Cell systems最新文献

Single-molecule RNA fluorescence in situ hybridization (RNA FISH)-based spatial transcriptomics methods have enabled the accurate quantification of gene expression at single-cell resolution by visualizing transcripts as diffraction-limited spots. Although these methods generally scale to large samples, image analysis remains challenging, often requiring manual parameter tuning. We present Piscis, a fully automatic deep learning algorithm for spot detection trained using a loss function, the SmoothF1 loss, that approximates the F1 score to directly penalize false positives and false negatives but remains differentiable and hence usable for training by deep learning approaches. Piscis was trained and tested on a diverse dataset composed of 358 manually annotated experimental RNA FISH images representing multiple cell types and 240 additional synthetic images. Piscis outperforms other state-of-the-art spot detection methods, enabling accurate, high-throughput analysis of RNA FISH-derived imaging data without the need for manual parameter tuning. A record of this paper's transparent peer review process is included in the supplemental information.

Crohn's disease (CD) is an inflammatory gastrointestinal disease affecting approximately 1 in 1,000 people in North America. Incidence of pediatric CD has been rising in recent decades, and this group is especially at risk of more severe disease development because of the association of CD with developmental deficits. Genome-scale metabolic models (GEMs) present an opportunity to investigate systems-level changes in metabolism in specific contexts, such as pediatric CD. In this work, we utilized pediatric and adult omics data to create an ileum-specific GEM, Ileum1. We also developed reaction inclusion analysis (RIA) to quantify broad metabolic differences of several clinical cohorts and used this method to compare hundreds of subject-specific GEMs. RIA predicted altered cholesterol metabolism in males with CD, and in vitro testing found that cholesterol synthesis inhibition prevented an increase of inflammatory cytokines. We used transcriptomics from adult subjects and found that metabolism is uniquely altered in adult CD. A record of this paper's transparent peer review process is included in the supplemental information.

Understanding bacterial gene function remains a major challenge. Double-mutant genetic interaction analysis addresses this challenge by uncovering the functional partners of targeted genes, enabling association of genes of unknown function with known pathways and unraveling of connections among well-studied pathways, but such approaches are difficult to implement at the genome scale. Here, we use double-CRISPR interference (CRISPRi) to systematically quantify genetic interactions at scale for the Bacillus subtilis cell envelope, including essential genes. We discover >1,000 genetic interactions, some known and others novel. Our analysis pipeline and experimental follow-ups reveal the shared and distinct roles of paralogous genes such as mreB and mbl in peptidoglycan and teichoic acid synthesis and identify additional genes involved in the well-studied process of cell division. Overall, our study provides valuable insights into gene function and demonstrates the utility of double-CRISPRi for high-throughput dissection of bacterial gene networks, providing a blueprint for future studies in diverse species. A record of this paper's transparent peer review process is included in the supplemental information.

Cancer patients frequently suffer from anemia and cancer-related pain, which can be treated by non-opioid analgesics such as diclofenac (DCF) and acetaminophen (APAP) attenuating inflammatory responses. The pro-inflammatory cytokine interleukin (IL)-6 triggers the expression of acute-phase proteins, including the iron regulator hepcidin. Using proteomics and dynamic pathway modeling, we show that DCF and APAP directly impact IL-6 signaling by enhancing the induction of the feedback-inhibitor suppressor of cytokine signaling 3 (SOCS3), reducing signal transducer and activator of transcription (STAT)3 phosphorylation, and decreasing the expression of most acute-phase proteins except for hepcidin. In primary human hepatocytes (PHHs), the impact depends on the patient-specific extent of SOCS3 induction, which is anti-correlated with hepcidin expression. Whereas, in liver cancer cells, DCF and APAP stabilize the interaction of autocrine secreted bone morphogenic protein (BMP) with its receptor, resulting in strongly amplified hepcidin expression. Our studies suggest that co-inhibition of the BMP receptor counteracts excessive hepcidin production upon treatment with pain-relieving drugs and could prevent iron-deficiency-caused anemia in liver cancer. A record of this paper's transparent peer review process is included in the supplemental information.

The circulating antibody (Ab) repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is widely used for profiling the sequence diversity of circulating Abs (Ab repertoire sequencing [Ab-seq]), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal Ab spike-ins in 70 combinations of concentration and oligoclonality, with and without polyclonal serum immunoglobulin G (IgG) background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 liquid chromatography-tandem MS (LC-MS/MS) runs. We quantified the dependence of Ab-seq identification on Ab sequence, concentration, protease, presence of background IgGs, and bioinformatics methods. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced Ab identification. De novo sequencing performed similarly to database-dependent methods at higher Ab concentrations, but de novo Ab reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based Ab profiling. A record of this paper's transparent peer review process is included in the supplemental information.

Bacterial host factors regulate the infection cycle of bacteriophages. Except for some well-studied host factors (e.g., receptors or restriction-modification systems), the contribution of the rest of the host genome on phage infection remains poorly understood. We developed phage-host analysis using genome-wide CRISPR interference and phage packaging ("PHAGEPACK"), a pooled assay that systematically and comprehensively measures each host gene's impact on phage fitness. PHAGEPACK combines CRISPR interference with phage packaging to link host perturbation to phage fitness during active infection. Using PHAGEPACK, we constructed a genome-wide map of genes impacting T7 phage fitness in permissive E. coli, revealing pathways that affect phage packaging. When applied to the non-permissive E. coli O121, PHAGEPACK identified pathways leading to host resistance; their removal increased phage susceptibility up to a billion-fold. Bioinformatic analysis indicates that phage genomes carry homologs or truncations of key host factors, potentially for fitness advantage. In summary, PHAGEPACK offers insights into phage-host interactions, phage evolution, and bacterial resistance.

Uncovering genotype-phenotype relationships is hampered by genetic redundancy. For example, most genes in Streptococcus pneumoniae are non-essential under laboratory conditions. A powerful approach to unravel genetic redundancy is by identifying gene-gene interactions. We developed a broadly applicable dual CRISPRi-seq method and analysis pipeline to probe genetic interactions (GIs) genome-wide. A library of 869 dual single-guide RNAs (sgRNAs) targeting high-confidence operons was created, covering over 70% of the genetic elements in the pneumococcal genome. Testing these 378,015 unique combinations, 4,026 significant GIs were identified. Besides known GIs, we found previously unknown positive and negative interactions involving genes in fundamental cellular processes such as division and chromosome segregation. The presented methods and bioinformatic approaches can serve as a roadmap for genome-wide gene interaction studies in other organisms. All interactions are available for exploration via the Pneumococcal Genetic Interaction Network (PneumoGIN), which can serve as a starting point for new biological discoveries. A record of this paper's transparent peer review process is included in the supplemental information.

To determine whether HIV persistence arises from the natural dynamics of memory (m)CD4+ T cells, we compare clonal dynamics of HIV proviruses and mCD4+ T cells from the same people living with HIV (PWH) on antiretroviral therapy and from matched HIV-seronegative people (N = 51). HIV proviruses are more clonal than mCD4+ T cells but similarly clonal to antigen-specific cells. Increasing reservoir clonality over time and differential decay of intact and defective proviruses are not explained by mCD4+ T cell kinetics alone. We develop and validate a stochastic model trained on 10 quantitative data metrics, which shows that negative selection against HIV-infected cells is necessary to explain all metrics. We estimate the strength of negative selection, finding that death of cells harboring intact and defective proviruses is infrequently (∼6% and ∼2% on average) due to HIV-specific factors. Thus, our data indicate that HIV persistence is mostly, but not entirely, driven by natural mCD4+ kinetics.

Controlling cell states is pivotal in biological research, yet understanding the specific perturbations that induce desired changes remains challenging. To address this, we present PAIRING (perturbation identifier to induce desired cell states using generative deep learning), which identifies cellular perturbations leading to the desired cell state. PAIRING embeds cell states in the latent space and decomposes them into basal states and perturbation effects. The identification of optimal perturbations is achieved by comparing the decomposed perturbation effects with the vector representing the transition toward the desired cell state in the latent space. We demonstrate that PAIRING can identify perturbations transforming given cell states into desired states across different types of transcriptome datasets. PAIRING is employed to identify perturbations that lead colorectal cancer cells to a normal-like state. Moreover, simulating gene expression changes using PAIRING provides mechanistic insights into the perturbation. We anticipate that it will have a broad impact on therapeutic development, potentially applicable across various biological domains.

Romero-Pérez et al. reveal that protein surface properties-hydrophilicity, negative charge, and disorder content-confer innate tolerance to desiccation, mirroring protein solubility principles. Tolerant proteins are enriched in metabolic enzymes needed for recovery after rehydration. These insights into proteins' "molecular armor" could be leveraged to improve biologics design.