TCF-1-Centered Transcriptional Network Drives an Effector versus Exhausted CD8 T Cell-Fate Decision.

TCF-1 is a key transcription factor in progenitor exhausted CD8 T cells (Tex). Moreover, this Tex cell subset mediates responses to PD-1 checkpoint pathway blockade. However, the role of the transcription factor TCF-1 in early fate decisions and initial generation of Tex cells is unclear. Single-cell RNA sequencing (scRNA-seq) and lineage tracing identified a TCF-1+Ly108+PD-1+ CD8 T cell population that seeds development of mature Tex cells early during chronic infection. TCF-1 mediated the bifurcation between divergent fates, repressing development of terminal KLRG1Hi effectors while fostering KLRG1Lo Tex precursor cells, and PD-1 stabilized this TCF-1+ Tex precursor cell pool. TCF-1 mediated a T-bet-to-Eomes transcription factor transition in Tex precursors by promoting Eomes expression and drove c-Myb expression that controlled Bcl-2 and survival. These data define a role for TCF-1 in early-fate-bifurcation-driving Tex precursor cells and also identify PD-1 as a protector of this early TCF-1 subset.

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.

Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers.

PD-1 blockade unleashes CD8 T cells1, including those specific for mutation-associated neoantigens (MANA), but factors in the tumour microenvironment can inhibit these T cell responses. Single-cell transcriptomics have revealed global T cell dysfunction programs in tumour-infiltrating lymphocytes (TIL). However, the majority of TIL do not recognize tumour antigens2, and little is known about transcriptional programs of MANA-specific TIL. Here, we identify MANA-specific T cell clones using the MANA functional expansion of specific T cells assay3 in neoadjuvant anti-PD-1-treated non-small cell lung cancers (NSCLC). We use their T cell receptors as a 'barcode' to track and analyse their transcriptional programs in the tumour microenvironment using coupled single-cell RNA sequencing and T cell receptor sequencing. We find both MANA- and virus-specific clones in TIL, regardless of response, and MANA-, influenza- and Epstein-Barr virus-specific TIL each have unique transcriptional programs. Despite exposure to cognate antigen, MANA-specific TIL express an incompletely activated cytolytic program. MANA-specific CD8 T cells have hallmark transcriptional programs of tissue-resident memory (TRM) cells, but low levels of interleukin-7 receptor (IL-7R) and are functionally less responsive to interleukin-7 (IL-7) compared with influenza-specific TRM cells. Compared with those from responding tumours, MANA-specific clones from non-responding tumours express T cell receptors with markedly lower ligand-dependent signalling, are largely confined to HOBIThigh TRM subsets, and coordinately upregulate checkpoints, killer inhibitory receptors and inhibitors of T cell activation. These findings provide important insights for overcoming resistance to PD-1 blockade.

A systematic evaluation of computational methods for cell segmentation.

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance 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 image channels, choice of 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, substantially reducing the time and effort for training cell segmentation models.

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.

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.

Palo: spatially aware color palette optimization for single-cell and spatial data.

SUMMARY: In the exploratory data analysis of single-cell or spatial genomic data, single-cells or spatial spots are often visualized using a two-dimensional plot where cell clusters or spot clusters are marked with different colors. With tens of clusters, current visualization methods often assign visually similar colors to spatially neighboring clusters, making it hard to identify the distinction between clusters. To address this issue, we developed Palo that optimizes the color palette assignment for single-cell and spatial data in a spatially aware manner. Palo identifies pairs of clusters that are spatially neighboring to each other and assigns visually distinct colors to those neighboring pairs. We demonstrate that Palo leads to improved visualization in real single-cell and spatial genomic datasets. AVAILABILITY AND IMPLEMENTATION: Palo R package is freely available at Github (https://github.com/Winnie09/Palo) and Zenodo (https://doi.org/10.5281/zenodo.6562505). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

findPC: An R package to automatically select the number of principal components in single-cell analysis.

SUMMARY: Principal component analysis is widely used in analyzing single-cell genomic data. Selecting the optimal number of principal components (PCs) is a crucial step for downstream analyses. The elbow method is most commonly used for this task, but it requires one to visually inspect the elbow plot and manually choose the elbow point. To address this limitation, we developed six methods to automatically select the optimal number of PCs based on the elbow method. We evaluated the performance of these methods on real single-cell RNA-seq data from multiple human and mouse tissues and cell types. The perpendicular line method with 30 PCs has the best overall performance, and its results are highly consistent with the numbers of PCs identified manually. We implemented the six methods in an R package, findPC, that objectively selects the number of PCs and can be easily incorporated into any automatic analysis pipeline. AVAILABILITY AND IMPLEMENTATION: findPC R package is freely available at https://github.com/haotian-zhuang/findPC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Preparation of PAMAM modified PVDF membrane and its adsorption performance for copper ions.

As one of the main pollutants of water pollution, the potential toxicity of heavy metal ions always threatens the safety of human and nature. Therefore, how to effectively remove heavy metal ions has become an important research topic in environmental protection. In the existing research, adsorption method is outstanding from many methods because of its high adsorption efficiency and easy operation. In this study, different generations of hyperbranched polyamide-amine (PAMAM) were grafted onto PVDF membrane to obtain the membrane with high adsorption capacity for heavy metal ions. The structure and physicochemical properties of the membranes were evaluated by means of fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (FE-SEM), element analyzer and X-ray photoelectron spectroscopy (EDX). At the same time, various factors affecting the adsorption process were studied, and it was found that the adsorption behavior of copper ion (Cu2+) on the membrane conformed to the pseudo-first-order kinetic model and Langmuir isotherm model. Moreover, after comparing the adsorption effect of the modified membranes grafted with different generations of PAMAM, it was found that the membrane grafted with the third generation PAMAM had the best adsorption when the solution pH was 5, and its maximum adsorption capacity could reach 153.8 mg/g. After five adsorption-desorption cycles, its adsorption capacity can reach 72.83% of the first test, indicating that it has good recycling performance. The results show that the adsorption membrane has good application potential and research value.

Global gene expression and chromatin accessibility of the peripheral nervous system in animal models of persistent pain.

BACKGROUND: Efforts to understand genetic variability involved in an individual's susceptibility to chronic pain support a role for upstream regulation by epigenetic mechanisms. METHODS: To examine the transcriptomic and epigenetic basis of chronic pain that resides in the peripheral nervous system, we used RNA-seq and ATAC-seq of the rat dorsal root ganglion (DRG) to identify novel molecular pathways associated with pain hypersensitivity in two well-studied persistent pain models induced by chronic constriction injury (CCI) of the sciatic nerve and intra-plantar injection of complete Freund's adjuvant (CFA) in rats. RESULTS: Our RNA-seq studies identify a variety of biological process related to synapse organization, membrane potential, transmembrane transport, and ion binding. Interestingly, genes that encode transcriptional regulators were disproportionately downregulated in both models. Our ATAC-seq data provide a comprehensive map of chromatin accessibility changes in the DRG. A total of 1123 regions showed changes in chromatin accessibility in one or both models when compared to the naïve and 31 shared differentially accessible regions (DAR)s. Functional annotation of the DARs identified disparate molecular functions enriched for each pain model which suggests that chromatin structure may be altered differently following sciatic nerve injury and hind paw inflammation. Motif analysis identified 17 DNA sequences known to bind transcription factors in the CCI DARs and 33 in the CFA DARs. Two motifs were significantly enriched in both models. CONCLUSIONS: Our improved understanding of the changes in chromatin accessibility that occur in chronic pain states may identify regulatory genomic elements that play essential roles in modulating gene expression in the DRG.

Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq.

Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling.

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

Sex differences in gene regulation in the dorsal root ganglion after nerve injury.

BACKGROUND: Pain is a subjective experience derived from complex interactions among biological, environmental, and psychosocial pathways. Sex differences in pain sensitivity and chronic pain prevalence are well established. However, the molecular basis underlying these sex dimorphisms are poorly understood particularly with regard to the role of the peripheral nervous system. Here we sought to identify shared and distinct gene networks functioning in the peripheral nervous systems that may contribute to sex differences of pain in rats after nerve injury. RESULTS: We performed RNA-seq on dorsal root ganglia following chronic constriction injury of the sciatic nerve in male and female rats. Analysis from paired naive and injured tissues showed that 1513 genes were differentially expressed between sexes. Genes which facilitated synaptic transmission in naïve and injured females did not show increased expression in males. CONCLUSIONS: Appreciating sex-related gene expression differences and similarities in neuropathic pain models may help to improve the translational relevance to clinical populations and efficacy of clinical trials of this major health issue.

PRMT5 is essential for the maintenance of chondrogenic progenitor cells in the limb bud.

During embryonic development, undifferentiated progenitor cells balance the generation of additional progenitor cells with differentiation. Within the developing limb, cartilage cells differentiate from mesodermal progenitors in an ordered process that results in the specification of the correct number of appropriately sized skeletal elements. The internal pathways by which these cells maintain an undifferentiated state while preserving their capacity to differentiate is unknown. Here, we report that the arginine methyltransferase PRMT5 has a crucial role in maintaining progenitor cells. Mouse embryonic buds lacking PRMT5 have severely truncated bones with wispy digits lacking joints. This novel phenotype is caused by widespread cell death that includes mesodermal progenitor cells that have begun to precociously differentiate into cartilage cells. We propose that PRMT5 maintains progenitor cells through its regulation of Bmp4 Intriguingly, adult and embryonic stem cells also require PRMT5 for maintaining pluripotency, suggesting that similar mechanisms might regulate lineage-restricted progenitor cells during organogenesis.

Single-cell regulome data analysis by SCRAT.

SUMMARY: Emerging single-cell technologies (e.g. single-cell ATAC-seq, DNase-seq or ChIP-seq) have made it possible to assay regulome of individual cells. Single-cell regulome data are highly sparse and discrete. Analyzing such data is challenging. User-friendly software tools are still lacking. We present SCRAT, a Single-Cell Regulome Analysis Toolbox with a graphical user interface, for studying cell heterogeneity using single-cell regulome data. SCRAT can be used to conveniently summarize regulatory activities according to different features (e.g. gene sets, transcription factor binding motif sites, etc.). Using these features, users can identify cell subpopulations in a heterogeneous biological sample, infer cell identities of each subpopulation, and discover distinguishing features such as gene sets and transcription factors that show different activities among subpopulations. AVAILABILITY AND IMPLEMENTATION: SCRAT is freely available at https://zhiji.shinyapps.io/scrat as an online web service and at https://github.com/zji90/SCRAT as an R package. CONTACT: hji@jhu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.

When analyzing single-cell RNA-seq data, constructing a pseudo-temporal path to order cells based on the gradual transition of their transcriptomes is a useful way to study gene expression dynamics in a heterogeneous cell population. Currently, a limited number of computational tools are available for this task, and quantitative methods for comparing different tools are lacking. Tools for Single Cell Analysis (TSCAN) is a software tool developed to better support in silico pseudo-Time reconstruction in Single-Cell RNA-seq ANalysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods. TSCAN is available at https://github.com/zji90/TSCAN and as a Bioconductor package.

Turning publicly available gene expression data into discoveries using gene set context analysis.

Gene Set Context Analysis (GSCA) is an open source software package to help researchers use massive amounts of publicly available gene expression data (PED) to make discoveries. Users can interactively visualize and explore gene and gene set activities in 25,000+ consistently normalized human and mouse gene expression samples representing diverse biological contexts (e.g. different cells, tissues and disease types, etc.). By providing one or multiple genes or gene sets as input and specifying a gene set activity pattern of interest, users can query the expression compendium to systematically identify biological contexts associated with the specified gene set activity pattern. In this way, researchers with new gene sets from their own experiments may discover previously unknown contexts of gene set functions and hence increase the value of their experiments. GSCA has a graphical user interface (GUI). The GUI makes the analysis convenient and customizable. Analysis results can be conveniently exported as publication quality figures and tables. GSCA is available at https://github.com/zji90/GSCA. This software significantly lowers the bar for biomedical investigators to use PED in their daily research for generating and screening hypotheses, which was previously difficult because of the complexity, heterogeneity and size of the data.