Max Land

Abstract Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene–gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8 + T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs.

Abstract Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.

The cytogenetic endpoints sister chromatid exchange (SCE) and chromosome aberrations are widely used as indicators of DNA damage induced by mutagenic carcinogens. Chromosome aberrations appear to result directly from DNA double-strand breaks, but the lesion(s) giving rise to SCE formation remains unknown. Most compounds that induce SCEs induce a spectrum of lesions in DNA. To investigate the role of double-strand breakage in SCE formation, we constructed a plasmid that gives rise to one specific lesion, a staggered-end ("cohesive") DNA double-strand break. This plasmid, designated pMENs, contains a selectable marker, neo, which is a bacterial gene for neomycin resistance, and the coding sequence for the bacterial restriction endonuclease EcoRI attached to the mouse metallothionein gene promoter. EcoRI recognizes G decreases AATTC sequences in DNA and makes DNA double-strand breaks with four nucleotides overhanging as staggered ends. Cells transfected with pMENS were resistant to the antibiotic G418 and contained an integrated copy of the EcoRI gene, detectable by DNA filter hybridization. The addition of the heavy metal CdSO4 resulted in the intracellular production of EcoRI, as measured by an anti-EcoRI antibody. Cytogenetic analysis after the addition of CdSO4 indicated a dramatic increase in the frequency of chromosome aberrations but very little effect on SCE frequency. Although there was some intercellular heterogeneity, these results confirm that DNA double-strand breaks do result in chromosome aberrations but that these breaks are not sufficient to give rise to SCE formation.