Cell Reports Methods最新文献

Under low-coverage or error-prone sequencing conditions, existing assembly frameworks often fail to simultaneously preserve genome integrity and biological variation. To address these, this work introduces a dynamic variable-order unitig-level assembly graph (DVOUG), which constructs an initial precise unitig-level assembly graph using a high k-value and progressively lowers the k-value in regions with low coverage or high noise. Experimental results show that DVOUG solves the problem of path entanglement when reconstructing short sequences under low coverage and significantly outperforms previous graphs in both genome assembly and DNA storage data reconstruction tasks, even under low coverage. In addition, DVOUG achieves more than 99% recall rate by graph neural networks (GNNs) for edge prediction, exceeding both unitig-level assembly graphs and traditional DBGs, while also reducing training time by 4×. In summary, DVOUG excels in handling complex noisy data, enhancing assembly accuracy, connectivity, and learnability, with strong potential for practical applications.

Numerous software tools have been published to aid organoid quantification. These tools generate estimates of total organoid number and morphological characteristics in images. However, there remains a need to estimate the number of organoid cells in a well for use in organoid-based experiments (e.g., co-cultures). We present OSCAR (organoid segmentation and cell number approximation using regression), a workflow for estimating organoid cell numbers from bright-field images. Step one is a Mask-R-CNN-based convolutional neural network for identifying organoids in bright-field images and estimating the area of each organoid. Step two is an empirical multiple linear regression model relating the number of cells in an organoid to its area. OSCAR's estimate of the total number of cells in a well was within ±16% of the real number of organoid cells. OSCAR is an online tool capable of generating this key metric and will contribute to the increased reliability of organoid-based assays.

Accurate measurement of biomolecular condensates' mechanical properties is essential to understand their behavior within cells. We present FlickerPrint, an open-source Python package to determine the interfacial tension and bending rigidity of thousands of condensates using flicker spectroscopy by analyzing their shape fluctuations in confocal microscopy images. We detail the workflow and computational requirements of FlickerPrint to scale up these individual measurements to the population level. Examples of experiments in live cells and in vitro that are suitable for analysis with FlickerPrint are provided, as well as scenarios where the package cannot be used. Using these examples, we show that the results obtained are robust to changes in imaging setup, including frame rate. This implementation enables a step change in measurement capability for two key properties of biomolecular condensates: interfacial tension and bending rigidity. Moreover, the tools in FlickerPrint are also applicable for analyzing other soft, fluctuating bodies, demonstrated here using vesicles.

Studying intercellular and interorgan interactions in animal models is key to understanding development, physiology, and disease. We introduce EyaHOST, a system for clonal combinatorial loss- and gain-of-function genetics in fluorescently labeled cells under QF2-QUAS eya promoter control. Distinct from mosaic analysis with a repressible cell marker (MARCM), it reserves the use of genome-wide GAL4-UAS tools to manipulate any host tissue. EyaHOST-driven Ras^V12 overexpression with scribble knockdown recapitulates key cancer features, including systemic catabolic switching and organ wasting. We demonstrate effective tissue-specific manipulation of host compartments, including homotypic epithelial neighbors, immune cells, fat body, and muscle. Organ-specific inhibition of autophagy or stimulation of growth signaling via PTEN knockdown in fat body or muscle prevents cachexia-like wasting. Additionally, tumors trigger caspase-driven apoptosis in the neighboring epithelium, and blocking apoptosis with p35 enhances tumor growth. EyaHOST provides a modular platform to dissect mechanisms of intercellular and interorgan communication under physiological or disease conditions.

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.

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.