Mapping Gene Expression in the Spatial Dimension

Improving Spatial Transcriptomics with Membrane-Based Boundary Definition and Enhanced Single-Cell Resolution

Accurately defining cell boundaries for spatial transcriptomics is technically challenging. The current major approaches are nuclear staining or mathematical inference, which either exclude the cytoplasm or determine a hypothetical boundary. Here, a new method is introduced for defining cell boundaries: labeling cell membranes using genetically coded fluorescent proteins, which allows precise indexing of sequencing spots and transcripts within cells on sections. Use of this membrane-based method greatly increases the number of genes captured in cells compared to the number captured using nucleus-based methods; the numbers of genes are increased by 67% and 119% in mouse and axolotl livers, respectively. The obtained expression profiles are more consistent with single-cell RNA-seq data, demonstrating more rational clustering and apparent cell type-specific markers. Furthermore, improved single-cell resolution is achieved to better identify rare cell types and elaborate spatial domains in the axolotl brain and intestine. In addition to regular cells, accurate recognition of multinucleated cells and cells lacking nuclei in the mouse liver is achieved, demonstrating its ability to analyze complex tissues and organs, which is not achievable using previous methods. This study provides a powerful tool for improving spatial transcriptomics that has broad potential for its applications in the biological and medical sciences.

Spatially Controlled MicroRNA Imaging in Mitochondria via Enzymatic Activation of Hybridization Chain Reaction

Live-cell imaging of RNA in specific subcellular compartments is essential for elucidating the rich repertoire of cellular functions, but it has been limited by a lack of simple, precisely controlled methods. Here such an approach is presented via the combination of hybridization chain reaction and spatially restricted enzymatic activation with organelle-targeted delivery. The system can localize engineered DNA hairpins in the mitochondria, where target RNA-initiated chain reaction of hybridization events is selectively activated by a specific enzyme, enabling amplified RNA imaging with high precision. It is demonstrated that the approach is compatible with live cell visualization and enables the regulatable imaging of microRNA in mitochondria. Since in situ activation of the signal amplification with enzyme eliminates the need for genetically encoded protein overexpression, it is envisioned that this simple platform will be broadly applicable for precise RNA imaging with subcellular resolution in a variety of biological processes.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology

In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.

Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences

Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence.

The MIF promoter SNP rs755622 is associated with immune activation in glioblastoma

Intratumoral heterogeneity is a defining hallmark of glioblastoma, driving drug resistance and ultimately recurrence. Many somatic drivers of microenvironmental change have been shown to affect this heterogeneity and, ultimately, the treatment response. However, little is known about how germline mutations affect the tumoral microenvironment. Here, we find that the single-nucleotide polymorphism (SNP) rs755622 in the promoter of the cytokine macrophage migration inhibitory factor (MIF) is associated with increased leukocyte infiltration in glioblastoma. Furthermore, we identified an association between rs755622 and lactotransferrin expression, which could also be used as a biomarker for immune-infiltrated tumors. These findings demonstrate that a germline SNP in the promoter region of MIF may affect the immune microenvironment and further reveal a link between lactotransferrin and immune activation.

Advances in spatial transcriptomics and related data analysis strategies

Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Shoot-root signal circuit: Phytoremediation of heavy metal contaminated soil

High concentrations of heavy metals in the environment will cause serious harm to ecosystems and human health. It is urgent to develop effective methods to control soil heavy metal pollution. Phytoremediation has advantages and potential for soil heavy metal pollution control. However, the current hyperaccumulators have the disadvantages of poor environmental adaptability, single enrichment species and small biomass. Based on the concept of modularity, synthetic biology makes it possible to design a wide range of organisms. In this paper, a comprehensive strategy of "microbial biosensor detection - phytoremediation - heavy metal recovery" for soil heavy metal pollution control was proposed, and the required steps were modified by using synthetic biology methods. This paper summarizes the new experimental methods that promote the discovery of synthetic biological elements and the construction of circuits, and combs the methods of producing transgenic plants to facilitate the transformation of constructed synthetic biological vectors. Finally, the problems that should be paid more attention to in the remediation of soil heavy metal pollution based on synthetic biology were discussed.