Cell Reports Methods最新文献

We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer. We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides. Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments. This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.

The lung immune system consists of both resident and circulating immune cells that communicate intricately. The immune system is activated by exposure to bacteria and viruses, when cancer initiates in the lung (primary lung cancer), or when metastases of other cancer types, including breast cancer, spread to and develop in the lung (secondary lung cancer). Thus, in these pathological situations, a comprehensive and quantitative assessment of changes in the lung immune system is of paramount importance for understanding mechanisms of infectious diseases, lung cancer, and metastasis but also for developing efficacious treatments. Unfortunately, lung tissue exhibits high autofluorescence, and this high background signal makes high-parameter flow cytometry analysis complicated. Here, we provide an optimized 30-parameter antibody panel for the analysis of all major immune cell types and states in normal and metastatic murine lungs using spectral flow cytometry.

Glucose metabolism has been well understood for many years, but some intriguing questions remain regarding the subcellular distribution, transport, and functions of glycolytic metabolites. To address these issues, a living cell metabolic monitoring technology with high spatiotemporal resolution is needed. Genetically encoded fluorescent sensors can achieve specific, sensitive, and spatiotemporally resolved metabolic monitoring in living cells and in vivo, and dozens of glucose metabolite sensors have been developed recently. Here, we highlight the importance of tracking specific intermediate metabolites of glycolysis and glycolytic flux measurements, monitoring the spatiotemporal dynamics, and quantifying metabolite abundance. We then describe the working principles of fluorescent protein sensors and summarize the existing biosensors and their application in understanding glucose metabolism. Finally, we analyze the remaining challenges in developing high-quality biosensors and the huge potential of biosensor-based metabolic monitoring at multiple spatiotemporal scales.

Single-cell RNA sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes such as cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in phenotypic resolution and functionality with respect to regression-based methods. Using ConDecon, we discover the implication of neurodegenerative microglia inflammatory pathways in the mesenchymal transformation of pediatric ependymoma and characterize their spatial trajectories of activation. The generality of this approach enables the deconvolution of other data modalities, such as bulk ATAC-seq data.

Single-fluorescent protein (FP)-based FLIM (fluorescence lifetime imaging microscopy) biosensors can visualize intracellular processes quantitatively. They require a single wavelength for detection, which facilitates multi-color imaging. However, their development has been limited by the absence of a general design framework and complex screening processes. In this study, we engineered FLIM biosensors for ATP (adenosine triphosphate), cAMP (cyclic adenosine monophosphate), citrate, and glucose by inserting each sensing domain into mTurquoise2 (mTQ2) between Tyr-145 and Phe-146 using peptide linkers. Fluorescence intensity-based screening yielded FLIM biosensors with a 0.5 to 1.0 ns dynamic range upon analyte binding, demonstrating that the mTQ2(1-145)-GT-X-EF-mTQ2(146-238) backbone is a versatile platform for FLIM biosensors, allowing for simple intensity-based screening while providing dual-functional biosensors for both FLIM and intensity-based imaging. As a proof of concept, we monitored cAMP and Ca²⁺ dynamics simultaneously in living cells by dual-color imaging. Our results complement recent studies, establishing mTQ2 as a valuable framework for developing FLIM biosensors.

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

The application of CRISPR-Cas systems to genome editing has revolutionized experimental biology and is an emerging gene and cell therapy modality. CRISPR-Cas systems target off-target regions within the human genome, which is a challenge that must be addressed. Phages have evolved anti-CRISPR proteins (Acrs) to evade CRISPR-Cas-based immunity. Here, we engineer an Acr (AcrIIA4) to increase the precision of CRISPR-Cas-based genome targeting. We developed an approach that leveraged (1) computational guidance, (2) deep mutational scanning, and (3) highly parallel DNA repair measurements within human cells. In a single experiment, ∼10,000 Acr variants were tested. Variants that improved editing precision were tested in additional validation experiments that revealed robust enhancement of gene editing precision and synergy with a high-fidelity version of Cas9. This scalable high-throughput screening framework is a promising methodology to engineer Acrs to increase gene editing precision, which could be used to improve the safety of gene editing-based therapeutics.

Loss of proteostasis is a hallmark of aging that underlies many age-related diseases. Different cell compartments experience distinctive challenges in maintaining protein quality control, but how aging regulates subcellular proteostasis remains underexplored. Here, by targeting the misfolding-prone Fluc^DM luciferase to the cytoplasm, mitochondria, and nucleus, we established transgenic sensors to examine subcellular proteostasis in Drosophila. Analysis of detergent-insoluble and -soluble levels of compartment-targeted Fluc^DM variants indicates that thermal stress, cold shock, and pro-longevity inter-organ signaling differentially affect subcellular proteostasis during aging. Moreover, aggregation-prone proteins that cause different neurodegenerative diseases induce a diverse range of outcomes on Fluc^DM insolubility, suggesting that subcellular proteostasis is impaired in a disease-specific manner. Further analyses with Fluc^DM and mass spectrometry indicate that pathogenic tau^V337M produces an unexpectedly complex regulation of solubility for different Fluc^DM variants and protein subsets. Altogether, compartment-targeted Fluc^DM sensors pinpoint a diverse modulation of subcellular proteostasis by aging regulators.

The etiology of Parkinson's disease (PD) remains elusive, and the limited availability of suitable animal models hampers research on pathogenesis and drug development. We report the development of a cynomolgus monkey model of PD that combines adeno-associated virus (AAV)-mediated overexpression of α-synuclein into the substantia nigra with an injection of poly(ADP-ribose) (PAR) into the striatum. Our results show that pathological processes were accelerated, including dopaminergic neuron degeneration, Lewy body aggregation, and hallmarks of inflammation in microglia and astrocytes. Behavioral phenotypes, dopamine transporter imaging, and transcriptomic profiling further demonstrate consistencies between the model and patients with PD. This model can help to determine the mechanisms underlying PD impacted by α-synuclein and PAR and aid in the accelerated development of therapeutic strategies for PD.

A key step in developing engineered B cells for therapeutic purposes is evaluation in immunocompetent, large-animal models. Therefore, we developed methods to purify, expand, and differentiate non-human primate (NHP; rhesus macaque) B cells. After 7 days in culture, B cells expanded 10-fold, differentiated into a plasma cell phenotype (CD38, CD138), and secreted immunoglobulin G. Using single-cell sequencing and flow cytometry, we verified the presence of plasma cell genes in differentiated NHP B cells and unearthed less-recognized markers, such as CD59 and CD79A. In contrast with human cells, we found that the immune checkpoint molecule CD274 (PD-L1) and major histocompatibility complex (MHC) class I molecules were upregulated in NHP plasma cells in the transcriptional data. Lastly, we established the conditions for efficient transduction of NHP B cells with adeno-associated virus (AAV) vectors, achieving a delivery rate of approximately 60%. We envision that this work will accelerate proof-of-concept studies using engineered B cells in NHPs.