, necessitating accurate analysis. Leveraging advances in deep learning, this study explores the application of a U-Net-based neural network for image cytometry to enhance the analysis of microglial phagocytosis. Murine microglia were imaged using the Olympus ScanR system, generating a substantial dataset for training a U-Net. The U-Net (AIstain) demonstrated superior performance in cell detection compared to live cell staining and the established segmentation tools SAM2 and Cellpose 3. Additionally, the model's applicability can be extended to other cell types, including leukemia and breast cancer cells, highlighting its versatility. AIstain provides a straightforward approach for the analysis of live cell images and microglial phagocytosis. This method enhances the precision of the results while simultaneously reducing the complexity of the experiment, thus facilitating substantial progress in the domain of neurobiological research.

Single-cell multiomics technologies generate paired measurements of different cellular modalities, such as gene expression and chromatin accessibility. However, multiomics technologies are more expensive than their unimodal counterparts, resulting in smaller and fewer available multiomics datasets. Here, we present scPairing, a deep learning model inspired by contrastive language-image pre-training (CLIP), which embeds different modalities from the same single cells onto a common embedding space. We leverage the common embedding space to generate novel multiomics data following bridge integration, a method that uses an existing multiomics bridge to link unimodal data. Through extensive benchmarking, we show that scPairing constructs an embedding space that fully captures both coarse and fine biological structures. We then use scPairing to generate new multiomics data from retina, immune, and renal cells. Furthermore, we extend scPairing to generate trimodal data. The generated multiomics datasets can facilitate the discovery of novel cross-modality relationships and the validation of existing biological hypotheses.

Mitochondria are central to cardiomyocyte function, and their spatial organization regulates nuclear signaling and gene transcription, holding potential for novel cardioprotective interventions. We developed a transmission electron microscopy platform optimized for resolving mitochondrial subpopulations and nuclear architecture in adult cardiomyocytes. This approach reliably captures longitudinal sections containing the center of the nucleus and perinuclear regions, enabling consistent imaging of subcellular nanostructures, assessment of pharmacological effects within the same organism, and visualization of extracellular vesicles carrying dysfunctional mitochondria. Integrated with an analysis workflow employing machine learning-based segmentation for annotation, the method allows automated quantification of mitochondrial and nuclear architecture and positioning. Using Drp1-deficient mice with impaired mitochondrial fission, we demonstrate this tool's ability to uncover nanoscale remodeling of mitochondria and nuclei under stress. Our platform overcomes challenges in electron microscopy analysis, providing a powerful resource to interrogate mitochondrial-nuclear dynamics in cardiac (patho)physiology. These insights will inform therapeutic targeting of bioenergetic failure.

Calcium fluorescence imaging enables us to investigate how individual neurons of live animals encode sensory input or drive specific behaviors. Extracting and interpreting large-scale neuronal activity from imaging data are crucial steps in harnessing this information. A significant challenge arises from uncorrectable tissue deformation, which disrupts the effectiveness of existing neuron segmentation methods. Here, we propose an open-source software, DynamicNeuronTracker (DyNT), which generates dynamic neuron masks for deforming and/or incompletely registered 3D calcium imaging data using patch-matching iterations. We demonstrate that DyNT accurately tracks densely populated neurons under positional jitters. DyNT also includes automated statistical analyses for interpreting neuronal responses to multiple sequential stimuli. We applied DyNT to analyze the responses of pheromone-sensing neurons in mice to controlled stimulation. We found that four bile acids and four sulfated steroids activated 15 subpopulations of sensory neurons with distinct combinatorial response profiles, revealing a strong bias toward detecting sulfated estrogen and pregnanolone.