Cell Reports Methods最新文献

Active endolysosomal pH regulation is essential for optimal enzymatic activity. To measure acidification, pH sensors can be delivered to acidic compartments using labeled dextran polymers or proteins. However, commercial probes have limited sensitivity in the acidic endolysosomal range or their fluorescence undergoes degradation. Herein, we introduce the new pH-sensitive probe ApHID, a green-emitting sensor with optimal dynamic range matching the acidity of endosomes and lysosomes. Acid pH indicator dye (ApHID) has a pKa near 5, increasing brightness with acidity, and withstands oxidation and photobleaching. We used ApHID dextrans to measure endolysosomal pH in macrophages and compared it to other commercially available sensors. ApHID reported pH accurately and stably over time in cell culture and was sensitive to subtle variations in organelle acidification in real time. Overall, ApHID circumvents limitations of currently available commercial probes and can provide utility in demanding applications such as intravital imaging of tissues.

Spatial proteomic and transcriptomic technologies enable high-throughput phenotyping of cells in situ, enabling quantification of spatial relationships among diverse cell populations. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge, we introduce Kontextual, a method that considers alternative frames of reference for contextualizing spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells that are consistent across regions. By modeling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the region selected. We demonstrate in spatial proteomics and transcriptomics datasets that modeling spatial relationships this way is biologically meaningful and can improve the prediction of patient prognosis in a classification setting.

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success rates, and extensive timelines of traditional drug development, while efficiently using the growing amount of available data. This review identifies four major factors that influence DTI predictions, highlights persistent challenges, and proposes insights and strategies from the perspectives of data, features, and experimental setups to address these challenges. Furthermore, it emphasizes the importance of refining established approaches-such as the "guilt-by-association" concept-to manage data sparsity, and integrating emerging technologies, including large language models and AlphaFold, to advance feature engineering. We hope that this work will provide valuable guidance and novel perspectives for advancing future research on DTI predictions.

Single-cell sequencing enables genome- and epigenome-wide profiling of thousands of individual cells, offering unprecedented biological insights. However, technical noise and batch effects obscure high-resolution structures, hindering rare-cell-type detection and cross-dataset comparisons. To comprehensively address these challenges, this study upgrades RECODE, a high-dimensional statistics-based tool for technical noise reduction in single-cell RNA sequencing (RNA-seq), to include a function called iRECODE, which simultaneously reduces technical and batch noise. Further, RECODE's applicability is extended to diverse single-cell modalities, including single-cell high-throughput chromosome conformation capture (Hi-C) and spatial transcriptomics. Recent improvements in the algorithm have substantially enhanced both accuracy and computational efficiency. The RECODE platform thus provides a robust and versatile solution for noise mitigation, enabling more accurate downstream analyses across transcriptomic, epigenomic, and spatial domains.

High-definition visualization techniques are critical for understanding the neuroanatomy of the spinal cord, an essential structure for sensorimotor and autonomic functions, in both healthy and pathological conditions. Magnetic resonance imaging (MRI) is a common method for visualizing neural structures in 3D. However, techniques for spinal cord MRI have historically achieved limited visualization of rootlets and nerves, especially at lower spinal levels, due to their highly complex and compact organization. Here, we developed a spinal in situ contrast 3D imaging (SpIC3D) method that allows visualization of spinal compartments in fixed animal and human specimens with high resolution (50 μm) at various spinal levels. Using SpIC3D, we achieved quantification of neuronal cell density in dorsal root ganglia, multi-segment identification of individual rootlets and roots, and volumetric reconstruction of multiple spinal structures for computational modeling. SpIC3D provides a basis for accelerated spinal pathology characterization and personalized spinal cord stimulation treatments.

We developed SpliPath as a generalizable framework to discover disease associations mediated by rare variants that induce experimentally supported mRNA splicing defects. Our approach integrates components of burden tests (BTs), traditional splicing quantitative trait locus (sQTL) analyses, and sequence-to-function AI models (SpliceAI and Pangolin). Central to the workings of SpliPath is our concept of collapsed rare variant splicing QTL (crsQTL). crsQTL groups rare variants that are predicted to alter splicing in the same way, specifically by linking them to shared splice junctions observed in independent (unpaired) RNA sequencing (RNA-seq) datasets. We demonstrate the utility of SpliPath through applications in amyotrophic lateral sclerosis (ALS). Through this, we showcase scenarios where SpliPath detects genetic associations that cannot be recovered by more simplistic combinations of BT and SpliceAI. We also nominate crsQTL for splice defects detected in large-scale analyses of ALS patient tissue.

In vitro human pluripotent stem cell (hPSC)-derived models have been crucial in advancing our understanding of the mechanisms underlying neurodevelopment, though knowledge of the earliest stages of brain formation is lacking. Micropatterning of cell populations as they transition from pluripotency through the process of neurulation can produce self-assembled neuroepithelial tissues (NETs) with precise spatiotemporal control, enhancing the fidelity of hPSC models to the early developing human brain and their use in phenotypic assessments. Here, we introduce an accessible, customizable, and scalable method to produce self-assembled NETs using bioprinting to rapidly deposit reproducibly sized extracellular matrix droplets. Matrix addition to the media provides a scaffold that promotes 3D tissue folding, reflecting neural tube development. We demonstrate that these scaffolded NETs (scNETs) exhibit key architectural and biological features of the human brain during normal and abnormal development-notably, hyperproliferation and structural malformations induced by TSC2 deficiency-and provide a robust drug screening tool.

Cytotoxic CD8⁺ T cells are essential mediators of immune responses against viral infections and tumors. Upon antigen encounter, antigen-specific CD8⁺ T cells undergo clonal expansion and produce effector cytokines, processes that require dynamic metabolic adaptation. However, profiling antigen-specific T cells at single-cell resolution remains technically challenging. We present a spectral flow cytometry-based workflow enabling metabolic profiling of antigen-specific CD8⁺ T cells identified via major histocompatibility complex (MHC) class I tetramers or CD137 upregulation. The approach integrates the analysis of metabolic protein expression to infer pathway activity, uptake of fluorescent probes to measure functional metabolism and metabolite utilization, and assays evaluating cellular energy metabolism. Applied to human and mouse samples, this method defined the metabolic profiles of cytomegalovirus-, SARS-CoV-2-, and tumor-specific CD8⁺ T cells across distinct activation states and tissues. By detailing each component of the workflow, we provide practical guidance for applying metabolic spectral flow cytometry to dissect disease mechanisms and therapeutic responses.

We present "Active-Seq" (azide click tagging for in vitro epigenomic sequencing), a base-conversion-free technology that enables the isolation of DNA containing unmodified CpG sites using a mutated bacterial methyltransferase enzyme and a synthetically prepared cofactor analog. Active-Seq is a robust epigenomic profiling platform with a simple and streamlined workflow, performed in tandem with sequencing library preparation and compatible with DNA input quantities as low as 1 ng. We establish a baseline for the performance of Active-Seq using model DNA oligos and further validate it against gold-standard whole-genome bisulfite sequencing data. We show robust performance of the platform across tissue-derived DNA and demonstrate enrichment of DNA at unmethylated, cell-type-specific marker regions of the epigenome, laying the foundation for the future application of this technology in tissue deconvolution applications. Finally, we apply the technology to cell-free DNA samples, outlining an approach for tumor-informed disease profiling in patients with colorectal cancer.

Melanocytic atypia often leads to diagnostic discordance, complicating its prediction by machine learning models. To overcome this, we paired H&E-stained histology images with contiguous or serial sections immunohistochemically (IHC) stained for melanocytic cells via antibodies for MelanA, MelPro, or SOX10. We developed a melanocytic atypia deep learning pipeline from real-world archives of 122 paired whole slide images from 61 confirmed melanoma in situ (MIS) cases at two institutions. Only 37.7% of pairs matched well enough for deep learning; nonetheless, MelanA+MelPro models achieved an average area under the receiver-operating characteristic (AUROC) = 0.948 and area under the precision-recall curve (AUPRC) = 0.611 (9.3× baseline) and SOX10 models achieved AUROC = 0.867 and AUPRC = 0.433 (7.3× baseline). Despite learning from biologically different nuclear versus cytoplasmic IHC stains, convolutional neural network models exhibited a convergent explainable AI rationale. The resulting multi-antibody virtual stains identified cytologic and small-scale architectural features directly from H&E images, supporting pathologists in assessing cutaneous MIS.