Cell Reports Methods最新文献

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

The type I CRISPR system has recently emerged as a promising tool, especially for large-scale genomic modification, but its application to generate model animals by editing zygotes had not been established. In this study, we demonstrate genome editing in zygotes using the type I-E CRISPR-Cas3 system, which efficiently generates deletions of several thousand base pairs at targeted loci in mice with 40%-70% editing efficiency without off-target mutations. To overcome the difficulties associated with detecting the variable deletions, we used a newly long-read sequencing-based multiplex genotyping approach. Demonstrating remarkable versatility, our Cas3-based technique was successfully extended to rats as well as mice, even by zygote electroporation methods. Knockin for SNP exchange and genomic replacement with a donor plasmid were also achieved in mice. This pioneering work with the type I CRISPR zygote editing system offers increased flexibility and broader applications in genetic engineering across different species.

We developed a method that utilizes fluorescent labeling of nuclear envelopes alongside cytometry sorting for the selective isolation of Purkinje cell (PC) nuclei. Beginning with SUN1 reporter mice, we GFP-tagged envelopes to confirm that PC nuclei could be accurately separated from other cell types. We then developed an antibody-based protocol to make PC nuclear isolation more robust and adaptable to cerebellar tissues of any genotypic background. Immunofluorescent labeling of the nuclear membrane protein RanBP2 enabled the isolation of PC nuclei from C57BL/6 cerebellum. By analyzing the expression of PC markers, nuclear size, and nucleoli number, we confirmed that our method delivers a pure fraction of PC nuclei. To demonstrate its applicability, we isolated PC nuclei from spinocerebellar ataxia type 7 (SCA7) mice and identified transcriptional changes in known and new disease-associated genes. Access to pure PC nuclei offers insights into PC biology and pathology, including the nature of selective neuronal vulnerability.

The ability of cells to sense and respond to mechanical forces is critical in many physiological and pathological processes. However, determining the mechanisms by which forces affect protein function inside cells remains challenging. Motivated by in vitro demonstrations of fluorescent proteins (FPs) undergoing reversible mechanical switching of fluorescence, we investigated whether force-sensitive changes in FP function could be visualized in cells. Guided by a computational model of FP mechanical switching, we develop a formalism for its detection in Förster resonance energy transfer (FRET)-based biosensors and demonstrate its occurrence in cellulo within a synthetic actin crosslinker and the mechanical linker protein vinculin. We find that in cellulo mechanical switching is reversible and altered by manipulation of cell force generation, external stiffness, and force-sensitive bond dynamics of the biosensor. This work describes a framework for assessing FP mechanical stability and provides a means of probing force-sensitive protein function inside cells.

Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration, with the ultimate goal of accelerating recovery from diseases or injuries. To accomplish this, regulators must be identified and manipulated to control cell fate. We propose Fatecode, a computational method that predicts cell fate regulators based only on single-cell RNA sequencing (scRNA-seq) data. Fatecode learns a latent representation of the scRNA-seq data using a deep learning-based classification-supervised autoencoder and then performs in silico perturbation experiments on the latent representation to predict genes that, when perturbed, would alter the original cell type distribution to increase or decrease the population size of a cell type of interest. We assessed Fatecode's performance using simulations from a mechanistic gene-regulatory network model and scRNA-seq data mapping blood and brain development of different organisms. Our results suggest that Fatecode can detect known cell fate regulators from single-cell transcriptomics datasets.

Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods and has quickly become the current state of the art in the field. Nevertheless, tight control of proximity-labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein of interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split/link design, we applied it to identify protein interactions of the glucocorticoid receptor and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid and non-structural protein 7 (NSP7) proteins by TurboID proximity labeling. Our results demonstrate that our T2A split/link provides an opportune control that builds upon previously established control requirements in the field.

Inorganic polyphosphate (polyP) is a ubiquitous polymer that controls fundamental processes. To overcome the absence of a genetically tractable mammalian model, we developed an inducible mammalian cell line expressing Escherichia coli polyphosphate kinase 1 (EcPPK1). Inducing EcPPK1 expression prompted polyP synthesis, enabling validation of polyP analytical methods. Virtually all newly synthesized polyP accumulates within the nucleus, mainly in the nucleolus. The channeled polyP within the nucleolus results in the redistribution of its markers, leading to altered rRNA processing. Ultrastructural analysis reveals electron-dense polyP structures associated with a hyper-condensed nucleolus resulting from an exacerbation of the liquid-liquid phase separation (LLPS) phenomena controlling this membraneless organelle. The selective accumulation of polyP in the nucleoli could be interpreted as an amplification of polyP channeling to where its physiological function takes place. Indeed, quantitative analysis of several mammalian cell lines confirms that endogenous polyP accumulates within the nucleolus.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules as gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at levels greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging by GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

PAX3/7 fusion-negative rhabdomyosarcoma (FN-RMS) is a childhood mesodermal lineage malignancy with a poor prognosis for metastatic or relapsed cases. Limited understanding of advanced FN-RMS is partially attributed to the absence of sequential invasion and dissemination events and the challenge in studying cell behavior, using, for example, non-invasive intravital microscopy (IVM), in currently used xenograft models. Here, we developed an orthotopic tongue xenograft model of FN-RMS to study cell behavior and the molecular basis of invasion and metastasis using IVM. FN-RMS cells are retained in the tongue and invade locally into muscle mysial spaces and vascular lumen, with evidence of hematogenous dissemination to the lungs and lymphatic dissemination to lymph nodes. Using IVM of tongue xenografts reveals shifts in cellular phenotype, migration to blood and lymphatic vessels, and lymphatic intravasation. Insight from this model into tumor invasion and metastasis at the tissue, cellular, and subcellular level can guide new therapeutic avenues for advanced FN-RMS.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8⁺ T cell types and potential prognostic marker genes.