Cell Reports Methods最新文献

Spatially resolved transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. However, current ST methods, particularly barcode-based methods, have limitations in reconstructing high-resolution images from barcodes sparsely distributed in slides. Here, we present SuperST, an algorithm that enables the reconstruction of dense matrices (higher-resolution and non-zero-inflated matrices) from low-resolution ST libraries. SuperST is based on deep image prior, which reconstructs spatial gene expression patterns as image matrices. Compared with previous methods, SuperST generated output images that more closely resembled immunofluorescence images for given gene expression maps. Furthermore, we demonstrated how one can combine images created by SuperST with computer vision algorithms. In this context, we proposed a method for extracting features from the images, which can aid in spatial clustering of genes. By providing a dense matrix for each gene in situ, SuperST can successfully address the resolution and zero-inflation issue.

Protein retention expansion microscopy (ExM) retains fluorescent signals in fixed tissue and isotropically expands the tissue to allow nanoscale (<70 nm) resolution on diffraction-limited confocal microscopes. Despite the numerous advantages of ExM, the protocol is time-consuming. Here, we adapted an ExM protocol to vibratome-sectioned brain tissue of Xenopus laevis tadpoles and implemented a microwave (M/W)-assisted protocol (^M/WExM) to reduce the workflow from days to hours. Our ^M/WExM protocol maintains the superior resolution of the original ExM protocol and yields a higher magnitude of expansion, suggesting that M/W radiation may also facilitate the expansion process. We then adapted the M/W protocol to the whole-mount brain of Drosophila melanogaster fruit flies, and successfully reduced the processing time of a widely used Drosophila IHC-ExM protocol from 6 to 2 days. This demonstrates that with appropriate adjustment of M/W parameters, this protocol can be readily adapted to different organisms and tissue types to greatly increase the efficiency of ExM experiments.

Detection of pathogenic DNA variants is vital in cancer diagnostics and treatment monitoring. While CRISPR-based diagnostics (CRISPRdx) offer promising avenues for cost-effective, rapid, and point-of-care testing, achieving single-nucleotide detection fidelity remains challenging. We present an in silico pipeline that scans the human genome for targeting pathogenic mutations in the seed region (ARTEMIS), the most stringent crRNA domain. ARTEMIS identified 12% of pathogenic SNVs as Cas12a recognizable, including 928 cancer-associated variants such as BRAF^V600E, BRCA2^E1953∗, TP53^V272M, and ALDH2^E504K. Cas12a exhibited remarkable tolerance to single mismatches within the seed region. Introducing deliberate synthetic mismatches within the seed region yielded on-target activity with single-nucleotide fidelity. Both positioning and nucleobase types of mismatches influenced detection accuracy. With improved specificity, Cas12a could accurately detect and semi-quantify BRAF^V600E in cfDNA from cell lines and patient liquid biopsies. These results provide insights toward rationalized crRNA design for high-fidelity CRISPRdx, supporting personalized and cost-efficient healthcare solutions in oncologic diagnostics.

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

Functional profiling of whole-metagenome shotgun sequencing (WMS) enables our understanding of microbe-host interactions. We demonstrate microbial functional information loss by current annotation methods at both the taxon and community levels, particularly at lower read depths. To address information loss, we develop a framework, RFW (reference-based functional profile inference on WMS), that utilizes information from genome functional annotations and taxonomic profiles to infer microbial function abundances from WMS. Furthermore, we provide an algorithm for absolute abundance change quantification between groups as part of the RFW framework. By applying RFW to several datasets related to autism spectrum disorder and colorectal cancer, we show that RFW augments downstream analyses, such as differential microbial function identification and association analysis between microbial function and host phenotype. RFW is open source and freely available at https://github.com/Xingyinliu-Lab/RFW.

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise remains unclear. Here, we utilize a small-molecule perturbation (5'-iodo-2'-deoxyuridine [IdU]) to amplify noise and assess noise quantification from numerous single-cell RNA sequencing (scRNA-seq) algorithms on human and mouse datasets and then compare it to noise quantification from single-molecule RNA fluorescence in situ hybridization (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise-without altered mean expression levels-for ∼90% of genes and that smFISH analysis verifies noise amplification for the vast majority of tested genes. Collectively, the analyses suggest that most scRNA-seq algorithms (including a simple normalization approach) are appropriate for quantifying noise, although all algorithms appear to systematically underestimate noise changes compared to smFISH. For practical purposes, this analysis further argues that IdU noise enhancement is globally penetrant-i.e., homeostatically increasing noise without altering mean expression levels-and could enable investigations of the physiological impacts of transcriptional noise.

In this work, we developed PolyMap (polyclonal mapping), a high-throughput method for mapping protein-protein interactions. We demonstrated the mapping of thousands of antigen-antibody interactions between diverse antibody libraries isolated from convalescent and vaccinated COVID-19 donors and a set of clinically relevant SARS-CoV-2 spike variants. We identified over 150 antibodies with a variety of distinctive binding patterns toward the antigen variants and found a broader binding profile, including targeting of the Omicron variant, in the antibody repertoires of more recent donors. We then used these data to select mixtures of a small number of clones with complementary reactivity that together provide strong potency and broad neutralization. PolyMap is a generalizable platform that can be used for one-pot epitope mapping, immune repertoire profiling, and therapeutic design and, in the future, could be expanded to other families of interacting proteins.

We apply a single-molecule chromatin fiber sequencing (Fiber-seq) protocol designed for amplification-free cell-type-specific mapping of the regulatory architecture at nucleosome resolution along extended ∼10-kb chromatin fibers to neuronal and non-neuronal nuclei sorted from human brain tissue. Specifically, application of this method enables the resolution of cell-selective promoter and enhancer architectures on single fibers, including transcription factor footprinting and position mapping, with sequence-specific fixation of nucleosome arrays flanking transcription start sites and regulatory motifs. We uncover haplotype-specific chromatin patterns, multiple regulatory elements cis-aligned on individual fibers, and accessible chromatin at 20,000 unique sites encompassing retrotransposons and other repeat sequences hitherto "unmappable" by short-read epigenomic sequencing. Overall, we show that Fiber-seq is applicable to human brain tissue, offering sharp demarcation of nucleosome-depleted regions at sites of open chromatin in conjunction with multi-kilobase nucleosomal positioning at single-fiber resolution on a genome-wide scale.

Library:library screening technologies hold substantial promise for paired antibody:antigen discovery, but challenges have persisted. In this issue of Cell Reports Methods, Wagner et al. introduce a method that combines antibody-ribosome-mRNA complexes, antigen cell surface display, and single-cell RNA sequencing to successfully screen diverse antibody gene libraries against a library of viral receptor proteins.

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module.