Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics.

Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).

A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma.

To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.

Loss of GM-CSF-dependent instruction of alveolar macrophages in COVID-19 provides a rationale for inhaled GM-CSF treatment.

GM-CSF promotes myelopoiesis and inflammation, and GM-CSF blockade is being evaluated as a treatment for COVID-19-associated hyperinflammation. Alveolar GM-CSF is, however, required for monocytes to differentiate into alveolar macrophages (AMs) that control alveolar homeostasis. By mapping cross-species AM development to clinical lung samples, we discovered that COVID-19 is marked by defective GM-CSF-dependent AM instruction and accumulation of pro-inflammatory macrophages. In a multi-center, open-label RCT in 81 non-ventilated COVID-19 patients with respiratory failure, we found that inhalation of rhu-GM-CSF did not improve mean oxygenation parameters compared with standard treatment. However, more patients on GM-CSF had a clinical response, and GM-CSF inhalation induced higher numbers of virus-specific CD8 effector lymphocytes and class-switched B cells, without exacerbating systemic hyperinflammation. This translational proof-of-concept study provides a rationale for further testing of inhaled GM-CSF as a non-invasive treatment to improve alveolar gas exchange and simultaneously boost antiviral immunity in COVID-19. This study is registered at ClinicalTrials.gov (NCT04326920) and EudraCT (2020-001254-22).

Distinct Transcriptional Programs in Ascitic and Solid Cancer Cells Induce Different Responses to Chemotherapy in High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) is responsible for the largest number of ovarian cancer deaths. The frequent therapy-resistant relapses necessitate a better understanding of mechanisms driving therapy resistance. Therefore, we mapped more than a hundred thousand cells of HGSOC patients in different phases of the disease, using single-cell RNA sequencing. Within patients, we compared chemonaive with chemotreated samples. As such, we were able to create a single-cell atlas of different HGSOC lesions and their treatment. This revealed a high intrapatient concordance between spatially distinct metastases. In addition, we found remarkable baseline differences in transcriptomics of ascitic and solid cancer cells, resulting in a different response to chemotherapy. Moreover, we discovered different robust subtypes of cancer-associated fibroblasts (CAF) in all patients. Besides inflammatory CAFs, vascular CAFs, and matrix CAFs, we identified a new CAF subtype that was characterized by high expression of STAR, TSPAN8, and ALDH1A1 and clearly enriched after chemotherapy. Together, tumor heterogeneity in both cancer and stromal cells contributes to therapy resistance in HGSOC and could form the basis of novel therapeutic strategies that differentiate between ascitic and solid disease. IMPLICATIONS: The newly characterized differences between ascitic and solid cancer cells before and after chemotherapy could inform novel treatment strategies for metastatic HGSOC.

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq.

BACKGROUND: Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called "hashing." RESULTS: Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. CONCLUSIONS: Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

Operational tolerance after hematopoietic stem cell transplantation is characterized by distinct transcriptional, phenotypic, and metabolic signatures.

The mechanisms underlying operational tolerance after hematopoietic stem cell transplantation in humans are poorly understood. We studied two independent cohorts of patients who underwent allogeneic hematopoietic stem cell transplantation from human leukocyte antigen-identical siblings. Primary tolerance was associated with long-lasting reshaping of the recipients' immune system compared to their healthy donors with an increased proportion of regulatory T cell subsets and decreased T cell activation, proliferation, and migration. Transcriptomics profiles also identified a role for nicotinamide adenine dinucleotide biosynthesis in the regulation of immune cell functions. We then compared individuals with operational tolerance and nontolerant recipients at the phenotypic, transcriptomic, and metabolomic level. We observed alterations centered on CD38+-activated T and B cells in nontolerant patients. In tolerant patients, cell subsets with regulatory functions were prominent. RNA sequencing analyses highlighted modifications in the tolerant patients' transcriptomic profiles, particularly with overexpression of the ectoenzyme NT5E (encoding CD73), which could counterbalance CD38 enzymatic functions by producing adenosine. Further, metabolomic analyses suggested a central role of androgens in establishing operational tolerance. These data were confirmed using an integrative approach to evaluating the immune landscape associated with operational tolerance. Thus, balance between a CD38-activated immune state and CD73-related production of adenosine may be a key regulator of operational tolerance.

Osteopontin Expression Identifies a Subset of Recruited Macrophages Distinct from Kupffer Cells in the Fatty Liver.

Metabolic-associated fatty liver disease (MAFLD) represents a spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis (NASH). Hepatic macrophages, specifically Kupffer cells (KCs), are suggested to play important roles in the pathogenesis of MAFLD through their activation, although the exact roles played by these cells remain unclear. Here, we demonstrated that KCs were reduced in MAFLD being replaced by macrophages originating from the bone marrow. Recruited macrophages existed in two subsets with distinct activation states, either closely resembling homeostatic KCs or lipid-associated macrophages (LAMs) from obese adipose tissue. Hepatic LAMs expressed Osteopontin, a biomarker for patients with NASH, linked with the development of fibrosis. Fitting with this, LAMs were found in regions of the liver with reduced numbers of KCs, characterized by increased Desmin expression. Together, our data highlight considerable heterogeneity within the macrophage pool and suggest a need for more specific macrophage targeting strategies in MAFLD.

A scalable SCENIC workflow for single-cell gene regulatory network analysis.

This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructs regulons (i.e., transcription factors and their target genes) assesses the activity of these discovered regulons in individual cells and uses these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon's target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in <2 h.