Cell Reports Methods最新文献

Dual-attribute immune cells possess advantageous features of cytotoxic T cells and natural killer (NK) cells and hold promise for advancing immunotherapy. Dual-attribute cell types such as invariant natural killer T cells, induced T-to-NK cells, and cytokine-induced killer cells have demonstrated efficacy and safety in preclinical and clinical studies. However, their limited availability hinders their widespread application. Human pluripotent stem cells (hPSCs) offer an ideal source. Here, we generate dual-attribute induced T-NK (iTNK) cells from hPSCs, expressing markers of both cytotoxic T and NK cells. Single-cell RNA and T cell receptor (TCR) sequencing analyses reveal that iTNK cells expressed signature genes associated with both NK and T cells and displayed a diverse TCR repertoire. iTNK cells release cytotoxic mediators, exert cytotoxicity against diverse tumor cell lines, and inhibit tumor growth in vivo. By harnessing adaptive and innate immune responses, hPSC-derived iTNK cells offer promising strategies for cancer immunotherapy.

Glycosylation is generally characterized and controlled as a critical quality attribute for therapeutic glycoproteins because glycans can impact protein drug-product efficacy, half-life, stability, and safety. Analytical procedures to characterize N-glycans are relatively well established, but the characterization of O-glycans is challenging due to the complex workflows and lack of enzymatic tools. Here, we present a simplified chemoenzymatic method to simultaneously profile N- and O-glycans from the same sample using a one-pot format by mass spectrometry (MS). N-glycans were first released by PNGase F, followed by O-glycopeptide generation by proteinase K, selective N-glycan reduction, and O-glycan release by β-elimination during permethylation of both N- and O-glycans. Glycan structural assignments and determination of N- to O-glycan ratio was obtained from the one-pot mass spectra. The streamlined, one-pot method is a reliable approach that will facilitate advanced characterizations for quality assessments of therapeutic glycoproteins.

The genome contains numerous regulatory elements that may undergo complex interactions and contribute to the establishment, maintenance, and change of cellular identity. Three-dimensional genome organization can be explored with fluorescence in situ hybridization (FISH) at the single-cell level, but the detection of small genomic loci remains challenging. Here, we provide a rapid and simple protocol for the generation of bright FISH probes suited for the detection of small genomic elements. We systematically optimized probe design and synthesis, screened polymerases for their ability to incorporate dye-labeled nucleotides, and streamlined purification conditions to yield nanoscopy-compatible oligonucleotides with dyes in variable arrays (NOVA probes). With these probes, we detect genomic loci ranging from genome-wide repetitive regions down to non-repetitive loci below the kilobase scale. In conclusion, we introduce a simple workflow to generate densely labeled oligonucleotide pools that facilitate detection and nanoscopic measurements of small genomic elements in single cells.

Existing models of the human skin have aided our understanding of skin health and disease. However, they currently lack a microbial component, despite microbes' demonstrated connections to various skin diseases. Here, we present a robust, standardized model of the skin microbial community (SkinCom) to support in vitro and in vivo investigations. Our methods lead to the formation of an accurate, reproducible, and diverse community of aerobic and anaerobic bacteria. Subsequent testing of SkinCom on the dorsal skin of mice allowed for DNA and RNA recovery from both the applied SkinCom and the dorsal skin, highlighting its practicality for in vivo studies and -omics analyses. Furthermore, 66% of the responses to common cosmetic chemicals in vitro were in agreement with a human trial. Therefore, SkinCom represents a valuable, standardized tool for investigating microbe-metabolite interactions and facilitates the experimental design of in vivo studies targeting host-microbe relationships.

The availability of data from profiling of cancer patients with multiomics is rapidly increasing. However, integrative analysis of such data for personalized target identification is not trivial. Multiomics2Targets is a platform that enables users to upload transcriptomics, proteomics, and phosphoproteomics data matrices collected from the same cohort of cancer patients. After uploading the data, Multiomics2Targets produces a report that resembles a research publication. The uploaded matrices are processed, analyzed, and visualized using the tools Enrichr, KEA3, ChEA3, Expression2Kinases, and TargetRanger to identify and prioritize proteins, genes, and transcripts as potential targets. Figures and tables, as well as descriptions of the methods and results, are automatically generated. Reports include an abstract, introduction, methods, results, discussion, conclusions, and references and are exportable as citable PDFs and Jupyter Notebooks. Multiomics2Targets is applied to analyze version 3 of the Clinical Proteomic Tumor Analysis Consortium (CPTAC3) pan-cancer cohort, identifying potential targets for each CPTAC3 cancer subtype. Multiomics2Targets is available from https://multiomics2targets.maayanlab.cloud/.

Intrinsically photosensitive retinal ganglion cells (ipRGCs) play a crucial role in several physiological light responses. In this study, we generate an improved Opn4^cre knockin allele (Opn4^cre(DSO)), which faithfully reproduces endogenous Opn4 expression and improves compatibility with widely used reporters. We evaluated the efficacy and sensitivity of Opn4^cre(DSO) for labeling in retina and brain and provide an in-depth comparison with the extensively utilized Opn4^cre(Saha) line. Through this characterization, Opn4^cre(DSO) demonstrated higher specificity in labeling ipRGCs with minimal recombination escape. Leveraging a combination of electrophysiological, molecular, and morphological analyses, we confirmed its sensitivity in detecting all ipRGC types (M1-M6) and defined their unique topographical distribution across the retina. In the brain, the Opn4^cre(DSO) line labels ipRGC projections with minimal labeling of cell bodies. Overall, the Opn4^cre(DSO) mouse line represents an improved tool for studying ipRGC function and distribution, offering a means to selectively target these cells to study light-regulated behaviors and physiology.

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.