Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1–5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.

Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze data from such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus-infected airway organoids, and (2) combinatorial cytokine stimulation of immune cells. Using CINEMA-OT, we discover diverging treatment responses and their associated cellular sub-populations. By applying CINEMA-OT to combinatorial experimental designs, we infer the specific cell-gene programs driving syngergistic responses.

An ackerman mobile robot system based on ROS and lidar was developed in this research. The i5 industrial control computer was taken as the core controller, and the Ubuntu system was installed inside, the control core of the driving part was the STM32 microcontroller. Using the external environment information obtained by lidar and IMU sensors, the corresponding SLAM algorithm was developed and designed under the Linux system based on the distributed framework of ROS operating system. Through establishing the feature map of environment, the system can locate position and posture of vehicle body in real time, and generate optimal path according to the given target point automatically identify and avoid surrounding obstacles. According to the state of the robot and the commands of the user, it can realize the functions of human-computer interaction, SLAM map scanning and drawing, Wi-fi remote control, real-time positioning, autonomous navigation, etc. efficiently and quickly.

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