Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs.

Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.

A novel cellulolytic/xylanolytic SbAA14 from Sordaria brevicollis with a branched chain preference and its synergistic effects with glycoside hydrolases on lignocellulose.

In this study, the novel auxiliary activity (AA) family 14 lytic polysaccharide monooxygenase (LPMO) SbAA14 from Sordaria brevicollis was successfully characterized. It was active against heteroxylan, xyloglucan and cellulose in ß-cellulose and released native oligosaccharides and corresponding C1- and/or C4-oxidized products. SbAA14 showed a branched chain preference, because partial removal of arabinosyl substituents from heteroxylan led to a decrease in activity. SbAA14 had synergistic effects with the debranching enzyme EpABF62C in an enzyme- and ascorbic acid-dependent manner. SbAA14 had synergistic effects with the GH10 endoxylanase EpXYN1, and the degree of synergy was greater with step-by-step addition than with simultaneous addition. SbAA14 could also synergize with Celluclast® 1.5 L on NaOH-pretreated wheat straw and on NaOH-pretreated and hydrogen peroxide-acetic acid (HPAC)-H2SO4-pretreated bamboo substrates. The greatest synergistic effect between SbAA14 and Celluclast® 1.5 L was observed for HPAC-H2SO4-200 mM pretreated bamboo, in which the degree of synergy reached approximately 1.61. The distinctive substrate preference of SbAA14 indicated that it is a novel AA14 LPMO that may act mainly on heteroxylan with numerous arabinosyl substituents between cellulose fibers rather than on recalcitrant xylan tightly associated with cellulose. These findings broaden the understanding of enigmatic AA14 LPMOs and provide new insights into the substrate specificities and biological functionalities of AA14 LPMOs in fungi.

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors.

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing.

Head and neck squamous cell carcinoma (HNSCC) is characterized by complex relations between stromal, epithelial, and immune cells within the tumor microenvironment (TME). To enable the development of more efficacious therapies, we aim to study the heterogeneity, signatures of unique cell populations, and cell-cell interactions of non-immune and immune cell populations in 6 human papillomavirus (HPV)+ and 12 HPV- HNSCC patient tumor and matched peripheral blood specimens using single-cell RNA sequencing. Using this dataset of 134,606 cells, we show cell type-specific signatures associated with inflammation and HPV status, describe the negative prognostic value of fibroblasts with elastic differentiation specifically in the HPV+ TME, predict therapeutically targetable checkpoint receptor-ligand interactions, and show that tumor-associated macrophages are dominant contributors of PD-L1 and other immune checkpoint ligands in the TME. We present a comprehensive single-cell view of cell-intrinsic mechanisms and cell-cell communication shaping the HNSCC microenvironment.

Switchable broadband terahertz spatial modulators based on patterned graphene and vanadium dioxide.

We numerically demonstrate a switchable broadband terahertz spatial modulator composed of ginkgo-leaf-patterned graphene and transition material vanadium dioxide (VO2). The phase transition property of VO2 is used to switch the spatial modulator from absorption mode to transmission mode, and the graphene behaves as dynamically adjustable material for a large scale of absorption and transmittance modulation. When VO2 is in the metallic state and the Fermi energy of graphene is set as 0.8 eV, the proposed modulator behaves as a broadband absorber with the absorbance over 85% from 1.33 to 2.83 THz. By adjusting the graphene Fermi level from 0 to 0.8 eV, the peak absorbance can be continuously tuned from 24.3% to near 100% under the absorption mode, and the transmittance at 2.5 THz can be continuously tuned from 87% to 35.5% under the transmission mode. To further increase the bandwidth, a three-layer-patterned-graphene is introduced into a new modulator design, which achieves a wide bandwidth of 3.13 THz for the absorbance over 85%. By the combination of the tunability of graphene and VO2, the proposed modulators not only can flexibly switch between dual-functional modulation modes of absorption and transmission but also possess deep modulation depth. Benefitting from the excellent modulation performance, the proposed switchable dual-functional spatial modulators may offer significant potential applications in various terahertz smart optoelectronic devices.

Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition.

BACKGROUND: Human tumor is a complex tissue with multiple heterogeneous hypoxic regions and significant cell-to-cell variability. Due to the complexity of the disease, the explanation of why anticancer therapies fail cannot be attributed to intrinsic or acquired drug resistance alone. Furthermore, there are inconsistent reports of hypoxia-induced kinase activities in different cancer cell-lines, where increase, decreases, or no change has been observed. Thus, we asked, why are there widely contrasting results in kinase activity under hypoxia in different cancer cell-lines and how does hypoxia play a role in anti-cancer drug sensitivity? RESULTS: We took a modeling approach to address these questions by analyzing the model simulation to explain why hypoxia driven signals can have dissimilar impact on tumor growth and alter the efficacy of anti-cancer drugs. Repeated simulations with varying concentrations of biomolecules followed by decision tree analysis reveal that the highly differential effects among heterogeneous subpopulation of tumor cells could be governed by varying concentrations of just a few key biomolecules. These biomolecules include activated serine/threonine-specific protein kinases (pRAF), mitogen-activated protein kinase kinase (pMEK), protein kinase B (pAkt), or phosphoinositide-4,5-bisphosphate 3-kinase (pPI3K). Additionally, the ratio of activated extracellular signal-regulated kinases (pERK) or pAkt to its respective total was a key factor in determining the sensitivity of pERK or pAkt to hypoxia. CONCLUSION: This work offers a mechanistic insight into how hypoxia can affect the efficacy of anti-cancer drug that targets tumor signaling and provides a framework to identify the types of tumor cells that are either sensitive or resistant to anti-cancer therapy.

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

Mathematical model of dynamic behavior of microbial desalination cells for simultaneous wastewater treatment and water desalination.

Microbial desalination cells (MDCs) are an emerging concept for simultaneous wastewater treatment and water desalination. This work presents a mathematical model to simulate dynamic behavior of MDCs for the first time through evaluating multiple factors such as organic supply, salt loading, and current generation. Ordinary differential equations were applied to describe the substrate as well as bacterial concentrations in the anode compartment. Local sensitivity analysis was employed to select model parameters that needed to be re-estimated from the previous studies. This model was validated by experimental data from both a bench- and a large-scale MDC system. It could fit current generation fairly well and simulate the change of salt concentration. It was able to predict the response of the MDC with time under various conditions, and also provide information for analyzing the effects of different operating conditions. Furthermore, optimal operating conditions for the MDC used in this study were estimated to have an acetate flow rate of 0.8 mL·min(-1), influent salt concentration of 15 g·L(-1) and salt solution flow rate of 0.04 mL·min(-1), and to be operated with an external resistor less than 30 Ω. The MDC model will be helpful with determining operational parameters to achieve optimal desalination in MDCs.