eccDNA-pipe: an integrated pipeline for identification, analysis and visualization of extrachromosomal circular DNA from high-throughput sequencing data.

Extrachromosomal circular DNA (eccDNA) is currently attracting considerable attention from researchers due to its significant impact on tumor biogenesis. High-throughput sequencing (HTS) methods for eccDNA identification are continually evolving. However, an efficient pipeline for the integrative and comprehensive analysis of eccDNA obtained from HTS data is still lacking. Here, we introduce eccDNA-pipe, an accessible software package that offers a user-friendly pipeline for conducting eccDNA analysis starting from raw sequencing data. This dataset includes data from various sequencing techniques such as whole-genome sequencing (WGS), Circle-seq and Circulome-seq, obtained through short-read sequencing or long-read sequencing. eccDNA-pipe presents a comprehensive solution for both upstream and downstream analysis, encompassing quality control and eccDNA identification in upstream analysis and downstream tasks such as eccDNA length distribution analysis, differential analysis of genes enriched with eccDNA and visualization of eccDNA structures. Notably, eccDNA-pipe automatically generates high-quality publication-ready plots. In summary, eccDNA-pipe provides a comprehensive and user-friendly pipeline for customized analysis of eccDNA research.

SPACEL: deep learning-based characterization of spatial transcriptome architectures.

Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.

Single-cell transcriptome profiling reveals immune and stromal cell heterogeneity in primary Sjögren's syndrome.

Primary Sjögren's syndrome (pSS) is a complex autoimmune disease characterized by lymphocytic infiltration and exocrine dysfunction, particularly affecting the salivary gland (SG). We employed single-cell RNA sequencing to investigate cellular heterogeneity in 11 patients with pSS and 5 non-SS controls. Notably, patients with pSS exhibited downregulated SOX9 in myoepithelial cells, potentially associated with impaired epithelial regeneration. An expanded ACKR1+ endothelial subpopulation in patients with pSS suggested a role in facilitating lymphocyte transendothelial migration. Our analysis of immune cells revealed expanded IGHD+ naive B cells in peripheral blood from patients with pSS. Pseudotime trajectory analysis outlined a bifurcated differentiation pathway for peripheral B cells, enriching three subtypes (VPREB3+ B, BANK1+ B, CD83+ B cells) within SGs in patients with pSS. Fibroblasts emerged as pivotal components in a stromal-immune interaction network, potentially driving extracellular matrix disruption, epithelial regeneration impairment, and inflammation. Our study illuminates immune and stromal cell heterogeneity in patients with pSS, offering insights into therapeutic strategies.

Memristor Based on Inorganic and Organic Two-Dimensional Materials: Mechanisms, Performance, and Synaptic Applications.

A memristor is a two-terminal device with nonvolatile resistive switching (RS) behaviors. Recently, memristors have been highly desirable for both fundamental research and technological applications because of their great potential in the development of high-density memory technology and neuromorphic computing. Benefiting from the unique two-dimensional (2D) layered structure and outstanding properties, 2D materials have proven to be good candidates for use in gate-tunable, highly reliable, heterojunction-compatible, and low-power memristive devices. More intriguing, stable and reliable nonvolatile RS behaviors can be achieved in multi- and even monolayer 2D materials, which seems unlikely to be achieved in traditional oxides with thicknesses less than a few nanometers because of the leakage currents. Moreover, such two-terminal devices show a series of synaptic functionalities, suggesting applications in simulating a biological synapse in the neural network. In this review article, we summarize the recent progress in memristors based on inorganic and organic 2D materials, from the material synthesis, device structure and fabrication, and physical mechanism to some versatile memristors based on diverse 2D materials with good RS properties and memristor-based synaptic applications. The development prospects and challenges at the current stage are then highlighted, which is expected to inspire further advancements and new insights into the fields of information storage and neuromorphic computing.

A synaptic memristor based on two-dimensional layered WSe2 nanosheets with short- and long-term plasticity.

Neural synapses with diverse synaptic functions of short- and long-term plasticity are highly desired for developing complex neuromorphic systems. A memristor with its two terminals serving as pre- and post-neurons, respectively, can emulate two neuronal-based synaptic functions. In this work, multilayer two-dimensional (2D) layered WSe2 nanosheets are synthesized by a salt-assisted chemical vapor deposition (CVD) method. Two-terminal memristors with a planar structure are fabricated based on the CVD-grown triangular WSe2 nanosheets. The fabricated devices exhibit typical bipolar nonvolatile resistive switching behaviors with a high current ON/OFF ratio of up to 6 × 103 and good retention and endurance properties, suggesting good stability and reliability of the WSe2-based memristors. Furthermore, the developed memristors demonstrate synaptic functions of short- and long-term plasticity (STP and LTP), as well as a transition from STP to LTP by applying consecutive pulse voltages. Moreover, the WSe2-based memristors exhibits biological synaptic functions of long-term potentiation and depression, and paired-pulse facilitation. Thus, our 2D WSe2 nanosheet based memristors not only exhibit stable and reliable nonvolatile resistive switching behaviors, but also show potential applications in mimicking biological synapses.

Reversible Transformation between Bipolar Memory Switching and Bidirectional Threshold Switching in 2D Layered K-Birnessite Nanosheets.

Birnessite-related manganese dioxides (MnO2) have recently been studied owing to their diverse low-dimensional layered structures and potential applications in energy devices. The birnessite MnO2 possesses a layered structure with edge-shared MnO6 octahedra layer stacked with interlayer of cations. The unique layered structure may provide some distinct electrical properties for the 2D layered nanosheets. In this work, layered K-birnessite MnO2 samples are synthesized by a hydrothermal method. The resistive switching (RS) devices based on single K-birnessite MnO2 nanosheets are fabricated by transferring the nanosheets onto SiO2/Si substrates through a facile and feasible method of mechanical exfoliation. The device exhibits nonvolatile memory switching (MS) behaviors with high current ON/OFF ratio of ∼2 × 105. And more importantly, reversible transformation between the nonvolatile MS and volatile threshold switching (TS) can be achieved in the single layered nanosheet through tuning the magnitude of compliance current (Icc). To be more specific, a relatively high Icc (1 mA) can trigger the nonvolatile MS behaviors, while a relatively low Icc (≤100 µA) can generate volatile TS characteristics. This work not only demonstrates the memristor based on single birnessite-related MnO2 nanosheet, but also offers an insight into understanding the complex resistive switching types and relevant physical mechanisms of the 2D layered oxide nanosheets.