Faculty Bibliography

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Transcriptional profiling of tumors has revealed a stress-like state among the cancer cells with the concerted expression of genes such as fos, jun, and heat-shock proteins, though this has been controversial given possible dissociation-effects associated with single-cell RNA sequencing. Here, we validate the existence of this state using a combination of zebrafish melanoma modeling, spatial transcriptomics, and human samples. We found that the stress-like subpopulation of cancer cells is present from the early stages of tumorigenesis. Comparing with previously reported single-cell RNA sequencing datasets from diverse cancer types, including triple-negative breast cancer, oligodendroglioma, and pancreatic adenocarcinoma, indicated the conservation of this state during tumorigenesis. We also provide evidence that this state has higher tumor-seeding capabilities and that its induction leads to increased growth under both MEK and BRAF inhibitors. Collectively, our study supports the stress-like cells as a cancer cell state expressing a coherent set of genes and exhibiting drug-resistance properties.

Alternative polyadenylation (APA) produces isoforms with distinct 3'-ends, yet their functional differences remain largely unknown. Here, we introduce the APA-seq method to detect the expression levels of APA isoforms from 3'-end RNA-Seq data by exploiting both paired-end reads for gene isoform identification and quantification. We detected the expression levels of APA isoforms in individual Caenorhabditis elegans embryos at different stages throughout embryogenesis. Examining the correlation between the temporal profiles of isoforms led us to distinguish two classes of genes: those with highly correlated isoforms (HCI) and those with lowly correlated isoforms (LCI) across time. We hypothesized that variants with similar expression profiles may be the product of biological noise, while the LCI variants may be under tighter selection and consequently their distinct 3' UTR isoforms are more likely to have functional consequences. Supporting this notion, we found that LCI genes have significantly more miRNA binding sites, more correlated expression profiles with those of their targeting miRNAs and a relative lack of correspondence between their transcription and protein abundances. Collectively, our results suggest that a lack of coherence among the regulation of 3' UTR isoforms is a proxy for selective pressures acting upon APA usage and consequently for their functional relevance.