Spatial transcriptomics reveals segregation of tumor cell states in glioblastoma and marked immunosuppression within the perinecrotic niche

Glioblastoma (GBM) remains an untreatable malignant tumor with poor patient outcomes, characterized by palisading necrosis and microvascular proliferation. While single-cell technology made it possible to characterize different lineage of glioma cells into neural progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal like (MES-like) states, it does not capture the spatial localization of these tumor cell states. Spatial transcriptomics empowers the study of the spatial organization of different cell types and tumor cell states and allows for the selection of regions of interest to investigate region-specific and cell-type-specific pathways. Here, we obtained paired 10x Chromium single-nuclei RNA-sequencing (snRNA-seq) and 10x Visium spatial transcriptomics data from three GBM patients to interrogate the GBM microenvironment. Integration of the snRNA-seq and spatial transcriptomics data reveals patterns of segregation of tumor cell states. For instance, OPC-like tumor and NPC-like tumor significantly segregate in two of the three samples. Our differentially expressed gene and pathway analyses uncovered significant pathways in functionally relevant niches. Specifically, perinecrotic regions were more immunosuppressive than the endogenous GBM microenvironment, and perivascular regions were more pro-inflammatory. Our gradient analysis suggests that OPC-like tumor cells tend to reside in areas closer to the tumor vasculature compared to tumor necrosis, which may reflect increased oxygen requirements for OPC-like cells. In summary, we characterized the localization of cell types and tumor cell states, the gene expression patterns, and pathways in different niches within the GBM microenvironment. Our results provide further evidence of the segregation of tumor cell states and highlight the immunosuppressive nature of the necrotic and perinecrotic niches in GBM.

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

The top 100 most-cited papers in incisional hernia: a bibliometric analysis from 2003 to 2023

PURPOSE

Incisional hernia (IH) is one of the most common complications after abdominal surgeries and may bring great suffering to patients. This study aims to evaluate the global trends in IH research from 2003 to 2023 and visualize the frontiers using bibliometric analysis.

METHODS

The literature search was conducted on the Web of Science for IH studies published from 2003 to 2023 and sorted by citation frequency. The top 100 most-cited articles were analyzed by the annual publication number, prolific countries and institutions, influential author and journal, and the number of citations through descriptive statistics and visualization.

RESULTS

The top paper was cited 1075 times and the median number of citations was 146. All studies were published between 2003 and 2019 and the most prolific year was 2003 with 14 articles. Jeekel J and Rosen M were regarded as the most productive authors with ten articles each and acquired 2738 and 2391 citations, respectively. The top three institutions with the most productive articles were Erasmus Mc, Carolinas Med Ctr, and Univ Utah, while the top three countries were the United States, Netherlands and Germany. The most frequent keyword was "incisional hernia" with 55 occurrences, followed by "mesh repair", "randomized controlled trial", and "polypropylene".

CONCLUSION

The 100 most-cited papers related to IH were published predominantly by USA and European countries, with randomized controlled trial (RCT) and observational study designs, addressing topics related to risk factors, complications, mesh repair, and mesh components.

The causal association between body fat distribution and risk of abdominal wall hernia: a two-sample Mendelian randomization study

PURPOSE

Obesity and a high body mass index (BMI) are considered as risk factors for abdominal wall hernia (AWH). However, anthropometric measures of body fat distribution (BFD) seem to be better indicators in the hernia field. This Mendelian randomization analysis aimed to generate more robust evidence for the impact of waist circumstance (WC), body, trunk, arm, and leg fat percentages (BFP, TFP, AFP, LFP) on AWH.

METHODS

A univariable MR design was employed and the summary statistics allowing for assessment were obtained from the genome-wide association studies (GWASs). An inverse variance weighted (IVW) method was applied as the primary analysis, and the odds ratio value was used to evaluate the causal relationship between BFD and AWH.

RESULTS

None of the MR-Egger regression intercepts deviated from null, indicating no evidence of horizontal pleiotropy (p > 0.05). The Cochran Q test showed heterogeneity between the genetic IVs for WC (p = 0.005; p = 0.005), TFP (p < 0.001; p < 0.001), AFP-L (p = 0.016; p = 0.015), LFP-R (p = 0.012; p = 0.009), and LFP-L (p < 0.001; p < 0.001). Taking the IVW random-effects model as gold standard, each standard deviation increment in genetically determined WC, BFP, TFP, AFP-R, AFP-L, LFP-R, and LFP-L raised the risk of AWH by 70.9%, 70.7%, 56.5%, 69.7%, 78.3%, 87.7%, and 72.5%, respectively.

CONCLUSIONS

This study proves the causal relationship between AWH and BFD, attracting more attention from BMI to BFD. It provides evidence-based medical evidence that healthy figure management can prevent AWH.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.

PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns

Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.

Identification of Regulatory Elements in Primary Sensory Neurons Involved in Trauma-Induced Neuropathic Pain

Chronic pain is a significant public health issue that is often refractory to existing therapies. Here we use a multiomic approach to identify cis-regulatory elements that show differential chromatin accessibility and reveal transcription factor (TF) binding motifs with functional regulation in the rat dorsal root ganglion (DRG), which contain cell bodies of primary sensory neurons, after nerve injury. We integrated RNA-seq to understand how differential chromatin accessibility after nerve injury may influence gene expression. Using TF protein arrays and chromatin immunoprecipitation-qPCR, we confirmed C/EBPγ binding to a differentially accessible sequence and used RNA-seq to identify processes in which C/EBPγ plays an important role. Our findings offer insights into TF motifs that are associated with chronic pain. These data show how interactions between chromatin landscapes and TF expression patterns may work together to determine gene expression programs in rat DRG neurons after nerve injury.

Risk factors for incisional hernia after gastrointestinal surgeries in non-tumor patients

PURPOSE

Incisional hernia (IH) is a common secondary ventral hernia after abdominal incisions and there is still little reliable evidence to predict and prevent IH. This study aimed to estimate risk factors of its incidence, especially concentrating on blood results.

METHODS

96 patients received midline laparotomy for gastrointestinal benign diseases and suffered from IH were enrolled in the IH group. A control group of 192 patients were randomly selected from patients underwent midline laparotomy for gastrointestinal benign diseases without IH.

RESULTS

Patients in the IH group exhibited higher age (P < 0.001), BMI (P < 0.001), hernia history (P = 0.001) and laparotomy history (P < 0.001). Rate of coronary heart disease (P = 0.046), hypertension (P < 0.001), diabetes (P = 0.008), incisional infection (P = 0.004) and emergency surgery (P = 0.041) were also higher in the IH group. Patients with IH had lower levels of Hb (P = 0.002), TP (P = 0.013), ALB (P < 0.001), A/G (P = 0.019), PA (P < 0.001), HDL-C (P = 0.008) and ApoA1 (P = 0.005). Meanwhile, patients in the control group bore lower levels of LDH (P = 0.008), GLU (P = 0.007), BUN (P = 0.048), UA (P = 0.021), TG (P = 0.011), TG/HDL-C (P = 0.002), TC/HDL-C (P = 0.013), ApoB/ApoA1 (P = 0.001) and Lp(a) (P = 0.001). A multivariate logistic regression revealed that high BMI, laparotomy history, incisional infection, decreased PA, elevated levels of UA, Lp(a) and ApoB/ApoA1 were independent risk factors of IH.

CONCLUSION

This is the first study to reveal the relationship between IH and serum biochemical levels, and give a prediction through the nomograph model. These results will help surgeons identify high-risk patients, and take measures to prevent IH during the perioperative period.

A systematic evaluation of computation methods for cell segmentation

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.

GPT-4V exhibits human-like performance in biomedical image classification

We demonstrate that GPT-4V(ision), a large multimodal model, exhibits strong one-shot learning ability, generalizability, and natural language interpretability in various biomedical image classification tasks, including classifying cell types, tissues, cell states, and disease status. Such features resemble human-like performance and distinguish GPT-4V from conventional image classification methods, which typically require large cohorts of training data and lack interpretability.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We assessed the performance of GPT-4, a highly potent large language model, for cell type annotation, and demonstrated that it can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations and has the potential to considerably reduce the effort and expertise needed in cell type annotation. We also developed GPTCelltype, an open-source R software package to facilitate cell type annotation by GPT-4.

T-cell receptor sequences correlate with and predict gene expression levels in T cells

T cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities. We analyzed fifteen single-cell immune profiling datasets to systematically investigate the association between T-cell receptor (TCR) sequences and the gene expression profiles of T cells. Our findings reveal that T cells sharing identical or similar TCR sequences tend to have highly similar gene expression profiles. Additionally, we developed a foundational model that leverages TCR information to predict gene expression levels in T cells.

[The efficacy of chemotherapy re-challenge in third-line setting for metastatic colorectal cancer patients: a real-world study]

Objective: To explore the efficacy of chemotherapy re-challenge in the third-line setting for patients with metastatic colorectal cancer (mCRC) in the real world. Methods: The clinicopathological data, treatment information, recent treatment efficacy, adverse events and survival data of mCRC patients who had disease progression after treatment with oxaliplatin-based and/or irinotecan-based chemotherapy and received third-line chemotherapy re-challenge from January 2013 to December 2020 at Tianjin Medical University Cancer Institute and Hospital were retrospectively collected. Survival curves were plotted with the Kaplan-Meier method, and the Cox proportional hazard model was used to analyze the prognostic factors. Results: A total of 95 mCRC patients were included. Among them, 32 patients (33.7%) received chemotherapy alone and 63 patients (66.3%) received chemotherapy combined with targeted drugs. Eighty-three patients were treated with dual-drug chemotherapy (87.4%), including oxaliplatin re-challenge in 35 patients and irinotecan re-challenge in 48 patients. The remaining 12 patients were treated with triplet chemotherapy regimens (12.6%). Among them, as 5 patients had sequential application of oxaliplatin and irinotecan in front-line treatments, their third-line therapy re-challenged both oxaliplatin and irinotecan; 7 patients only had oxaliplatin prescription before, and these patients re-challenged oxaliplatin in the third-line treatment. The overall response rate (ORR) and disease control rate (DCR) reached 8.6% (8/93) and 61.3% (57/93), respectively. The median progression free survival (mPFS) and median overall survival (mOS) were 4.9 months and 13.0 months, respectively. The most common adverse events were leukopenia (34.7%) and neutropenia (34.7%), followed by gastrointestinal adverse reactions such as nausea (32.6%) and vomiting (31.6%). Grade 3-4 adverse events were mostly hematological toxicity. Cox multivariate analysis showed that gender (HR=1.609, 95% CI: 1.016-2.548) and the PFS of front-line treatments (HR=0.598, 95% CI: 0.378-0.947) were independent prognostic factors. Conclusion: The results suggested that it is safe and effective for mCRC patients to choose third-line chemotherapy re-challenge, especially for patients with a PFS of more than one year in front-line treatments.

A tissue ubiquitous gene set for cellular senescence

An accurate gene set for cellular senescence is crucial for identifying and studying senescent cells in single-cell RNA-seq datasets. We integrated nine existing senescence gene sets and identified a core senescence gene set comprising four genes: CDKN1A, CDKN2A, IL6, and CDKN2B. We found that these genes are ubiquitously associated with cellular senescence across human and mouse tissues. Using this gene set, we identified cell types enriched with senescent cells and cell-cell communication targets and pathways associated with cellular senescence in human and mouse single-cell datasets.

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions while adjusting for batch effects, and to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.

Kernel-based PMP structure for nonlinear industrial quality-related process monitoring

Quality-related process monitoring as a supervised technology has increasingly attracted attention in complex industries. Various approaches have been studied to cope with this issue. Nevertheless, these methods cannot reasonably decompose the process variable space, resulting in deficiencies in monitoring quality-related faults. To handle this issue, this paper presents an orthogonal kernel partial least squares improved kernel least squares with a preprocessing-modeling-postprocessing (PMP) structure to implement quality-related process monitoring with more proper decomposition and more straightforward monitoring logic. Compared with the previous approaches, a nonlinear preprocessing technology is presented to eliminate the quality-unrelated knowledge of process variables, enormously enhancing the interpretability of modeling and improving the monitoring efficiency. Then, a proper decomposition is presented to decompose the kernel matrix into two orthogonal parts, significantly improving the monitoring performance. The theoretical analysis of the proposed method is provided in this paper. Finally, two cases indicate the validity and superiority of the proposed method.

Lung tumor-infiltrating Treg have divergent transcriptional profiles and function linked to checkpoint blockade response

Regulatory T cells (Treg) are conventionally viewed as suppressors of endogenous and therapy-induced antitumor immunity; however, their role in modulating responses to immune checkpoint blockade (ICB) is unclear. In this study, we integrated single-cell RNA-seq/T cell receptor sequencing (TCRseq) of >73,000 tumor-infiltrating Treg (TIL-Treg) from anti-PD-1-treated and treatment-naive non-small cell lung cancers (NSCLC) with single-cell analysis of tumor-associated antigen (TAA)-specific Treg derived from a murine tumor model. We identified 10 subsets of human TIL-Treg, most of which have high concordance with murine TIL-Treg subsets. Only one subset selectively expresses high levels of TNFRSF4 (OX40) and TNFRSF18 (GITR), whose engangement by cognate ligand mediated proliferative programs and NF-κB activation, as well as multiple genes involved in Treg suppression, including LAG3. Functionally, the OX40hiGITRhi subset is the most highly suppressive ex vivo, and its higher representation among total TIL-Treg correlated with resistance to PD-1 blockade. Unexpectedly, in the murine tumor model, we found that virtually all TIL-Treg-expressing T cell receptors that are specific for TAA fully develop a distinct TH1-like signature over a 2-week period after entry into the tumor, down-regulating FoxP3 and up-regulating expression of TBX21 (Tbet), IFNG, and certain proinflammatory granzymes. Transfer learning of a gene score from the murine TAA-specific TH1-like Treg subset to the human single-cell dataset revealed a highly analogous subcluster that was enriched in anti-PD-1-responding tumors. These findings demonstrate that TIL-Treg partition into multiple distinct transcriptionally defined subsets with potentially opposing effects on ICB-induced antitumor immunity and suggest that TAA-specific TIL-Treg may positively contribute to antitumor responses.

Nuclear PTEN Regulates Thymidylate Biosynthesis in Human Prostate Cancer Cell Lines

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor governs a variety of biological processes, including metabolism, by acting on distinct molecular targets in different subcellular compartments. In the cytosol, inactive PTEN can be recruited to the plasma membrane where it dimerizes and functions as a lipid phosphatase to regulate metabolic processes mediated by the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin complex 1 (mTORC1) pathway. However, the metabolic regulation of PTEN in the nucleus remains undefined. Here, using a gain-of-function approach to targeting PTEN to the plasma membrane and nucleus, we show that nuclear PTEN contributes to pyrimidine metabolism, in particular de novo thymidylate (dTMP) biosynthesis. PTEN appears to regulate dTMP biosynthesis through interaction with methylenetetrahydrofolate dehydrogenase 1 (MTHFD1), a key enzyme that generates 5,10-methylenetetrahydrofolate, a cofactor required for thymidylate synthase (TYMS) to catalyze deoxyuridylate (dUMP) into dTMP. Our findings reveal a nuclear function for PTEN in controlling dTMP biosynthesis and may also have implications for targeting nuclear-excluded PTEN prostate cancer cells with antifolate drugs.

Parents of preschoolers use multiple strategies to feed their children: Findings from an observational video pilot study

The current study leveraged observational data collection methods to fill gaps in our understanding of parent approach to feeding as well as child responses to various parental approaches. Specifically, the study aimed to: 1) characterize the broad range of food parenting practices used by parents of preschoolers during shared mealtimes at home, including differences by child gender, and 2) describe child responses to specific parent feeding practices. Forty parent-child dyads participated by recording two in-home shared meals. Meals were coded using a behavioral coding scheme that coded the occurrence of 11 distinct food parenting practices (e.g. indirect and direct commands, praise, bribes) and eight child responses (e.g., eat, refuse, cry/whine) to food parenting practices. Results revealed that parents engaged in a broad range of food parenting practices at meals. On average, parents in our sample used 10.51 (SD 7.83; Range 0-30) total food parenting practices per mealtime with a mean use of 3.38 (SD 1.67; Range 0-8) unique food parenting practices per mealtime. Use of indirect and direct commands to eat were most common; direct and indirect commands were used by 97.5% (n = 39) and 87.5% (n = 35) of parents at meals, respectively. No statistically significant differences were observed by child gender. No one specific feeding practice consistently yielded compliance or refusal to eat from the child, instead child responses were often mixed (e.g., compliance followed by refusal and/or refusal followed by compliance). However, use of praise to prompt eating was the practice that most often resulted in child compliance; 80.8% of children complied following parent's use of praise as a prompt to eat. Findings deepen our understanding of the types and frequency of food parenting practices used by parents of preschoolers during meals eaten at home and illuminate child responses to specific food parenting practices.

Spatial mapping of cellular senescence: emerging challenges and opportunities

Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.

Measurement of Direct-Photon Cross Section and Double-Helicity Asymmetry at sqrt[s]=510 GeV in p[over →]+p[over →] Collisions

We present measurements of the cross section and double-helicity asymmetry A_{LL} of direct-photon production in p[over →]+p[over →] collisions at sqrt[s]=510  GeV. The measurements have been performed at midrapidity (|η|<0.25) with the PHENIX detector at the Relativistic Heavy Ion Collider. At relativistic energies, direct photons are dominantly produced from the initial quark-gluon hard scattering and do not interact via the strong force at leading order. Therefore, at sqrt[s]=510  GeV, where leading-order-effects dominate, these measurements provide clean and direct access to the gluon helicity in the polarized proton in the gluon-momentum-fraction range 0.02<x<0.08, with direct sensitivity to the sign of the gluon contribution.

[Clinical efficacy and safety analysis of 125I seed implantation in the treatment of mediastinal lymph node metastasis of lung cancer]

Objective: To investigate the clinical efficacy and safety of ¹²⁵I seed implantation in the treatment of mediastinal lymph node metastasis of lung cancer. Methods: Clinical data of 36 patients who underwent CT-guided ¹²⁵I seed implantation for mediastinal lymph node metastasis of lung cancer from August 2013 to April 2020 in three hospitals of the Northern radioactive particle implantation treatment collaboration group were retrospectively collected, including 24 males and 12 females, aged 46 to 84 years. Cox regression model was used to analyze the relationship between local control rate, survival rate and tumor stage, pathological type, postoperative D90, postoperative D100 and other variables, and to analyze the occurrence of complications. Results: The objective response rate of CT-guided ¹²⁵I seed implantation in the treatment of mediastinal lymph node metastasis of lung cancer was 75% (27/36), the median control time was 12 months, the 1-year local control rate was 47.2% (17/36), and the median survival time was 17 months. The 1-year and 2-year survival rates were 61.1% (22/36) and 22.2% (8/36) respectively. Univariate analysis showed that in the treatment of mediastinal lymph node metastasis with CT-guided ¹²⁵I implantation, factors related to local control included tumor stage (HR=5.246, 95%CI: 2.243-12.268, P<0.001), postoperative D90 (HR=0.191, 95%CI: 0.085-0.431, P<0.001), postoperative D100 (HR=0.240, 95%CI: 0.108-0.533, P<0.001); The factors affecting survival were tumor stage (HR=2.712, 95%CI: 1.356-5.425, P=0.005), postoperative D90 (HR=0.110, 95%CI: 0.041-0.294, P<0.001), postoperative D100 (HR=0.212, 95%CI: 0.092-0.489, P<0.001). Multivariate analysis showed that tumor stage (HR=5.305, 95%CI: 2.187-12.872, P<0.001) and postoperative D100 (HR=0.237, 95%CI: 0.099-0.568, P<0.001) were correlated with local control rate. Tumor stage (HR=2.347, 95%CI: 1.095-5.032, P=0.028) and postoperative D90 (HR=0.144, 95%CI: 0.051-0.410, P<0.001) were correlated with survival. In terms of complications, 9 of the 36 patients had pneumothorax, and 1 of them was cured by closed thoracic drainage for severe pneumothorax; 5 cases developed pulmonary hemorrhage and 5 cases developed hemoptysis, which recovered after hemostasis treatment. One case developed pulmonary infection and recovered after anti-inflammatory treatment. No radiation esophagitis and radiation pneumonia occurred; No grade 3 or higher complications occurred. Conclusion: ¹²⁵I seed implantation in the treatment of lung cancer mediastinal lymph node metastasis has a high local control rate and controllable adverse effects.

Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis

Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We demonstrate that GPT-4, a highly potent large language model, can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations, and has the potential to considerably reduce the effort and expertise needed in cell type annotation.

DNA-initiated epigenetic cascades driven by C9orf72 hexanucleotide repeat

The C9orf72 hexanucleotide repeat expansion (HRE) is the most frequent genetic cause of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Here, we describe the pathogenic cascades that are initiated by the C9orf72 HRE DNA. The HRE DNA binds to its protein partner DAXX and promotes its liquid-liquid phase separation, which is capable of reorganizing genomic structures. An HRE-dependent nuclear accumulation of DAXX drives chromatin remodeling and epigenetic changes such as histone hypermethylation and hypoacetylation in patient cells. While regulating global gene expression, DAXX plays a key role in the suppression of basal and stress-inducible expression of C9orf72 via chromatin remodeling and epigenetic modifications of the promoter of the major C9orf72 transcript. Downregulation of DAXX or rebalancing the epigenetic modifications mitigates the stress-induced sensitivity of C9orf72-patient-derived motor neurons. These studies reveal a C9orf72 HRE DNA-dependent regulatory mechanism for both local and genomic architectural changes in the relevant diseases.

Integrating multiple single-cell multi-omics samples with Smmit

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.

Distinct Myeloid Derived Suppressor Cell Populations Promote Tumor Aggression in Glioblastoma

The diversity of genetic programs and cellular plasticity of glioma-associated myeloid cells, and thus their contribution to tumor growth and immune evasion, is poorly understood. We performed single cell RNA-sequencing of immune and tumor cells from 33 glioma patients of varying tumor grades. We identified two populations characteristic of myeloid derived suppressor cells (MDSC), unique to glioblastoma (GBM) and absent in grades II and III tumors: i) an early progenitor population (E-MDSC) characterized by strong upregulation of multiple catabolic, anabolic, oxidative stress, and hypoxia pathways typically observed within tumor cells themselves, and ii) a monocytic MDSC (M-MDSC) population. The E-MDSCs geographically co-localize with a subset of highly metabolic glioma stem-like tumor cells with a mesenchymal program in the pseudopalisading region, a pathognomonic feature of GBMs associated with poor prognosis. Ligand-receptor interaction analysis revealed symbiotic cross-talk between the stemlike tumor cells and E-MDSCs in GBM, whereby glioma stem cells produce chemokines attracting E-MDSCs, which in turn produce growth and survival factors for the tumor cells. Our large-scale single-cell analysis elucidated unique MDSC populations as key facilitators of GBM progression and mediators of tumor immunosuppression, suggesting that targeting these specific myeloid compartments, including their metabolic programs, may be a promising therapeutic intervention in this deadly cancer.

One-Sentence Summary

Aggressive glioblastoma harbors two unique myeloid populations capable of promoting stem-like properties of tumor cells and suppressing T cell function in the tumor microenvironment.

JAK-STAT activation contributes to cytotoxic T cell-mediated basal cell death in human chronic lung allograft dysfunction

Chronic lung allograft dysfunction (CLAD) is the leading cause of death in lung transplant recipients. CLAD is characterized clinically by a persistent decline in pulmonary function and histologically by the development of airway-centered fibrosis known as bronchiolitis obliterans. There are no approved therapies to treat CLAD, and the mechanisms underlying its development remain poorly understood. We performed single-cell RNA-Seq and spatial transcriptomic analysis of explanted tissues from human lung recipients with CLAD, and we performed independent validation studies to identify an important role of Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling in airway epithelial cells that contributes to airway-specific alloimmune injury. Specifically, we established that activation of JAK-STAT signaling leads to upregulation of major histocompatibility complex 1 (MHC-I) in airway basal cells, an important airway epithelial progenitor population, which leads to cytotoxic T cell-mediated basal cell death. This study provides mechanistic insight into the cell-to-cell interactions driving airway-centric alloimmune injury in CLAD, suggesting a potentially novel therapeutic strategy for CLAD prevention or treatment.

DNA methylation entropy is associated with DNA sequence features and developmental epigenetic divergence

Epigenetic information defines tissue identity and is largely inherited in development through DNA methylation. While studied mostly for mean differences, methylation also encodes stochastic change, defined as entropy in information theory. Analyzing allele-specific methylation in 49 human tissue sample datasets, we find that methylation entropy is associated with specific DNA binding motifs, regulatory DNA, and CpG density. Then applying information theory to 42 mouse embryo methylation datasets, we find that the contribution of methylation entropy to time- and tissue-specific patterns of development is comparable to the contribution of methylation mean, and methylation entropy is associated with sequence and chromatin features conserved with human. Moreover, methylation entropy is directly related to gene expression variability in development, suggesting a role for epigenetic entropy in developmental plasticity.

GeneTuring tests GPT models in genomics

Generative Pre-trained Transformers (GPT) are powerful language models that have great potential to transform biomedical research. However, they are known to suffer from artificial hallucinations and provide false answers that are seemingly correct in some situations. We developed GeneTuring, a comprehensive QA database with 600 questions in genomics, and manually scored 10,800 answers returned by six GPT models, including GPT-3, ChatGPT, and New Bing. New Bing has the best overall performance and significantly reduces the level of AI hallucination compared to other models, thanks to its ability to recognize its incapacity in answering questions. We argue that improving incapacity awareness is equally important as improving model accuracy to address AI hallucination.

Controlling taxa abundance improves metatranscriptomics differential analysis

BACKGROUND

A common task in analyzing metatranscriptomics data is to identify microbial metabolic pathways with differential RNA abundances across multiple sample groups. With information from paired metagenomics data, some differential methods control for either DNA or taxa abundances to address their strong correlation with RNA abundance. However, it remains unknown if both factors need to be controlled for simultaneously.

RESULTS

We discovered that when either DNA or taxa abundance is controlled for, RNA abundance still has a strong partial correlation with the other factor. In both simulation studies and a real data analysis, we demonstrated that controlling for both DNA and taxa abundances leads to superior performance compared to only controlling for one factor.

CONCLUSIONS

To fully address the confounding effects in analyzing metatranscriptomics data, both DNA and taxa abundances need to be controlled for in the differential analysis.

Dynamic Self-Triggered Impulsive Synchronization of Complex Networks With Mismatched Parameters and Distributed Delay

Synchronization of complex networks with nonlinear couplings and distributed time-varying delays is investigated in this article. Since the mismatched parameters of individual systems, a kind of leader-following quasisynchronization issues is analyzed via impulsive control. To acquire appropriate impulsive intervals, the dynamic self-triggered impulsive controller is devoted to predicting the available instants of impulsive inputs. The proposed controller ensures the control effects while reducing the control costs. In addition, the updating laws of the dynamic parameter is settled in consideration of error bounds to adapt to the quasisynchronization. With the utilization of the Lyapunov stability theorem, comparison method, and the definition of average impulsive interval, sufficient conditions for realizing the synchronization within a specific bound are derived. Moreover, with the definition of average impulsive gain, the parameter variation scheme is extended from the fixed impulsive effects case to the time-varying impulsive effects case. Finally, three numerical examples are given to show the effectiveness and the superiority of proposed mathematical deduction.

[Validation of calculation method for dose distribution around radioactive iodine-125 particles based on AAPM TG43 report]

Objective: According to the formula provided by the TG43 report [AAPM TG43 (2004)] proposed by the American Association of Physicists in Medicine (AAPM) in 2004, we calculated the dose distribution around the radioactive iodine-125 particles, and verified the calculation accuracy of the radioactive iodine-125 particles treatment planning system. Methods: AAPM TG43 (2004) report provides two calculation methods when calculating the dose around a single radioactive source. The calculation method that does not consider the geometric structure of the radioactive source is called point source calculation method, and the calculation method that considers the geometric structure of the radioactive source is called line source calculation method. Assuming a single Amersham 6711 radioactive iodine-125 particle with an activity of 100 U, the following point doses were calculated according to the two calculation methods provided by AAPM TG43 (2004) report, at 0°, 90° directions, distances 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5 and 6 cm; In the direction of 45°, the doses at 0.71, 1.41, 2.12, 2.83, 3.54, 4.24, 4.95, 5.66, 6.36, 7.07, 7.78 and 8.49 cm. On the clinically used brachytherapy planning system variseeds 8.0, the above two calculation methods are used to calculate the corresponding activity and the dose around the corresponding type of radioactive iodine-125 particles, and the function of capturing points to templates built in the planning system is used to accurately find the above corresponding point position, using a single measurement of the above corresponding point dose; and comparation of the results were performed to see if there is a statistical difference. Results: The AAPM TG43 report uses point source calculation method to calculate the dose of single Amersham 6711 radioactive iodine-125 particles with activity of 100 U at 0° and 90° directions. The points with the same distance and the same dose are 8 082.18, 1 870.08, 756.58, 381.47, 217.11, 131.91, 86.55, 58.32, 39.97, 27.42, 19.74, 14.13 Gy, respectively, at 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5 and 6 cm away from them. In the 45° direction, the doses at the distances of 0.71, 1.41, 2.12, 2.83, 3.54, 4.24, 4.95, 5.66, 6.36, 7.07, 7.78 and 8.49 cm are 3 957.37, 865.83, 329.99, 155.69, 84.10, 48.50, 28.49, 17.80, 11.37, 7.38, 4.98 and 3.39 Gy, respectively; For line source calculation method, radioactive particles are at the same distance as above. The doses at each point in the direction of 0° are 3 128.71, 755.44, 330.30, 180.53, 107.74, 68.56, 46.40, 32.22, 22.70, 16.00, 11.51, 8.24 Gy, respectively. The doses at each point in the direction of 90° are 8 306.46, 1 981.01, 802.74, 405.38, 230.60, 140.03, 91.83, 61.84, 42.36, 29.05, 20.91, 14.97 Gy; In the 45° direction, the dose at the corresponding distance as above is 4 020.78, 877.43, 333.49, 156.93, 84.69, 48.81, 28.65, 17.89, 11.42, 7.41, 4.99 and 3.40 Gy, respectively. The maximum dose difference (0.3%) between the two methods is 7.78 cm in the 45° direction, the maximum difference (-0.3%) between the two methods is 8.49 cm in the 45° direction, and the value of other sampling points is less than 0.3%. The closer the Amersham 6711 iodine-125 particles are to the source in the directions of 0°, 45°, and 90°, the faster the dose will drop, and the dose will drop gradually as the distance increases. Conclusion: The brachytherapy planning system variseeds 8.0 and the AAPM TG43 report calculate a maximum dose difference of 0.3%, which can accurately calculate the dose distribution around radioactive iodine-125 seeds, and provide a reliable tool for the clinical implementation of radioactive iodine-125 particles implantation for tumor treatment.

Abstract 595: Distinct tumor infiltrating Treg lineages are associated with response to anti-PD1 checkpoint blockade in NSCLC

Immune-checkpoint blockade (ICB) has proved a major success, especially in highly mutated tumors such as lung cancer. Nevertheless, not all patients respond to ICB. It is possible that regulatory T cells (Tregs) play a role in this lack of response by suppressing tumor-reactive cytotoxic T cells, however the specific mechanisms that lead to this suppression remain elusive. It is therefore necessary to understand the functional programming and suppressive nature of Treg subsets in the tumor microenvironment to define targetable molecules for future biomarker-driven therapeutics. In this study we performed single cell RNA-sequencing on T cells isolated from resected tissue and peripheral blood from 15 neoadjuvant nivolumab (anti-PD1)-treated and 10 untreated non-small cell lung cancer (NSCLC) patients. We identified and analyzed 71,251 CD4+ FoxP3+ Tregs. Refined clustering was performed, and we used pseudotime and differential gene analyses to understand the transcriptional relationship between clusters and patient groups. With our highly refined clustering approach, we identified 8 separate Treg clusters that reflect differing functionalities within the tumor microenvironment. We demonstrate distinct Treg subsets that diverge towards either an activated state, expressing members of the tumor necrosis factor receptor (TNFR) superfamily: OX40, 41BB, GITR, or a resting state. Patients who respond to ICI have a decreased activated Treg score and demonstrate RNA velocity trajectory from activated Tregs towards more in-active and resting populations. Untreated patients conversely show a high activated Treg score with RNA velocity demonstrating activated Tregs are a terminal differentiation state. We hypothesize that ICI treatment pushes Tregs away from an activated phenotype towards more quiescent and that the efficiency of this transition may predict response to ICI. We plan to stimulate receptors associated with non-response using TNFR agonist ligands and hypothesize that their induced signaling will result in transcriptional program changes altering the suppressive ability of Tregs. In addition, we show that Tregs who experience antigen within the tumor microenvironment are more suppressive than bystander Tregs that home to the tumor without antigen stimulation. Together, this study provides an in-depth look at the Treg-derived suppressive mechanisms governing their function in the TME of anti-PD-1-treated vs. untreated tumors. This in-depth analysis of tumor Tregs has identified specific targetable biomarkers which could be used to improve ICB response while mitigating off-target immune adverse events by specifically inhibiting a small subset of Tregs without disturbing systemic immune homeostasis.

Citation Format: Arbor G. Dykema, Jiajia Zhang, Boyang Zhang, Taibo Li, Justina X. Caushi, Laurene S. Cheung, Hongkai Ji, Zhicheng Ji, Kellie N. Smith, Drew M. Pardoll. Distinct tumor infiltrating Treg lineages are associated with response to anti-PD1 checkpoint blockade in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 595.