Search

A compacted body was fabricated by pulverulent polyimide (PI) block copolymers using solid-like state compression molding (SCM) technique. Polymer heated to solid-like state, i.e. the high-elastic non-melting state above the glass transition temperature ( Tg) and well below melting temperature, could achieve plasticity due to dramatic decreases in elastic modulus. Tensile properties were taken as response values, and the results of single-factor experiments indicated that molding temperature was the dominant parameter on mechanical performances, followed by molding pressure and holding time. Within this context, the SCM process possesses a longer processing time window whereas the processing temperature is narrow. The manufacturing defects induced by inappropriate processing conditions also hurt the tribological performance of PIs. Particles in a solid-like state could coalesce tightly only by exerting both high temperature and pressure in the SCM process. Thermoforming mechanism examined by atomic-scale molecular dynamics simulation indicated that non-bonding interaction forces, especially van der Waals forces play a key role in fusing among polymeric particles. This study is devoted to establishing the interdependence of structure-formability-property for high-temperature polymers that are not melt processible.

Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells and in multiple samples, facilitate the study of gene expression and gene regulatory activities on a population scale. Existing integration methods can integrate either multiple samples or multiple modalities, but not both simultaneously. To address this limitation, we developed Smmit, a computational pipeline that leverages existing integration methods to simultaneously integrate both samples and modalities and produces a unified representation of reduced dimensions. We demonstrate Smmit’s capability to integrate information across samples and modalities while preserving cell type differences in two real datasets. Smmit is an R software package that is freely available at Github:https://github.com/zji90/Smmit

Table S1. Differential expression analysis for CD8 T cells.

Table S5. List of Genes Used to Generate NK Cell Activation Scores.

Table S2. Differential expression analysis for Treg CD4 T cells.

Table S3. Differential expression analysis for non-Treg CD4 T cells.

Immune therapies have had limited efficacy in high-grade serous ovarian cancer (HGSC), as the cellular targets and mechanism(s) of action of these agents in HGSC are unknown. Here we performed immune functional and single-cell RNA sequencing transcriptional profiling on novel HGSC organoid/immune cell co-cultures treated with a unique bispecific anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibody compared with monospecific anti-PD-1 or anti-PD-L1 controls. Comparing the functions of these agents across all immune cell types in real time identified key immune checkpoint blockade (ICB) targets that have eluded currently available monospecific therapies. The bispecific antibody induced superior cellular state changes in both T and natural killer (NK) cells. It uniquely induced NK cells to transition from inert to more active and cytotoxic phenotypes, implicating NK cells as a key missing component of the current ICB-induced immune response in HGSC. It also induced a subset of CD8 T cells to transition from naïve to more active and cytotoxic progenitor-exhausted phenotypes post-treatment, revealing the small, previously uncharacterized population of CD8 T cells responding to ICB in HGSC. These state changes were driven partially through bispecific antibody-induced downregulation of the bromodomain-containing protein BRD1. Small-molecule inhibition of BRD1 induced similar state changes in vitro and demonstrated efficacy in vivo, validating the co-culture results. Our results demonstrate that state changes in both NK and a subset of T cells may be critical in inducing an effective anti-tumor immune response and suggest that immune therapies able to induce such cellular state changes, such as BRD1 inhibitors, may have increased efficacy in HGSC.Significance:This study indicates that increased efficacy of immune therapies in ovarian cancer is driven by state changes of NK and small subsets of CD8 T cells into active and cytotoxic states.

Table S4. Differential expression analysis for NK cells.

Supplementary Materials and Methods, Figures, and Figure and Table legends

Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). We developed a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Immune therapies have had limited efficacy in high-grade serous ovarian cancer (HGSC), as the cellular targets and mechanism(s) of action of these agents in HGSC are unknown. Here we performed immune functional and single-cell RNA sequencing transcriptional profiling on novel HGSC organoid/immune cell co-cultures treated with a unique bispecific anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibody compared with monospecific anti-PD-1 or anti-PD-L1 controls. Comparing the functions of these agents across all immune cell types in real time identified key immune checkpoint blockade (ICB) targets that have eluded currently available monospecific therapies. The bispecific antibody induced superior cellular state changes in both T and natural killer (NK) cells. It uniquely induced NK cells to transition from inert to more active and cytotoxic phenotypes, implicating NK cells as a key missing component of the current ICB-induced immune response in HGSC. It also induced a subset of CD8 T cells to transition from naïve to more active and cytotoxic progenitor-exhausted phenotypes post-treatment, revealing the small, previously uncharacterized population of CD8 T cells responding to ICB in HGSC. These state changes were driven partially through bispecific antibody-induced downregulation of the bromodomain-containing protein BRD1. Small-molecule inhibition of BRD1 induced similar state changes in vitro and demonstrated efficacy in vivo, validating the co-culture results. Our results demonstrate that state changes in both NK and a subset of T cells may be critical in inducing an effective anti-tumor immune response and suggest that immune therapies able to induce such cellular state changes, such as BRD1 inhibitors, may have increased efficacy in HGSC. Significance: This study indicates that increased efficacy of immune therapies in ovarian cancer is driven by state changes of NK and small subsets of CD8 T cells into active and cytotoxic states.

The Cistrome Data Browser (DB) at the website (cistrome.org/db) provides about 56,000 published human and mouse ChIP-seq, DNase-seq, and ATAC-seq chromatin profiles, which we have processed using uniform analysis and quality control pipelines. The Cistrome DB Toolkit at the website (dbtoolkit.cistrome.org) was developed to allow users to investigate fundamental questions using this data collection. In this tutorial, we describe how to use the Cistrome DB to search for publicly available chromatin profiles, to assess sample quality, to access peak results, to visualize signal intensities, to explore DNA sequence motifs, and to identify putative target genes. We also describe the use of the Toolkit module to seek the factors most likely to regulate a gene of interest, the factors that bind to a given genomic interval (enhancer, SNP, etc.), and samples that have significant peak overlaps with user-defined peak sets. This tutorial guides biomedical researchers in the use of Cistrome DB resources to rapidly obtain valuable insights into gene regulatory questions

The hydraulic support column bears loading and makes reciprocating motion ceaselessly for extended periods, so its service life is far shorter than that of the overall hydraulic support. This paper offers a comparative study on the surface coating of hydraulic support columns with hard chrome plating and high-velocity oxygen fuel (HVOF) thermal spraying refabricating to analyze the impact of different refabricating processes on the microstructure, hardness, corrosion resistance, and wear resistance of the coating (plating). The result shows that the structure of the HVOF coating is uniformly compact, and the HVOF WC10Co4Cr coating has better wear resistance, more than four times that of hard chrome plating. In the neutral salt spray test, the HVOF Ni60 coating shows rustiness at 720 h of the test, which suggests its corrosion resistance is nearly five times that of hard chrome plating. Hence, under the harsh corrosive wear environment, the refabricating HVOF Ni60 is a more suitable replacement for the hydraulic support column coating than the hard chrome plating. Thus, the HVOF Ni60 coating could be an effective replacement for hard chrome plating.

Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). However, tumor scRNA analysis relies on clustering cells and annotating with markers from experts. Subsequent challenges include inconsistent cell type and state definition, different marker usage among studies, and that clustering doesn’t find continuous cell states. We developed a data-driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labeling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. For example, the T cell co-signaling meta-component reflects co-expressed gene modules of multiple immune checkpoint blockade targets. Also, the tumor-infiltrating macrophage meta-components found macrophage states in different metabolism preferences, rather than the pro-inflammatory versus anti-inflammatory classifications. We also observed certain macrophage states associated with tumor prognosis and cancer immunotherapy response. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Lastly, leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy. Citation Format: Yi Zhang, Guanjue Xiang, Yijia Alva Jiang, Allen Lynch, Zexian Zeng, Chenfei Wang, Wubing Zhang, Jingyu Fan, Jiajinlong Kang, Shengqing Gu, Changxin Wan, Boning Zhang, Shirley Liu, Myles Brown, Clifford Meyer. MetaTiME: meta-components of the tumor immune microenvironment [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr B65.

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow (http://github.com/liulab-dfci/MAESTRO) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

Characterization of the genomic distances over which transcription factor (TF) binding influences gene expression is important for inferring target genes from TF chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here we systematically examine the relationship between thousands of TF and histone modification ChIP-seq data sets with thousands of gene expression profiles. We develop a model for integrating these data, which reveals two classes of TFs with distinct ranges of regulatory influence, chromatin-binding preferences, and auto-regulatory properties. We find that the regulatory range of the same TF bound within different topologically associating domains (TADs) depend on intrinsic TAD properties such as local gene density and G/C content, but also on the TAD chromatin states. Our results suggest that considering TF type, binding distance to gene locus, as well as chromatin context is important in identifying implicated TFs from GWAS SNPs., Characterization of the distance over which TF binding influences gene expression is important for inferring target genes. Here the authors study this relationship using thousands of genomic data sets, finding two classes of TFs with distinct ranges of regulatory influence modulated by chromatin states of topologically associated domains.

Despite growing numbers of immune checkpoint blockade (ICB) trials with available omics data, it remains challenging to evaluate the robustness of ICB response and immune evasion mechanisms comprehensively. To address these challenges, we integrated large-scale omics data and biomarkers on published ICB trials, non-immunotherapy tumor profiles, and CRISPR screens on a web platform TIDE (http://tide.dfci.harvard.edu). We processed the omics data for over 33K samples in 188 tumor cohorts from public databases, 998 tumors from 12 ICB clinical studies, and eight CRISPR screens that identified gene modulators of the anticancer immune response. Integrating these data on the TIDE web platform with three interactive analysis modules, we demonstrate the utility of public data reuse in hypothesis generation, biomarker optimization, and patient stratification.

Table S1. New function modules and datasets in the TIDE web server.

Additional file 3. Supplementary figures.

Additional file 4. Supplementary materials.

Additional file 11. Review history.

Additional file 1. Figure S1-S6.

The Cistrome Data Browser (DB) is a resource of human and mouse cis-regulatory information derived from ChIP-seq, DNase-seq and ATAC-seq chromatin profiling assays, which map the genome-wide locations of transcription factor binding sites, histone post-translational modifications and regions of chromatin accessible to endonuclease activity. Currently, the Cistrome DB contains approximately 47,000 human and mouse samples with about 24,000 newly collected datasets compared to the previous release two years ago. Furthermore, the Cistrome DB has a new Toolkit module with several features that allow users to better utilize the large-scale ChIP-seq, DNase-seq, and ATAC-seq data. First, users can query the factors which are likely to regulate a specific gene of interest. Second, the Cistrome DB Toolkit facilitates searches for factor binding, histone modifications, and chromatin accessibility in any given genomic interval shorter than 2Mb. Third, the Toolkit can determine the most similar ChIP-seq, DNase-seq, and ATAC-seq samples in terms of genomic interval overlaps with user-provided genomic interval sets. The Cistrome DB is a user-friendly, up-to-date, and well maintained resource, and the new tools will greatly benefit the biomedical research community. The database is freely available at http://cistrome.org/db, and the Toolkit is at http://dbtoolkit.cistrome.org.

We develop Lisa (epigenetic Landscape In silico Sequence Analysis) to predict transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosts the performance of imputed TR cistromes, and outperforms alternative methods in identifying the perturbed TRs., This work was supported by grants from the NIH (U24 HG009446 to XLS, U24 CA237617 to XSL and CAM), National Natural Science Foundation of China (31801110 to S.M.) and the Shanghai Sailing Program (18YF1402500 to QQ).

We developed Lisa (http://lisa.cistrome.org) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses compendia of public histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Then using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes, and outperformed alternative methods in identifying the perturbed TRs.