Faculty Bibliography

As tumors grow, they interact with cells neighboring the tumor, but it is unclear how these interactions influence tumor progression. To investigate this, we applied spatial transcriptomics and scRNA-seq to a zebrafish model of BRAF[V600E]-driven melanoma. Using spatial transcriptomics, we identified a unique "interface" cell state localized to the tumor boundary. We used scRNA-seq to find that the interface is composed of specialized tumor and microenvironment cells that upregulate a common gene set. We found evidence of an "interface" population in patient samples, suggesting it is a conserved feature of human melanoma. In both fish and humans, interface cells are characterized by significant upregulation of the chromatin modifier HMGB2, which is prognostic in human melanoma. Loss of HMGB2 impairs invasion of human melanoma cells in vitro. HMGB2 is preferentially enriched in confined tumor cells subjected to high levels of mechanical force as they invade into neighbouring tissues. When we subjected human melanoma cells to confinement in vitro, we discovered that confinement causes HMGB2 to become cytoplasmic, where previous work suggests it may be secreted to be taken up by neighbouring cells. We are currently investigating how mechanical force signals through HMGB2 at the tumor border to induce changes in gene expression, chromatin accessibility, and cell state in both the tumor and surrounding cells. Together, our work suggests that HMGB2 may be a novel mechanosensor of the mechanical microenvironment across tumors, and demonstrates the power of spatial and single-cell transcriptomics in uncovering the biology underlying tumor cell behavior in vivo

Innate immune recognition of bacterial pathogens is a key determinant of the ensuing systemic response, and host or pathogen heterogeneity in this early interaction can impact the course of infection. To gain insight into host response heterogeneity, we investigate macrophage inflammatory dynamics using primary human macrophages infected with Group B Streptococcus. Transcriptomic analysis reveals discrete cellular states within responding macrophages, one of which consists of four sub-states, reflecting inflammatory activation. Infection with six additional bacterial species-Staphylococcus aureus, Listeria monocytogenes, Enterococcus faecalis, Yersinia pseudotuberculosis, Shigella flexneri, and Salmonella enterica-recapitulates these states, though at different frequencies. We show that modulating the duration of infection and the presence of a toxin impacts inflammatory trajectory dynamics. We provide evidence for this trajectory in infected macrophages in an in vivo model of Staphylococcus aureus infection. Our cell-state analysis defines a framework for understanding inflammatory activation dynamics in response to bacterial infection.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

Of all mammalian organs, the testis has long been observed to have the most diverse gene expression profile. To account for this widespread gene expression, we have proposed a mechanism termed 'transcriptional scanning', which reduces germline mutation rates through transcription-coupled repair (TCR). Our hypothesis contrasts with an earlier observation that mutation rates are overall positively correlated with gene expression levels in yeast, implying that transcription is mutagenic due to effects dominated by transcription-coupled damage (TCD). Here we report evidence that the compound effects of both TCR and TCD during spermatogenesis modulate human germline mutation rates, with TCR dominating in most genes, thus supporting the transcriptional scanning hypothesis. Our analyses address potentially confounding factors, distinguish the differential mutagenic effects acting on the highly expressed genes and the low-to-moderately expressed genes, and resolve concerns relating to the validation of the results using a de novo mutation dataset. We also discuss the theoretical possibility of transcriptional scanning hypothesis from an evolutionary perspective. Together, these analyses support a model by which the coupling of transcription-coupled repair and damage establishes the pattern of germline mutation rates and provide an evolutionary explanation for widespread gene expression during spermatogenesis.

Complex dynamic processes such as development involve the deployment of gene regulatory pathways that transform the spatial arrangement of cells. Disentangling these genetic programs is at the core of many biological problems. Stereo-seq is a promising spatial transcriptomics method, as demonstrated by three papers in this issue of Developmental Cell, each in a distinct biological context.

Single cell biology has the potential to elucidate many critical biological processes and diseases, from development and regeneration to cancer. Single cell analyses are uncovering the molecular diversity of cells, revealing a clearer picture of the variation among and between different cell types. New techniques are beginning to unravel how differences in cell state-transcriptional, epigenetic, and other characteristics-can lead to different cell fates among genetically identical cells, which underlies complex processes such as embryonic development, drug resistance, response to injury, and cellular reprogramming. Single cell technologies also pose significant challenges relating to processing and analyzing vast amounts of data collected. To realize the potential of single cell technologies, new computational approaches are needed. On March 17-19, 2021, experts in single cell biology met virtually for the Keystone eSymposium "Single Cell Biology" to discuss advances both in single cell applications and technologies.