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Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

SummaryTissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts “multicellular gene expression patterns” that vary across different biological samples. These patterns capture how changes in one cell type are connected to changes in other cell types. The multicellular patterns can be further associated with known covariates (e.g., disease, treatment, or technical batch effects) and used to stratify heterogeneous samples. We first validated the performance of scITD usingin vitroexperimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern that appears to potentiate renal involvement for patients with anti-dsDNA autoantibodies. This pattern was characterized by an expansion of activated memory B cells along with helper T cell activation and is predicted to be mediated by an increase in ICOSLG-ICOS interaction between monocytes and helper T cells. Finally, we applied scITD to two PBMC datasets from patients with COVID-19 and identified reproducible multicellular patterns that stratify patients by disease severity. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. However, their relationship and relative contribution to tumor evolution and therapy resistance are not well-understood. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and non-genetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of CNVs from scRNA-seq data. To resolve tumor clonal architecture, Numbat exploits the evolutionary relationships between subclones to iteratively infer the single-cell copy number profiles and tumor clonal phylogeny. Analyzing 21 tumor samples composed of multiple myeloma, breast, and thyroid cancers, we show that Numbat can accurately reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We uncover additional subclonal complexity contributed by allele-specific alterations, and identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. We hope that the increased power to characterize genomic aberrations and tumor subclonal phylogenies provided by Numbat will help delineate contributions of genetic and non-genetic mechanisms in cancer.