In Situ Sequencing: A High-Throughput, Multi-Targeted Gene Expression Profiling Technique for Cell Typing in Tissue Sections

HOX gene expression in the developing human spine

Positional coding along the anterior-posterior axis is regulated by HOX genes, whose 3' to 5' expression correlates with location along this axis. The precise utilisation of HOX genes in different human cell types is not fully understood. Here, we use single-cell and spatial-transcriptomics, along with in-situ sequencing, to create a developmental atlas of the human fetal spine. We analyse HOX gene expression across cell types during development, finding that neural-crest derivatives unexpectedly retain the anatomical HOX code of their origin while also adopting the code of their destination. This trend is confirmed across multiple organs. In the axial plane of the spinal cord, we find distinct patterns in the ventral and dorsal domains, providing insights into motor pool organisation and loss of collinearity in HOXB genes. Our findings shed new light on HOX gene expression in the developing spine, highlighting a HOX gene 'source code' in neural-crest cell derivatives.

How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases?

Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.

Immune mapping of human tuberculosis and sarcoidosis lung granulomas

Tuberculosis (TB) and sarcoidosis are both granulomatous diseases. Here, we compared the immunological microenvironments of granulomas from TB and sarcoidosis patients using in situ sequencing (ISS) transcriptomic analysis and multiplexed immunolabeling of tissue sections. TB lesions consisted of large necrotic and cellular granulomas, whereas "multifocal" granulomas with macrophages or epitheloid cell core and a T-cell rim were observed in sarcoidosis samples. The necrotic core in TB lesions was surrounded by macrophages and encircled by a dense T-cell layer. Within the T-cell layer, compact B-cell aggregates were observed in most TB samples. These B-cell clusters were vascularized and could contain defined B-/T-cell and macrophage-rich areas. The ISS of 40-60 immune transcripts revealed the enriched expression of transcripts involved in homing or migration to lymph nodes, which formed networks at single-cell distances in lymphoid areas of the TB lesions. Instead, myeloid-annotated regions were enriched in CD68, CD14, ITGAM, ITGAX, and CD4 mRNA. CXCL8 and IL1B mRNA were observed in granulocytic areas in which M. tuberculosis was also detected. In line with ISS data indicating tertiary lymphoid structures, immune labeling of TB sections expressed markers of high endothelial venules, follicular dendritic cells, follicular helper T cells, and lymph-node homing receptors on T cells. Neither ISS nor immunolabeling showed evidence of tertiary lymphoid aggregates in sarcoidosis samples. Together, our finding suggests that despite their heterogeneity, the formation of tertiary immune structures is a common feature in granulomas from TB patients.

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics

Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.

Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications

Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.

Structured Illumination Microscopy Improves Spot Detection Performance in Spatial Transcriptomics

Spatial biology is a rapidly growing research field that focuses on the transcriptomic or proteomic profiling of single cells within tissues with preserved spatial information. Imaging-based spatial transcriptomics uses epifluorescence microscopy, which has shown remarkable results for the identification of multiple targets in situ. Nonetheless, the number of genes that can be reliably visualized is limited by the diffraction of light. Here, we investigate the effect of structured illumination (SIM), a super-resolution microscopy approach, on the performance of single-gene transcript detection in spatial transcriptomics experiments. We performed direct mRNA-targeted hybridization in situ sequencing for multiple genes in mouse coronal brain tissue sections. We evaluated spot detection performance in widefield and confocal images versus those with SIM in combination with 20×, 25× and 60× objectives. In general, SIM increases the detection efficiency of gene transcript spots compared to widefield and confocal modes. For each case, the specific fold increase in localizations is dependent on gene transcript density and the numerical aperture of the objective used, which has been shown to play an important role, especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to improve spot detection and overall data quality in spatial transcriptomics.

European Society of Toxicologic Pathology (Pathology 2.0 Molecular Pathology Special Interest Group): Review of In Situ Hybridization Techniques for Drug Research and Development

In situ hybridization (ISH) is used for the localization of specific nucleic acid sequences in cells or tissues by complementary binding of a nucleotide probe to a specific target nucleic acid sequence. In the last years, the specificity and sensitivity of ISH assays were improved by innovative techniques like synthetic nucleic acids and tandem oligonucleotide probes combined with signal amplification methods like branched DNA, hybridization chain reaction and tyramide signal amplification. These improvements increased the application spectrum for ISH on formalin-fixed paraffin-embedded tissues. ISH is a powerful tool to investigate DNA, mRNA transcripts, regulatory noncoding RNA, and therapeutic oligonucleotides. ISH can be used to obtain spatial information of a cell type, subcellular localization, or expression levels of targets. Since immunohistochemistry and ISH share similar workflows, their combination can address simultaneous transcriptomics and proteomics questions. The goal of this review paper is to revisit the current state of the scientific approaches in ISH and its application in drug research and development.

Simultaneous detection of miRNA and mRNA at the single-cell level in plant tissues

Detecting the simultaneous presence of a microRNA (miRNA) and a mRNA in a specific tissue can provide support for the prediction that the miRNA regulates the mRNA. Although two such methods have been developed for mammalian tissues, they have a low signal-noise ratio and/or poor resolution at the single-cell level. To overcome these drawbacks, we develop a method that uses sequence-specific miRNA-locked nucleic acid (LNA) and mRNA-LNA probes. Moreover, it augments the detection signal by rolling circle amplification, achieving a high signal-noise ratio at the single-cell level. Dot signals are counted for determining the expression levels of mRNA and miRNA molecules in specific cells. We show a high sequence specificity of our miRNA-LNA probe, revealing that it can discriminate single-base mismatches. Numerical quantification by our method is tested in transgenic rice lines with different gene expression levels. We conduct several applications. First, the spatial expression profiling of osa-miR156 and OsSPL12 in rice leaves reveals their specific expression in mesophyll cells. Second, studying rice and its mutant lines with our method reveals opposite expression patterns of miRNA and its target mRNA in tissues. Third, the dynamic expression profiles of ZmGRF8 and zma-miR396 during maize leaf development provide evidence that zma-miR396 regulates the preferential spatial expression of ZmGRF8 in bundle sheath cells. Finally, our method can be scaled up to simultaneously detect multiple miRNAs and mRNAs in a tissue. Thus, it is a sensitive and versatile technique for studying miRNA regulation of plant tissue development.

Spatial and temporal heterogeneity in the lineage progression of fine oligodendrocyte subtypes

Background

Oligodendrocytes are glial cells that support and insulate axons in the central nervous system through the production of myelin. Oligodendrocytes arise throughout embryonic and early postnatal development from oligodendrocyte precursor cells (OPCs), and recent work demonstrated that they are a transcriptional heterogeneous cell population, but the regional and functional implications of this heterogeneity are less clear. Here, we apply in situ sequencing (ISS) to simultaneously probe the expression of 124 marker genes of distinct oligodendrocyte populations, providing comprehensive maps of the corpus callosum, cingulate, motor, and somatosensory cortex in the brain, as well as gray matter (GM) and white matter (WM) regions in the spinal cord, at postnatal (P10), juvenile (P20), and young adult (P60) stages. We systematically compare the abundances of these populations and investigate the neighboring preference of distinct oligodendrocyte populations.

Results

We observed that oligodendrocyte lineage progression is more advanced in the juvenile spinal cord compared to the brain, corroborating with previous studies. We found myelination still ongoing in the adult corpus callosum while it was more advanced in the cortex. Interestingly, we also observed a lateral-to-medial gradient of oligodendrocyte lineage progression in the juvenile cortex, which could be linked to arealization, as well as a deep-to-superficial gradient with mature oligodendrocytes preferentially accumulating in the deeper layers of the cortex. The ISS experiments also exposed differences in abundances and population dynamics over time between GM and WM regions in the brain and spinal cord, indicating regional differences within GM and WM, and we found that neighboring preferences of some oligodendroglia populations are altered from the juvenile to the adult CNS.

Conclusions

Overall, our ISS experiments reveal spatial heterogeneity of oligodendrocyte lineage progression in the brain and spinal cord and uncover differences in the timing of oligodendrocyte differentiation and myelination, which could be relevant to further investigate functional heterogeneity of oligodendroglia, especially in the context of injury or disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01325-z.

Matisse: a MATLAB-based analysis toolbox for in situ sequencing expression maps

BACKGROUND

A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computational techniques are developed to facilitate data analysis. There is also a need for versatile user friendly tools that can be used for a de novo exploration of datasets.

RESULTS

Here we present MATLAB-based Analysis toolbox for in situ sequencing (ISS) expression maps (Matisse). We demonstrate Matisse by characterizing the 2-dimensional spatial expression of 119 genes profiled in a mouse coronal section, exploring different levels of complexity. Additionally, in a comprehensive analysis, we further analyzed expression maps from a second technology, osmFISH, targeting a similar mouse brain region.

CONCLUSION

Matisse proves to be a valuable tool for initial exploration of in situ sequencing datasets. The wide set of tools integrated allows for simple analysis, using the position of individual reads, up to more complex clustering and dimensional reduction approaches, taking cellular content into account. The toolbox can be used to analyze one or several samples at a time, even from different spatial technologies, and it includes different segmentation approaches that can be useful in the analysis of spatially resolved transcriptomic datasets.

Automated identification of the mouse brain's spatial compartments from in situ sequencing data

Background

Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments.

Results

Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles.

Conclusion

We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.