Cell Reports Methods最新文献

We developed a rat dorsal root ganglion (DRG)-derived sensory nerve organotypic model by culturing DRG explants on an organoid culture device. With this method, a large number of organotypic cultures can be produced simultaneously with high reproducibility simply by seeding DRG explants derived from rat embryos. Unlike previous DRG explant models, this organotypic model consists of a ganglion and an axon bundle with myelinated A fibers, unmyelinated C fibers, and stereo-myelin-forming nodes of Ranvier. The model also exhibits Ca²⁺ signaling in cell bodies in response to application of chemical stimuli to nerve terminals. Further, axonal transection increases the activating transcription factor 3 mRNA level in ganglia. Axons and myelin are shown to regenerate 14 days following transection. Our sensory organotypic model enables analysis of neuronal excitability in response to pain stimuli and tracking of morphological changes in the axon bundle over weeks.

Small noncoding RNAs (sncRNAs) regulate biological processes by impacting post-transcriptional gene expression through repressing the translation and levels of targeted transcripts. Despite the clear biological importance of sncRNAs, approaches to unambiguously define genome-wide sncRNA:target RNA interactions remain challenging and not widely adopted. We present CIMERA-seq, a robust strategy incorporating covalent ligation of sncRNAs to their target RNAs within the RNA-induced silencing complex (RISC) and direct detection of in vivo interactions by sequencing of the resulting chimeric RNAs. Modifications are incorporated to increase the capacity for processing low-abundance samples and permit cell-type-selective profiling of sncRNA:target RNA interactions, as demonstrated in mouse brain cortex. CIMERA-seq represents a cohesive and optimized method for unambiguously characterizing the in vivo network of sncRNA:target RNA interactions in numerous biological contexts and even subcellular fractions. Genome-wide and cell-type-selective CIMERA-seq enhances researchers' ability to study gene regulation by sncRNAs in diverse model systems and tissue types.

Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.

Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.

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.