SCIPAC: quantitative estimation of cell-phenotype associations.

Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.

Protein Signature Differentiating Neutrophils and Myeloid-Derived Suppressor Cells Determined Using a Human Isogenic Cell Line Model and Protein Profiling.

Myeloid-derived suppressor cells (MDSCs) play an essential role in suppressing the antitumor activity of T lymphocytes in solid tumors, thus representing an attractive therapeutic target to enhance the efficacy of immunotherapy. However, the differences in protein expression between MDSCs and their physiological counterparts, particularly polymorphonuclear neutrophils (PMNs), remain inadequately characterized, making the specific identification and targeting of MDSCs difficult. PMNs and PMN-MDSCs share markers such as CD11b+CD14-CD15+/CD66b+, and some MDSC-enriched markers are emerging, such as LOX-1 and CD84. More proteomics studies are needed to identify the signature and markers for MDSCs. Recently, we reported the induced differentiation of isogenic PMNs or MDSCs (referred to as iPMNs and iMDSCs, respectively) from the human promyelocytic cell line HL60. Here, we profiled the global proteomics and membrane proteomics of these cells with quantitative mass spectrometry, which identified a 41-protein signature ("cluster 6") that was upregulated in iMDSCs compared with HL60 and iPMN. We further integrated our cell line-based proteomics data with a published proteomics dataset of normal human primary monocytes and monocyte-derived MDSCs induced by cancer-associated fibroblasts. The analysis identified a 38-protein signature that exhibits an upregulated expression pattern in MDSCs compared with normal monocytes or PMNs. These signatures may provide a hypothesis-generating platform to identify protein biomarkers that phenotypically distinguish MDSCs from their healthy counterparts, as well as potential therapeutic targets that impair MDSCs without harming normal myeloid cells.

Ketogenic Diet Alters the Epigenetic and Immune Landscape of Prostate Cancer to Overcome Resistance to Immune Checkpoint Blockade Therapy.

Resistance to immune checkpoint blockade (ICB) therapy represents a formidable clinical challenge limiting the efficacy of immunotherapy. In particular, prostate cancer poses a challenge for ICB therapy due to its immunosuppressive features. A ketogenic diet (KD) has been reported to enhance response to ICB therapy in some other cancer models. However, adverse effects associated with continuous KD were also observed, demanding better mechanistic understanding and optimized regimens for using KD as an immunotherapy sensitizer. In this study, we established a series of ICB-resistant prostate cancer cell lines and developed a highly effective strategy of combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic KD (CKD), or dietary supplementation of the ketone body ß-hydroxybutyrate (BHB), which is an endogenous HDACi. CKD and BHB supplementation each delayed prostate cancer tumor growth as monotherapy, and both BHB and adaptive immunity were required for the antitumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that HDACi and ketogenesis enhanced ICB efficacy through both cancer cell-intrinsic mechanisms, including upregulation of MHC class I molecules, and -extrinsic mechanisms, such as CD8+ T-cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen-presenting cells, and diminished neutrophil infiltration. Overall, these findings illuminate a potential clinical path of using HDACi and optimized KD regimens to enhance ICB therapy for prostate cancer. SIGNIFICANCE: Optimized cyclic ketogenic diet and 1,3-butanediol supplementation regimens enhance the efficacy of immune checkpoint blockade in prostate cancer through epigenetic and immune modulations, providing dietary interventions to sensitize tumors to immunotherapy.

Neutrophils resist ferroptosis and promote breast cancer metastasis through aconitate decarboxylase 1.

Metastasis causes breast cancer-related mortality. Tumor-infiltrating neutrophils (TINs) inflict immunosuppression and promote metastasis. Therapeutic debilitation of TINs may enhance immunotherapy, yet it remains a challenge to identify therapeutic targets highly expressed and functionally essential in TINs but under-expressed in extra-tumoral neutrophils. Here, using single-cell RNA sequencing to compare TINs and circulating neutrophils in murine mammary tumor models, we identified aconitate decarboxylase 1 (Acod1) as the most upregulated metabolic enzyme in mouse TINs and validated high Acod1 expression in human TINs. Activated through the GM-CSF-JAK/STAT5-C/EBPß pathway, Acod1 produces itaconate, which mediates Nrf2-dependent defense against ferroptosis and upholds the persistence of TINs. Acod1 ablation abates TIN infiltration, constrains metastasis (but not primary tumors), bolsters antitumor T cell immunity, and boosts the efficacy of immune checkpoint blockade. Our findings reveal how TINs escape from ferroptosis through the Acod1-dependent immunometabolism switch and establish Acod1 as a target to offset immunosuppression and improve immunotherapy against metastasis.

Overcome Prostate Cancer Resistance to Immune Checkpoint Therapy with Ketogenic Diet-Induced Epigenetic Reprogramming.

Advanced prostate cancer (PCa) is overwhelmingly resistant to immune checkpoint blockade (ICB) therapy, representing a formidable clinical challenge. In this study, we developed a syngeneic murine PCa model with acquired ICB resistance. Using this model, synergistic efficacy was achieved by combining anti-PD1 and anti-CTLA4 antibodies with histone deacetylase inhibitor (HDACi) vorinostat, a cyclic ketogenic diet (CKD), or supplementation of ketone body ß-hydroxybutyrate (BHB, endogenous HDACi) via 1,3-butanediol-admixed food. CKD and BHB supplementation delayed PCa tumors as monotherapy, and both BHB and adaptive immunity are required for the anti-tumor activity of CKD. Single-cell transcriptomic and proteomic profiling revealed that the HDACi and ketogenesis-enhanced ICB therapy involves cancer-cell-intrinsic (upregulated MHC class I molecules) and extrinsic mechanisms (CD8 + T cell chemoattraction, M1/M2 macrophage rebalancing, monocyte differentiation toward antigen presenting cells, and diminished neutrophils). Overall, these findings underscore the potential of using HDACi and optimized KD to enhance ICB therapy for PCa.

SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data.

MOTIVATION: Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as 'batch effects', need to be removed before integration to avoid misleading interpretation of the data. Although many batch-effect-removal methods have been developed, there is still a large room for improvement: most existing methods only give dimension-reduced data instead of expression data of individual genes, are based on computationally demanding models and are black-box models and thus difficult to interpret or tune. RESULTS: Here, we present a new batch-effect-removal method called SCIBER (Single-Cell Integrator and Batch Effect Remover) and study its performance on real datasets. SCIBER matches cell clusters across batches according to the overlap of their differentially expressed genes. As a simple algorithm that has better scalability to data with a large number of cells and is easy to tune, SCIBER shows comparable and sometimes better accuracy in removing batch effects on real datasets compared to the state-of-the-art methods, which are much more complicated. Moreover, SCIBER outputs expression data in the original space, that is, the expression of individual genes, which can be used directly for downstream analyses. Additionally, SCIBER is a reference-based method, which assigns one of the batches as the reference batch and keeps it untouched during the process, making it especially suitable for integrating user-generated datasets with standard reference data such as the Human Cell Atlas. AVAILABILITY AND IMPLEMENTATION: SCIBER is publicly available as an R package on CRAN: https://cran.r-project.org/web/packages/SCIBER/. A vignette is included in the CRAN R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The GR2D2 estimator for the precision matrices.

Biological networks are important for the analysis of human diseases, which summarize the regulatory interactions and other relationships between different molecules. Understanding and constructing networks for molecules, such as DNA, RNA and proteins, can help elucidate the mechanisms of complex biological systems. The Gaussian Graphical Models (GGMs) are popular tools for the estimation of biological networks. Nonetheless, reconstructing GGMs from high-dimensional datasets is still challenging. The current methods cannot handle the sparsity and high-dimensionality issues arising from datasets very well. Here, we developed a new GGM, called the GR2D2 (Graphical $R^2$-induced Dirichlet Decomposition) model, based on the R2D2 priors for linear models. Besides, we provided a data-augmented block Gibbs sampler algorithm. The R code is available at https://github.com/RavenGan/GR2D2. The GR2D2 estimator shows superior performance in estimating the precision matrices compared with the existing techniques in various simulation settings. When the true precision matrix is sparse and of high dimension, the GR2D2 provides the estimates with smallest information divergence from the underlying truth. We also compare the GR2D2 estimator with the graphical horseshoe estimator in five cancer RNA-seq gene expression datasets grouped by three cancer types. Our results show that GR2D2 successfully identifies common cancer pathways and cancer-specific pathways for each dataset.

How to organise travel restrictions in the new future: lessons from the COVID-19 response in Hong Kong and Singapore.

It has been nearly 2 years since the first case of COVID-19 was reported. Governments worldwide have introduced numerous non-pharmaceutical interventions (NPIs) to combat this disease. Many of these NPIs were designed in response to initial outbreaks but are unsustainable in the long term. Governments are exploring how to adjust their current NPIs to resume normal activities while effectively protecting their population. As one of the most controversial NPIs, the implementation of travel restrictions varies across regions. Some governments have abandoned their previous travel restrictions because of the induced costs to society and on the economy. Other areas, including Hong Kong (Special Administrative Region of China) and Singapore, continue employing these NPIs as a long-term disease prevention tactic. However, the multidimensional impacts of travel restrictions require careful consideration of how to apply restrictions more appropriately. We have proposed an adapted framework to examine Hong Kong and Singapore's travel restrictions. We aimed to study these two regions' experiences in balancing disease control efforts with easing the burden on lives and livelihoods. Based on the experiences of Hong Kong and Singapore, we have outlined six policy recommendations to serve as the cornerstone for future research and policy practices.