Simon J. McGowan

Single cell spatial interrogation of the immune-structural interactions in COVID −19 lungs is challenging, mainly because of the marked cellular infiltrate and architecturally distorted microstructure. To address this, we develop a suite of mathematical tools to search for statistically significant co-locations amongst immune and structural cells identified using 37-plex imaging mass cytometry. This unbiased method reveals a cellular map interleaved with an inflammatory network of immature neutrophils, cytotoxic CD8 T cells, megakaryocytes and monocytes co-located with regenerating alveolar progenitors and endothelium. Of note, a highly active cluster of immature neutrophils and CD8 T cells, is found spatially linked with alveolar progenitor cells, and temporally with the diffuse alveolar damage stage. These findings offer further insights into how immune cells interact in the lungs of severe COVID-19 disease. We provide our pipeline [Spatial Omics Oxford Pipeline (SpOOx)] and visual-analytical tool, Multi-Dimensional Viewer (MDV) software, as a resource for spatial analysis.

Summary: Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete understanding of potentially druggable immune mediators of disease. To advance this, we present a comprehensive multi-omic blood atlas in patients with varying COVID-19 severity and compare with influenza, sepsis and healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity revealed cells, their inflammatory mediators and networks as potential therapeutic targets, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Tensor and matrix decomposition of the overall dataset revealed feature groupings linked with disease severity and specificity. Our systems-based integrative approach and blood atlas will inform future drug development, clinical trial design and personalised medicine approaches for COVID-19.

DNA folding within nuclei is a highly ordered process, with implications for gene regulation and development. An array of chromosome conformation capture (3C) methods have been developed to investigate how DNA is packaged within nuclei and to interrogate specific interactions. While these methods use different approaches to examine target loci (many-versus-all) or the entire genome (all-versus-all), they all rely on the core principle of endonuclease digestion and proximity-based ligation to re-arrange genomic order to reflect the three-dimensional nuclear conformation. This sequence reorganization creates novel chimeric DNA fragments which require specialist bioinformatic tools to analyze and visualize. Despite this need for specialist bioinformatic skills, the core biological importance of genome folding has seen widespread methodological uptake. To service the needs of experimentalists using the many-versus-all Capture-C family of methods we have developed CaptureCompendium; a toolkit of software to simplify the design, analysis and presentation of 3C experiments.

Background: Cancer patients often display dysfunctional antitumor T-cell responses. Because noteworthy benefits of immune checkpoint pathway blockade, such as programmed cell death protein 1 (PD-1) inhibitors, have been achieved in multiple advanced cancers, the next critical question is which mono-blockade or combinatorial blockade regimens may reinvigorate antitumor T-cell immunity in those cancer patients while limiting immune-related adverse effects. Method: This study recruited, in total, 172 primary cancer patients (131 were blood-tumor-matched patients) who were treatment-naïve prior to the surgeries or biopsies covering the eight most prevalent types of cancer. With access to fresh surgical samples, this study simultaneously investigated the ex vivo expression level of eight known immune checkpoint receptors [PD-1, cytotoxic T-lymphocyte antigen-4 [CTLA-4], T-cell immunoglobulin and mucin-domain containing-3 [Tim-3], 2B4, killer cell lectin like receptor G1 [KLRG-1], TIGIT, B- and T-lymphocyte attenuator [BTLA], and CD160] on tumor-infiltrating T cells (TILs) and paired circulating T cells in blood from a 131-patient cohort. Results: We found increased an expression of PD-1 and Tim-3 but a decreased expression of BTLA on TILs when compared with peripheral blood from multiple types of cancer. Moreover, our co-expression analysis of key immune checkpoint receptors delineates “shared” subsets as PD-1+Tim-3+TIGIT+2B4+KLRG-1–CTLA-4– and PD-1+TIGIT+2B4+Tim-3–KLRG-1–CTLA-4– from bulk CD8 TILs. Furthermore, we found that a higher frequency of advanced differentiation stage T cells (CD27–CCR7–CD45RA–) among the “shared” subset (PD-1+Tim-3+TIGIT+2B4+KLRG-1–CTLA-4–) in bulk CD8 TILs was associated with poorly differentiated cancer type in cervical cancer patients. Conclusions: To our knowledge, our study is the first comprehensive analysis of key immune checkpoint receptors on T cells in treatment-naïve, primary cancer patients from the eight most prevalent types of cancer. These findings might provide useful information for future design of mono-blockade/combinatorial blockades and/or genetically modified T-cell immunotherapy.

Genome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling.

Rheumatology key message. Clinical variability in Majeed syndrome, favourable response to IL-1 blockade and recommendations for genetic screening.

The 3D structure of chromatin in the nucleus is important for gene expression and regulation. Chromosomal conformation capture techniques, such as Hi-C, generate large amounts of data showing interaction points on the genome but these are hard to interpret using standard tools. We have developed CSynth, a high performance 3D genome browser and real time chromatin restraint-based modeller to visualise dynamic and interactive models of chromatin capture data. CSynth does its calculations in the GPU hence is much faster than existing modelling software to infer and visualise the chromatin structure which also allow real-time interaction with the modelling parameters. It also allows straightforward comparison of interaction data and the results of third party 3D modelling outputs. In addition we include an option to view and manipulate these complicated structures using Virtual Reality (VR) allowing scientists to immerse themselves in the models for further understanding. This VR component has also proven to be a valuable teaching and public engagement tool. CSynth is web based and available to use at http://csynth.org .

SUMMARY: Single-cell RNA-sequencing has emerged as a powerful tool to resolve transcriptional heterogeneity. However, its application to study cancerous tissues is currently hampered by the lack of coverage across key mutation hotspots in the vast majority of cells, which prevents correlation of genetic and transcriptional readouts from the same single cell. To overcome this, we developed TARGET-seq, a method for the high-sensitivity detection of multiple mutations within single-cells from both genomic and coding DNA, in parallel with unbiased, high-depth whole transcriptome analysis. We demonstrate how this technique uniquely resolves transcriptional and genetic tumor heterogeneity in myeloproliferative neoplasm stem/progenitor cells, providing insights into deregulated pathways of mutant and non-mutant cells. TARGET-seq provides a powerful tool to resolve molecular signatures of genetically distinct subclones of tumor cells.